Identifying, Measuring, and Valuing Knowledge-Based Intangible Assets: New Perspectives (Premier Reference Source)

Identifying, Measuring, and Valuing KnowledgeBased Intangible Assets: New Perspectives Belén Vallejo-Alonso University of the Basque Country, Spain Arturo Rodríguez-Castellanos University of the Basque Country, Spain Gerardo Arregui-Ayastuy University of the Basque Country, Spain

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Published in the United States of America by Business Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com/reference Copyright © 2011 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Identifying, measuring, and valuing knowledge-based intangible assets : new perspectives / Belén Vallejo-Alonso, Arturo Rodríguez-Castellanos and Gerardo Arregui-Ayastuy, editors. p. cm. Includes bibliographical references and index. Summary: "The main scope of the book is to highlight the importance of intangible resources in business management, evidenced in their measurement and financial valuation, and the need for a strategic analysis that enables them to be identified and then assessed"--Provided by publisher. ISBN 978-1-60960-054-9 (hardcover) -- ISBN 978-1-60960-056-3 (ebook) 1. Intangible property--Management. 2. Intangible property--Valuation. I. Vallejo Alonso, Belin. II. Rodríguez Castellanos, Arturo. III. ArreguiAyastuy, Gerardo, 1962HD53.I34 2010 658.4'038--dc22 2010017212

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List of Reviewers Gloría Aparicio, University of the Basque Country, Spain Nekane Aramburu, University of Deusto, Spain Andrés Araujo, University of the Basque Country, Spain Blanca Arosa, University of the Basque Country, Spain Imanol Basterretxea, University of the Basque Country, Spain Constantin Bratianu, Academy of Economic Sudies, Romania Epaminondas Christofilopoulus, Foundation for Research and Technology, Greece Petrie Coetzee, Tshwane University of Technology, South Africa Kimiz Dalkir, McGill University, Canada Susanne Durst, University of Liechtenstein, Principality of Liechtenstein G. Scott Erickson, Ithaca College, USA Péter Fehér, University of Economic Sciences and Public Administration, Hungary Domingo García-Merino, University of the Basque Country, Spain Fernando E. García-Muiña, Rey Juan Carlos University, Spain Annie Green, George Washington University, USA Adamantios Koumpis, Altec S.A., Greece Harri Laihonen, Tampere University of Technology, Finland Jon Landeta, University of the Basque Country, Spain Victor López, University of Castilla-La Mancha, Spain Pedro López, Complutense University of Madrid, Spain Larry Lucardie, Uppsala University, The Netherlands Jesus Matey, University of the Basque Country, Spain Androklis Mavridis, Aristotle University of Thessaloniki, Greece Susan McIntyre, Defence Research and Development Centre for Security Science, Canada Carlos Merino, Universidad Autónoma de Madrid, Spain Premysl Parka, Tomas Bata University in Zlin, Czech Republic Eva Pelechano-Barahona, Rey Juan Carlos University, Spain Susana Elena Pérez, Institute for Prospective Technological Studies, Spain Agnieta B. Pretorius, Tshwane University of Technology, South Africa Josune Sáenz, University of Deusto, Spain Enrico Scarso, University of Padua, Italy

Christiaan Stam, Holland University of Applied Sciences, The Netherlands Behrang Zadjabbari, Curtin University of Technology, Australia Patrocinio Zaragoza, University of Alicante, Spain Campbell Warden, University of La-Laguna, Spain Piotr Wisniewski, Economic Institute of the Polish National Bank, Poland

Table of Contents

Foreword .............................................................................................................................................. xv Preface ................................................................................................................................................. xvi Acknowledgment ............................................................................................................................... xxv Section 1 Identifying Intangibles Chapter 1 A New Perspective of the Intellectual Capital Dynamics in Organizations ........................................... 1 Constantin Bratianu, Academy of Economic Studies, Romania Chapter 2 Knowledge Flow Audit: Indentifying, Measuring and Managing Knowledge Asset Dynamics .......... 22 Harri Laihonen, Tampere University of Technology, Finland Matti Koivuaho, Tampere Power Utility Ltd, Finland Chapter 3 Relational Capabilities: Value Creation through Knowledge Management ......................................... 43 Patrocinio Zaragoza-Sáez, University of Alicante, Spain Enrique Claver-Cortés, University of Alicante, Spain Chapter 4 Intangible Assets and Company Succession: Are There any Differences between Buy-In and Buy-Out Initiatives?........................................................................................................... 64 Susanne Durst, University of Liechtenstein, Principality of Liechtenstein

Section 2 Measuring Intangibles Chapter 5 Towards a New Approach for Measuring Innovation: The Innovation-Value Path .............................. 87 Josune Sáenz, University of Deusto, Spain Nekane Aramburu, University of Deusto, Spain Chapter 6 Production Cognitive Capital as a Measurement of Intellectual Capital ............................................ 112 Leonardo P. Lavanderos, Sintesys Corporation, Chile Eduardo S. Fiol, Sintesys Corporation, Chile Chapter 7 Making Sense of Knowledge Productivity ......................................................................................... 133 Christiaan D. Stam, INHolland University of Applied Sciences, The Netherlands Chapter 8 Measuring Intangible Assets: Assessing the Impact of Knowledge Management in the S&T Fight against Terrorism .................................................................................................... 156 Kimiz Dalkir, McGill University, Canada Susan McIntyre, Defence Research and Development Canada – Centre for Security Science, Canada Chapter 9 Visualising the Hidden Value of Higher Education Institutions: How to Manage Intangibles in Knowledge-Intensive Organisations ............................................................................................... 177 Susana Elena Pérez, Institute for Prospective Technological Studies (IPTS) - Joint Research Centre, Spain Campbell Warden, University of La Laguna, Spain Chapter 10 The Complex Issue of Measuring KM Performance: Lessons from the Practice............................... 208 Enrico Scarso, University of Padua, Italy Ettore Bolisani, University of Padua, Italy Antonella Padova, Ernst & Young, Italy Section 3 Financial Valuation of Intangibles Chapter 11 Engineering Business Reasoning, Analytics and Intelligence Network (E-BRAIN): A New Approach to Intangible Asset Valuation Based on Einstein’s Perspective.............................. 232 Annie Green, George Washington University, Institute of Knowledge and Innovation (IKI), USA

Chapter 12 Measuring and Managing Intellectual Capital for both Development and Protection ....................... 254 G. Scott Erickson, Ithaca College, USA Helen N. Rothberg, Marist College, USA Chapter 13 Measuring and Valuing Knowledge-Based Intangible Assets: Real Business Uses ........................... 268 Steve Pike, ICS Ltd., UK Göran Roos, ICS Ltd., UK Chapter 14 Financial Risks and Intangibles .......................................................................................................... 294 David Ceballos, University of Barcelona, Spain Ada Ch. Quesada, University of Zulia, Venezuela Dídac Ramírez, University of Barcelona, Spain Chapter 15 Motives for the Financial Valuation of Intangibles: Reasons and Results.......................................... 309 Jose Domingo García-Merino, University of the Basque Country, Spain Gerardo Arregui-Ayastuy, University of the Basque Country, Spain Arturo Rodríguez-Castellanos, University of the Basque Country, Spain Belén Vallejo-Alonso, University of the Basque Country, Spain Chapter 16 Model of a Knowledge Management Support System for Choosing Intellectual Capital Assessment Methods .............................................................................................................. 336 Agnieta B. Pretorius, Tshwane University of Technology, South Africa F.P. (Petrie) Coetzee, Tshwane University of Technology, South Africa Compilation of References ............................................................................................................... 360 About the Contributors .................................................................................................................... 401 Index ................................................................................................................................................... 408

Detailed Table of Contents

Foreword .............................................................................................................................................. xv Preface ................................................................................................................................................. xvi Acknowledgment ............................................................................................................................... xxv Section 1 Identifying Intangibles Chapter 1 A New Perspective of the Intellectual Capital Dynamics in Organizations ........................................... 1 Constantin Bratianu, Academy of Economic Studies, Romania Most pioneers of the intellectual capital studies developed static models able to describe the structure and the operational power of this new concept. Their contributions have been based on individual experience of dealing with tangible assets. According to these models, there is no time variable in the intellectual capital interpretation, and therefore there is no change or transformation. Intellectual capital is considered a stock with the following generic structure: human capital, structural capital, and relational capital. The purpose of this chapter is to present a dynamic model of the organizational intellectual capital, based on a new concept of integrators, and a new functional structure. Integrators are powerful fields of forces acting upon the employees of a company in order to generate synergy. Among the most important integrators we may think of leadership, management, processes and organizational culture. The new structure is based on knowledge, intelligences and values, as independent basic building blocks. Chapter 2 Knowledge Flow Audit: Indentifying, Measuring and Managing Knowledge Asset Dynamics .................................................................................................................................... 22 Harri Laihonen, Tampere University of Technology, Finland Matti Koivuaho, Tampere Power Utility Ltd, Finland The purpose of this chapter is twofold. Theoretically it hybridizes two management concepts: Intellectual capital (IC) and knowledge flows. By combining these two concepts, the authors seek to illustrate

the dynamics of organizations’ intellectual capital. In addition to the theoretical and conceptual contribution, this chapter introduces an empirical setting for testing the framework. The purpose of the empirical illustration is not to provide exhaustive and hands-on guidelines for managing knowledge flows but to increase managers’ awareness of this highly relevant issue and to offer some suggestions for possible development measures. The knowledge flow audit helps to pinpoint the processes in which IC transforms into value or into some other form. It is based on a fundamental assumption; the dynamics of IC can be demonstrated by examining knowledge flows. Empirical results from the conducted case studies indicate that the knowledge flow audit as a whole and especially the related knowledge flow survey can be successfully used for recognizing and mapping out the dynamics of knowledge assets within a short time period. According to the feedback received from the case studies, the audit provides important information for management purposes by describing the status and accumulation of knowledge assets. Chapter 3 Relational Capabilities Value Creation through Knowledge Management .......................................... 43 Patrocinio Zaragoza-Sáez, University of Alicante, Spain Enrique Claver-Cortés, University of Alicante, Spain Linking the knowledge-based view and the intellectual capital view of the firm, this chapter has as its purpose to underline the relevance of a specific component of intellectual capital, namely relational capital, in the knowledge acquisition and transfer processes as well as its influence on a firm’s value creation. We used a qualitative research based on a multiple case study, and six Spanish knowledgeintensive firms were analyzed in depth. The results show that the main relational capabilities used by firms to create value through knowledge management are: relationships with customers, suppliers and stakeholders; acquisition of established firms; setting-up of joint ventures; collaboration with Universities, national and international institutions; participation in forums and conferences; publications; advice given by consultants and experts; and benchmarking practices. These capabilities allow firms to acquire and transfer knowledge from the environment where they develop their activity with the aim of obtaining benefits such as innovations; customers, suppliers and stakeholders’ satisfaction; an improvement in the firm’s image and credibility; new knowledge; and learning. Chapter 4 Intangible Assets and Company Succession: Are There any Differences between Buy-In and Buy-Out Initiatives?........................................................................................................... 64 Susanne Durst, University of Liechtenstein, Principality of Liechtenstein A successful company succession depends on a multitude of different aspects. In the case of external succession, certainly, the available funds represent a critical factor. Nevertheless, it can be argued that the decision to acquire a company is based on other factors as well. This chapter rests upon the hypothesis that a potential external successor will be only interested in those companies offering promising prospects. Thus, it is expected that the decision to takeover a company is rooted in the target firm’s inherent intangible assets which justify a financial investment in return. Data are collected through interviews with eight external successors from Germany who pursued buy-in respectively buy-out initiatives in small and medium-sized enterprises. The study’s findings highlight those intangible assets

that are regarded as critical in the external succession process. This helps us to obtain a more complete picture about the issue of company succession. Section 2 Measuring Intangibles Chapter 5 Towards a New Approach for Measuring Innovation: The Innovation-Value Path .............................. 87 Josune Sáenz, University of Deusto, Spain Nekane Aramburu, University of Deusto, Spain The aim of this paper is to provide the foundations of a new measurement system that will help companies to diagnose and manage their innovation performance from a holistic perspective. Adopting a resource-based view of the firm (and more precisely, a dynamic capability approach), the measurement system proposed is intended to show whether the company has the right combination of resources (both tangible and intangible) in order to foster effective and efficient innovation, as well as the degree of mastery achieved in the combination and orchestration of those resources (i.e. capability excellence), the outputs obtained and their influence on value creation and on competitive advantage. Chapter 6 Production Cognitive Capital as a Measurement of Intellectual Capital ............................................ 112 Leonardo P. Lavanderos, Sintesys Corporation, Chile Eduardo S. Fiol, Sintesys Corporation, Chile At present, knowledge plays a key role in the new economies. Nevertheless, its measurement as Intellectual Capital has not been possible from a certainty vision for the states, events and entities, leaving aside the complexity of the organizations. This work proposes a paradigmatic shift where the fundamental base is the relational–semiotic condition of human organizations; any deviation from its strategic goals could be explained through the gap between language and action levels. Defined as Coherence and Congruity Management, the process named NETOUT, allows reducing incoherence through the participation in decisional modeling, and transferring repulsion interactions to organization areas that re-signify the conflict. Configurations arising from coherence are a Production Cognitive Capital and constitute a measurement of Intellectual Capital. Chapter 7 Making Sense of Knowledge Productivity ......................................................................................... 133 Christiaan D. Stam, INHolland University of Applied Sciences, The Netherlands In the knowledge economy knowledge productivity is the main source of competitive advantage and thus the biggest management challenge. Based on a review of the concept from two distinct perspectives, knowledge productivity is defined as the process of knowledge-creation that leads to incremental and radical innovation. The two main elements in this definition are ‘the process of knowledge creation’ and ‘incremental and radical innovation’. The main aim of this chapter is to contribute to a better

understanding of the concept of knowledge productivity in order to support management in designing policies for knowledge productivity enhancement. After elaborating on the concept of knowledge productivity, the two main elements are combined in a conceptual framework – the knowledge productivity flywheel. This framework appeared to be an effective model for supporting initiatives that aim for enhancing knowledge productivity. Chapter 8 Measuring Intangible Assets: Assessing the Impact of Knowledge Management in the S&T Fight against Terrorism .................................................................................................... 156 Kimiz Dalkir, McGill University, Canada Susan McIntyre, Defence Research and Development Canada – Centre for Security Science, Canada At present, there are no standards for assessing the value of intangible assets or intellectual capital. Historically, a number of frameworks have evolved, each with a different focus and a different assessment methodology. In order to assess that knowledge management initiatives contributed to the fight against terrorism in Canada, a results-based framework was selected, customized and applied to CRTI (a networked science and technology program to counter terrorism threats). This chapter describes the step by step process of how the results-based framework was applied to measure the value contributed by knowledge-based assets. A combination of qualitative, quantitative and anecdotal assessment techniques was used and a map was employed to visualize the evaluation results. The strengths and weaknesses of this particular approach are discussed and specific examples from CRTI are presented to illustrate how other organizations can use this method to assess the value-added to innovation and research and development using a results-based framework. Chapter 9 Visualising the Hidden Value of Higher Education Institutions: How to Manage Intangibles in Knowledge-Intensive Organisations ............................................................................................... 177 Susana Elena Pérez, Institute for Prospective Technological Studies (IPTS) - Joint Research Centre, Spain Campbell Warden, University of La Laguna, Spain European universities are immersed in an intensive transformation process to order to transform themselves into more autonomous and competitive organisations. Adapting to the new demands implies the introduction of management systems, traditionally used by firms, in order to govern universities according to criteria of efficiency and effectiveness. In recent years, the idea of managing and reporting on intangibles and Intellectual Capital in universities has been acquiring progressive importance in Europe. The Chapter provides a comparative analysis of the most significant European experiences in managing and reporting Intellectual Capital in higher education institutions addressing two main issues: the identification of the benefits and obstacles of implementing IC frameworks in these particular institutions and reflect on the necessary degree of standardisation of indicators to allow comparability. To this purpose, three types of initiatives are analysed: the case of Austrian universities, which are compelled by law to report annually on their IC; five initiatives developed by individual institutions on a voluntary basis, and an attempt to build a homogeneous IC framework for European universities.

Chapter 10 The Complex Issue of Measuring KM Performance: Lessons from the Practice............................... 208 Enrico Scarso, University of Padua, Italy Ettore Bolisani, University of Padua, Italy Antonella Padova, Ernst & Young, Italy Most companies that are deeply investing in Knowledge Management (KM) initiatives encounter substantial difficulties in assessing the effectiveness of these programmes. Actually, measuring the impact of KM projects is still a puzzling problem both at the conceptual and operative level. However, measuring their performance is necessary for monitoring their progress and for successfully managing and allocating resources, as well as to maintain the support and commitment by the top management. Although several KM performance evaluation approaches have been proposed in literature, they are still far from becoming an established practice. The chapter aims at discussing this issue by placing it in a business context. First, the literature on KM performance evaluation is briefly reviewed, and the main methods currently used are classified. Then, the practical experience of a multinational company is discussed, with the purpose to describe the problems that practitioners face in their daily experience, and provide insights into the possible improvements of KM performance measurement. Section 3 Financial Valuation of Intangibles Chapter 11 Engineering Business Reasoning, Analytics and Intelligence Network (E-BRAIN): A New Approach to Intangible Asset Valuation Based on Einstein’s Perspective.............................. 232 Annie Green, George Washington University, Institute of Knowledge and Innovation (IKI), USA This chapter details a performance-based theoretical model of intangible asset valuation – Engineering – Business Reasoning, Analytics and Intelligence Network (E-BRAIN). E-BRAIN’s origin started with the construction of a validated taxonomy of intangible asset value drivers: Framework of Intangible Valuation Areas (FIVA) (Green 2008). E-BRAIN is a culmination of research and practice and offers valuable insights into the emerging discipline and field of intangible assets. Using systems engineering and organization memory (cognition) as the foundation for its structure, the model identifies the path from intangible key performance indicators to performance measurement. This chapter introduces EBRAIN as a systemic and holistic approach to intangible asset valuation that starts with a set of metrics by which business leaders can account for intangible or non-financial factors that affect value creation in the knowledge era business. Chapter 12 Measuring and Managing Intellectual Capital for both Development and Protection... .................... 254 G. Scott Erickson, Ithaca College, USA Helen N. Rothberg, Marist College, USA This chapter considers the strategic management of intellectual capital, balancing the need to develop knowledge assets with the need to protect them. In making more strategic decisions, metrics on the

level of intellectual capital and degree of knowledge management necessary to compete in an industry are required, as are those on the threat from competitive intelligence activity. We develop the case for appropriate metrics that accomplish these purposes, noting both potential and limitations. We also consider alternatives, additional data that could contribute to the usefulness and understanding of the core metrics, and provide suggestions for further research. Chapter 13 Measuring and Valuing Knowledge-Based Intangible Assets: Real Business Uses ........................... 268 Steve Pike, ICS Ltd., UK Göran Roos, ICS Ltd., UK This chapter offers a practical guide to the structure, taxonomy, measurement and use of intellectual capital (IC) in business. It traces the roots of IC and exposes and explains the remarkable lack of consensus that has been allowed to develop over the years and the methods used to try to measure it. In keeping with the practical, yet grounded, approach of the chapter, the chapter focuses on business innovation from an IC perspective. Most importantly, through a case study, the chapter introduces a practical means of measuring IC and modelling businesses predictively connecting soft issues such as human capital and relationship management with hard financial output. Recognising that IC is still an evolving discipline, the chapter offers a number of areas for future research and case study. Chapter 14 Financial Risks and Intangibles .......................................................................................................... 294 David Ceballos, University of Barcelona, Spain Ada Ch. Quesada, University of Zulia, Venezuela Dídac Ramírez, University of Barcelona, Spain This paper briefly analyses the potential impact of financial risks on the valuation of intangibles from a theoretical and heuristic approach. We justify the financial risk that has the greatest impact on the value of intangibles for a wide range of intangibles and types of valuation models. Four types of financial risks are considered for the analysis of three principal types of intangibles (resource, capacity and asset). We present a study applied to six examples of intangibles and eleven categories of valuation methods.The results are coherent with the literature because the common examples of valuation of intangibles use the recommendable methods according to the lower impact of financial risks. Chapter 15 Motives for the Financial Valuation of Intangibles: Reasons and Results.......................................... 309 Jose Domingo García-Merino, University of the Basque Country, Spain Gerardo Arregui-Ayastuy, University of the Basque Country, Spain Arturo Rodríguez-Castellanos, University of the Basque Country, Spain Belén Vallejo-Alonso, University of the Basque Country, Spain

This paper aims to analyze the Basque Country companies’ view about the financial valuation of intangibles relevance and its influence on business performance. To achieve this objective, a field study has been done with 440 telephone calls to Basque Country companies’ financial managers. Then, their responses and theirs firm’s performance are analyzed. The results show that the companies that are interested in the financial valuation of the intangibles, especially for internal motivation, perform better; however, this improvement is not statistically significant. Otherwise, the companies that are more interested in the valuation of their intangibles for external reasons need to provide information to stakeholders about their ability to generate income. Chapter 16 Model of a Knowledge Management Support System for Choosing Intellectual Capital Assessment Methods .............................................................................................................. 336 Agnieta B. Pretorius, Tshwane University of Technology, South Africa F.P. (Petrie) Coetzee, Tshwane University of Technology, South Africa Existing literature propagates a variety of methods for assessment of intellectual capital (IC). This research argues that, due to complexities involved in selecting and customizing an appropriate method or combination of methods for assessing intellectual capital, mechanisms are needed for managing and applying the evolving body of knowledge concerning such assessment. The assumption of complexity is supported by the results obtained from a survey (employing a self-administered questionnaire as instrument for data collection). This research proceeds to develop a model, referred to as a conceptual design, for a system to (i) provide management support to the process of selecting and customizing an appropriate method (or combination of methods) for assessment of intellectual capital, (ii) utilize past knowledge and expertise to accelerate and improve decision-making, (iii) promote synergism through integration of methods, and (iv) manage the evolving body of knowledge concerning the assessment of IC. Compilation of References ............................................................................................................... 360 About the Contributors .................................................................................................................... 401 Index ................................................................................................................................................... 408

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Foreword

The financial crisis that marks the end of the first decennium of the 21st century proves that intangible factors rule the economy: trust, information, knowledge, wisdom. For over 20 years these factors have been identified as the intangible assets our knowledge-based economy is built on and the crisis once again shows that the success of companies depends on their ability to create unique intangibles and turn them into value. However, the financial crisis also shows there is a second class of intangible factors that is equally important: e.g. a culture of greed, a management style focused on realizing short-term success, a system of bonuses that encourages fraud and misdemeanour. These are the intangible liabilities that our capitalistic system has produced and that have caused the collapse of multi-billion dollar companies like Enron, Lehman Brothers and Northern Rock. As human beings we have come a long way. Our most important means for the creation of wealth have become intangible. Hurray! Not so long ago we created wealth by hunting for deer and making fire by pounding two rocks together. Now we create it with intangible resources like knowledge, information and relationships. But, at the same time the biggest threats to our wealth have also become intangible: lack of trust in our money, lack of moral consciousness among stock brokers that try to earn millions by speculating at the fall of the Euro, lack of check and balances in our financial system. Although you can not drop these intangible liabilities on your foot, they can hurt like hell. So intangibles create and destroy. That is why this is the right book published at the right time. We know intangibles are the most important assets and liabilities in our economy. However, that is about all we know. We do not really know how to correctly identify intangibles, how to measure them exactly, or how to value them accurately. What we do know is that identifying, measuring and valuing intangible are difficult and related tasks. This book provides an overview of how far the intellectual capital community has come in finding solutions for identifying, measuring and valuing intangibles. It is an honest book that does not try to fool you with easy answers. At the same time it is a practical book with plenty of examples of organizations that have developed practical tools to create insight into their most important assets and liabilities: intangibles. Daniel Andriessen Amsterdam, May 10, 2010 Daniel Andriessen is Professor of intellectual capital at INHolland University of Applied Sciences, The Netherlands, and director of the INHolland Centre for Research in Intellectual Capital, a research group set up to study the impact of the intangible economy on people and organizations. More information on his work can be found at his website: www.weightlesswealth.com

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Preface

IDENTIFICATION, MEASUREMENT AND FINANCIAL VALUATION OF INTANGIBLES: DIFFICULT AND RELATED TASKS The management and valuation of intangible resources—also called intangibles—is undoubtedly a major business preoccupation. This is particularly true for knowledge-based resources, also known as intellectual capital (IC) (Hussi, 2004; Kaufmann and Schneider, 2004). A company’s intangible resources often account for a greater proportion of its overall total resources than its tangible assets do. However, the value of most intangibles does not appear on financial statements, largely because the lack of transparency and the absence of a benchmark market make it difficult to value them (Lev and Zarowing, 1998). Some authors see no need for explicit reports on the value of a firm’s intellectual capital, arguing that the market already does this by valuing its securities. This view would be correct if stock markets were continuously efficient, but this has proven not to be the case. Furthermore, the market always values the set of a firm’s intangibles, which means the problem of valuing them individually persists. Moreover, stock market valuations are not applicable to unquoted SME, being comparable listed companies hard to find. In the 1990s, demands from the corporate world prompted academic research to seek ways of reflecting the value of intangibles in financial statements (García-Ayuso, Monterrey and Pineda, 1997; Lev and Zarowin, 1998; Lev, Sarath and Sougiannis, 1999; Lev, 2001b; Cañibano et al., 2002). Unfortunately, the problem has largely resisted efforts to find a solution. The lack of an explicit valuation of intangible assets may encourage information asymmetries and inefficiencies on stock markets. Experience shows that when the value of intangible assets is included in the market analysis, forecasts on the future business performance improve, which highlights their importance in making the market efficient, reducing information asymmetries and thus the adverse selection risk. Apart from the advantages for financial market performance to be gained from fuller information about a firm’s intangibles, detailed knowledge of such intangibles inside the company is also very important: • • •

For managers, shareholders and employees to know the true value of their company. To encourage the preservation, regeneration and strengthening of the firm’s intangibles, and thus help to increase present and future corporate profits. To show the firm’s guarantees when seeking new financing, either through debt or equity. True information about the value of intangibles reduces information asymmetries, making it easier to access financial resources in better cost conditions.

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• •

To negotiate the company value in mergers or acquisitions. Where applicable, to compare it with the stock value and check to what extent this is due to the real value of the company or to “market feeling”.

There are two general procedures for intangibles valuation: value measurement and financial valuation (Andriessen, 2004a). Value measurement basically includes two tasks: one is identifying and placing the intangibles in a structured order, i.e. discovering the type of intangibles in the company, the ones that generate basic competencies, the relationships between them and so on. The other involves looking for indicators that facilitate the development of the most important intangibles and comparing the company situation with other benchmark organizations. As these indicators are mainly ratios, the measurement of intangibles is basically non-monetary. Brooking (1996), Edvinsson and Malone (1997) Scandia Navigator, Kaplan and Norton (1997) Balanced Scorecard, Roos et al. (1997), Sveiby (1997) Intangible Assets Monitor, Joia (2000), Viedma (2001) Intellectual Capital Benchmarking System, Bueno (2003) Intellectus Model, Bounfour (2003) IC-dVAL Approach, Jacobsen, et al (2005) IC RatingTM Model, Sveiby (2005) and Nazari & Herremans (2007) Extended VAIC Model, have all made interesting contributions on these issues. Financial valuation seeks to establish a monetary valuation of intangibles. There are several ways of arriving at this valuation. Unfortunately, they all have advantages and drawbacks, which means the search for methods and models for the financial valuation of intangibles that are both true and simple is by no means an easy task. Tobin (1969), Caballer and Moya (1997), Stewart (1997), Khoury (1998), Rodov and Leliaert (2002), Lev (2001a), Lev (2001b), Gu and Lev (2001), Andriessen and Tissen (2000), Nevado and López (2002), Andriessen (2004b), Roos et al. (2005), Rodríguez-Castellanos and Araujo (2005), Milost (2005), Rodríguez-Castellanos et al. (2007), Bose and Thomas (2007) and García-Merino et al. (2008) have all made contributions to this subject. In recent years, the options methodology, originally conceived to value options on financial assets (Black and Scholes, 1973; Merton, 1973), has also been used to value other types of assets, including investment projects and tangible assets, leading to what are known as Real Options Approach (Dixit and Pindyck, 1994; Kogut and Kulatilaka, 1997; Luehrman, 1998; Amram and Kulatilaka, 1999). Furthermore, the underlying characteristics of these options can also be applied to knowledge assets, thereby facilitating their valuation as options (Bose and Oh, 2003). In fact, some elements of intellectual capital have obvious option characteristics. This is the case of patents, intellectual property, R&D, IT, flexibility of industrial organization, human resources management, etc. Contributions on this issue are in Pakes (1986), Damodaran (2002), Kossovsky (2002), Mitchel and Hamilton (1988), Newton and Pearson (1994), Bodner and Rouse (2007), Benaroch (2002), Nembhard et al. (2005), Bhattacharya and Wright (2005) and Jacobs (2007). To measure and financially value intangibles, first they have to be identified and listed. In most of the works on intangible referred to above, general models are used to identify intangibles in companies and organizations. While acknowledging the undeniable value and usefulness of such models, preparing a comprehensive list may be very difficult and ultimately unrewarding; differences in competitive capabilities would lead to differences in key intangibles from one company to another. Some important intangibles that enable the company to obtain competitive advantages should almost certainly not be individualised, being the result of a combination of a number of elements. The identification, measurement and financial valuation of intangibles must be considered related tasks. It is essential to try to establish a framework that benefits from the progress made in each one, by interrelating them.

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As can be seen from the above statement, the literature on intangibles has grown steadily in recent years. However, developments have not been entirely satisfactory, and more work is needed on models and frameworks that take account of the interrelation between the identification, measurement and financial valuation of intangibles, as well as on methods that have practical utility in business management.

AIM AND STRUCTURE OF THE BOOK The aim of this book is to highlight the importance of intangible resources in business management, and the need for a strategic analysis that enables them to be identified and then assessed, that is, measured and valued. This book also contributes to show the difficulties involved in the identification, measurement and financial valuation of intangibles and the possible solutions to them. These solutions come as developing new models and frameworks for managing and assessing a firm’s knowledge and other intangibles. This book contains 16 chapters, gathered under three section headings, corresponding to each of the three aspects previously exposed as the most relevant ones for the aim of this book: identification, measurement and financial valuation of intangibles. These three aspects are closely related between themselves; in fact, each of the chapters of this book makes contributions to more than one of the aspects or even to the three ones. The criterion used to include them in each of the section headings was to estimate the aspect in which they contribute in a more relevant way. In Section 1, Identifying Intangibles, new perspectives for the identification and analysis of the business intangibles are exposed, also the identification of specific intangibles and the difficulties to identify them under particular conditions are studied in depth. Section 2, Measuring Intangibles, contains, on the one hand, new approaches for intangibles measurement; and, on the other hand, the analysis of implementation experiences on intangibles measurement in specific companies under particular circumstances. Section 3, Financial Valuation of Intangibles, includes critical analysis of the methods for the financial valuation of intangibles developed to the present time, as well as proposals of new approaches to overcome the detected limitations of the formers. This section also contains the analysis of a specific aspect of the financial valuation: the risk, an analysis of the motivations to carry out the valuation process, and a proposal of a generic method to select the specific methods for the measurement and valuation of intangibles depending on the particular circumstances of the companies. Section 1 contains four chapters, as summarized below. Chapter 1 presents a dynamic model of the organizational intellectual capital, based on a new concept of integrators. Among the most important integrators the author emphasizes leadership, management, processes and organizational culture. The new structure is based on knowledge, intelligence and values as independent basic building blocks. Chapter 2 proposes a new framework, named knowledge flow audit, for identifying and managing the dynamics of IC trough the analysis of knowledge flows. In addition to the theoretical and conceptual contributions, this chapter introduces an empirical setting for testing the proposed framework. Chapter 3 links the knowledge-based view with the intellectual capital view of a firm to underline the relevance of the relational capital in knowledge acquisition and transfer processes as well as its influence on the firm’s value creation. To accomplish this aim, the authors use a qualitative research approach based on a multiple case study and an in-depth analysis of six knowledge-intensive firms.

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Chapter 4 poses the identification of critical intangibles under a specific circumstance: the company succession through an external takeover. The analysis relies on the hypothesis that a potential external successor will only be interested in those companies offering promising prospects in the form of valuable intangibles. The findings of interviews with eight external successors highlight those intangible assets that are regarded as critical in the external succession process. In Section 2 there are six chapters, as summarized below. Chapter 5 aims to provide the foundations of a new measurement system, grounded on the dynamic capabilities approach, to show whether a company has the right combination of resources (both tangible and intangible) in order to foster effective and efficient innovation, as well as the degree of mastery achieved in the combination and orchestration of those resources, the outputs obtained and their influence on value creation and on competitive advantage. Chapter 6 proposes a paradigmatic shift in measurement of Intellectual Capital where the fundamental base is the relational–semiotic condition of human organizations. The author, trough the process named NETOUT, seeks reducing incoherence through the participation in decisional modeling and transferring repulsion interactions to organization areas that re-signify the conflict. The resulting configurations are a Production Cognitive Capital and constitute a measurement of Intellectual Capital. Chapter 7 seeks to contribute to a better understanding of the concept of knowledge productivity —defined as the process of knowledge-creation that leads to incremental and radical innovation—in order to support management in designing policies for its enhancement. The authors combine the two main elements of the concept in a conceptual framework –the knowledge productivity flywheel– that appear to be an effective model for supporting initiatives that aim for enhancing knowledge productivity. Chapter 8 describes the process of how a results-based framework was applied to measure the knowledge-based assets value contribution to CRTI (a networked science and technology program to counter terrorism threats). The authors use a combination of qualitative, quantitative and anecdotal assessment techniques and employ a map to visualize the results evaluation. The authors also discuss the strengths and weaknesses of the approach and present specific examples from CRTI to illustrate how this method can be used by other organizations. Chapter 9 provides a comparative analysis of the most significant European experiences in managing and reporting Intellectual Capital in higher education institutions addressing two main issues: the identification of the benefits and obstacles of implementing IC frameworks in these particular institutions and the reflect on the degree of standardization of indicators necessary to allow comparability. Chapter 10 is centred in KM performance evaluation approaches. First, the authors review the literature on this subject and classify the main methods currently used. Then, the practical experience of a multinational company is discussed, with the purpose to describe the problems that practitioners face in their daily experience. Finally, insights into the possible improvements of KM performance measurement are provided. In Section 3 there are six chapters, as summarized below. Chapter 11 details a performance-based theoretical model of intangible assets valuation (E-BRAIN). Using systems engineering and organization memory (cognition) as the foundation for its structure, EBRAIN identifies the path from intangible key performance indicators to performance measurement. The author claims that the model offers a set of metrics by which business leaders can account for intangible factors that affect value creation in the knowledge era. Chapter 12 considers the strategic management of intellectual capital, balancing the need to develop knowledge assets against the need to protect them. The authors develop metrics for assessing the level

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of intellectual capital, the degree of knowledge management necessary to compete in an industry and the threat from the competitive intelligence activity. Chapter 13 offers a practical guide to the structure, taxonomy, measurement and use of intellectual capital (IC) in business. It traces the roots of IC and exposes and explains the remarkable lack of consensus that has been allowed to develop over the years and the methods used to try to measure it. Through a case study, the chapter introduces a practical means of measuring IC and modelling businesses predictively connecting soft issues such as human capital and relationship management with hard financial output. This chapter offers a number of areas for future research and case study. Chapter 14 analyses the potential impact of financial risks on the valuation of intangibles from a theoretical and heuristic approach. The authors consider four types of financial risks for the analysis of three principal types of intangibles (resource, capacity and asset), six additional examples of intangibles and eleven categories of valuation methods. The authors claim that the results obtained are coherent with the literature as the common examples of intangibles valuation use the recommended methods according to the lower impact of financial risks. Chapter 15 analyses the companies’ view about the financial valuation of intangibles relevance and its influence on business performance. The results show that the companies that are interested in the financial valuation of the intangibles, especially for internal motivation, perform better; however, this improvement is not statistically significant. Otherwise, the companies that are more interested in the valuation of their intangibles for external reasons need to provide information to stakeholders about their ability to generate income. Chapter 16 proposes a model, referred to as a conceptual design, for a system to (i) provide management support to the process of selecting and customizing an appropriate method (or combination of methods) for assessment of intellectual capital, (ii) utilize past knowledge and expertise to accelerate and improve decision-making, (iii) promote synergism through integration of methods, and (iv) manage the evolving body of knowledge concerning the assessment of IC.

FINAL REMARKS This book deals jointly with the three essential features in the literature on intangibles, i.e. identification, measurement and financial valuation, and their inter-relations. This enables readers to overcome the conceptual division in watertight compartments that current literature offers. It also publicizes the progress made in the three fields and the new valuation and management models and frameworks currently being proposed. Up to the present time, current literature lacks any work that synthesizes the three approaches included in this proposal, i.e. the identification of intangibles with a strategic approach, the measurement of intangibles and, finally, the financial valuation of intangibles in an organization. To that end, we consider this book to be unique and a major contribution in this field. The objective of the editors while compiling this book was to include the most advanced researches in each issue. But simultaneously, we also sought the book be applied, that is, to be useful for business management by proposing models that can be applied in firms and which take account of their needs and requirements. Beforehand, these two objectives appeared to be difficult to achieve. However, we do believe we have totally achieved our purpose for this study. The search for applicability in the methods and models proposed makes the book of enormous interest not only for researchers in the field, but also, and specially, for executives and practitioners.

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Therefore, we consider this book to be of great utility for: •

•

• •

Executives and practitioners interested in learning about the models and frameworks proposed for identifying intangibles, measuring and financially valuing them, and the advantages for management the models might provide. Academics seeking to exchange ideas about new challenges and perspectives in identifying, measuring and valuing intangibles and trying to innovate in their fields of research, by finding new management frameworks in the knowledge economy. MBA students who want to compare and analyze the old and new paradigms on identifying, measuring and valuing intangibles within companies. Doctoral students researching the identification, measurement and valuation of intangibles.

For all of them, we hope the reading of this book is a source of satisfaction, findings and stimulation to seek new models and methods that allow progress to be made on the task of intangibles management, which is the fundamental source of wealth in the knowledge economy. Improving the intangibles management will lead, as a last resort, to improve human welfare.

REFERENCES Amram, M., & Kulatilaka, N. (1999). Real Options: Managing strategic investment in an uncertain world. Boston, MA: Harvard Business School Press. Andriessen, D. (2004a). IC valuation and measurement: classifying the state of the art. Journal of Intellectual Capital, 5(2), 230-242. Andriessen, D. (2004b). Making sense of intellectual capital. Burlington, MA: Butterworth-Heinemann. Andriessen, D., & Tissen, R. (2000). Weightless Wealth. Find your Real Value in a Future of Intangible Assets. London, UK: Pearson Education. Benaroch, M. (2002). Managing information technology investment risk: a real options perspective. Journal of Management Information Systems, 19(2), 43-84. Bhattacharya, M., & Wright, P. M. (2005). Managing human assets in an uncertain world: applying real options theory to HRM. The International Journal of Human Resource Management, 16(6), 929 -948. Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81, 637-654. Bodner, D. A., & Rouse, W. B. (2007). Understanding R&D value creation with organizational simulation. System Engineering, 10(1), 64-82 Bose, S., & Oh, K. B. (2003). An empirical evaluation of option pricing in intellectual capital. Journal of Intellectual Capital, 4(3), 382-395. Bose, S., & Thomas, K. (2007). Valuation of intellectual capital in knowledge-based firms. Management Decision, 45(9), 1484-1496.

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Bounfour, A. (2003). The IC-dVAL approach. Journal of Intellectual Capital, 4(3), 396-412. Brooking, A. (1996). Intellectual Capital. New York, NY: Thomson Business Press. Bueno, E. (Dir.). (2003). Model for the measurement and management of Intellectual Capital: Intellectus Model. Intellectus Documents, 5. Madrid, Spain: Knowledge Society Research Centre. Caballer, V., & Moya, I. (1997). Companies valuation: an analogical stock market empirical approach. In Topsacalian, P. (Ed.), Contemporary Developments in Finance. Paris, France: Éditions ESKA. Cañibano, L., Sánchez, P., García-Ayuso, M., & Chaminade, C. (2002). MERITUM Project. Guidelines for Managing and Reporting on Intangibles (Intellectual Capital Report). Madrid, Spain: Vodafone Foundation. Damodaran, A. (2002). Investment Valuation: Tools and Techniques for Determining the Value of Any Asset. 2nd ed. New York, NY: Wiley. Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty. Princeton, NJ: Princeton University Press. Edvinsson, L., & Malone, M. S. (1997). Intellectual Capital: Realizing Your Company’s True Value by Finding Its Hidden Brainpower. New York, NY: Harper Business. García-Ayuso, M., Monterrey, J., & Pineda, C. (1997). Empirical evidence on the Convex Relationship between Prices and Earnings: The Role of Abnormal Earnings in Equity Valuation (Working Paper). Seville, Spain: University of Seville. García-Merino, D., Rodríguez-Castellanos, A., Vallejo-Alonso, B. & Arregui-Ayastuy, G. (2008). Importancia y valoración de los intangibles: la percepción de los directivos en el País Vasco. Estudios de Economía Aplicada, 26(3), 27-55. Gu, F., & Lev, B. (2001). Intangible Assets: Measurement, Drivers, Usefulness (Working Paper, April). New York, NY: New York University. Hussi, T. (2004). Reconfiguring knowledge management –combining intellectual capital, intangible assets and knowledge creation. Journal of Knowledge Management, 8(2), 36-52. Jacobs, B. (2007). Real options and human capital investment. CESifo Working Paper Series, 1982. Retrieved from http://ssrn.com/abstract=988022 Jacobsen, K., Hofman-Bang, P., & Nordby, R. (2005). The IC RatingTM model by Intellectual Capital Sweden. Journal of Intellectual Capital, 6(4), 570-587. Joia, L. A. (2000). Measuring intangible corporate assets. Linking business strategy with intellectual capital. Journal of Intellectual Capital, 1(1), 68-84. Kaplan, R., & Norton, D. (1997). The Balanced Scorecard. Boston, MA: Harvard Business School Press. Kaufmann, L., & Schneider, Y. (2004). Intangibles. A synthesis of current research. Journal of Intellectual Capital, 5(3), 366-388.

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Khoury, S. (1998). Valuing intellectual properties. In Sullivan, P. H. (Ed.) (1998). Profiting from intellectual capital: extracting value from innovation (pp. 335-356). New York, NY: John Wiley & Sons. Kogut, B., & Kulatilaka, N. (1997). Options Thinking and Platform Investments: Investing in Opportunity. California Management Review, winter, 52-71. Kossovsky, N. (2002). Fair value of intellectual property: an options-based valuation of nearly 8000 intellectual property assets. Journal of Intellectual Capital, 3(1), 62-70. Lev, B. (2001a, May). Intangible assets: measurement, drivers, usefulness. Paper presented at the Advances in the Measurement of Intangibles (Intellectual) Capital Conference, New York, NY. Lev, B. (2001b). Intangibles: Management, Measurement, Reporting. New York, NY: Brookings Institution. Lev, B., & Zarowing, P. (1998). The Boundaries of financial reporting and how to extend them (Working Paper). New York, NY: New York University. Lev, B., Sarath, B., & Sougianis, T. (1999). R&D Reporting biases and their consequences (Working Paper). New York, NY: New York University. Luehrman, T. A. (1998). Investment opportunities as real options: Getting started on the numbers. Harvard Business Review, 76(4), 51-67. Merton, R. (1973). The theory of rational option pricing. Bell Journal of Economics and Management Science, 4(spring), 141-183. Milost, F. (2005). Evaluation of Intellectual Capital. In Z. Vodovnik (Ed.), Intellectual Capital and Knowledge Management (pp. 353-363). Portorož, Slovenia: Faculty of Management Koper, University of Primorska. Mitchel, G. R., & Hamilton, W. F. (1988). Managing R&D as a strategic option. Research-Technology Management, 27, 15-22. Nazari J. A., & Herremans, I. M. (2007). Extended VAIC model: measuring intellectual capital components. Journal of Intellectual Capital, 8, 595-609. Nembhard, D. A., Nembhard, H. B. & Qin, R. (2005). A real options model for workforce cross-training. The Engineering Economist, 50, 95-116. Nevado, D., & López, V. R. (2002). Capital Intelectual. Valoración y Medición. Madrid: Prentice-Hall. Newton, D. P., & Pearson, A. W. (1994). Application of option pricing theory to R&D. R&D Management, 24, 83-89. Pakes, A. (1986). Patents as options: some estimates of the value of holding European patents stocks. Econometrica, 54, 755-784. Rodov, I., & Leliaert, P. H. (2002). FiMIAM: financial method of intangible assets measurement. Journal of Intellectual Capital, 3(2), 323-336. Rodríguez-Castellanos, A., & Araujo, A. (2005). Métodos para la valoración económico-financiera de los intangibles. In Rocafort, A. (Ed.), Doctor Mario Pifarré Riera. La Ciencia de la Contabilidad (pp. 763-783). Barcelona, Spain: University of Barcelona Press.

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Rodríguez-Castellanos, A., Arregui-Ayastuy, G., & Vallejo-Alonso, B. (2007). The financial valuation of intangibles: A method grounded on an IC-Based Taxonomy. In Joia, L. A. (Ed.), Strategies for information technology and intellectual capital: Challenges and opportunities (pp. 66-90). Londres: Information Science Reference. Roos, G., Pike, S., & Fernström, L. (2005).Valuation and reporting of intangibles - state of the art in 2004. International journal learning and intellectual capital, 1, 21-48. Roos, J., Roos, G., Dragonetti, N. C., & Edvinsson, L. (1997). Intellectual Capital: Navigating in the New Business Landscape. Houndmills, MA: Macmillan. Stewart, T. A. (1997). Intellectual Capital: The New Wealth of Organizations. New York, NY: Doubleday. Sveiby, K. (1997). The New Organizational Wealth: Managing and Measuring Knowledge-Based Assets. Brisbane, Australia: Berrett Koehler. Sveiby, K. E. (2005). Methods for Measuring Intangible Assets. Retrieved from http://www.sveiby.com/ articles/IntangibleMethods.htm Tobin, J. (1969). A general equilibrium approach to monetary theory. Journal of Money, Credit and Banking, 1, 15-29. Viedma, J. M. (2001). ICBS Intellectual Capital Benchmarking System. Journal of Intellectual Capital, 2(2), 148-164.

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Acknowledgment

Editing a book is a collective project, in which all the participants play an important role. For this reason, we would like to thank all the authors who believed in this project and gave their full encouragement for the success of this publication and thank to the reviewers their collaboration and patience during the process. Without they this book we’ll not be possible. In particular, we would like to express our gratitude to Domingo Garcia-Merino who, since the beginning of this endeavour, helped us with his responsiveness and good will. We would like to thank all the members of the Research Group in Financial Valuation of Intangibles of the University of the Basque Country that we belong to, in particular, Rebeca López, Nerea San Martín and Lidia García for their help in handling the activities involved in this project. And we also like to thank the team at IGI Global, especially Elizabeth Ardner and Dave DeRicco, for their dedication and good will. Last, but not least, we also like to thank the University of the Basque Country and the Emilio Soldevilla Foundation for the Research and the Development in Business Economics (FESIDE) for their support to our research. Belén Vallejo-Alonso Arturo Rodriguez-Castellanos Gerardo Arregui-Ayastuy Editors Research Group in Financial Valuation of intangibles (VALINTE) University of the Basque Country Business Administration Faculty, Bilbao –SpainApril 2010

Section 1

Identifying Intangibles

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Chapter 1

A New Perspective of the Intellectual Capital Dynamics in Organizations Constantin Bratianu Academy of Economic Studies, Romania

ABSTRACT Most pioneers of the intellectual capital studies developed static models able to describe the structure and the operational power of this new concept. Their contributions have been based on individual experience of dealing with tangible assets. According to these models, there is no time variable in the intellectual capital interpretation, and therefore there is no change or transformation. Intellectual capital is considered a stock with the following generic structure: human capital, structural capital, and relational capital. The purpose of this chapter is to present a dynamic model of the organizational intellectual capital, based on a new concept of integrators, and a new functional structure. Integrators are powerful fields of forces acting upon the employees of a company in order to generate synergy. Among the most important integrators we may think of leadership, management, processes and organizational culture. The new structure is based on knowledge, intelligences and values, as independent basic building blocks.

INTRODUCTION The concept of intellectual capital (IC) is a fuzzy concept, due to its various definitions and theories developed so far. Although it is a powerful concept, it is difficult to figure out how to measure it, due to its intangible nature. As Sullivan remarked (1998, p.19), “discussing intellectual capital management can be a frustrating experiDOI: 10.4018/978-1-60960-054-9.ch001

ence. Conversations that begin with apparent understanding can soon become confused and unclear”. This explanation comes from the semantic dynamics of the concept in time, and from the spectrum of meanings attached to this concept in different organizational contexts. Although IC studies have an intrinsic interdisciplinary nature, there is little cross-communication between researchers representing different backgrounds. However, despite all of these difficulties, we can intuitively understand the core meaning of this

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A New Perspective of the Intellectual Capital Dynamics in Organizations

powerful concept. Difficulties appear when we want to measure and to manage properly this IC at the organizational level. Most pioneers of the IC studies developed a static model of this concept, based on their experience on tangible assets. According to this model, there is no time variable in IC interpretation, and therefore there is no change or transformation. This is the simplest thinking model with respect to time, and it is a result of the static or inertial way of thinking our environment (Bratianu, 2007a). IC is considered as a stock at the organizational level using the metaphor of tangible assets (Edvinsson & Malone, 1997; Stewart, 1999; Sveiby, 1997). The knowledge as a resource metaphor underlying the concept of IC reveals the first root of the IC concept: the resource-based view (Andriessen, 2006; Andriessen, 2008; Andriessen & Van den Boom, 2007). IC is considered as a given potential composed of: human capital, structural capital and customer or relational capital. There are some variations of this structuring, but the basic ideas are the same. Organizational experience demonstrates every day that knowledge is in a continuous transformation process at both individual and organizational levels. In his metaphorical analysis of IC, Andriessen (2006) showed that Nonaka and Takeuchi used in their works some dynamic metaphors like “knowledge as a process” and “knowledge as action”. As Kianto explains, in this perspective “knowledge is understood as emerging from the ongoing interactions between the organizational members, and the focus is not on the intangible assets per se but on the organizational capabilities to leverage, develop and change intangible assets for value creation” (Kianto, 2007, p.3). However, even if the knowledge interpretation is changed from stock to flow, the IC model remains in the same static structure due to its paradigmatic interpretation. Meanwhile, research focus has been changed from the IC operational structure toward IC evaluation and measuring methods and models (Andriessen & Tissen, 2000;

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Arregui-Ayastuy et al. 2009; Green & Revilak, 2009; Mertins et al. 2009; Rodov & Leliaert, 2002; Rodriguez-Castellanos et al. 2007). “Basically, measuring consists in two tasks: one being the attempt to identify intangibles and put them into a structured order; and the other is the search for indicators that enables us to measure them” (Arregui-Ayastuy et al., 2009, p.53). The purpose of this chapter is to introduce a new perspective of the dynamic structure of the organizational intellectual capital. There are three new elements of this approach: 1. 2.

3.

The interpretation of organization as a multitude of fields of forces. The concept of integrator that operates at the organizational level to yield the intellectual capital potential. The organizational structure of the intellectual capital based on three independent entities: knowledge, intelligence and cultural values.

In our view, an integrator is a powerful field of forces capable of combining two or more elements into a new entity, based on interdependence and synergy. These elements may have a physical or virtual nature, and they must possess the capacity of interacting in a controlled way. The interdependence property is necessary for combining all elements into a system. The synergy property makes it possible to generate an extra energy or power from the working system. It makes the difference between a linear system and a nonlinear one. The dynamic characteristic of this new approach comes from the operational nature of the organizational integrators. The IC is not a stock obtained as a summation of different given constituents; it is a resulting potential of a dynamic field of forces generated by the organizational integrators. This potential can be less or larger than the aggregated sum of all organizational components. In the first case integrators have a negative operational intensity (Albrecht, 2003), while in

A New Perspective of the Intellectual Capital Dynamics in Organizations

the second case they have a positive operational intensity generating synergy and syntropy. This chapter will explain the dynamics of the pattern and the way in which integrators contribute to the formation and development of the IC, according to the operational power of each integrator. The most powerful integrator is leadership since it contains both cognitive and emotional fields of forces, and its action is strongly nonlinear. For the same organization, a great leader can increase the potential of the intellectual capital, while a weak leader will get a much lower potential for it. This new perspective has pragmatic implications for executives since it shows the ways to increase the potential of the organizational intellectual capital.

THE INTELLECTUAL CAPITAL ARENA Defining Intellectual Capital In a well documented paper dedicated to the IC research, Roos and Pike stated that “one of the reasons why it exists at all is that despite everything that has occurred, been invented and reported, intellectual capital as a discipline has still to attain widespread recognition in the business community. It is still far from attaining widespread understanding or use” (Roos & Pike, 2007, p.2). Reviewing 220 papers published over 7 years in the Journal of Intellectual Capital, they concluded that the most concerning issue of this research was “the enduring confusion concerning the categorization of IC and the taxonomies utilized” (Roos & Pike, 2007, 17). The IC arena reflects three types of confrontations, which are actually interlinked from conceptual and operational viewpoints: defining the concept of IC, structuring the organizational IC, valuing and measuring the IC of an organization. The diversity of definitions and interpretations comes from the fact that the research field of IC is by its nature an interdisciplinary domain allowing researchers

from all kind of disciplines to enter this arena and to express their own concepts and theories. Thus, the same reality is reflected simultaneously in many cognitive mirrors resulting in a wealth of images. Since there is no such thing as the best mirror or the best shaped image, we have to live with this diversity and to make efforts only to understand their fundamental features. In a theoretical analysis of this variety of definitions and conceptual views concerning IC, Arenas and Lavanderos (2008) identified several schools of thought: symbolic, connectionist, enactive and relational. The most contributions have come so far from the symbolic school that considers knowledge as being an object. We may illustrate this approach by considering one of Stewart’s formulations: ”Intellectual capital is the sum of everything everybody in a company knows that gives it a competitive edge…Intellectual capital is intellectual material–knowledge, information, intellectual property, experience–that can be put to use to create wealth.” (Stewart, 1999, p.XI). In this perspective, knowledge can be added up like apples and the IC is the result of this summation process. This formulation is based on two assumptions: a) knowledge is like any physical object which can be subject to arithmetic operations; b) IC is a result of a linear process of aggregation. Concerning the first point of view it is an interesting remark made by Fisher in the beginning of the last century, presented by Nerdrum and Erikson (2001, p.127): “A stock of wealth existing at an instant of time is called capital. A flow of services through a period of time is called income”. Thus, the concept of capital is strongly related to the concept of stock. No wonder that so many authors provide definitions based on this interpretation. A similar perspective is described by Saint-Onge, another pioneer of developing IC research field, in an interview taken by Chatzkel (2000, p.103): “As such, the intellectual capital framework is a representation of the ‘stocks’ of intangible assets, while knowledge is the electrical current that runs between these assets to grow the human,

3

A New Perspective of the Intellectual Capital Dynamics in Organizations

structural and customer capital”.Andriessen (2006) underlines the metaphorical nature of IC, based on the seminal works of Lakoff and Johnson (1980, 1999). He considers that the concept of IC refers to the resources of a company, i.e. on the metaphor of “knowledge as a resource”, but it is also more specific as it expresses “knowledge as capital”. In this metaphor, “capital” represents the source semantic domain, and “knowledge” represents the target semantic domain. One of the most important characteristics of the source domain with respect to management and organizations is that of linearity, since it dominated for about one hundred years the managerial thinking. In order to understand if this property can be projected into the target domain, Bratianu (2009) performed a theoretical investigation of the necessary mathematical conditions for a given field to be considered a linear space. He considered step by step the rules for addition and multiplication required by the linear space, and he analyzed the way in which these rules are satisfied or not in the target domain. This way, Bratianu (2009, p.422) demonstrated that the defining rules of a linear space cannot be satisfied by the “knowledge” domain, which means that the knowledge field is strongly nonlinear. In other words, “linearity is like a frontier in the metaphor ‘knowledge as capital’. Understanding knowledge means to break away with the classical linear thinking, and to embrace the new nonlinear thinking”. In conclusion, all definitions considering IC a result of a linear aggregation of knowledge assets of a generic organization are limited by the linearity frontier. Roos et al. (2005, p. 19) present a more general definition for the IC: “Intellectual capital can be defined as all nonmonetary and nonphysical resources that are fully or partly controlled by the organization and that contribute to the organization’s value creation”. This definition is like an umbrella for many others, but it remains linked to the resource based view of an organization. A total different view is presented by Harrison

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and Sullivan (2006, p.21): “from the perspective of managers within the organization, there are only two kinds of intangibles management: the management of intellectual property (IP) and the management of the rest of the company’s intangibles, its I-stuff. We came to understand that the path to sophistication in I-stuff management is different from that for IP management”. Thus, the IC of an organization is broken into distinct components, based on the criterion of knowledge codification and ownership. There are at least three key differences between IP and I-stuff coming from: (1) definition, (2) focus, and (3) capability for leverage. The most important issue is that all intellectual property component can be codified following a set of legally mandated requirements, in order to obtain ownership protection. The other IC component, i.e. I-stuff has no such codification requirements. Actually it is rather difficult to codify it, since I-stuff may include relationships and know-how, which encompass both values and intuition as well as physical skills and capabilities. It may be individual pieces of knowledge or know-how; it may be a community of practice within the organization; or it may be an organization-wide capability.

Structuring Intellectual Capital One of the pioneers of intellectual capital, Karl Erik Sveiby classified the invisible assets of an organization into three components: employee competence, internal structure, and external structure (Sveiby, 1997). In his view, employee competence reflects the capacity of people to produce tangible and intangible assets. Some authors may say that employee competence does not belong to organization but only to individuals, and thus it cannot be considered as an asset. However, Sveiby argues that employee competence should be included in the balance sheet since it is hard to conceive of any organization without people. The internal structure includes patents, concepts, methods, models, organizational procedures and processes

A New Perspective of the Intellectual Capital Dynamics in Organizations

descriptions, as well as administrative systems. All of these elements are created by employees and owned by the company. Also, Sveiby considers that organizational culture is a part of this internal structure. Actually, this internal structure together with employees constitutes what is generally understood as being the organization. The external structure includes relationships with customers and suppliers. It also contains brand names, trademarks, and the company’s reputation. External structures are based mostly on knowledge flows created by intangible assets. This classification has been made based on the axiomatic view that any organization is characterized by an internal environment, an external environment and an interface between them. Also, any internal environment is composed of people and a functional structure. Actually, this is the systemic approach to organizations: each organization is considered a functional triad composed of a system, a structure and a process. This axiomatic view developed by Sveiby has been accepted by consensus by most researchers, with some variations concerning the basic structure and the denomination of components. The intellectual capital can be considered of being composed in its essence of: human capital, struc-

tural capital and relational capital (Figure 1). It is a convergence toward the Edvinsson’s taxonomy (Roos & Pike, 2007). Human capital is just another name, may be more comprehensive given to what Sveiby was considering the employee competence. Human capital matters because it is the source of innovation and renewal of the company. Main components of this human capital are: tacit and explicit knowledge, brain power or processing capacity, empathy, ability to build personal networks, social intelligence, motivation, ability to innovate, ability to adapt, ability to share knowledge, ability to learn, ability to communicate, endurance and perseverance. Structural capital is generated by the organizational and functional structure of organization. It is that part of the IC that remains in place when all employees go home. It is the support or infrastructure that companies provide to their human capital. It is the nonhuman embodiment of the accumulated knowledge that has been developed by the organization and suppliers to the organization in order to create value for organization. It is composed of functional connections between people, organizational regulations and behavior, software and data bases, communication networks, company know-how, brands and patents, organizational

Figure 1.

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A New Perspective of the Intellectual Capital Dynamics in Organizations

culture with all of its components. Sharing and transferring knowledge requires structural capital. It is the software environment which multiply or not the individual knowledge and intelligence. It is an organizational knowledge based competence to provide a competitive advantage for the company. Relational capital is derived from the Sveiby’s external structure. Thus, it is oriented toward customers and market. Relational capital reflects the organization business relations with all its stakeholders. However, the most important relations are established with the company suppliers and customers. It is closely related to relationship marketing and business intelligence. Relational capital becomes extremely important in e-business, where external structure is much more developed than the internal structure. The competitive analysis of the market, the open innovation process and strategy elaboration are all based on the potential of relational capital. A different perspective on structuring the IC potential of an organization is developed by Schiuma et al. (2008), and Schiuma (2009). Their model is based on the concept of knowledge asset: “We define a knowledge asset as any resource made of or incorporating knowledge which provides an ability to carry out a process or a function aimed to create and/ or deliver value. It can be a tangible or an intangible resource” (Schiuma et al., 2008, p.287). Thus, these authors consider that many tangible resources can be interpreted as knowledge assets, since they embody codified knowledge. It is a rather confusing definition since, ultimately, any physical object embody more or less knowledge. There is actually very difficult to separate those tangible assets which are declared knowledge assets, and those tangible assets which cannot be accepted as knowledge assets. Moreover, their definition makes reference to the codified knowledge, but many tangible assets incorporate the worker’s tacit knowledge. Ignoring this need of an operation criterion to separate tangible assets into those which are eligible for knowledge assets and those

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which are not, these authors developed a the model of the knoware tree. This tree contains the following branches: wetware–knowledge assets related to human resources, hardware–physical infrastructure embodying critical knowledge, netware–knowledge assets related to relationships, and software–intangible infrastructure representing critical knowledge. The domain containing wetware and netware is called the structural knoware. The domain containing hardware and software is called stakeholder knoware. The intersection of these two domains represents the set of knowledge assets. This model helps managers to understand the importance of knowledge assets in value creation and realizing the competitive advantage: “The ability of an organization to harness knowledge assets dynamics lies at the core or organizational value creation capacity. Knowledge assets are the building blocks of organizations’ capabilities that affect the success of an organization in the economic and competitive environment” (Schiuma, 2009, p.290).

Valuing and Measuring Intellectual Capital Experience demonstrates how difficult is to evaluate or measure something which is intangible. Thus, the tendency has been to use by extension systems and methods developed for tangible objects for valuing and measuring intangible objects. The most important observation we have to make is that most of the measuring systems devised and used for tangible objects are based on linear properties and linear thinking, while the IC filed is full of nonlinear intangibles. The linearity property used in the physical space becomes a limitation, a frontier in the knowledge field which is strongly nonlinear. “Organizational knowledge is not a result of summing up the individual contributions of all the employees, like in Physics. It is a result of their integration and of the synergy effect” (Bratianu, 2009, p. 422). The synergy effect makes actually the difference

A New Perspective of the Intellectual Capital Dynamics in Organizations

between a linear and a nonlinear system. Thus, the linearity frontier cannot go beyond the static interpretation of the IC, interpretation based on the assumption that knowledge is like any other tangible asset. The starting point in performing valuation and measurement in the IC domain should not be the physical world but the main goal of developing them. In other words, what is the motivation behind developing models and methods for valuation and measurement of business intangibles. One of the most frequently given answer is that “what you can measure, you can manage, and what you want to manage, you have to measure. IC theory represents the fusion between these two streams of thought. IC is concerned with how better to manage and measure knowledge and other intangibles in the company” (Roos et al, 1997, p.7). In this context, valuation of IC is important for understanding the competitiveness of the company with respect to other companies on the market, and measuring the IC is important for the company’s management to create strategies able to yield its competitive advantage. Valuation is a process requesting an object to be valued, an operational framework, and a criterion that reflects the usefulness or desirability of the object. If instead of a certain criterion we may define a metric that relates to an observable phenomenon, then the generic valuation process becomes a measurement process. In a general context, measurement means to make use of a set of observations to reduce uncertainty where the result is expressed as a quantity (Hubbard, 2007). Reduction in uncertainty is critical in business, since decision making is strongly related to the degree of uncertainty and the risks associated to it. The main problems of designing a model for measuring IC is to define a set of meaningful indicators, able to reflect the essence of the company intangibles. These indicators may be expressed in different units: pure number, percentages, ratios, number of hours, of workers, of books, of published papers, of computers, of patents, of in-

novations etc. There is a tendency to increase the number of indicators in order to get more information. However, the more indicators one uses the more difficult would be to get adequate statistical data for them and then, to compare two different companies base on such a variety of indicators. In designing an IC index it is recommended to think first of the critical knowledge one should obtain and then to define a minimum number of indicators able to reflect that critical knowledge. In order to illustrate this important issue, I shall refer to the 63rd Regulation of the Ministry of Education, Science and Culture of the Republic of Austria concerning the Intellectual Capital Report Act - ICRA, designed for universities. It is stated that “The IC report aims at presenting, evaluating and communicating intangible assets, performance processes and their consequences and services as a qualitative and quantitative basis for generating and entering a performance agreement” (ICRA). However, looking at the indicators defined for this IC Index one would easily observes the discrepancy between the goal and the means. Many indicators have nothing to do with IC of the university, and those possible indicators which could reflect much better the potential of IC are not there. For illustration I present in the next paragraph the indicators defined for measuring the structural capital of the university. The lesson is that tangibility and linearity remains two important barriers in understanding the real essence of the IC. Just look at the last indicator: II.2.11 Floor space in m². According to it, a university with a larger floor space would have necessarily a larger IC potential. This kind of interpretation suggests a clear linearization of the IC domain, destroying this way the main specific of it, and also the vulnerability of the static structuring of IC into human capital, structural capital and relational capital. Section “II.2 Intellectual property: structural capital” includes the following indicators: II.2.1.Funding for measures promoting equal opportunities for men and women and affirma-

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A New Perspective of the Intellectual Capital Dynamics in Organizations

tive action for women in Euro; II.2.2 Funding for measures advancing gender specific education and research/ development and promotion of the arts in Euro; II.2.3 Number of staff active at special institutions; II.2.4 Number of staff active in institutions for students with special needs or with chronic disorders, or both; II.2.5 Funding for specific measures for students with special needs or chronic disorders, or both, in Euro; II.2.6 Funding for measures for balancing work/studies and family/private life for females and males in Euro; II.2.7 Cost for available online research data bases in Euro; II.2.8 Cost for available scientific/art journals in Euro; II.2.9 Total funding for large equipment for research and development / development and promotion of the arts; II.2.10 Proceeds from sponsoring in Euro; II.2.11 Floor space in m².

A NEW DYNAMIC PERSPECTIVE ON INTELLECTUAL CAPITAL Organizational Integrators In the IC literature it is almost axiomatic the fact that the IC is conceived as a given potential, obtained by linear aggregation of all knowledge components. As Stewart put it, IC represents the sum of everybody’s knowledge that gives the company a competitive advantage (Stewart, 1999). This is a result of the linear thinking, based on the proportionality relation between the outcomes of a process and its inputs (Bratianu, 2007a; Bratianu & Murakawa, 2004). The everyday experience demonstrates that the IC of any organization is not a linear aggregation of the individual knowledge and intelligence components, and therefore its value is not the sum of these possible components. If in a linear space we have: 4 employees + 6 employees = 10 employees, in a nonlinear space we have: the value of 4 knowledge workers + the value of 6 knowledge workers ≠ the value of 10 knowledge workers. Thus, the value of the

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IC potential cannot be equal to the sum of all individual knowledge value. In such a nonlinear space we find in any organization the resulting IC might be disappointing if the Albrecht Law will apply: “Intelligent people, when assembled into an organization, will tend toward collective stupidity” (Albrecht, 2003, p.4). Thus, even if the constitutive components are valuable from knowledge and intelligence viewpoints, the IC potential is much less than the linear sum of them all. This organizational incapacity is not a necessary or inevitable part of the life of an enterprise. It could be only a result of a lack of leadership and of an efficient management. Business means people and people means motivation and brain power which are strongly nonlinear phenomena. Putting them to work together is not enough to get the best out of them and to create a high potential of organizational IC. Integrators. The synergy effect can be generated only when there is a field of forces able to align all the efforts made by all employees, in a perfect timing and rhythm set up by the management. This field of forces is capable of integrating individual knowledge, individual energy and power generated by each employee during the work, and individual motivation to be the best. The final energy and motivation effect represents more than just the summation of individual energy and motivation contribution of each employee. The combining process of individual contributions is highly nonlinear in such an organizational environment, and the final result depends strongly on the power of the integration field of forces. We consider to be both of theoretical and practical interests in understanding and performing much better the organizational analysis and IC creation to introduce as a new construct the concept of “integrator”. By definition, An integrator is a powerful field of forces capable of combining two or more elements into a new entity, based on interdependence and synergy. These elements may have a physical or virtual

A New Perspective of the Intellectual Capital Dynamics in Organizations

nature, and they must possess the capacity of interacting in a controlled way. To understand this new construct think about the gravity field of the Earth, or the magnetic field created by a magnet. These fields are able to structure in a certain way all the elements whose physical properties are sensible to them. For instance, a piece of metal can be moved and arranged according to the force lines of the magnetic field, while a piece of wood would not be influenced by such a magnetic field. On the other hand, thinking at organizational level the interdependence property is necessary for combining all elements into a system. The synergy property makes it possible to generate an extra energy or power from the working system. It makes the difference between a linear system and a nonlinear one. In the case of a linear system, the output is obtained through a summation process of the individual outputs. For instance, a mechanical system made of rigid frames works in a linear regime, while a complex electrical system works in a strongly nonlinear regime. In the first case, there is only interdependence and no synergy. In the second case, there is both interdependence and synergy. In organizational behavior, we can talk about linear work in groups and nonlinear work in teams. In the first case, sharing the same goal but not the same responsibility leads to interdependence and a linear behavior. In the second case, sharing the same goal and the same responsibility leads to interdependence and synergy, which means a nonlinear behavior. However, synergy is not a guaranteed effect. It must be obtained by an intelligent team management. We can say that this team management acts as an integrator at the team level. In the case of a nonlinear system, the output might be larger or smaller than the sum of all individual outputs. For instance, when the integrator’s operational value is positive, the final result is larger than just the sum of all aggregated components, and when the operational value is negative, the final result is smaller. In this last

situation, the integrator’s action is actually to disintegrate the organization. A good example could be in this case the top management of ENRON. The theory of integrators is strongly related to strategic thinking and strategic management. In the internal environment analysis of any company we talk about operational capabilities and dynamic capabilities (Carpenter, 2007; Hitt et al, 1999; Teece, 2009). Operational capabilities refer to a firm’s skill or capacity in using both tangible and intangible resources in an integral way to create goods and services. A synonym that is frequently used to describe the same concept is competences. By contrast, dynamic capabilities refer to high level activities that reflect the management capacity to sense and seize opportunities. As Teece (2009, p.54) emphasizes, “Dynamic capability is a meta-competence that transcends operational competence. It enables firms to not just invent but also to innovate profitably”. However, in these approaches both operational capabilities and dynamic capabilities are assigned to the whole organization. This is a rather vague approach since in any organizations there are many fields of forces generating processes, but only some of them can develop such capabilities. Introducing the concept of integrator we can identify and define each field of forces that is able to integrate tangible and intangible resources and develop operational and dynamic capabilities. Integrators are actually the generators of any process leading to synergies in organization. Integrators are much more powerful than aggregators since integration is a nonlinear process while aggregation is a linear one. For instance, when we talk about management we must be able to distinguish between administrators, managers, entrepreneurs and leaders. While administrators aggregate resources and perform routines, leaders integrate tangible and intangible resources and deploy them along the line forces of their vision. Thus, leaders can be considered integrators. The theory of integrators is a step further of the theory of core competences (Hamel & Prahalad,

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A New Perspective of the Intellectual Capital Dynamics in Organizations

1994), the theory of dynamic capabilities (Teece, 2009), and the theory of balanced scorecard (Kaplan & Norton, 1996), at least from the following viewpoints: •

•

•

•

•

The organization is considered as a multitude of fields of forces convergent with its mission, but acting in different ways due to their different nature and existence laws. These fields of forces are nonuniform and dynamic. Their gradients generate fluxes that create processes with different operational intensities. These fields of forces may have tangible resources as basic generators, or intangible resources like knowledge. Whenever such fields of forces are generated by intangible resources, they have a nonlinear nature. In this situation, results are not anymore proportional to the initial efforts. Some of these fields of forces are powerful enough to combine two or more elements into new entities, based on interdependence and synergy. They are called integrators. Integrators play the fundamental role in generating the intellectual capital of an organization. For the same given resources an organization may have a low or a high potential of the intellectual capital, as a direct result of integrators’ operation.

The theory of integrators contains many ideas from the strategic management and from the above mentioned theories, but it frames the new field perspective of the organization by comparison with the energy field in physics. Even knowledge is considered as a field of ideas and emotions, a vision beyond that of stocks and flows used in literature. In the next paragraphs we shall describe some of the most important integrators for any organization: technology and associated processes, management and leadership, vision and mission, and organizational culture.

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Technology and associated processes. In classical industrial companies, technology and its associated processes put people to work together in different chain sequences and assembly line. These are linear systems based on interdependence and technological fluxes. These companies have a linear organizational structure such that people can change their places or can be replaced by others without any change in the final result, as much as their contributions are according to their job requirements. Think about the famous Ford’s assembly line for automobiles, where each worker was assembling only one piece to the whole car body. There is no synergy for such a mechanical assembly line. Let us consider now a modern car manufacturing company, where all processes and technologies have been interconnected based on the concurrent engineering philosophy. That means to create a powerful IT system as a core framework and to allow many processes to develop simultaneously and interactively, generating this way the synergy effect. Also, using computer integrated manufacturing (CIM), modern plants realize a total integration of product design and engineering, process planning, and manufacturing by means of complex computer systems (Krajewski & Ritzman, 1999). In this new production context, technology and its associated work processes act as a powerful integrator. The main role is played by the IT system, which is an excellent explicit knowledge integrator. In the new economy organizations where the intangible resources became much more important than the tangible ones, the synergy effect of the IT is felt stronger, and integration power increased almost exponentially. The future will open new opportunities for this integrator, increasing its role in acting not only upon individual knowledge, but also upon individual intelligence (Hayes-Roth, 2006). Management and leadership. Management is, by its own nature an integrator, much more powerful than technology and its associated processes. It acts upon the individual knowledge transforming it into team and organizational knowledge, and upon

A New Perspective of the Intellectual Capital Dynamics in Organizations

the individual intelligence transforming it into team and organizational intelligence. The technology integrator is capable to act only upon the explicit knowledge, which is codified in a certain way. The management integrator can act upon both explicit and tacit knowledge, generating explicit organizational knowledge and tacit organizational knowledge. The management process is intimately related to the production process, such that in an old type of manufacturing plant there is an old type of industrial management. In this situation, if the technology is very close to a linear system, the management will be predominantly linear and the synergy effect will be very small. Of course, workers are not machines but their activities are designed to be fuelled mostly by their energy and practical knowledge. The integrator will produce little organizational knowledge. On the other hand, in the new economy companies, where the technology integrator is highly nonlinear, the management must be also highly nonlinear in order to match the process requirements. The final output in this situation contains large synergy and the organizational knowledge contributes greater to the IC. However, we may find some anomalies as well. For instance, a university is a highly nonlinear value system. However, if the academic management is based on linear thinking patterns, and linear decision making processes, the integration effect will be very small. I am considering especially universities from the former socialist countries, where the linear thinking and decision making is still very powerful and very inefficient. In these situations, the academic management is a poor integrator with very little synergy effects on the organizational intellectual capital. I am not going to open the debate concerning the overlapping meanings of management and leadership, or their definitions. But I am going to consider a continuum between management and leadership, with a driving force oriented from the left hand side toward the right hand side. Far away to the left, I shall consider the linear management, and far to the right, I shall consider leadership.

Somewhere in the middle is situated the nonlinear management. The industrial era management is situated to the left, while the new economy management is situated in the middle. That means that leadership is a much stronger integrator than the new management since it acts especially on the individual intelligence and the individual core values of employees. While the management is emphasizing the integration process of individual knowledge and individual intelligence, leadership is emphasizing especially the integration process of individual intelligence and individual core values. Also, management is using mostly the cognitive knowledge, while leadership is using mostly the emotional knowledge. Thus, leadership is a strong integrator with a powerful impact on the generation of organizational IC. Great companies have great leaders, capable to inspire all the employees with their force of vision and motivation (Mankes, 2005; Collins & Porras, 2002; Welch & Welch, 2005). Great companies run by leaders succeed in generating greater IC than companies run by managers. Thus, from a practical point of view, in order to increase the organizational intellectual output, it is necessary to move from the operational management toward the strategic management and leadership. Vision and mission. Just continuing this above idea, moving toward strategic management, I shall put forward the vision and mission statement for a company. Vision or the strategic intent is the “leveraging of a firm’s internal resources, capabilities, and core competencies to accomplish the firm’s goals in the competitive environment” (Hitt et al., 1999, p.24). Vision means a projection into the future of the company, a projection capable of a strong motivation and inspiration for all employees. An application of this vision in terms of products to be offered and markets to be served constitutes the company mission. Thus, the strategic mission is externally focused. An effective strategic mission establishes a company’s individuality, and it should be inspiring, exciting and relevant to all stakeholders (Dess et al.,

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A New Perspective of the Intellectual Capital Dynamics in Organizations

2006; Thompson & Strickland, 2001). Together, the company vision and mission constitutes a powerful integrator, which acts especially on the emotional intelligence and core values of all the employees and stakeholders. Great leaders know how to use this integrator in generating valuable organizational intelligence and driving forces for elaborating and implementing successful strategies. Since emotions have a strong nonlinear nature, this integrator is capable of generating much more synergy than the previous integrators acting mostly on knowledge. Organizational culture. Peters and Waterman were among the most convincing authors in emphasizing the great importance of corporate culture in achieving excellence. As they conclude in their research of the best-run companies, “The excellent companies are marked by very strong cultures, so strong that you either buy into their norms or get out. There’s no halfway house for most people in the excellent companies” (Peters & Waterman, 1982, p.77). A strong organizational culture is a system of core values, traditions, symbols, rituals and informal rules that spells out how people are to behave most of the time. Companies that have developed their personality by shaping values, making heroes, spelling out rites and rituals, and acknowledging the cultural network have an edge over the others. These companies have values to pass along their life, not just products and profits. Organizational culture is a very powerful integrator since it acts especially on the individual intelligence and individual core values, generating the spirit of excellence. However, the organizational culture can produce also adverse results if its core values are based on fear and punishment, and there is a mismatch between corporate interests and individual core values. Great leaders have always understood the importance of the corporate culture and thus they contributed first in developing a strong, and stimulating culture. As an integrator, organizational culture contributes especially in building up an IC with a great potential for innovation. Also, it can play a

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significant role in strategic and change management, in being a source of high reliability, and in crafting a successful organizational behavior (Griffin & Moorhead, 2006; Weick, K.E., 2001).

The New IC Structuring As shown previously, the static structure of IC presented in Figure 1 contains as building blocks human capital, structural capital and relational capital. However, these basic constituents of IC are not independent entities, making the whole scheme vulnerable from semantic point of view, and from interpreting the measurement results. Actually, their interdependence lead to the fact some of their inner constituents are counted several times when measuring is performed. This situation may lead to wrong interpretations and less adequate decisions. We shall try to go a step further in decomposing each of these entities and analyze their interdependence. Human capital is a dynamic result of the operation intensity of these integrators presented above upon the employees of the company. For each employee the IC contribution can be thought in terms of knowledge, intelligence and values. Thus, through the action of each integrator, the individual contributions of employees will be integrated into organizational knowledge, organizational intelligence and organizational values. That means that at the level of a generic organization the human capital can be decomposed into its basic constituents: knowledge, intelligence and values (Figure 2). These new basic constituents are independent entities and they eliminate the risk of overlapping measurements. Knowledge. Knowledge is a fuzzy concept, yet it is a fundamental concept in understanding intellectual capital. It is fuzzy because during its long history, it has been defined and used by specialists coming from different scientific fields, who defined the concept from their own perspective (i.e., philosophy, epistemology, psychology, mathematics, physics, etc.), and tried very hard

A New Perspective of the Intellectual Capital Dynamics in Organizations

Figure 2.

to match their definition to the research goal. Also, the concept is context dependent, which means to adapt its meanings to a given social, economical, political, cultural and scientific environment. Due to this cultural and scientific dependence, Nonaka and Takeuchi (1995) identified two streams of semantic evolution: the Cartesian dualism of body and mind, and the Japanese oneness of the body and mind. Descartes’ ideas about knowledge have been integrated into a very simple and intuitive expression which made history: Cogito ergo sum. This expression which means ‘I think, therefore I am’ constitutes the kernel of Descartes’ theory of knowledge, and contains what it is most important in his philosophy. Cogito ergo sum makes mind more certain than matter and my mind (for me) more certain than the minds of others. The conclusion is that my existence is derived from the fact that I think. As Russell (1972, p.565) comments the consequences of Descartes statement Cogito ergo sum: “If I ceased to think, there would be no evidence of my existence. I am a thing that thinks, a substance of which the whole nature or essence consists in thinking, and which needs no place or material think for its existence. The soul, therefore,

is wholly distinct from the body and easier to know than the body; it would be what it is even if there were no body.” Thus, the Cartesian doubt contributed in a definite way to this general perspective that ‘mind’ and ‘body’ are and operate as two distinct entities, and only the ‘mind’ is responsible for knowledge generation and knowledge processing. Thus, knowledge is a field of concepts and ideas, produced in a rational way. In the Japanese tradition, there is a strong emphasis on the ‘oneness of body and mind’. They are integrated into one entity and knowledge can be acquired through direct experience of the body. This tradition emphasizing bodily experience has contributed to the development of Zen Buddhism in medieval times. It is an ultimate state of being that Zen practitioners seek by means of internal meditation and disciplined life. According to this tradition, ‘samurai’ had to develop their wisdom through physical education. Physical exercises contributed not only to the building and strengthening samurai bodies but also to the formation of their character, which included a certain way of thinking. Thus, in contrast to Descartes emphasis on the mind, the Japanese epistemology tends to value the embodiment of direct, personal experience.

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A New Perspective of the Intellectual Capital Dynamics in Organizations

In Zen Buddhist, training students are required to devote themselves to the world of nonlogic throughout their learning process. In his famous Book of Five Rings, the legendary Miyamoto Musashi stresses the importance of developing understanding and strategy in martial arts through direct experience (Kaufman, 1994, p.13): “In order to be able to determine the possible outcomes of combat situations you must constantly maintain the proper attitude by practicing diligently. You can only fight the way you practice. By maintaining the proper attitude, you will always practice diligently with the proper spirit and ensure your ability to become that much stronger.” By comparing these two different streams of thoughts, it results that in the Cartesian view mind is fully rational, and knowledge has the same nature. It can be obtained through a knowledge transfer process from other people or through an internal reasoning process. We deal with ‘explicit knowledge’, i.e., knowledge which has a rational root and which can be transferred, explained, shared, accumulated and processes as it is. In the Japanese view, mind and body integrate themselves into a coherent process of knowing. Thus, knowledge can be obtained individually through a direct experience, or it can be obtained through a transfer process. Polanyi (1983) defined the knowledge obtained through a direct experience of life as tacit knowledge. The other type is defined as being explicit knowledge. The oneness conception about our thinking and understanding life consider that knowledge should be the outcome of the fusion process between tacit knowledge and explicit knowledge (Allee, 1997; Baumard, 2001; Davenport & Prusak, 2000; Nonaka & Takeuchi, 1995; Polanyi, 1983). Think about people living in equatorial zones where there is never snow. They heard, read and may be viewed in movies snow. Thus, they have the explicit part of the concept of snow. Yet, they have never had the chance to touch, to smell or taste snow. Thus, their understanding about snow is incomplete and their concept about snow is somehow different

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than that of people living in countries where wintertime brings in snow. In this perspective, the tacit knowledge is very powerful: “I shall reconsider human knowledge by starting from the fact that we can know more we can tell. This fact seems obvious enough; but it is not easy to say exactly what it means” (Polanyi, 1983, p.4). Knowledge integrates both theoretical and practical aspects of life and sciences. It is both rational and nonrational, abstract and concrete, based on inner feelings and the impact of the environment upon us. In Honda Company, there is a tradition called y-gawa according to which people working especially in R&D have training sessions for three days, in which they must be able to share their knowledge beyond the rational mind (Kobayashi, 2009). In the theory of integrators perspective knowledge is considered a field of ideas and emotions which can be defined at individual and organizational levels. Thus, we deal both with cognitive and emotional knowledge. Intelligence. Intelligence is also a fuzzy and debatable concept. As Hawkins remarks, “Fortunately, we live at a time when the problem of understanding intelligence can be solved. Our generation has access to a mountain of data about the brain, collected over hundreds of years, and the rate at which we are gathering more data is accelerating. The United States alone has thousands of neuroscientists. Yet, we have no productive theories about what intelligence is or how the brain works as a whole” (Hawkins, 2004, p.2). In the classic psychometric view, intelligence is defined operationally as the ability to answer items on tests of intelligence. Binet created for the first time these IQ (intelligence quotient) test about 100 years ago. Since then, many contributors refined the IQ tests and developed them as a powerful tool of predicting job performance. These tests measure a general intelligence factor called g, sometimes composed of two parts (i.e., Gf: fluid intelligence; Gc: crystallized intelligence). However, we have to make a clear distinction between the concept of intelligence, IQ and g factor. Intelligence, in

A New Perspective of the Intellectual Capital Dynamics in Organizations

this classic view, represents a cognitive ability of a given person; The IQ is an index whose value represents a certain ability to solve different problems imagined by psychologists as items in the test formulation; g is a theoretical construct to represent a general cognitive ability of a given individual (Jensen, 1998). Gardner, a professor from Harvard University came up with another perspective. He made actually a semantic reengineering of this concept, by defining multiple intelligences (Gardner, 1983). In his view, “Multiple intelligences theory pluralizes the traditional concept. An intelligence is a computational capacity–a capacity to process a certain kind of information–that originates in human biology and human psychology” (Gardner, 2006, p.6). An intelligence reflects the ability of solving problems and crafting products in a given community and cultural environment. Gardner defined the following intelligences: musical, bodily-kinesthetic, logical-mathematical, linguistic, spatial, interpersonal and intrapersonal. Each intelligence reflects a certain capacity to process information and knowledge and to contribute in solving a problem or to fashion a product. I would like to emphasize the fact that this new view is compatible with the framework of knowledge. If the classical intelligent theory was related actually to the capacity of processing explicit knowledge, the multiple intelligences can process both tacit and explicit knowledge. In this new perspective it is also possible to accept the emotional intelligence concept, dealing mostly with the individual experience and behavioral pattern (Goleman, 1995). In the theory of organizational intellectual capital knowledge and intelligence are considered as two independent entities, since intelligence acts as an intangible processor of knowledge. Values. Value is a fuzzy concept, yet a fundamental one in the decision-making process, and in defining the behavior pattern. We refer here to the moral and ethical values, not to the financial worth of goods and services. Thus, values represent beliefs about what is right and wrong and what is important in life. Any judgement based

on such beliefs and not on facts is called a value judgement. As such, values should be understood as driving forces in decision making. As Keeney showed “Values are more fundamental to a decision problem than are alternatives. Just ask yourself why you should ever make the effort to choose an alternative rather than simply let whatever happens happen. The answer must be that the consequences of the alternatives may be different enough in terms of your values to warrant attention” (Keeney, 1992, p.3). The relative desirability of consequences resulted from a decision is based on values. This may explain the importance attached to the full spectrum of individual values, in order to anticipate the vectors of this individual decision-making process. The value system works like a kernel of our personality, being deep rooted in our education from early childhood. This is a system of evaluative beliefs concerning the relative worth of a person, place, event or thing. We are born in a given cultural matrix and we receive all these cultural, ethical, moral and religious values from family, school, church, community, university and company. Values are learned, usually from people whom we love, respect or highly trust. Many of us might therefore look upon the altering of a value system not only as a rejection of a specific value, but also as an unconscious rejection of the person from whom we learned that value. This explains why most attempts at altering a value through direct confrontation are typically met with failure. Yet, values can be changed and managing excellence is based on creating a system of values able to sustain such an outstanding goal. If we agree that human capital at both individual and organizational levels can be structured into these categories–knowledge, intelligence and values–then, it easy to demonstrate that structural capital and relational capital can be structured into same constituents. Thus, knowledge, intelligence and values become the building blocks of each component of the IC: human capital is composed of knowledge, intelligence and values; structural

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A New Perspective of the Intellectual Capital Dynamics in Organizations

Figure 3.

capital is composed of knowledge, intelligence and values; relational capital is composed of knowledge, intelligence and values. That means that human capital, structural capital and relational capital are not independent entities and that any evaluation model of them will not be able to avoid overlapping of the basic constituents (i.e. knowledge, intelligence and values). This new structuring features are shown in Figure 3, with all functional connections. Considering the new independent entities of knowledge, intelligence and values as basic building blocks, and introducing the concept of integrators as well, we get a new configuration of the organizational IC, which is illustrated in Figure 4. In this new perspective, the potential of the IC is not a direct result of the static addition of the human capital, structural capital and relational capital, but a dynamic evaluation of the knowledge, intelligence and corporate values as resulting from the action of the organizational integrators. For

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the same inputs in organization we may have a different potential value for the IC, as the result of integrators. Also, this potential value is not fixed in time but variable, as a result of the field forces interaction. That means dynamics. The resulting IC is composed of: organizational knowledge, organizational intelligence and organizational values. Organizational knowledge contains both cognitive and emotional knowledge, knowledge being considered as a nonlinear field of concepts, ideas and emotions. Organizational intelligence contains cognitive and emotional intelligence, being able thus to process both cognitive and emotional knowledge. Finally, the organizational values mean systems of business and social values. Now, the corporate social responsibility is directly related to these organizational values. This new interpretation of the organizational IC can be correlated easily with the Nonaka’s SECI cycle of knowledge transformation (Nonaka & Takeuchi, 1995), and with the knowledge dynamics theories (Bratianu &

A New Perspective of the Intellectual Capital Dynamics in Organizations

Figure 4.

Andriessen, 2008; Nissen, 2006). This new perspective opens new directions for the IC evaluation without overlapping entities and repetitive implicit addition of same intangibles.

FUTURE RESEARCH DIRECTIONS The new dynamic perspective presented in this chapter requires future research concerning: 1. 2. 3.

4.

Identifying the most important integrators for each organization; Evaluating the operational power of different integrators in organizations; Developing measuring models and methods for organizational knowledge, organizational intelligence and organizational values. Defining new indicators able to match the nonlinearity of each component of IC, and going beyond the linear thinking model in

5.

understanding and interpreting these new results. Using all of these results for a better understanding of intelligent organizations and making the step forward from knowledge intensive organizations toward intelligent organizations.

CONCLUSION Most of the researchers use a static model of the IC, considering it as a stock, like in the case of tangible resources. In the same time, they consider IC of being composed of: human capital, structural capital and relational capital, with minor variations. Static models are appealing due to their simplicity and easy use in developing metrics for IC evaluation. However, organizational IC has a dynamic nature and thus, a dynamic model is more adequate for its understanding and use.

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A New Perspective of the Intellectual Capital Dynamics in Organizations

This chapter presents a new dynamic structure of the IC based on the concept of integrator, and on a new reference system in considering the building blocks of knowledge, intelligence and values. In our view, an integrator is a powerful field of forces capable of combining two or more elements into a new entity, based on interdependence and synergy. These elements may have a physical or virtual nature, and they must possess the capacity of interacting in a controlled way. Knowledge, intelligence and values are considered at the individual level of all constituents of a given company, and then at its organizational level. Dynamic processes are on the vertical axis in transforming individual contributions into organizational contributions, and on the horizontal axis in transforming knowledge into action through intelligence processing, and decision making guided by the organizational core values.

Andriessen, D., & Tissen, R. (2000). Weightless wealth. Find your real value in a future of intangible assets. London: Pearson Education.

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Kaufmann, L., & Schneider, Y. (2004). Intangibles. A synthesis of current research. Journal of Intellectual Capital, 5(3), 366–388. doi:10.1108/14691930410550354 Keeney, R. L. (1992). Value-focused thinking. A path to creative decisionmaking. Cambridge: Harvard University Press. Kianto, A. (2007). What do we really mean by the dynamic dimension of intellectual capital? Proceedings of the IC-Congress, INHolland University of professional education, Haarlem, The Netherlands, 3-4 May 2007. Kobayashi, S. (2009). Honda’s corporate culture: challenge, creativity & innovation. Conference delivered at the Academy of Economic Studies, Bucharest, 12 November. Krajewski, L. G., & Ritzman, L. P. (1999). Operations management. Strategy and analysis (5th ed.). Reading: Addison-Wesley. Lakoff, G., & Johnson, M. (1980). Metaphors we live by. Chicago: Chicago University Press. Lakoff, G., & Johnson, M. (1999). Philosophy in the flesh. New York, NY: Basic books. Mankes, J. (2005). Executive intelligence. New York, New York: Collins. Merins, K., Will, M., & Meyer, C. (2009). InCAS: Intellectual capital statement. Measuring intellectual capital in European small and medium sized enterprises. In: C. Stam & D. Andriessen (eds.) Proceedings of the European Conference on Intellectual Capital, INHolland University of Applied Sciences, Haarlem, The Netherlands, 28-29 April 2009 (pp.355-362). Nerdrum, L., & Erikson, T. (2001). Intellectual capital: a human perspective. Journal of Intellectual Capital, 2(2), 127–135. doi:10.1108/14691930110385919

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Nissen, M. E. (2006). Harnessing knowledge dynamics. London: IRM Press. Nonaka, I., & Takeuchi, H. (1995). The knowledge creating company. How Japanese companies create the dynamics of innovation. Oxford: Oxford University Press. Peters, T., & Waterman, R. H. (1982). In search of excellence. Lessons from America’s best run companies. London: Harper Collins Business. Petty, R., & Guthrie, J. (2000). Intellectual capital review. Measurement, reporting and management. Journal of Intellectual Capital, 1(2), 155–176. doi:10.1108/14691930010348731 Polanyi, M. (1983). The tacit dimension. Gloucester: Peter Smith. 63rd Regulation of the Ministry of Education, Science and Culture on Intellectual Capital Reports (Intellectual Capital Report Act – ICRA). Rodov, I. & leliaert, P.H. (2002). FiMIAM: financial method of intangible assets measurement. Journal of Intellectual Capital, 3(2), 323–336. doi:10.1108/14691930210435642 Rodriguez-Castellanos, A., Arregui-Ayastuy, G., & Vallejo-Alonso, B. (2007). The financial valuation of intangibles: a method grounded on an IC-based taxonomy. In Joia, L. A. (Ed.), Strategies for information technology and intellectual capital: challenges and opportunities (pp. 66–90). Roos, G., & Pike, S. (2007). Intellectual capital research: a personal view. In: Proceedings of the IC-Congress, INHolland University of professional education, Haarlem, The Netherlands, 3-4 May 2007. Roos, G., Pike, S., & Fernström, L. (2005). Managing intellectual capital in practice. Amsterdam: Elsevier.

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Roos, J., Roos, G., Edvinsson, L., & Dragonetti, N. (1997). Intellectual capital: navigating in the new business landscape. London: Macmillan. Russell, B. (1972). A history of western philosophy. New York: Simon and Schuster. Schiuma, G. (2009). The managerial foundations of knowledge assets dynamics. Knowledge Management Research & Practice, 7, 290–299. doi:10.1057/kmrp.2009.21 Schiuma, G., Lerro, A., & Carlucci, D. (2008). The knoware tree and the regional intellectual capital index. Journal of Intellectual Capital, 9(2), 283–300. doi:10.1108/14691930810870346 Stewart, T. A. (1999). Intellectual capital. The new wealth of organizations. London: Nicholas Brealey. Sullivan, P. H. (1998). Profitting from intellectual capital. Extracting value from innovation. New York: John Wiley & Sons, Inc. Sveiby, E. K. (1997). The new organizational wealth. Managing & measuring knowledge-based assets. San Francisco, California: Berret-Koehler Publishers, Inc. Teece, J. D. (2009). Dynamic capabilities & atrategic management. Oxford: Oxford University Press. Thompson, A. A. Jr. & Strickland III, A.J. (2001). Strategic management. Concepts and cases. 12th edition. Boston: McGraw Hill Irvin. Weick, K. E. (2001). Making sense of the organization. Malden, Massachusetts: Blackwell Publishing.

Welch, J., & Welch, S. (2005). Winning. New York: Harper Business.

KEY TERMS AND DEFINITIONS Dynamic Capability: The enterprise’s ability to sense, seize, and adapt in order to generate and exploit internal and external enterprise-specific competences, and to addresses the enterprise’s changing environment. Integrator: A powerful field of forces capable of combining two or more elements into a new entity, based on interdependence and synergy. These elements may have a physical or virtual nature, and they must possess the capability of interacting in a controllable way. Static Model: A model based on variables that have no variation in time. Dynamic Model: A model based on variables that have variations in time. Intellectual Capital: All nonmonetary and nonphysical resources that are fully or partly controlled by the organization and that contribute to the organization’s value creation. It is usually structured into: human capital, structural capital, and relational capital. Intellectual Capital Dynamics: The integration of the individual knowledge, intelligence, and values of all employees within a given organization, and a given time period. Vision: Leveraging of a firm‘s internal resources, capabilities, and core competencies to accomplish the firm’s goals in the competitive environment.

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Chapter 2

Knowledge Flow Audit:

Indentifying, Measuring and Managing Knowledge Asset Dynamics Harri Laihonen Tampere University of Technology, Finland Matti Koivuaho Tampere Power Utility Ltd, Finland

ABSTRACT The purpose of this chapter is twofold. Theoretically it hybridizes two management concepts: Intellectual capital (IC) and knowledge flows. By combining these two concepts, the authors seek to illustrate the dynamics of organizations’ intellectual capital. In addition to the theoretical and conceptual contribution, this chapter introduces an empirical setting for testing the framework. The purpose of the empirical illustration is not to provide exhaustive and hands-on guidelines for managing knowledge flows but to increase managers’ awareness of this highly relevant issue and to offer some suggestions for possible development measures. The knowledge flow audit helps to pinpoint the processes in which IC transforms into value or into some other form. It is based on a fundamental assumption; the dynamics of IC can be demonstrated by examining knowledge flows. Empirical results from the conducted case studies indicate that the knowledge flow audit as a whole and especially the related knowledge flow survey can be successfully used for recognizing and mapping out the dynamics of knowledge assets within a short time period. According to the feedback received from the case studies, the audit provides important information for management purposes by describing the status and accumulation of knowledge assets.

INTRODUCTION It has been argued in the literature that knowledge assets are dynamic in nature; they interact and depend on each other to create value (e.g. Moustaghfir, 2008; Roos & Roos, 1997). Earlier DOI: 10.4018/978-1-60960-054-9.ch002

literature has also acknowledged the important role of learning mechanisms and knowledge management processes as enablers of this interconnectivity (Moustaghfir, 2008; Carlucci et al., 2004; McGaughey, 2002; Marr & Schiuma, 2001). One central starting point of this chapter is the need for a better understanding of knowledge asset dynamics (cf. Schiuma, 2009). This chapter

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Knowledge Flow Audit

contributes to the existing literature by proposing that the dynamics of knowledge assets can be demonstrated by examining knowledge flows. Knowledge flows are seen as the concrete embodiments of interaction through which data, information and knowledge are transferred from one entity to another. The knowledge flow approach is moreover supported by the earlier literature on intellectual capital, which has proposed that IC is concerned with the management of human and non-human knowledge flow across organizational levels in order to create value for organizations (Kong & Prior, 2008; Petty & Gutherie, 2000; Choo & Bontis, 2002). Another starting point for this chapter is the fact that in modern organizations, knowledge flows constitute complex networks and the efficient management of these networks becomes a crucial success factor for any organization. This paper takes a holistic view of organizations’ knowledge processes instead of concentrating on only certain functions such as knowledge creation, accumulation or utilization. The ultimate goal of the knowledge flow approach proposed is to understand how knowledge flows as the concrete embodiments of knowledge transfer and interaction could be used for understanding and improving the efficiency and productivity of organizations. Theoretically, this chapter presents a conceptualization which supports the processes of identification, measurement and management of knowledge asset dynamics. In practice, the chapter hybridizes the concepts of “intellectual capital” and “knowledge flows” into a single framework. It is argued that by combining these two concepts it is possible to gain an understanding of the dynamics of the organizations’ knowledge assets. This argument is based on the idea that knowledge flows emerge from the organizations’ need to utilize, develop and transfer knowledge. Similar thinking has been suggested by Kong and Prior (2008), although they have approached the issue more from the angle of IC, whereas this paper has its main focus on knowledge flows.

It is suggested that new knowledge is gained through knowledge acquisition processes that can be interpreted as inbound knowledge flows from the viewpoint of a given organization. The newly acquired knowledge is then processed, transferred and analyzed within an organization in several ways. These processes are enabled by internal knowledge flows. Individuals process and assimilate the information and finally the new knowledge will be incorporated into organizations’ internal structures such as organization culture, values, processes and information systems. Finally, the external utilization of knowledge takes place through outward knowledge flows. These flows constitute all those knowledge products that organizations produce as solutions to their customers’ needs. As an important contribution to the literature, the chapter also describes the empirical setting developed for testing the theoretical frame. This so-called knowledge flow audit (Laihonen & Koivuaho, 2009; Koivuaho & Laihonen, 2009) also provides management with important information concerning the knowledge transfer processes of an organization. The knowledge flow audit has so far been implemented in two organizations operating in the welfare sector and the early results are promising. Above all, it seems that the audit process has been especially successful in launching knowledge-based thinking in the case organizations. In organizations that are typically seen as labor-intensive this can be seen as a major achievement. It is hypothesized that this will probably increase the commitment to accumulate knowledge and lead to a more effective utilization of knowledge assets in the long run. Nevertheless, further development of the method is still needed, especially concerning the measurement of the dynamics. Without proper measuring methods and tools the effects of interventions might remain untapped. From the managerial perspective, the knowledge flow audit represents a bottom-up approach and gathers information extensively from the

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employees. This approach was chosen due to the self-organizing nature of knowledge networks (Laihonen, 2006). The audit process could also be implemented the other way around. The process could be started by defining the most important knowledge assets of the organization and concentrating exclusively on these. This would be a more traditional approach, especially from the IC point of view (e.g. Danish guidelines), but would most probably not give as truthful a picture of knowledge flows than the bottom-up approach chosen. The identification of the most essential knowledge flows should be in line with the strategically important knowledge assets as well. If not, this should definitely have managerial implications. The audit process has been developed mainly from the viewpoint of welfare and health care organizations. The health sector is interesting due to its high overall impact on GDP. Even though its recognized impact on societal and technical innovations has had a somewhat residual role in the contemporary organization theory and management. There are several reasons for this minor role. Health care organizations are often considered as static, tradition guided systems, where the managers need to have a good understanding of medical science. They have also been considered to be isolated from competition (Porter & Teisberg, 2006). At the same time treatment technologies, payment mechanisms and consumer preferences are evolving rapidly (Shortell & Kaluzny, 2006). However, as the key inputs in the care processes are a combination of complex knowledge, manual labor and technology, the role of knowledge management is crucial. The intellectual capital approach could help managers to visualize the importance of knowledge assets in their organizations (Kong & Prior, 2008). Despite the current focus on health services, the conceptual examination and the process of knowledge flow audit are at least partly applicable to knowledge-intensive services. Since knowledge-intensive work focuses on team effort

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and iterative working methods, it is of critical importance to develop the sharing, distribution, and dissemination of knowledge. The projectbased nature of many contemporary organizations challenges traditional models of management that are based on the rigorous design and division of labor. The knowledge flow approach offers a novel way to understand the underlying interactions of complex organizations.

THEORETICAL FRAME Intellectual Capital Intellectual capital is a multi-disciplinary concept and its content varies depending on the approach (Kujansivu, 2008). Kujansivu has recognized that at least the following terms have been used, mostly interchangeably to refer to intellectual capital: intangible assets (e.g. Sveiby, 1997; Lev, 2001), knowledge assets (e.g. Teece, 2000; Marr et al., 2004; Schiuma, 2009) and intangibles (e.g. Lev, 2001; Meritum, 2001). Intellectual capital has been defined as “the economic value of organizational (“structural”) capital and human capital” (Petty & Gutherie 2000, p.158). In this definition, structural capital refers to software systems, distribution networks and supply chains. Human capital, in the same definition, includes human resources within the organization (i.e. employees’ resources) and resources external to the organization (i.e. customers and suppliers). Other authors (e.g. Edvinsson & Malone, 1997; Roos & Roos, 1997) have also distinguished structural and human capital as the main categories of IC. In addition to these two categories, Petty & Gutherie point out that there are other forms of intangible assets such as a company’s reputation. Brooking (1996) has defined market assets as one component of IC alongside intellectual property assets, humancentered assets and infrastructure assets. An often used categorization of IC divides it into

Knowledge Flow Audit

Table 1. Three components of intellectual capital and some examples of related intangible resources (Kujansivu, 2008; Lönnqvist, 2004) Human capital − Knowledge and competencies − Experience − Education − Personal characteristics

Relational capital − Relationships with customers and other stakeholders − Contracts and arrangements with stakeholders − Organization’s image and brands

three main categories: human capital, relational capital and structural capital (e.g. Sveiby, 1997; Lönnqvist, 2004; Kujansivu, 2008). When market assets are interpreted as relational capital, these categories include all the individual intangible resources addressed in the definitions presented above. This categorization is the basis for this paper, i.e. the study of the dynamics of IC in the context of non-profit welfare organizations. Some general examples of the intangible resources of an organization are presented in Table 1. An interesting approach presented in the literature, especially from the viewpoint of knowledge flows, is the concept of dynamic intellectual capital. This has been used to refer organizations’ actions through which value is created, intellectual capital maintained or transformed, or for describing organizations’ ability to continuously learn and innovate (Ståhle & Grönroos, 2000; Ståhle et al. 2003; Pöyhönen, 2004; Kianto, 2007). These actions or their embodiments could be interpreted as knowledge flows, the concrete embodiments of the actions that are used for creating value from the intangible resources. The distinction between potential and realized intellectual capital (Ståhle & Grönroos, 2000) is also interesting from the perspective of knowledge flow. By this Ståhle and Grönroos mean that until it has been transformed into economic value (i.e. financial results, or in the case of nonprofit organizations into health, mutual good etc.) intellectual capital is only potential. It could be interpreted that the concept of knowledge flows

Structural capital − Values and culture − Processes and systems − Management philosophy − Immaterial properties − Documented information

could help in revealing the relationships between these two forms of intellectual capital. Knowledge flows can be seen as the concrete embodiment of those transformation processes used for utilizing and transforming knowledge assets into value. Consequently, from the theoretical perspective, knowledge flows could provide interesting means of developing the above mentioned theoretical approaches.

Knowledge Flows as the Concrete Embodiments of Knowledge Transfer It has been widely acknowledged in the existing knowledge management literature that knowledge transfer enables the exploitation and application of existing knowledge for organizational purposes (e.g. Cohen & Levinthal, 1990; Grant, 1996; Argote & Ingram, 2000; Kumar & Ganesh, 2009). However, the literature on knowledge transfer still lacks a clear distinction between the concepts of “knowledge sharing”, “knowledge transfer” and “knowledge flow”, as Kumar and Ganesh (2009) have pointed out. Based on a conceptual analysis they conclude that these concepts have a common trait – they all refer to an exchange of knowledge, where “knowledge is given by one or more entities and received by another” (ibid. p. 163). Knowledge flow comprises the set of processes, events, and activities through which data, information, and knowledge are transferred from one entity (person or system) to another (Mu et al., 2008). Knowledge flow and especially knowledge transfer have been an active research

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Knowledge Flow Audit

area within the stream of knowledge management over the years (e.g. Nonaka, 1994; Grant, 1996; Kogut & Zander, 1996; Szulanski, 1996; Gupta & Govindarajan, 2000; Spencer, 2000; Tsai, 2000; Reagans & McEvily, 2003; Mu et al., 2008). The task of defining the concept of “knowledge flow” is demanding. However, a good overview of the concept can be attained by listing three properties that describe the nature and importance of knowledge flows. •

•

•

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Knowledge flows as a way to create knowledge. It has been argued that new knowledge is created in interaction. If knowledge flows are seen as the concrete embodiments of interaction, this leads to the argument that knowledge flows are necessary for the creation of new knowledge. In the SECI model (Nonaka & Takeuchi, 1995) knowledge flows and is transformed in four different processes known as socialization, externalization, combination and internalization. Knowledge is created through these processes in which knowledge flows enable the interaction required in the transformations. Knowledge flows as a way to transfer knowledge. Knowledge transfer occurs at various levels: between individuals, from individuals to explicit sources, from individuals to groups, within groups, between groups and from the group to the whole organization (Alavi & Leidner, 2001). Therefore, knowledge transfer is a basic property of all communication, prevents people from reinventing the wheel and further is an important factor in the quest for productivity enhancement. Knowledge flows as a way to understand knowledge as a process. There is a growing stream of literature in which knowledge itself is seen as a process. Knowledge is reproduced in a continuous interaction and manifested momentarily as concrete

flows between the sender and the receiver of knowledge (Boland & Tenkasi, 1995). From this viewpoint, knowledge flows are an integral part of organizational culture, routines and practices. These together form the basic infrastructure of an organization. Therefore, it could be argued that the network of knowledge flows creates an enabling infrastructure for all the operations of organizations, especially in knowledgeintensive organizations. These three properties lead the way to a general understanding of the importance of knowledge flows for knowledge sharing and transfer. However, the diversity of transferred knowledge within and between modern organizations is enormous and therefore it is necessary to briefly specify what the term “knowledge flow” means. Here the term refers to any form of knowledge transfer. Figure 1 presents and exemplifies the linkage between the concept of knowledge flows and a more traditional distinction between data, information and knowledge (Nonaka & Takeuchi, 1995). When analyzing knowledge transfer, it is important to understand who transfers knowledge to whom (actors), what is transferred and in which context (content and context) and which media is the most suitable in a given context (media or channel) (Albino et al. 1999; Jasimuddin, 2005). Concerning actors, the three most important factors affecting knowledge transfer are openness or willingness to share knowledge, trust and previous knowledge (Wathne et al., 1996). Each of these characteristics correlates positively with knowledge transfer (e.g. Cohen & Levinthal, 1990; Dodgson, 1993; Ring & van der Ven, 1994; Wathne et al., 1996). The cognitive capabilities of the actors also decisively influence the success of transfer (e.g. Cohen & Levinthal, 1990; Szulanski, 1996; Simonin, 1999; Gupta & Govindarajan, 2000; Tsai, 2000; Foss & Pedersen, 2002). Concerning the content of the transfer, it has been shown that it is more challenging to transfer

Knowledge Flow Audit

Figure 1. Diversity of knowledge flows (translated from Laihonen, 2009)

tacit knowledge (Polanyi, 1972; Nonaka & Takeuchi, 1995) than explicit knowledge (e.g. von Hippel, 1994; Simonin, 1999; Argote & Ingram, 2000; McEvily & Chakravarthy, 2002). The ambiguity of knowledge has also been identified as a characteristic hampering the transfer (e.g. Zander & Kogut 1995; Szulanski, 1996 and 2000; Stein & Ridderstrale, 2001). In addition, the value of transferred information matters – the more valuable the information, the more interested the receiver is (Gupta & Govindarajan, 2000). Finally, the transfer media or the actual means and structures used for transferring the information have an important role from the viewpoint of the transfer process. The literature has recognized two central characteristics of the media that the transfer depends on, namely capacity and richness (Albino et al., 1999). Capacity refers, first, to a media’s ability to noiseless transfer and second, to the redundancy of information transferred. Richness on the other hand refers to

the possibility to exchange mental representations during the transfer process (Daft & Lengel, 1986; (Wathne et al., 1999). On a general level knowledge flows and their relation to the knowledge assets of an organization can be simplified into a three-part model that will be presented in the next section.

Theoretical Frame for the Knowledge Flow Audit By combining the aforementioned approaches of intellectual capital and knowledge flows into a single framework, the link between transformations of the three categories of IC as well as their formation to value through knowledge flows can be recognized. The framework can be used for analyzing the inbound, internal and outbound knowledge flows (Koivuaho & Laihonen, 2006). These flows act as the primus motor for all knowledge-based value creation. After this, the fundamental ques-

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Knowledge Flow Audit

tion of knowledge management is: how should the knowledge processes of an organization be organized to support the utilization and development of knowledge assets? Knowledge flows emerge especially from the need to utilize, develop and transfer knowledge. Inbound flows refer to knowledge that the organization receives from outside its boundaries (knowledge acquisition). Outbound flows refer to knowledge that organization shares with its environment or sells as knowledge products (knowledge utilization). Internal flows consist of knowledge sharing within an organization i.e. between its employees supported by the information systems and processes (transformation of IC). In this approach, the organization serves as the nodal point of knowledge flows. The organizational boundary is an important element in the framework since it is assumed that human capital and relational capital are the principal forms of IC through which the eventual value of the IC will be realized. They are the forms of IC that operate at the interface between an organization and its environment. Structural capital, on the other hand, is seen as an internal resource, creating value for internal customers. The theoretical framework is presented in Figure 2. Figure 2. Theoretical frame for knowledge flow audit

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Concerning inbound flows, interesting questions include at least the following: what kind of knowledge flows and external knowledge do organizations use for developing their intellectual capital? Internal knowledge flows, on the other hand, are crucial in the development, transformation and transfer of intellectual capital within the organization. Human capital becomes concrete and valuable through interaction and these interaction events may be supported by processes and procedures embedded in organizations as structural capital. Finally, outbound flows may be seen as the flux of intellectual capital into value. Knowledge-intensive organizations utilize their intellectual capital in many ways. Members of the organization may, for example, sell expertise as knowledge products or in professional seminars and training programs and thereby disseminate knowledge and create value. The knowledge flow audit to be described in the next chapter has been developed for purposes of testing in practice the theoretical framework presented above. In addition to this purely theoretical aim, there is also an increasing practical need to identify, measure and manage complex networks of knowledge flows in knowledgeintensive organizations. Thus, the knowledge flow

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audit also aims at providing practical information for managerial needs.

THE KNOWLEDGE FLOW AUDIT AND AN EMPIRICAL SETTING Objectives and General Setting for the Knowledge Flow Audit The knowledge flow audit has been developed for purposes of mapping out the continuously changing net of knowledge flows that organizations use for developing and utilizing their knowledge assets. The audit seeks answers the following questions: •

•

•

Through what kind of inbound knowledge flows are knowledge assets renewed and accumulated? Through what kind of internal knowledge flows are knowledge assets developed and refined? Through what kind of outbound knowledge flows are knowledge assets converted into value?

These concrete questions lead the way towards more interesting and general questions such as how organizations actually handle information and knowledge. How is knowledge accumulated and created and how is it transformed into services? The ultimate target is to understand how value is created, but first, knowledge flows must be identified. The knowledge flow audit consists of three process steps (see Figure 3). The process starts with a preliminary study. This phase is qualitative and includes one to three meetings with the key personnel of an organization and a focus group discussion with the executive group. In these meetings, participants are asked and encouraged to describe the processes and practices concerning internal interaction as well as organizations’ connections with external stakeholders. During this step, a general understanding about the knowledge flows of an organization can be gained. Process step I, has been in a key role in the audits implemented in welfare organizations. In this step, the most crucial knowledge flows and knowledge assets are identified by the workshop participants. In a sense, this means prioritization, which is essential because it is not possible to

Figure 3. Knowledge flow audit (Laihonen & Koivuaho, 2009)

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map all the knowledge flows within the organization. From the IC management point of view, there are some particular issues that have definitely affected the formalization process in the given welfare organizations. First, these service organizations are fairly small (e.g., compared with the global companies for which many of the IC models were originally designed). Thus, their managerial information needs may be fairly simple and they probably have limited resources for developing and maintaining IC management models, which suggests that the managerial tools should not be too complex or resource consuming. Second, many of these organizations may lack a target management or measurement culture. This causes some implementation challenges when introducing new measures or management methods. In the second step of the audit, a tailor-made knowledge flow survey will be implemented. This survey ascertains employees’ perceptions of the state of knowledge flows. The survey will be sent to a selected group of employees. Ideally, it should be sent to all employees. The purpose of the survey is to gain a wider and more profound understanding of employees’ perceptions of the status of knowledge assets and their dynamics. The survey consists of statements concerning knowledge flows. The number of statements to which employees respond on a five-point likert scale, can be justified according to the needs of the organization. Statements are divided into blocks of questions: background questions, questions concerning general factors affecting knowledge sharing and transfer, inbound knowledge flows, internal knowledge flows and outbound knowledge flows. Finally, in the third step results will be analyzed, reported and the employees have an opportunity to discuss the results. A lot of valuable information can be gained during the feedback discussions with both employees and managers. This phase could also include public seminars or workshops. In addition, a research report for the purposes of

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the case organization will be submitted. From the managerial perspective, this phase is essential. It promotes interaction among and between employees and managers.

About the Case Organizations As mentioned earlier, the knowledge flow audit has been implemented so far in two welfare organizations. These organizations were pilot organizations in a 3-year research project that aimed at developing IC measures for non-profit elderly care organizations. The knowledge flow audit was implemented at the beginning of each case study. The case organizations were: Jyllin Kodit and the Rehabilitation Institute Apila. Although both organizations are non-profit organizations operating in the elderly care sector their operational focus is somewhat different. Brief descriptions of the case organizations partly explain these differences. Jyllin Kodit is an old people’s home founded in 1965 and owned by the Jalmari Jylli Foundation. Jyllin Kodit offers care, nursing, nutrition, research and rehabilitation services for elderly people. The organization has five special care units that function as group homes with a total of 80 people living in dependent or highly dependent care. In addition, Jyllin Kodit offers recreational activities and short-term rehabilitation for elderly people. The organization has been active in its development activities and has had several development projects e.g. related to management, the well-being of the personnel, incentive systems and rehabilitation of the elderly. Jyllin Kodit has about 60 full-time employees and a group of volunteers who participate actively in the operations of the organization e.g. by offering leisure activities. The Rehabilitation Institute Apila was founded in 1963 and is owned by The Finnish Rheumatism Association. Apila helps people suffering from musculoskeletal diseases by offering short term rehabilitation. Apila offers recreational activities and rehabilitation services aimed at maintaining and improving an individuals’ working capacity.

Knowledge Flow Audit

Figure 4. Some examples of the areas covered by the knowledge flow survey

Since 2004 respite care for the elderly (geriatric rehabilitation) and family care have been rapidly increasing sectors. The facility has 116 places for clients using rehabilitation and recreational services and serves over 1800 clients annually. The facility employs about 80 professionals with different areas of expertise. The facility also functions as the national resource centre for uncommon rheumatic diseases and inflammatory muscular diseases. The key difference between the case organizations is the length of the client contact. In Jyllin Kodit people spend the rest of their lives as residents, whereas in Rehabilitation Institute Apila the average duration of a rehabilitation period is two weeks. With this difference, these case studies provide different angles on the intangible resources of elderly care.

The Knowledge Flow Survey in Practice The knowledge flow survey has a crucial role in the knowledge flow audit. It is a questionnaire that can be carried out electronically or on paper. The survey is a rapid method for acquiring an understanding of the state of the organization’s

knowledge flows. The survey can also be implemented independently, although in this case the organization specific features cannot be taken into account. Nevertheless, an independent implementation could also shed light on important issues related to the dynamics of the organization’s knowledge assets and might help in identifying the most crucial development areas. Some examples of the concrete areas covered by the knowledge flow survey are illustrated in Figure 4. The first section of the knowledge flow survey maps out the basic components concerning knowledge sharing and transfer in organizations. From the IC point of view, these components belong to the category of structural capital. The main focus of this part is to find out how components such as organizational values, strategy and working environment support knowledge sharing and transfer. Generally, these factors constitute the basic frame for all the organizational actions and determine the direction in which the organization heading. Therefore it is important also to measure the effectiveness of these enabling factors from the viewpoint of employees. This is one reason for the chosen bottom-up approach of the knowledge flow survey. Conflicting value conceptions among employees would most likely lead to inef-

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ficient knowledge transfer within a work community. The effectiveness of values, strategy and goals is elicited by asking the employees whether they are aware of these and furthermore if they consider that they guide their actions. During the qualitative phase employees stressed that a favorable environment for knowledge transfer relies on clearly communicated values, well-defined strategy and clear organizational goals. They thought that these also enhance trust and create a shared context within the work community. Although statistical significances and correlations could not be used due to the small size of the case organizations, the qualitative analysis of the results as a whole still strongly supported the original hypothesis that management should pay careful attention to the above-mentioned components, also from the viewpoint of knowledge transfer. In addition, it is important that the working environment and atmosphere are such that employees thrive at the workplace. From the knowledge flow perspective it would also be important that employees should not think that they are underestimated and that no cliques should form. Again, these factors relate to trust issues between employees. In the worst case, they would certainly cause barriers to the flow of knowledge. To sum up the first part of the survey, it could be concluded that the traditional methods of management, such as the creation of a solid value base and the clear communication of strategy and goals are important enablers of knowledge transfer. They are crucial in creating a working environment where employees are keen to co-operate and utilize their personal knowledge assets. Without proper management, the potential of these knowledge assets might remain untapped. In the second part of the knowledge flow survey, the inbound knowledge flows of organizations are studied. This part consists of about 20 statements addressing, for example, issues like external training and education, client feedback, the role of new employees and students as knowledge

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sources. In addition, inter-organizational development projects, special field publications and Internet-based databases are examples of external knowledge sources whose roles and importance are elicited from the respondent employees. The purpose is to find out which are important for the personnel, and, further, which knowledge flows most effectively support organizational learning and renewal. Huber (1991) has stated, “an organization learns if any of its units acquires knowledge that it recognizes as potentially useful to the organization” (p.89). From this viewpoint, knowledge acquisition (i.e. inbound knowledge flows) is a key process of organizational learning. Huber continues by arguing “more organizational learning occurs when more of the organization’s components obtain this knowledge and recognize it as potentially useful” (p.89). Based on these arguments of Huber, this part of the knowledge flow questionnaire aims at identifying inbound knowledge flows that the organization and its employees use to acquire new knowledge. New knowledge in this case refers to the material that is new to the collectivity in question (Pentland, 1995). In knowledge-intensive work, it is important for all employees to keep up with the development of their respective areas of expertise, thereby increasing the organization’s human capital. In the case studies conducted, the majority of the employees agreed at least to some extent with the statements concerning the role of new employees and students as knowledge sources. Especially in the case of these knowledge sources, a supportive and encouraging working environment is crucial as an enabling factor for knowledge transfer between new and “old” employees. Without immediate interaction, openness and trust, inexperienced employees could easily be forgotten and excluded from the knowledge-sharing point of view. From the relational capital perspective, it is important to keep in mind that for most organizations customers are their most important

Knowledge Flow Audit

interest group. Therefore, organizations should acknowledge that customer feedback and interaction should also form an inflow of external information and knowledge. The wider maintenance of external relationships is typically among the managerial responsibilities and therefore in both case organizations most of the employees had no opinions about the knowledge flows of relational capital. In addition, the size of the organization as well as the focus of its operations is an important aspect that should be taken into account when formulating the questionnaire. For example, in the case of elderly people and customers suffering from different degrees of memory disorder, the role of customer opinion and the possibilities for customer-to-customer interaction should be approached very differently than in the case of different types of services. The same also goes for other service-specific statements used in the survey; they should be adjusted case by case based on the first process step of the audit. The third section of the questionnaire elicits internal knowledge flows. External actors such as customers are naturally very important for any organization, but on the other hand, without the internal knowledge flows organizations would find themselves reinventing the wheel repeatedly. From this perspective, it is crucial to assess the status of internal interaction and knowledge transfer. From the viewpoint of human capital, internal knowledge flows refer to the ways of structuring the personnel’s knowledge and expertise. On the other hand, internal flows are also used for utilizing existing knowledge already stored in organizational memory (e.g. Walsh & Ungson, 1991; Stein & Zwass, 1995). These examples illustrate how IC transforms from human capital to structural capital and vice versa. In the knowledge flow survey, almost 40 statements are used for mapping out the status of internal interaction and knowledge flows. In the case organizations, employees reported that the interactivity of communication increases their willingness to receive new information.

Therefore, it is important that knowledge transfer within an organization is interactive and that each individual should feel that he or she benefits from that interaction. This is extremely important, especially in organizations where employees operate in multidisciplinary teams. In many knowledgeintensive organizations, multidisciplinary teams are typically used due to the fragmentation of expertise between professional groups. In addition, teams are used for increasing internal diversity and thereby potential for innovations (Laihonen & Koivuaho, 2005). For management this sends a clear message. There should be opportunities (context and media), most importantly time and structures (e.g. meetings, process maps, information systems, documentation and clear reporting practices) for knowledge sharing. The relationship between internal knowledge flows and relational capital is not as obvious as their connection to other forms of intellectual capital. As one example of this relationship, internal flows could be seen as the channels for structuring brands and reputation. The concrete embodiments of these flows could be the preparing of the organization’s marketing plan, the maintenance of a customer relationship system or the documentation of personal contacts and networks. The latter example points out how closely human and relational capital are connected. The final section of the questionnaire examines outbound knowledge flows. Knowledge-intensive organizations typically create value by utilizing and sharing previously acquired knowledge and expertise. Therefore, from the knowledge flow viewpoint the organizational boundary is an important element distinguishing internal knowledge transfer from the solutions (knowledge) offered to solve customers’ problems. According to the theoretical frame presented earlier, human capital and relational capital are the principal forms of IC through which the eventual value of the organizational knowledge assets is realized. These operate at the interface between an organization and its environment. By mapping out the outbound

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Knowledge Flow Audit

knowledge flows, it is possible to create a snapshot of the knowledge processes used for value creation. In the knowledge flow questionnaire, about 20 statements address outbound knowledge flows. In the case organizations, client interaction was seen as the most important outflow of knowledge and expertise. Some examples of outbound knowledge flows in the welfare sector includes the utilization of personal expertise in customer work, interaction with other external interest groups, participation in external workshops as an expert or advisor, the guidance of customers in general well-being, knowledge sharing with students and participation in public health work. Again, for a management consulting firm the statements would be very different. However, concerning marketing, purchasing of services, public relations and many other typical organizational tasks, the knowledge flows might be quite similar. More generally, in many service organizations, it is typical that employees utilize their knowledge assets in this way. In addition to clients, it is important to assess the relative importance of other external interest groups from the viewpoint of knowledge sharing and transfer. In the case of very intensive interaction with certain parties, it may prove worthwhile to invest in structuring connections of this kind. Although structural capital is interpreted as an internal resource, it might be possible to use, for example, computer-based

systems for automating such processes so that structural capital could flow outwards without any human involvement. Assessment could also help in revealing barriers or bottlenecks in knowledge transfer. In the next section, some lessons learned from implemented knowledge flow audits will be summarized.

WHAT DOES THE KNOWLEDGE FLOW AUDIT TELL US? The knowledge flow audit has been implemented twice so far and the preliminary results are encouraging. During these audits, several knowledge flows in both organizations were recognized, analyzed and reported. Figure 5 summarizes the results of the two knowledge flow audits and exemplifies the concrete embodiments of the knowledge flows and corresponding knowledge assets dynamics in the case organizations. As the figure shows, the knowledge flow audit could paint an extensive picture of the knowledge assets of an organization and therefore it supports management in its role of ensuring continuous learning by pointing out possible deficiencies in the knowledge flow. From the practical perspective, the knowledge flow audit provides important information about the dynamics of organizations’ knowledge assets.

Figure 5. Summary of the results of two knowledge flow surveys

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The knowledge flow audit is able to map out especially knowledge flows concerning human and structural capital. However, the second process step, the knowledge flow survey, has not proven very successful in mapping out the relational capital of an organization. The qualitative data of the first process step has proven much more effective in identifying the knowledge flows of relational capital. This should be taken into account when developing the audit further. Probably this relates to the fact that in service organizations value is created in close interaction with clients and outbound knowledge flows are in most cases channeled through human capital. Yet, in order to gain a more objective and comprehensive understanding of the state of relational capital, there should probably be a separate questionnaire for external stakeholders. During the feedback discussions with the participating employees, a few important issues were recognized. First, there was firm agreement that the knowledge flow survey in particular forces employees to think about their work from the knowledge perspective. This could also be seen in the responses to the open-ended questions of the questionnaire in both case organizations. The employees clearly analyzed their operations as knowledge-intensive and evinced several important notions about the organizations’ knowledge flows and processes. This will most probably lead to enhancements in knowledge transfer processes and further in the smoothness of service production. Second, again in both organizations, the knowledge flow audit helped employees to recognize the importance of knowledge transfer and communication. Although they were all aware of the importance of proper documentation and knowledge sharing, the constant haste sets great challenges for compliance with these practices. Therefore, it would be extremely important to clearly communicate and explain why certain practices exist. Especially in the fields that are not very knowledge-intensive, management

should pay close attention to the reasons for the importance and the purpose of documentation. Employees are much more motivated to fill in forms or a certain field in the database if they understand why this has to be done. The third and final issue recognized relates to the external knowledge sources and their important role as enabling structures for renewal and learning. Knowledge-intensive organizations continuously need new information and knowledge to keep their innovation processes running. Knowledge assets need refreshment just as well as human beings. Therefore, it is important that inbound knowledge flows do indeed exist and are managed although employees are in a hurry, clients require attention and the outbound knowledge flows are the ones that enable the inflow of cash. The balance between inbound, outbound and internal knowledge flows guarantees the dynamics of organizations’ knowledge assets and enables its robustness to be sustained.

CONCLUSION Theoretical Implications This chapter analyzed the components of intellectual capital from the viewpoint of knowledge flows. The chapter proposes that knowledge flows constitute the dynamics of intellectual capital. It is assumed that in organizations in which knowledge is an asset as well as an output knowledge flows can be seen as a highly productive target for improvements. Those improvements are assumed to have positive effects on the value creation processes of intellectual capital and on the performance of the organization. The theoretical contribution of this chapter lies in the framework, which combines the concepts and approaches of knowledge flows and intellectual capital. Furthermore, it provides a concise theoretical analysis of the phenomena of organizational value creation through knowledge

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flows. For future research, the results of this chapter indicate that there are synergies to be found in the research fields of intellectual capital and knowledge transfer. The definition of dynamic intellectual capital especially and the distinction between potential and realized intellectual capital seem to offer an appropriate mindset for the study of intellectual capital from the knowledge flow viewpoint. Knowledge flows could be seen as enabling factors used for realizing potential capital. The analysis presented in this chapter also supports the hypothesis that knowledge flows as the concrete embodiments of knowledge transfer and interaction could be a useful concept for concretizing the dynamic nature of knowledge assets. Although the main contribution of this chapter lies in the identification of knowledge flows, it also described a method that is aimed to measure the status of knowledge flows. Measurement could be seen as a crucial point, especially from the management perspective. An old management adage says that “You can’t manage what you don’t measure”. This is certainly true, but not an easy task in the case of intangible knowledge assets. However, difficulties should not be the reason for avoiding measurement. The knowledge flow audit is a method that has been developed to map out the dynamics of intellectual capital in knowledgeintensive organizations. The knowledge flow audit may yield a snapshot-like image of the flows, or when used longitudinally, also of the rate of change of different factors. In light of the early analysis it seems as a promising opening in the field of measuring knowledge asset dynamics. As it has been shown in this chapter, the knowledge flow audit could also be used for gathering data for research purposes. Although the knowledge flow audit in general represents a bottom-up approach to management, there is a risk that by concentrating on the key employees and their perceptions during the preliminary phase some crucial knowledge flows may go identified. This is due to the fact that in multiprofessional team organizations, where tasks

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are not as strictly defined as in pure profession based organizations or structures, managers most probably are not aware of all the knowledge flows that relate to the operative work. Another deficiency of the knowledge flow audit from the viewpoint of generalization relates to its scalability. The approach proposed might not be applicable as such in any organization. Especially, when the size and complexity of the organization and the number of knowledge flows increases, it becomes impossible to map out all of organizations’ knowledge flows. In this case, it might be necessary to distinguish between different organizational functions, e.g. the knowledge flows of operative processes, the knowledge flows of supportive processes or the knowledge flows of strategic development. This might also help in avoiding the problems related to the differences in knowledge flows across the line of businesses (cf. welfare sector and management consulting). In a more commercial environment knowledge needs may differ significantly from the need in the welfare sector. For example, customer interaction with patients suffering from Alzheimer’s disease is obviously very different than with the top executives in large companies. Therefore, the qualitative phase of the audit plays a crucial role as a basis of the knowledge flow survey. Yet another approach would be to focus on a certain organizational unit. These approaches would yield a more accurate picture of the respective knowledge flows. For future research, this chapter has recognized multiple interesting themes that should be studied more carefully. At least the following questions arise from the analysis of this chapter: How does knowledge transfer affect the value creation of an organization in practice? How to make visible the linkages between enhanced knowledge transfer and productivity improvements? How to map and measure the knowledge flows of customer interaction? How to measure the effects of outbound knowledge flows?

Knowledge Flow Audit

Managerial Implications From the practical viewpoint, the knowledge flow audit may help to concretize concepts such as intellectual capital and knowledge assets that might otherwise be quite difficult to communicate. During the audit process, these concepts could be translated into the language that employees use in their everyday work. This will probably increase the commitment to accumulate knowledge and lead to more effective utilization of knowledge assets. Therefore, the knowledge flow audit seems to be a good starting point for any development activities related to intellectual capital. This has been strongly supported by the feedback discussions with the managers of the case organizations. In both case organizations especially the knowledge flow survey was found to be a practical way of gaining an overview of organizations’ knowledge flows in short space of time. The managers thought that it provided them with valuable information to support their decision-making. Therefore, the first managerial implication of the knowledge flow audit relates to the concretization of the strategic approach of intellectual capital management. It cannot be stressed enough that in many organizations the significance and meaning of knowledge assets are not obvious to the personnel. In this way, the practical approach of the knowledge flow audit offers a simple yet effective tool for concretizing IC. The same also applies to knowledge transfer processes. The knowledge flow audit provides a tool that helps organizations to comprehensively map their knowledge processes. Many organizations operate in networked business environments where the management of knowledge flows in many cases refers to the development of enabling infrastructures. Customer feedback and continuous co-operation with other actors in the field and therefore also boundary crossing knowledge flows are strategically important factors. Hence, the understanding and the rationalization of interaction processes and knowledge flows have a

central role among methods and tools for improving productivity. The rationalization here refers to the elimination of unnecessary work phases or processes in order to make knowledge transfer more efficient. Most importantly, the audit process as a whole encourages each and every employee to participate and to articulate their personal experiences and viewpoints concerning the organization’s knowledge flows. Therefore, it is highly recommended that the questionnaire should be completed by as many employees as possible. This forces employees to consider their own work from the viewpoint of knowledge, which might lead them to question the old and possibly outdated working methods and information in general. In complex systems such as organizations even the smallest changes may lead to significant changes. The main limitation of the knowledge flow audit from the managerial perspective relates to its capability to identify knowledge flows related to the relational capital of an organization. In light of the results it seems that outbound knowledge flows of relational capital are extremely difficult for most of the personnel to identify. It also seems that in most cases relational capital is channeled through human capital. This could be typical for service organizations, where value is created in close interaction with clients. This assumption, however, needs further investigation in different service organizations. According to the feedback discussions, relational capital also permeates organizations’ environment and thereby affects all interaction with the external sources without actually forming into knowledge flows. Nevertheless, in order to achieve a more objective and comprehensive analysis of the state of relational capital there should be a questionnaire to be completed by external stakeholders. In a summary, the managerial implications originating from the conducted knowledge flow audits could be encapsulated into the following statements concerning the management of knowledge flows:

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•

•

• •

Knowledge transfer and knowledge flows can be supported by an enabling infrastructure that supports and encourages interaction and knowledge sharing Continuous learning can be fostered by strengthening and managing inbound knowledge flows Internal interaction requires both capabilities and opportunities Management of outbound knowledge flows facilitate customer interaction and value co-creation

Efficient management of the dynamics of intellectual capital and the corresponding knowledge flows requires a case-specific balance between the above-mentioned components. In a continuously changing environment this balance is dynamic and therefore the management of knowledge flows should be an activity.

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Polanyi, M. (1974). Personal Knowledge: Towards a Post-Critical Philosophy. Chicago: University of Chicago Prepp. Porter, M., & Teisberg, E. (2006). Redefining Health Care. Creating Value-Based Competition on Results. Boston: Harvard Business School Press. Pöyhönen, A. (2004). Modeling and Measuring Organizational Renewal Capability. Acta Universitatis Lappeenrantaensis 200, (Doctoral dissertation) Lappeenranta: Lappeenranta University of Technology. Reagans, R., & McEvily, B. (2003). Network structure and knowledge transfer: the effects of cohesion and range. Administrative Science Quarterly, 48, 240–267. doi:10.2307/3556658 Ring, P. S., & van de Ven, A. (1994). Developmental processes of cooperative interorganizational relationships. Academy of Management Review, 19(1), 90–118. doi:10.2307/258836 Roos, G., & Roos, J. (1997). Measuring your company’s intellectual performance. Long Range Planning, 30(3), 413–426. doi:10.1016/S00246301(97)90260-0 Schiuma, G. (2009). Strategies for Assessing Organisational Knowledge Assets. In Lytras, D. M., & Ordonez de Pablos, P. (Eds.), Knowledge Ecology in Global Business: Managing Intellectual Capital (pp. 26–41). Hershey, PA: IGI Global. doi:10.4018/978-1-60566-270-1.ch003 Shortell, S., & Kaluzny, A. (Eds.). (2006). Health Care Management. Organization Design and Behaviour. Thomson Delmar Learning. Simonin, B. L. (1999).Ambiguity and the process of knowledge transfer in strategic alliances. Strategic Management Journal, 20, 595–623. doi:10.1002/ (SICI)1097-0266(199907)20:73.0.CO;2-5

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KEY TERMS AND DEFINITIONS Intellectual Capital: an umbrella concept that refers specifically to organizations’ intangible resources. Intellectual capital consists of human, structural and relational capital. Knowledge Assets: knowledge-based resources that organizations utilize in their valuecreation processes. Knowledge Transfer: a process where knowledge is being exchanged within or between organizations. Knowledge Flows: the concrete embodiments of knowledge transfer.

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Knowledge Flow Audit

Knowledge Flow Audit: a method for mapping the dynamics of knowledge assets i.e. knowledge flows.

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Knowledge Flow Survey: a questionnaire for acquiring an understanding of the state of the organization’s knowledge flows.

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Chapter 3

Relational Capabilities: Value Creation through Knowledge Management Patrocinio Zaragoza-Sáez University of Alicante, Spain Enrique Claver-Cortés University of Alicante, Spain

ABSTRACT Linking the knowledge-based view and the intellectual capital view of the firm, this chapter has as its purpose to underline the relevance of a specific component of intellectual capital, namely relational capital, in the knowledge acquisition and transfer processes as well as its influence on a firm’s value creation. The authors used a qualitative research based on a multiple case study, and six Spanish knowledge-intensive firms were analyzed in depth. The results show that the main relational capabilities used by firms to create value through knowledge management are: relationships with customers, suppliers and stakeholders; acquisition of established firms; setting-up of joint ventures; collaboration with Universities, national and international institutions; participation in forums and conferences; publications; advice given by consultants and experts; and benchmarking practices. These capabilities allow firms to acquire and transfer knowledge from the environment where they develop their activity with the aim of obtaining benefits such as innovations; customers, suppliers and stakeholders’ satisfaction; an improvement in the firm’s image and credibility; new knowledge; and learning.

INTRODUCTION Intangible resources have acquired great relevance in recent years due to their strategic value (Barney, 1991; Grant, 1991), and they have also become important value creation factors (Lev & Daum, 2004). These intangibles are known as ‘intellecDOI: 10.4018/978-1-60960-054-9.ch003

tual capital’. It is widely accepted that intellectual capital divides intangibles into three blocks or components: human capital, structural capital and relational capital (Roos, Pike & Fernström 2005; KSRC, 2003; Bontis, 1999; Johnson, 1999; Sveiby, 1997) Most of the literature about intellectual capital has focused on describing its nature, on classifying and measuring its intangibles and on highlighting

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Figure 1.

the importance that intellectual capital reports have both for the firm itself and for its stakeholders (Sudarsanam, Sorwar & Marr, 2006; Roos, Pike & Fernström, 2005; Burgman, Roos, Ballow & Thomas, 2005; Pike, Fernström & Roos, 2005; Marr, Gray & Neely, 2003; Sveiby, 2001; Edvinsson & Malone, 1997; Stewart, 1997). The development of intellectual capital models has also deserved special attention in literature (KSRC, 2003; Brooking, 1996; Bontis, 1996; Edvinson & Malone, 1997). However, while a great majority of studies on intellectual capital highlight its relevance in the creation of firm value, practically no theoretical or empirical works have highlighted the mechanisms through which that is possible. We need to know and understand what drives value in order to deliver value to stakeholders, whatever form that value may take (Roos, Pike & Fernström, 2005). That is why this chapter tries to fill the gap existing in literature considering that the value creation generated by intellectual capital is possible thanks to the knowledge management processes previously generated by the firm. This study has as its main objective to highlight the importance that the set of relationships created between the firm and its environment or external network has for both knowledge management and value creation. In other words, we

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try to underline the relevance corresponding to a specific component of intellectual capital, namely relational capital, in the knowledge acquisition and transfer processes as well as its influence on a firm’s value creation. Based on the previous arguments, this chapter focuses on answering the following three research questions: (1) what are the main capabilities which form the relational capital of knowledge-intensive firms? (2) how does relational capital help develop knowledge acquisition and transfer capabilities? (3) in what way does relational capital contribute to a firm’s value creation through knowledge management? (see Figure 1.) The present paper contributes to the literature in several ways. Firstly, the literature on intellectual capital is extensive and mainly focuses on highlighting the importance of intangibles as well as on the development of models for classifying and measuring intellectual assets. Nevertheless, this study adopts a different perspective from the theoretical point of view, bridging the gap between the knowledge-based view and the intellectual capital-based view of the firm for the purpose of showing the relevance that intangible resources and capabilities belonging to relational capital have in knowledge management by the firm. Secondly, we make an effort to fill the gap existing in literature considering that the value creation

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generated by intellectual capital is possible thanks to the knowledge creation and transfer processes previously generated by the firm. Thirdly, from an empirical point of view, since empirical works in the field of intellectual capital have been very scarce so far, we carry out a number of in-depth case studies meant not only to identify the main relational capabilities but also to show how they contribute to value creation through the acquisition and transfer of knowledge between the external network and the firm. A search of the literature has failed to identify any previous research works which specifically examine the relational capital-knowledge management-value creation link using a comparative case study as we have done here. Finally, the findings obtained in this study can help managers recognize the value of relational capital for knowledge acquisition and transfer as well as its influence on value creation, which should encourage them to take a leading role in the promotion of relationships between firms and their external network with a view to increase relational capital. We have structured the chapter in five sections. After the initial introductory section, there is a literature review which highlights the theoretical background of the study. A description of the methodology used, which describes the population under study as well as the information collection and data analysis procedures, comes next. The presentation of the results obtained follows. The paper finishes with a discussion, a summary of the most significant conclusions along with the main managerial implications drawn from the study, and a reference to its limitations, the future research lines, and the references used.

LITERATURE REVIEW The Knowledge Society is characterized by the appearance of a new business environment in which globalization, the liberalization of several economic sectors and the widespread access to

information, among other factors, have reduced firms’ competitive advantages. Before the dynamism in this environment, the resource-based view of the firm establishes that the firm’s endogenous factors constitute a more solid foundation to maintain their competitive advantages (Amit & Shoemaker, 1993; Peteraf, 1993; Barney, 1991; Grant, 1991; Wernerfelt, 1984). Some contributions made after this theory have permitted to discern three extensions of it. Firstly, the dynamic capabilities theory, according to which firms must permanently generate new resources and capabilities with the aim of increasing its initial stock and remaining competitive in the long term (Wang & Ahmed, 2007; Eisenhart & Martin, 2000; Teece, Pisano & Shuen, 1997); secondly, the knowledge-based view of the firm, which sees knowledge as the basic production factor and the main strategic resource (Zack, 1999; Spender, 1996; Grant, 1996; Drucker, 1993; Starbuck, 1992); and thirdly, the intellectual capital-based view of the firm, which focuses exclusively on those resources and capabilities which have an intangible nature (Reed, Lubatkin & Srinivasan, 2006). At present, knowledge stands out as the basic production factor and the main strategic resource (Zack, 1999; Spender, 1996; Grant, 1996; Drucker, 1993; Starbuck, 1992). Knowledge management and intellectual capital have generally been studied independently. However, since intangible resources are based on knowledge, we should consider the complementariness existing between these two theories. Reed, Lubatkin and Srinivasan (2006) refer to this complementariness when they state that the intellectual capital-based view’s focus is on the stocks and flows of knowledge capital embedded within an organization. Along these lines, intellectual capital could be defined as the sum of all the knowledge and knowing capabilities which can give a company a competitive advantage (Yound, Subramaniam & Snell, 2004; Nahapiet & Ghoshal, 1998; Stewart, 1997).

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Relational Capabilities

Relational Capital Firms adopt different approaches to accumulate and use their knowledge, and these approaches manifest themselves as distinct aspects of intellectual capital (Davenport & Prusak, 1998; Schultz, 1961). As shown in the previous section, there are three basic components of intellectual capital which represent all the expressions of a firm’s knowledge stocks (Martín de Castro & López Sáez, 2008). Human capital is the knowledge, abilities and skills residing within and used by individuals as well as their capacity to generate them (KSRC, 2003; Schultz, 1991). In turn, structural capital is the institutionalized knowledge and codified experience residing within and used through databases, patents, manuals, structures, systems and processes (Subramaniam & Youndt, 2005; Youndt, Subramaniam & Snell, 2004). Finally, relational capital refers to the value that relationships with its environment have for a firm (KSRC, 2003). Taking into account the purpose of this chapter, we will focus only on the relational capital part of intellectual capital. The reason for this choice lies in the fact that the difficulties encountered by a firm when trying to generate internally all the knowledge that it requires to achieve competitive advantages lead that firm to establish cooperation links with its external network. According to Lev & Daum (2004), we adopt a more dynamic approach and think that intangible assets represent capabilities and ‘potential’ for future growth and income. Therefore, we consider that relational capabilities provide useful intelligence and contacts which can increase the level of knowledge and experience within the firm (Hayer & Ibeh, 2006) and therefore contribute to create value for the firm itself and for its stakeholders. One can find various points of view concerning relational capital in literature. On the one hand, relational capital is described as the relationships that a firm has with its customers (Edvinsson & Malone, 1997); on the other hand, relational capital includes all the firm’s external relationships

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(Bontis, 1996), thus resembling the concept of social capital proposed by sociologists. Nahapiet & Ghoshal (1998) consider that social capital is an intermediate form of intellectual capital consisting of knowledge in groups and people networks. Such relationships are not confined to internal knowledge exchanges among employees, but also extend to linkages with customers, suppliers, alliance partners, and the like (Youndt, Subramaniam & Snell, 2004). An inherent characteristic of the knowledge associated with social capital is its evolution through interactions among individuals or groups who tend not to follow predetermined rules and procedures to access, share, or transact information (Subramaniam & Youndt, 2005).

The Relational Capital-Knowledge Management-Value Creation Link Several works have thoroughly described the effects of relational capital on knowledge management–mainly on knowledge creation and innovation (Chen, Chang & Hung, 2008; Lee, Wong & Chong, 2005; Nahapiet & Ghoshal, 1998). The acquisition and transfer of relevant knowledge are two of the main dynamic capabilities that firms can now resort to for the acquisition and transfer of new assets that will allow them to remain competitive in the long term (Wang & Ahmed, 2007; Eisenhart & Martin, 2000; Teece, Pisano & Shuen, 1997). More specifically, knowledge acquisition has to do with the absorption by the firm of the existing knowledge which resides within individuals or organizations placed outside the firm. Knowledge transfer implies the transfer of either expertise or external market data with a high strategic value between firms and their environment. Inkpen and Tsang (2005) mention the organization’s ability to acquire new knowledge from the network and to facilitate knowledge transfer among network members. But, what are the relational capabilities which allow firms to acquire and transfer knowledge from/to the environment? Our interest focuses on getting to know the relational

Relational Capabilities

capabilities that facilitate the acquisition and transfer of the most tacit knowledge. Although information and communication technologies are the mechanisms most commonly used to accumulate and transfer knowledge, they have limitations to share this type of knowledge, due to its peculiar characteristics. For that reason, we focus on those capabilities which enable individuals and firms to develop closer relationships. The firm’s external network is constituted by those external agents that interact with it in its daily transactions and consequently form part of its supply chain. Suppliers, customers, distributors, competitors and R&D centers stand out as some of the most widely recognized factors because of the role that they play through the suggestion of ideas for the development of new capabilities (Sanna-Randaccio & Veugelers, 2007; Schmid & Schurig, 2003; Frost, Birkinshaw & Ensign, 2002; Mascarenhas, Baveja & Jamil, 1998; Ghoshal & Nohria, 1989, 1994; Dosi, 1988). The environment equally provides the firm with new knowledge coming from consultants and experts, as well as from universities, state agencies and technological centers (Tödling, Lehner & Kaufmann, 2009; Knudsen, 2007; Kaufmann, McAndrews & Wang, 2000). Benchmarking, the acquisition of firms, and the setting-up of strategic alliances (mainly joint ventures) also contribute to supply new knowledge. Informal relationships emphasize a more voluntary, personal and intensive cooperation mode. Thus, the above forms of interaction between firms promote the trust required for the acquisition and transfer of the most tacit knowledge. Relational resources and capabilities encompass all those relationships which the organization has with entities outside it and which influence the firm’s ability to create value. The individual organization will have to define the dimensions that are relevant to it when it comes to value creation (Roos, Pike & Fernström, 2005). Since firms can exert an influence on how they relate to their environment, the empirical

research undertaken by us in this study seeks to list the main relational capabilities which facilitate the acquisition of knowledge toward the firm as well as the transfer of knowledge to the environment, improving its capacity to generate value. This value can materialize in higher profitability; product, process and service innovation; greater added value to customers; and an improvement of the firm’s image along with its financial results (Pertusa-Ortega, Zaragoza-Sáez & Claver-Cortés, 2010; Lev & Daum, 2004; Grant & Baden-Fuller, 1995; Nonaka & Takeuchi, 1995; Nonaka, 1991, 1994). In other words, intellectual capital requires utilizing and managing knowledge to achieve a competitive advantage.

METHODOLOGY Sample We decided to use the case study as our research method throughout the development of the empirical work because the characteristics of case studies make it possible to come closer to the study object. Sampling is crucial for a case study, since the choice of a sample has an effect on the results obtained (Miles & Huberman, 1994). Different cases have been selected seeking to obtain a diverse sample that can provide many possibilities for comparison, as this enables a richer theory development (Strauss & Corbin, 1990; Eisenhardt, 1989). The intention was to contrast firms placed in various sectors and different in terms of intangibles management levels through comparative case studies. This allows for cross-site comparisons and gives the researcher the chance to see idiosyncratic aspects of any one site in perspective (Miles, 1979). The basis for case selection was a non-random sample. We tried to identify firms with a leadership in knowledge and intellectual capital fields within the specific sectors under analysis. This

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Table 1. Firms under study Firms

ELECTROTÉCNICA ARTECHE HERMANOS S.A. (EAHSA) (Manufacture of electric motors, transformers and generators) UNIÓN FENOSA (UF) (Production and distribution of electricity and gas) PricewaterhouseCoopers (PwC) (Management and business assessment. Auditing) SIEMENS ESPAÑA S.A. (Electrical installations. Manufacture of electric motors, transformers and generators) TELEFÓNICA GROUP (Telephone services, Internet, content production and dissemination and directories) SANTANDER GROUP (Banking activities in general and activities related to the management of investments and pension funds in particular)

Geographical area

Munguía (Biscay, Basque Country) – EAHSA Madrid- UNIÓN FENOSA Madrid- PRICEWATERHOUSECOOPERS Tres Cantos (Madrid)- SIEMENS S.A. Madrid-Telefónica Group Madrid-Santander Group

Type of interview

Semi-structured or in-depth interview

leadership means that they are firms which have achieved a wide recognition of their knowledge management and intellectual capital practices in their respective sectors. Under this premise, we prepared a list of ten Spanish firms which had to fulfill two essential requirements in order to become candidates to appear in this study: owning a proactive attitude in knowledge matters and working hard to make their experiences in that field known to their stakeholders. Their proactivity becomes clear in the fact that these are companies where intangibles management has become a part of their day-to-day operation and also in the fact that they usually perform actions meant to create, transfer and apply knowledge insofar as is possible. We chose those cases which offered good opportunities for learning, and followed the recommendation that their number should be neither less than four nor more than ten (Eisenhardt, 1989). The reason for choosing Spanish firms was our interest in identifying the link between relational capital, knowledge management and

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value creation in Spain, as no empirical works had linked these topics within that geographical context until then. Finally six firms agreed to participate in the study, three of them belonging to the secondary sector (electric and electronic components, production of electricity, and gas) and the other three located in the tertiary sector (professional services and auditing, telecommunications, and banking). They form part of high-tech, knowledge-intensive sectors and are characterized by their high competitiveness and the outstanding level of success reached in the areas of knowledge management and intellectual capital. Table 1 shows the description of firms involved in the study.

Data Collection In order to avoid a potential bias introduced by researchers themselves and/or by informants, we collected the data using the ‘triangulation technique’, which combines three methodologies:

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•

•

•

In-depth interview, with the Chief Knowledge Officer (CKO) or the person responsible for knowledge and intellectual capital issues within the firm. We used a standard questionnaire with open-ended questions, adapted and customized for each one of the enterprises. Observation, which implies visits to the premises and contacts with employees. Although this is a secondary source to obtain data, it not only allows researchers to discover the truth about the study object but also helps them achieve a better understanding of the case. Consulting documents drawn from the Internet as well as from publications –both on an internal level (Intranet, formal and informal reports) and on an external one (websites, books, published articles, annual reports and corporate management reports).

Due to the qualitative character of most data, triangulation permits to increase construct validity. As for internal validity, the researcher can ensure it being in permanent contact with the interviewees during the process of analysis, because they can provide more data to fill any possible new information gaps that might arise. In turn, external validity comes as a result of multiple case studies and the analysis of findings. The reliability problem was addressed preparing a detailed case study protocol common to all the enterprises and following the information obtained from the documents and the transcription of the interviews. Finally, we sent the case study to the interviewees so that they could supervise and okay it or make any comments, if necessary, in the hope that this would make our results more reliable.

Data Analysis The extended case method (Burewoy, 1991) has served as a guide to data analysis. This method-

ological approach uses empirical data gathered through case studies to reconceptualize and extend theory. The extended case method consists of two ‘running exchanges’: (a) between the literature review and data analysis; and (b) between data analysis and data collection (López-Gamero, Zaragoza-Sáez, Claver-Cortés & Molina-Azorín, 2009). During the first phase of data analysis, we explored the relevant concepts and theories found in the literature. In turn, the second phase consisted in the description of cases based on the identified patterns. The research concern was to identify issues in the areas of interest rather than drawing conclusions about the strength of managers’ views. The interview began with respondents answering general questions in order to know if CKOs had been increasingly confronted by business decisions with knowledge and intellectual capital implications. More specific questions were asked as the interview progressed (see Table 2). CKOs were interviewed face to face. Each interview lasted four hours on average, and was later transcribed. During the visits to the facilities, it was possible to speak to other members of the firm’s staff, who also highlighted some of the knowledge and intangible capital tasks that they carried out. Furthermore, the drawing of a matrix provided a visual identification of the similarities and differences between the firms examined. The third step was to analyze the interviewees’ feedback on the first draft of case descriptions to check their validity. We kept in touch with interviewees via telephone or e-mail to clarify any aspects that might arise right up until they completed and approved the final report on each case. The fourth step was the comparative analysis of all cases. Some results derived from the literature review were systematically compared with the evidence from each case for the purpose of assessing how satisfactorily or poorly they fitted in with the case data (Eisenhardt, 1989). The fifth step was the construction of a table which summarized the findings.

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FINDINGS The analysis of the firms made it possible to answer the research questions posed, and the results showed the main capabilities forming their relational capital, which facilitate knowledge

flows between the firms under study and the environment which surrounds them. They are capabilities of a strategic nature that result from business experience. Tacit knowledge and the idiosyncratic nature of a large part of it make the relational capital of these firms difficult to imitate,

Table 2. Questions asked to interviewees* Relational capability

After the study, the resources and capabilities of an intangible nature observed were classified into three blocks: human capital, structural capital, and relational capital. Within the latter block are collected the relational capabilities presented in this chapter.

Knowledge acquisition capability

1. Does your firm acquire knowledge from the competitive environment? If the answer is yes, please indicate which alternative/s is/are usually employed and how the acquisition process takes place. ◦ Passive learning ⁃ Consultants and experts ⁃ Professional conferences ⁃ Universities  ⁃ Seminars ⁃ Specialized journals ⁃ Publications in general ◦ Active learning ⁃ Benchmarking ◦ Interactive learning ⁃ Interactions with customers, suppliers and competitors ⁃ Local clusters and industrial districts ⁃ Acquisitions of firms ⁃ Strategic alliances (joint ventures) ◦ Others________________________________________ 2. Do you consider that customers are an important source of knowledge for your firm? Why? 3. Do you consider that suppliers are an important source of knowledge for your firm? Why? 4. If your firm has acquired another firm, was the acquisition determined by the characteristics of the knowledge which your firm wished to obtain, or by other reasons? 5. To what extent does the local environment in which the firm develops its activity constitute a source for the acquisition of knowledge?

Knowledge transfer capability

1. Does the firm transfer its knowledge toward its competitive environment? If the answer is yes, please indicate which alternative/s is/are usually employed and how the transfer process takes place. ⁃ Professional conferences ⁃ Universities ⁃ Seminars ⁃ Specialized journals ⁃ Publications in general ⁃ Benchmarking ⁃ Interactions with customers, suppliers and competitors ⁃ Local clusters and industrial districts ⁃ Acquisitions of firms ⁃ Strategic alliances (joint ventures) ⁃ Technological platform ⁃ Others________________________________________ 2. Do any procedures exist in your firm to transfer toward customers the knowledge required for the correct development of their activities? 3. Do any procedures exist in your firm to transfer toward suppliers the knowledge required for the correct development of their activities? 4. If your firm has acquired another firm, was there an intention to transfer the existing knowledge in the firm of origin toward the acquired one? 5. To what extent does the firm transfer its knowledge to the local environment in which it develops its activity?

continued on following page

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Table 2. continued Value creation

1. Please specify the benefits brought to your firm by the acquisition of knowledge from the context that it carries out through the alternatives mentioned above: ⁃ Profitability increase ⁃ Cost reduction ⁃ Differentiation ⁃ Product innovation ⁃ Process innovation ⁃ Service innovation ⁃ Corporate image improvement ⁃ Customer satisfaction ⁃ Others_______________________ 2. Please specify the benefits brought to your firm by the transfer of knowledge toward the environment that it carries out through the alternatives mentiond above: ⁃ Profitability increase ⁃ Cost reduction ⁃ Differentiation ⁃ Product innovation ⁃ Process innovation ⁃ Service innovation ⁃ Corporate image improvement ⁃ Customer satisfaction ⁃ Others_______________________

*We have only included those questions asked to interviewees which have to do with the part of the study shown in the present chapter.

as a result of which this capital becomes a source of competitive advantage for them. Table 3 offers a summarized view of the most significant results associated with each of the research issues put forward. Next, we offer a detailed presentation of the results obtained in the present study.

Relationships with Customers The six firms analyzed pay special attention to relationships with their customers. The latter constitute a very important source of potential knowledge, since their needs and demands drive the firm to innovate and create new solutions for the purpose of satisfying them, simultaneously succeeding in differentiating themselves from competitors. There is a dual knowledge flow between the firm and its customers in EAHSA. On the one hand, because the firm’s products are not standard, customers specify in detail what they wish, thus triggering a knowledge flow that the firm has to interpret during the elaboration of the product. On

the other hand, once the product is in the hands of customers, the firm places at their disposal pieces of advice on functioning and technical assistance services. EAHSA elaborates technical assistance handbooks to inform and train customers, ensuring the optimum operation of all measurement and protection equipments. UF also makes an effort to understand and anticipate its customers’ needs, working closely with them in order to offer services which can satisfy their needs. It uses an Intranet and call-centers to know the needs of their customers at all times. The relationship with customers acquires essential importance in PwC because it permits to detect their specific needs and that means a stimulus for the firm to generate new knowledge that adds value to it. Due to the knowledge owned by PwC and to its good relationships with national and international regulatory entities, customers demand to have access to this corporate knowledge. To that end, the firm transfers knowledge to them formally through mechanisms such as: newsletters; customer extranets; reports on certain

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Table 3. Findings RELATIONAL CAPABILITY

KNOWLEDGE MANAGEMENT

VALUE CREATION

Knowledge acquisition capability

Knowledge transfer capability

RELATIONSHIPS WITH CUSTOMERS

- Customers specify their needs - Intranet and call-centers to know customers’ needs - Comunities of Practices - Requests and complaints from customers (opportunity to learn and improve) - Surveys on customer satisfaction

- Functioning and technical assistance services (safety and learning handbooks) - Newsletters - Customer Extranet - Reports on certain topics - E-learning platform for customers - Advice, help forums and meetings to assist customers - Personalized attention - Face-to-face with customers

- Product innovation - Service innovation - Customer satisfaction - Customer safety - Firm’s differentiation - Cost reduction - Profitability increase - Learning - Adaptation to customers’ needs - Trust and long-term collaboration between firm and customers

RELATIONSHIPS WITH SUPPLIERS

- New knowledge acquisition from unexpertise areas

- Flows of knowledge to improve suppliers’ processes - Development of technologies to facilitate communication with suppliers

- Taking advantage of the opportunities that suppliers provide from a logistic point of view - Development of local suppliers - New knowledge from unexpertise areas - Improvement of suppliers’ relationships in the long term

ACQUISITION OF FIRMS AND SETTING-UP OF JOINT VENTURES

- New knowledge from the acquired firms - New knowledge from sectors where the firm is not present - New partners who know the country, the market and the customers

- Transfer of knowledge to the less efficient and less modern firms acquired

- Market share improvement - Acquisition of new knowledge - Reduction of the risks associated with investments (through joint ventures)

RELATIONSHIPS WITH UNIVERSITIES, NATIONAL AND INTERNATIONAL INSTITUTIONS

- The knowledge from universities and institutions complements the existing one - Talent acquisition from universities - Acquisition of information and new competencies - Privileged environment for research, training and innovation

- Attendance to conferences, forums and seminars to transfer the most specilized knowledge - Transfer of knowledge about knowledge management and intellectual capital issues - Transfer of best practices - Teaching activities

- Increased knowledge base - Better image and reputation - Development of joint projects - Being up-to-date with new breakthroughs - Improvement of the existing competencies through the recruitment of talents and the results obtained in R&D projects

- Annual report - Corporate government report - Corporate social responsibility report - Firm’s magazine - Reports on financial, technological and business issues - Intellectual capital model

- Stakeholder satisfaction - Society satisfaction - Information that complements the traditional financial statements - Improved firm’s image - Improved firm’s credibility

PUBLICATIONS

continued on following page

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Relational Capabilities

Table 3. continued RELATIONSHIPS WITH STAKEHOLDERS AND INVESTORS

- Advice and information to shareholders - Periodical information about the firm to stakeholders - Informative brochures - Mailbox, help number and webpage to supply maximum transparency - Meetings with inverstors

- Stakeholder satisfaction - Improved firm’s image - Improved firm’s credibility

RELATIONSHIPS WITH THE LOCAL ENVIRONMENT

- Knowledge about customers, competitors, the market and the institutions located in a specific context

- Acquisition of valuable strategic knowledge before competitors - Gaining access to sources of competitive advantage in some regions

CONSULTANTS’ ADVICE

- New knowledge about aspects in which the firm is unexperienced

- Gaining access to tacit and experimental knowledge

BENCHMARKING

- New external and experimental knowledge

- Learning - Improvement of the activities performed

topics (financial, legal, tax-related, etc.) or the elearning platform for customers (B2B). Thanks to all this, it is now possible to access the firm’s knowledge from outside. The same as EAHSA, in Siemens there is a dual flow of knowledge between firm and customers. The ‘Communities of Practices’ (CoPs) with customers play a key role in this respect. On the one hand, the firm gets to know its customers’ expectations as well as their desires for differentiation, which forces the business organization not only to generate new knowledge but also to innovate and remain more and more competitive. On the other hand, Siemens supplies knowledge through the provision of advice, help forums and meetings organized to make it easier for customers to achieve a correct and effective functioning of the products offered. Customers receive safety handbooks and periodical publications in which emphasis is placed on safety issues and information is provided about Siemens’ image or about its new products. Customer attention along with the monitorization of incidents facilitate the

integration and knowledge of both organizations, thus permitting a long-term collaboration which optimizes and reduces costs, increasing the profitability of investments on infrastructure and training. Customers also constitute a significant source of knowledge in Telefónica Group as their knowledge and demands lead the firm to innovate and create new solutions. Before launching a product, Telefónica Group approaches the market in an attempt to identify the products desired by its customers, whose requests and complaints can provide a good opportunity to learn and improve. Most of Santander Group’s customers are private individuals, although there are also a large number of firms and institutions which have peculiar characteristics and consequently require a more specific treatment with products adapted to their needs. With the aim of channeling all the information coming from the customers toward the Group, the organization tries to exploit the relationships which take place at the office. They offer a close, personalized treatment to bridge

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the gap between the firm and its customers, thus strengthening the link with the latter through a better service that can increase those customers’ degree of satisfaction and reinforce their relationship with the firm in the long term. This is also achieved through the creation of offices with specific and exclusive characteristics, seeking to be near certain groups of potential value (International Express Offices, especially dedicated to the immigrant population in Spain and offices ascribed to Universities). It becomes essential to know what customers want when it comes to designing new products, for which employees must act as their advisors. Surveys on customer satisfaction along with the correct processing of the complaints identified provide a basic source of information to correct the mistakes made and also to improve the attributes of products and services.

Relationships with Suppliers Four of the firms analyzed state that suppliers become key channels for knowledge acquisition and transfer. In the case of EAHSA, foreign subsidiaries try to absorb as much knowledge as possible from the local suppliers with a high technological level and take advantage of the opportunities which they provide from a logistic point of view. The Spanish parent company is the central place for the purchase of critical products. Nevertheless, foreign subsidiaries have as their mission to develop local suppliers, to support them and to ensure the flow of knowledge between them, always encouraging knowledge flows in both directions. In UF, suppliers represent an important part of the firm’s value chain due to the potential of their knowledge in certain areas, which the firm does not possess. In Siemens, alliances with suppliers and other partners, according to the strategy adopted by each division, allow the optimization of the supply chain, the development of new business opportunities and a continuous improvement in

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the orientation toward the market and toward customers. Telefónica Group develops a series of tools meant to spread improvements to the whole supply chain and to collaborate with its suppliers. Among them stand out the following: B2B e-commerce, which has as its aim to automate and optimize the whole purchasing process and establish new and better communication and collaboration channels; the supplier’s portal, the main information channel between this firm and its suppliers, offering access to global information about how to become a supplier of the Group, the purchasing process and the main projects that the firm is undertaking in relation to suppliers; and suppliers’ development, through which Telefónica Group puts at the disposal of suppliers, with no extra costs for them, a team of people and a methodology that helps the providing firm implement ongoing improvement processes.

Acquisition of Firms and Setting-Up of Joint Ventures Most of EAHSA’s foreign subsidiaries come from acquisitions of firms already established in their countries of origin. With them EAHSA seeks to improve its market share, not only extending the knowledge already available in the parent company to the new locations but also acquiring still-not-owned knowledge with these firms. The acquisition of small business units has also been a formula used by PwC to acquire knowledge and grow within its own business line. Siemens claims that the acquisition of firms makes it possible to extrapolate the specific knowledge existing in those firms, which enjoy a good position within the industry. With such acquisitions, the firm purchases the knowledge available in sectors where the firm is still not present. Through the setting-up of joint ventures, Siemens tries to reduce the risks associated with investments and incorporate partners who know the country, the market and the customers.

Relational Capabilities

As for UF, one cannot say that the acquisitions carried out in the energy sector have as their ultimate aim to acquire knowledge. In this sector, the knowledge strengths are in Spain, and UF transfers them to the less efficient and less modern companies that it acquires abroad.

Relationships with Universities and National and International Institutions For EAHSA, its external relationships complement the knowledge existing within the Group. For this reason, it is involved on a permanent basis with different institutions and associations as a member and/or collaborator in national and international programs, both in the development of new technologies and in the area of business innovation. Among these institutions and associations, stand out R&D centers, electric organizations, universities, and management and quality institutions. With those stakeholders increasingly concerned about knowledge management and intellectual capital issues in mind, EAHSA transfers its knowledge through conferences and seminars imparted in forums, at the University of Mondragón, and through the visits they receive. UF maintains relationships with national and international institutions which participate in projects and initiatives at all levels of the sector where the firm operates. It has links with Universities to select and train employees, and to collaborate in the development of projects. It also has alliances with more than ten Spanish and foreign Universities. PwC develops new competencies externally through the relationships with Universities and Business Schools and the attendance to seminars and congresses. For this firm, it is basic to maintain good relationships with national regulatory organisms and, because it is a leader in knowledge management practices, PwC shares this knowledge and offers its experience in this field. It externally promotes its whole knowledge management proj-

ect along with the activities performed by the firm. To that end, they write books and articles, participate in forums and workshops, and keep links with Universities and Business Schools. PwC collaborated in the creation of the first Spanish intellectual capital model (the Intelect Model) and it has a strong relationship with the ESADE and IESE Business Schools in the fields of teaching and elaboration of studies and research works. For Siemens, the involvement in associations, business schools and forums related to marketing, knowledge management or engineering allows it to stay up-to-date in those areas. This gives Siemens the possibility to acquire and implement the breakthroughs along with the new knowledge which is progressively being developed before its competitors. Siemens actively collaborates with teaching institutions that impart courses about state-of-the-art technologies, taking part in conferences and sponsoring public events, seminars, publications, fairs and congresses with the aim of transferring its achievements in the areas of technology and sustainable management. Telefónica Group considers that the presence in meetings, forums and conferences, as well as in national and international institutions, enables it to capture information coming from other firms’ best practices. These external relationships are a potential source of knowledge, not only in aspects linked to the telecommunications sector but also regarding all those initiatives related to the Information and Knowledge Society which are of vital importance for the transformation of firms. Special attention must also be paid to the collaboration that Telefónica Group keeps with Universities seeking to promote training and innovation. The ‘Telefónica’ Chairs encourage telephone-related innovation, promoting basic and applied research, fostering the training of future professionals, and helping SMEs to acquire training in new technologies. The Group equally makes an effort to maintain suitable communication channels which enable it to fulfill its transparency commitment with stakeholders. This is why Telefónica Group

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participates in forums and conferences through which it transfers all its experience in the telecommunications sector along with that stemming from its own internal transformation process. Santander Group is present in nearly all the events organized about topics such as knowledge and intellectual capital, human resources, training, e-learning, and new technologies. It had an active participation in the development of the Intellectus Model, as well as in the various encounters which take place throughout the year. Regarding its relationship with the University, Santander Group has a triple vision, seeing it as: an empowering, training and meeting center for the professionals, entrepreneurs, intellectuals and executives of tomorrow; a key institution to guarantee the social wealth of nations and their economic competitiveness; and a source as well as a privileged environment for research, creation and innovation. Consequently, the Group maintains the only collaboration project between a firm and the University world which focuses a large part of its efforts on Social Responsibility matters: Santander Universidades. This links the Group with European and Latin American Universities by means of education and research support agreements and the Portal Universia. The materialization of its support in the funding for events, grants and libraries–as well as in technological infrastructures–is highly rewarded with the recruitment of talent for the Group and the results of R&D projects, which largely improve the existing competencies and additionally contribute to their renewal.

Publications UF considers that the elaboration of its own intellectual capital model and the publication of its indicators in the Annual Report constitute a valuable information tool for all stakeholders. It permits to know, understand and share the keys to the success of its whole business, apart from being a medium of communication for shareholders and for the financial community that–by means of

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the evolution of intangibles, the indicators which measure them and the projects which improve them–gains access to transparent information which is also complementary to the traditional financial statements. In order to provide investors with more agile and complete information about the activities as well as the financial and economic evolution of the firm, the Corporate Government Report offers a detailed account of the management carried out in this area. PwC acts as a meeting point in the business world through the dissemination of legislative, technological and business-related news in its publications. This firm is responsible for many publications and studies in the area of knowledge management and for digital contents which are available both nationally and internationally. PwC also publishes the magazine “IDEAS, Visions and Strategies for the 21st Century Leaders”, which has as its aim to become a referent in the world of business administration and management. Siemens thinks that it must supply information toward the outside world in order to achieve credibility among its shareholders and satisfaction among its stakeholders. This firm is fully aware that the interest groups not only want to know the financial information about the company but also everything that has to do with the social and environmental aspects, as well as with intangible asset management, innovation capacity, intellectual capital, transparency and ethics. In order to satisfy all groups, Siemens includes information about some of its intangibles indicators and everything that refers to the activities undertaken within the environmental area in its Annual Report. Taking into account the importance of intangible assets and the need to offer shareholders and investors more information than that provided by the traditional accounting statements, Santander Group elaborated its own intellectual capital model. The information about the Group’s intangible assets is presented through a series of indicators and the Corporate Social Responsibility Report

Relational Capabilities

is the way to make them known to shareholders, to investors and to the society as a whole.

Relationships with Shareholders and Investors Shareholders and investors need to be duly informed in order to make the appropriate decisions regarding the firm. For this reason, Telefónica Group has available several channels to provide them with information adapted to their needs, among which stand out: Shareholder’s Office (a service which facilitates and disseminates all the information that is relevant to shareholders and specifically deals with all their consultations, managing them through its call-center, its webpage or through postal mail), Area of Relationships with Investors (which has as its most relevant function to design and execute Telefónica’s communication program toward the national and international financial markets), and several informative brochures. The Office of Information to the Shareholder is used by UF for the purpose of providing all the advice and information demanded by shareholders. Santander Group supplies maximum transparency to its shareholders through the mailbox and the help telephone for shareholders, the reports, webpage and also through meetings with investors, analysts, rating agencies and investors specialized in social responsibility.

Local Environment, Consultants and Experts, and Benchmarking EAHSA considers that the local context where its plants stand is very important because it determines the specific knowledge that these plants own in relation to the country’s businesses (knowledge about customers, competitors, and the market) and with its institutions (institutional framework, government, restrictions, etc.). The knowledge about various aspects which characterize the countries where the Group is present constitutes a very useful

and specific set of knowledge portfolio that can help it expand even more in those regions. It also becomes a very valuable strategic asset before its competitors. This knowledge gives EAHSA the possibility to gain access to sources of competitive advantage in the geographical areas in question. EAHSA and Santander Group use the advice given by consultants and experts assuming that they can help them clarify and acquire knowledge about aspects in which these firms still have not accumulated enough experience. Thanks to those consultants and experts, the firm can have access to tacit and experimental knowledge that they do not own. Santander Group and Siemens also use benchmarking regularly with the aim of obtaining external knowledge and trying to learn more about certain issues.

DISCUSSION AND CONCLUSION We have examined three research questions in this chapter: (1) what are the main capabilities which form the relational capital of knowledgeintensive firms? (2) how does relational capital help develop knowledge acquisition and transfer capabilities? (3) in what way does relational capital contribute to a firm’s value creation through knowledge management? Our findings answer these questions and suggest that relational capital is an important part of intellectual capital by means of which knowledge processes and firm value are improved. 1.

What are the main capabilities which form the relational capital of knowledge-intensive firms?

The study reveals that the main capabilities composing the relational capital of the firms under analysis are: relationships with customers, suppliers and shareholders; acquisition of established firms; setting-up of joint ventures; collaboration

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with Universities, national and international management institutions, R & D and quality centers; participation in seminars, workshops and conferences; edition of publications; search for the advice of consultants and experts; and benchmarking practices. 2.

How does relational capital help develop knowledge acquisition and transfer capabilities?

The six firms examined identify most of these capabilities as enablers to acquire and transfer knowledge from the environment where they develop their activity. They all consider that customers represent a significant potential source of knowledge, since their needs and demands push the firm to innovate and also to create new solutions with the aim of satisfying them, which in turn allows the firm to differentiate itself from its competitors. Furthermore, three of them show a dual knowledge flow through which the firm not only obtains knowledge from its environment but also supplies knowledge to customers seeking to ensure the correct maintenance and functioning of the products that it commercializes. According to four of the firms, suppliers are important channels for knowledge acquisition and transfer due to the potential which can stem from the knowledge that the supplier has in certain specific areas and which the firm does not own. Thanks to the acquisition of firms and the setting-up of joint ventures, firms extend their knowledge to the new firms while at the same time they obtain the knowledge that they still do not have from those new firms. The collaboration with Universities, as well as the participation in conferences, workshops and seminars, allows these organizations to share their knowledge in different business areas within their environment and to acquire new knowledge. Nevertheless, not all the capabilities coming from relational capital permit the acquisition of knowledge available in the environment or the

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transfer of knowledge toward that environment. On the one hand, the value that publications and relationships with stakeholders and investors have for the firm is only possible through the knowledge transfer carried out by the firm toward its environment. The firms examined recognize that publications serve as a mechanism which provides information about the firm’s activities and as a means to transfer knowledge toward stakeholders (mainly shareholders) and toward the society as a whole. They are equally aware of the fact that the firm’s credibility and the improvement of its image become enhanced thanks to the mechanisms established to have a good relationship with stakeholders and investors. On the other hand, the links maintained with the local context in which the firm develops its activity, the advice given by consultants, and benchmarking are relational capabilities which supply value to the firm thanks merely to the acquisition of knowledge that the latter achieves through them. The firms studied recognize that, thanks to these capabilities, it is possible to obtain external knowledge and also to learn more about certain issues. 3.

In what way does relational capital contribute to a firm’s value creation through knowledge management?

The firms under analysis obtain several advantages from their capacity to relate with the environment and from the development of knowledge management processes to capitalize the knowledge that exists both inside and outside them. The relationships maintained with the environment provide an opportunity to acquire new knowledge that the firm does not own, thus enlarging its initial knowledge base and improving its products and processes. Such relationships make it possible for the firm to transfer its experience toward other organizations and stakeholders with the aim of improving their resources and capabilities. Innovation, customers, suppliers and stakeholders’

Relational Capabilities

satisfaction, improvement of the firm’s image and credibility, competitive advantages, achievement of new knowledge and learning, are some of the main benefits mentioned by the firms under study.

Managerial Implications The present paper has contributed to the literature in several ways. Firstly, from a theoretical point of view, we link the knowledge-based view and the intellectual capital-based view of the firm for the purpose of showing the importance that intangible resources and capabilities belonging to the relational capital have for knowledge acquisition and transfer by the firm. Secondly, this paper tries to fill the gap existing in literature considering that the value creation generated by intellectual is possible thanks to the knowledge management and transfer processes previously generated by the firm. Thirdly, from an empirical point of view, we carry out in-depth case studies meant not only to identify the main elements of relational capital but also to show how they help acquire and transfer knowledge between the external network and the firm. A search of the literature has failed to identify any previous research works which specifically examine the relational capital-knowledge management-value creation link using a comparative case study as we have done here. Finally, the findings obtained in this study also have managerial implications. Findings can help managers recognize the value of relational capital for knowledge acquisition and transfer, which should encourage them to take a leading role in the promotion of relationships between firms and their external network so as to increase relational capital and firm value. More specifically, managers need to be aware of the fact that the acquisition of knowledge, maninly tacit knowledge, from customers and suppliers will positively influence innovations. In order to achieve this aim, they will have to create a suitable infrastructure that can make easier the collaboration between the firm and its

environment, fostering agile and effective communication. The information and communication technologies play a very important role in the acquisition and transfer of explicit knowledge, but closer relationships between individuals and face-to-face encounters are needed for tacit knowledge. Managers must recognize that the knowledge resources and capabilities obtained with the acquisition of established firms and the setting-up of strategic alliances can revitalize the acquiring organizations and favour their long-term survival. This is why they must take advantage of the opportunities that the environment offers in this area. It is important for managers to establish links with Universities, Business Schools and official institutions, thanks to which firms will obtain up-to-date knowledge about the research carried out in their field of activity. This will give them the chance to acquire and implement the breakthroughs and new knowledge that is being gradually developed, both nationally and internationally–before their competitors. The publication of intellectual capital indicators and the dissemination of legislative, technological and business-related news will provide the environment with explicit, transparent information that can complement the firm’s financial statements. Furthermore, managers must be aware of the fact that the environment where the firm develops its activities, the benchmarking practices, and the advice given by consultants and experts constitute important sources to access specific, tacit and experimental knowledge that the firm does not own. Finally, some limitations and future lines of research need to be mentioned. The main limitations stem from the specific nature of the multiplecase approach and the fact that the firms analyzed stand out as being some of the most advanced in knowledge management and intellectual capital issues within their respective sectors. Future research will focus on increasing the number of cases studied with a view to find new elements

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and patterns of behavior in the relational capital field as well as on using other methodologies which can allow us to spread the results obtained to a wider population.

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Tödling, F., Lehner, P., & Kaufmann, A. (2009). Do different types of innovation rely on specific kinds of knowledge interactions? Technovation, 29(1), 59–71. doi:10.1016/j.technovation.2008.05.002 Wang, C. L., & Ahmed, P. K. (2003). Structure and structural dimensions for knowledge-based organizations. Measuring Business Excellence, 7(1), 51–62. doi:10.1108/13683040310466726 Wernerfelt, B. (1984). A resource based view of the firm. Strategic Management Journal, 5(2), 171–180. doi:10.1002/smj.4250050207 Yin, R. (1994). Case study research: design and methods (2nd ed.). Newbury Park: Sage Publications. Youndt, M. A., Subramaniam, M., & Snell, S. A. (2004). Intellectual capital profiles: an examination of investments and returns. Journal of Management Studies, 41(2), 335–361. doi:10.1111/j.1467-6486.2004.00435.x Zack, M. (1999). Developing a knowledge strategy. California Management Review, 41(3), 125–145.

KEY TERMS AND DEFINITIONS Intellectual Capital: The sum of all the knowledge and knowing capabilities which can give a company a competitive advantage (Yound, Subramaniam & Snell, 2004; Nahapiet & Ghoshal, 1998; Stewart, 1997).

Relational Capital: For the purpose of this chapter, relational capital is the part of intellectual capital that includes intangibles from the firm’s external relationships (Bontis, 1996). Knowledge: Knowledge originates from creativity, individual experiences and organizational learning, and it can be found not only in the written documents but also in the routines, tasks, processes, practices, rules and values that shape an organization. Knowledge Management: Set of business policies and actions undertaken to aid the creation of knowledge, its transfer to all company members, and its subsequent implementation, with the aim of achieving distinctive competencies that provide the company with a long-term competitive advantage. Knowledge Acquisition: In the context of this chapter, it has to do with the absorption by the firm of the existing knowledge residing within individuals or organizations outside the firm. Knowledge Transfer: It implies the transfer of either expertise or external market data with a high strategic value. Case Study: According to Yin (1994), it is an empirical study which examines a contemporary phenomenon within its real context, especially when the limits between the phenomenon and its scope are not clearly defined and multiple sources of evidence are used.

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Chapter 4

Intangible Assets and Company Succession:

Are There any Differences between Buy-In and Buy-Out Initiatives? Susanne Durst University of Liechtenstein, Principality of Liechtenstein

ABSTRACT A successful company succession depends on a multitude of different aspects. In the case of external succession, certainly, the available funds represent a critical factor. Nevertheless, it can be argued that the decision to acquire a company is based on other factors as well. This chapter rests upon the hypothesis that a potential external successor will be only interested in those companies offering promising prospects. Thus, it is expected that the decision to takeover a company is rooted in the target firm’s inherent intangible assets which justify a financial investment in return. Data are collected through interviews with eight external successors from Germany who pursued buy-in respectively buy-out initiatives in small and medium-sized enterprises. The study’s findings highlight those intangible assets that are regarded as critical in the external succession process. This helps us to obtain a more complete picture about the issue of company succession.

INTRODUCTION1 Demographic developments cause an increasing number of firms which are waiting to be transferred to new ownership (Commission of the European Communities, 2006). This coincides with an observable missing motivation/aptitude of family members to take over a family business

DOI: 10.4018/978-1-60960-054-9.ch004

(Poza, 2007). In such a situation, potential external (non-family) successors attract notice. A successful company succession depends on a multitude of different aspects. In the case of nonfamily succession, of course, the available funds represent a critical factor. Nevertheless, it can be argued that the decision to acquire a company is based on other factors as well. It is hypothesized that a potential external successor will be interested in those companies offering promising prospects. Thus, it is expected that the decision is rooted in

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the target firm’s inherent intangible assets which justify a financial investment in return. In view of the increasing relevance of knowledge resources to firms, it is suggested that the intangible assets are primarily influencing external successors to go further in the succession process. As intangible assets are considered the types of resource potential investors are looking for, it is expected that this could be transferred to external successors in small and medium-sized enterprises (SMEs) as well. Generally, in the case of company succession a primary concern is whether the target company has the potential for a sustained existence. Consequently, a business transfer is also considered a failure when the company enters a state of crisis or disappears from the market, respectively shortly after the succession has taken place. One explanation for this short-term failure might be that the successor has not thoroughly analyzed the firm’s assets. Maybe he/she relied too strongly upon past business performance thereby overlooking prospects. Consequences of a high number of such failed business transfers are obvious. A literature review in terms of the meaning of intangible assets in company succession has shown that this area is apparently overlooked so far. Instead, a central focus on legal, financial, tax and on family related issues can be observed (Morris, Williams & Nel, 1996). Especially the latter issue represents a research area of great interest. In addition, the succession literature reveals a focal point on the incumbent’s perspective. If successors are discussed it happens from the family firm’s point-of view, and the emphasis is on family successors rather than on non-family successors. In terms of the importance of the knowledge transfer in view of company succession, increasing research activities have taken place recently (e.g., Cabrera-Suárez, De Saá-Pérez, & García-Almeida, 2001). However, until now, these studies have in common that they represent conceptual papers. Referring to demographic trends and the meaning of successful succession processes for

national economies this particular focus on the family and their interests are unsatisfying. In this connection a need for more research in this area was identified, paying particular attention to the external successor’s point of view, which until now has tended to be underrepresented in research activities on the issue of company succession. In view of the general agreement regarding the central importance of succession issues (Kesner & Sebora, 1994), the lack of information available on intangibles in the succession context represents a deficiency which is intended to be tackled through this research. To this end the following central research question was posed: What role do intangible assets play in succession processes involving small and medium-sized enterprises as seen from the perspective of potential external successors? Thereby, the focus is on those intangibles making the company attractive from the standpoint of an external person pursuing a buy-in or a buy-out initiative, respectively. The findings presented in this chapter provide a new perspective on company succession, specifically in regard to selection processes used by external successors, and thus this research contributes to the literature in several ways. Firstly, an alternative approach to dealing with company succession in small and medium-sized enterprises is proposed by adopting the perspective of external successors (non-family successors), considering their proceeding. Secondly, the traditional view of company succession is enlarged by considering intangible assets as being the decisive elements in the preparation stage. Finally, the findings provide insights into critical intangible assets in terms of company selection.

BACKGROUND Intangible Assets The terms “globalization” and “information technology” as key driving forces have mainly triggered dramatic changes in the structure of 65

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companies. In order to remain competitive these changes have challenged companies of all sizes to shift their perspective from tangible to intangible resources. In the meantime intangible assets (IA) are considered as more important than in the 1960s, 1970s, or 1980s (Lev, 2001); though their relevance is not new (Martín-de-Castro, NavasLópez, López Sáez, & Alama-Salazar, 2006). However, nowadays a systematic handling of these resources is regarded as decisive to remain competitive (Wiig, 1997). Edvinsson (2005) links intangibles to a new management perspective that is targeted to longterm rather short-term profit increase, thus sustainability is the key. This perspective is found in many German SMEs in which management behaviour is based on a more long-term and ethical view rather than on satisfying financial investors´ requirements (Edvinsson & Kivikas, 2007). According to Nonaka, Toyama, & Konno (2001) IA represent the type of resource a potential investor is looking for. They further assert that the creation and utilization of intangibles as the core activities of a company in order to secure its continuity. Even though more and more organizations and scholars identify the prospects of taking into account IA a great problem still exists: the common language among practitioners and scholars is still missing (Marr & Chatzkel, 2004). One reason for this could be that differences arise from differing viewpoints of different interest groups or disciplines, respectively: strategy and measurement. The former is concerned with optimizing the management of knowledge resources in the company in order to improve performance. The latter focuses on the setting of standards for organizational accounting in order to give stakeholders a more comprehensive picture of intangible assets in terms of traditional monetary data (Petty & Guthrie, 2000). Consequently, different definitions are in place. For the purpose of this study IA are, based on Andriessen (2004) and Lev (2001), defined as the core non-monetary resources, lack-

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ing physical substance that are able to contribute to future benefits in SMEs. According to many authors, intangible resources can be classified into a number of distinct types of non-physical asset. These classification schemes aim to give a better understanding of what intangible assets consists of. Recently, it appears that the classification of these resources into human capital, structural capital and relational capital is increasingly used as a standard perspective (Edvinsson & Kivikas, 2007). Human capital comprises the organizational members’ competence, ability, and skills. It is viewed as central as it represents the foundation of innovation and changes (Bontis, 2002). Structural capital describes everything that supports the employees´ productivity, such as organizational structure and processes, software, intellectual property and corporate culture (Marr, 2005). Finally, relational capital embodies all the relationships with customers, suppliers and other critical partners (Roos, Bainbridge, & Jacobsen, 2001). The literature review related to the relevance of intangible aspects in terms of company succession in general has shown relatively little interest as yet. Instead, specifically in Germany, a focus on legal, financial, tax and family issues is observable (Amelingmeyer & Amelingmeyer, 2005). This is surprising since on intangible assets it is assumed that they are the key drivers of company value creation (Gupta & Roos, 2001).

The Relevance of Intangibles to SMEs According to Gibson (2004), the relevance of intangibles, in spite of the company size, depends very much on the industry the company is working in. Consequently, a small service-oriented company will put higher emphasis on intangibles such as employees, relationships and information compared with firms coming from other industries. Roos, Pike, & Fernström (2005) agree on the service aspect, which represents an integral part

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in many SMEs´ offerings, and thus their higher dependency on intangible assets compared to large businesses. On the other hand, Roos et al. (2005) spot a correlation between firm size and dependency on intangible assets. According to them, the dependency on intangible assets increases the smaller the company is. This would mean that the relevance of intangibles is not a matter of industry but primarily of company size. This would also suggest that intangibles are more relevant in SMEs than in large companies. The stage of the company’s life cycle may also have an influence on the relevance of intangible assets. In order to start a company the entrepreneur will mainly exploit his/her education, working experience and so forth. Thus, the emphasis will be on human capital. However, in the later stages, other forms of intangibles become important (Peña, 2002). As the focus of this chapter is on company succession, it can be argued that the target companies can be mainly described as being matured. Thus, in this stage it can be assumed that due to learning and experience the company is made up of all the three dimensions of the intellectual capital classification scheme (Peña, 2002). The stage of maturity often means a turning point for the company as here the decision is made whether the company strives for higher profitability or decline (Kuratko & Hodgetts, 2004). However, in companies, in which the right time for succession has been missed, representing a typical problem of many SMEs, it can be expected that the period of decline has already been started (Meis, 2000). Yet, it is doubtful if a prospective external successor will take the risk of acquiring such a company after a thorough company analysis. In terms of IA Robert Huggins Associates (2005) point to the situation that so far the issues of SMEs have predominantly been excluded from empirical studies in this field of research. Consequently, despite normative approaches, only little is known about the actual relevance of intangible assets to SMEs.

Company Succession Company succession can be defined as the simultaneous transition of property and/or management of a firm from one person to another (Ip & Jacobs, 2006). Szyperski and Nathusius (1999) consider company succession as a derivative corporate foundation compared to the original corporate foundation, in which an entirely new company is created. Succession is less frequent in SMEs than in large companies, thus practical experience is relatively low (Kesner & Sebora 1994). Moreover, the pool of potential successors for SMEs is smaller than that for larger public firms (Le Breton-Miller, Miller, & Steier, 2004). Thus in the worst cases, the company is closed or the owner continues to lead the enterprise beyond the pensionable age. Moreover, the centrality of the owner is another reason making succession in SMEs more difficult (Commission of the European Communities, 2006). Company succession has implications for all the parties involved and may symbolize an episode of danger to the further survival of the company (Shepherd & Zacharakis, 2000). Because of the increasing number of firms waiting to be transferred (Commission of the European Communities, 2006) and a lack of interest of many family successors in taking over the family firm (Poza, 2007), non-family successors come in the limelight and are in a position to choose the company, which best matches their expectations. This ‘investor market’ is confirmed by taking a glance at the online business exchange “next-change” provided by, amongst others, the German Federal Ministry of Economics and Technology, which shows a surplus of firms waiting to be transferred. The majority of researchers agree that succession is the result of a process and not a single event (Handler, 1994). Thereby, the academic literature provides several models aiming at illustrating this process (e.g., Handler (1989) in Handler, 1994; Ip & Jacobs, 2006). This study is based on the model developed by Ballarini and

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Keese (2006) which divides the succession process into five stages which are planning, preparation, realization, establishment and consolidation. The relevant phase for this study is the preparation stage. In this phase, the successor seeks and analyzes companies of interest. Accordingly, the focus is on decisive aspects within the company analysis process which take the successor from the preparation stage to the next stage (realization). Usually, two types of succession can be distinguished which are family succession and nonfamily succession. Family succession describes the transfer of the company to family member(s) (Sharma, Chrisman, & Chua, 2003); while in non-family situations, the company is transferred to external individual(s). These activities can be further divided into buyers from inside the company (buy-out) and buyers from outside the firm (buy-in). Against the background presented above, company succession seems to be of considerable relevance and external succession an increasing and critical subject within this area. However, the literature review conducted revealed that the articles dealing with the personal side of this topic are strongly focused on the other side of the business transfer process, namely the standpoint of the incumbent owner (Birley, 1986; Schulte & Wille, 2006). If the successor is analyzed, this happens from the perspective of family succession (Scholes, Westhead, & Burrows, 2008). However, in view of the demographic trends as described above an understanding of external successors perspectives is need in order to obtain a complete picture of company succession.

METHODOLOGY The Guiding Framework The classification scheme dividing intellectual capital into human capital, structural capital and relational capital was seen as suitable to link the

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research into the existing body of knowledge. Taking this scheme as starting point, previous empirical studies were reviewed focusing on IA having an influence on company success. Thereby, particular attention was paid to studies bearing relation to SMEs. The rationale for this proceeding was that if these intangibles do have an influence on company success they should also be of interest in the external successors´ selection process. Based on this proceeding intangibles that may be expected to be relevant in terms of company succession were identified. Figure 1 depicts the intangible assets employed for the construction of the guiding framework. Human capital is divided into employees and owner. Interestingly, none of the studies analyzed took into consideration the critical relevance of the person “owner”. However, due to the owner’s central position in many SMEs (Ballarini & Keese, 2006) this person cannot be neglected in terms of company succession; accordingly, the person “owner” was included. Structural capital includes four aspects which are innovative capabilities, company culture, knowledge management, and organizational structure. Finally, relational capital consists of customers and networks.

Strategy of Inquiry and Context of Analysis Given the situation that there is relatively little existing literature about the topic of interest an explorative (qualitative) research approach appeared to be appropriate. Nevertheless, the classification scheme dividing intellectual capital into human capital, structural capital and relational capital frequently used in the IA research community was utilized to guide the qualitative research. With the selection of a qualitative approach, it was supposed that the author is in the position to get close to participants and their thinking in order to scrutinize the entire research problem (Maykut & Morehouse, 1994).

Intangible Assets and Company Succession

Figure 1. Guiding framework

Sample Because this research was interested in the meaning intangibles have upon external successors´ selection process, individuals or teams of successors who have already taken over a SME were selected as the unit of analysis. In Germany, legal facts related to company succession are obliged to be registered but not the take-over itself (Schulte & Wille, 2006). Consequently, the researchers had to contact persons or institutions closely related to this topic. The identification of successors was carried out with the help of German trade associations. For a successor to be considered, he/she had to fulfill the following criteria: • •

•

The successor was a non-family (external) successor (buy-in and buy-out initiatives); He/she had acquired the entire company or a significant share of it which provide them the control of ownership and of management; The firm taken over had a total number of employees fewer than 250.

Thus the sampling strategy applied was what Patton (2002) referred to as criterion sampling. This strategy of purposive sampling comprises the selection of cases that meet some predefined criteria. Thereby by meeting these conditions the quality of the cases in terms of the research topic was regarded as being assured. After eight participants (four buy-ins and four buy-outs) had become involved the researcher decided to end the process of data collection. Because the data material received was rich in detail the researcher had a good confidence in the data. The notion behind purposive sampling justified this sample size, as it encourages the researcher to select data rich in detail about the research topic (Saunders, Lewis, & Thornhill, 2007).

Data Collection and Analysis Data were collected through interviews following a semi-structured format. This approach is appropriate when only little is known about the subject in focus (Maykut & Morehouse, 1994). The interview guide focused upon the following points:

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Intangible Assets and Company Succession

Figure 2. General characteristics of interviewees

• • • •

Decisive factors to analyze the company in more detail; Relevance of the intangibles in the guiding frameworks; Relevance of other intangibles not accounted for by the guiding framework; Weighting of intangible assets with regard to the final decision.

The interviews lasted from 1-2 hours were tape recorded and then transcribed. They took place between Mai and November 2007. The analysis of data was conducted by using a combination of inductive and deductive thematic analysis. Thematic analysis involves the searching for themes which appear to be important to the understanding of the phenomenon in focus (Fereday & MuirCochrane, 2006). General characteristics of the eight successors involved are summarized in Figure 2. Figure 2 illustrates that the period of succession covers the years 1995–2007. Referring to the aspect ‘number of employees’ a focus on small companies can be observed. In view of the type of succession buy-in and buy-out initiatives were given. The last aspect ‘management’ illustrates whether the succession took part alone or in a team. Consequently, the criteria set above were fulfilled.

FINDINGS This section consists of the presentation and analysis of the study’s findings. They are struc70

tured according to the research questions and the guiding framework.

Decisive Factors to Analyse the Company in More Detail Buy-In Initiatives One buy-in candidate stated the following: The topic future…prospect. The subject environment, the subject water cleaning. Water is going to become… a scarce good (…) If you turn on the water tap you have to pay for the water and you have to pay the same amount or even a higher price for the sewage. Then I think water cleaning is a forward-looking business (…) This environmental idea was critical for me. (case 5) Informant 3 named the number of employees (20) that were easily comprehensible, the financial figures which were right and “with the predecessor I had the feeling that there was an honest person involved”. Informant 6 actually wanted to start a new business. This idea changed as she was informed that the company she admired was looking for a successor. She explained that she has known the company since she started in the business and it constitutes the only firm in Germany which she would have taken over. For informant 1 to whom the succession meant a complete reorientation, the industry was the critical point, since his intention was to run a profitable production company. Based on prior industry research he was

Intangible Assets and Company Succession

convinced that this type of company would earn money short-term.

Buy-Out Initiatives Informant 7 appreciated the combination of strong staff and a product disposing of a unique selling proposition. For him these two aspects promised excellent opportunities for development. Interviewee 2 stated that the firm symbolizes a piece of home to her. She had always dreamt of running it. Consequently, as she was informed about the impending succession she went for it. On the other hand, informant 4 was already actively involved in the management of the company, thus together with his former and current partner he has taken steps for company development. Informant 8 said that based on his long-term activity in the company he knew that the company owns a competitive advantage within the sector.

Analyses The statements reveal a variety of starting-points. This is reasonable as each successor pursues other objectives or motivations respectively. Based on this similarities are less of a given.

Relevance of the Owner Buy-In Initiatives Informant 1 described the predecessor as open and honest. He anticipated the predecessor’s willingness to let go. They both are still in contact. On that matter he explained: If you deal openly and honestly with each other, you are able to look your opponent in the eyes even later on. Informant 3 confirmed it. He described the predecessor as an honest person who was very interested in finding a suitable solution for both

of them. He emphasized that he was able to cope with his forerunner very well. In this context, informant 5 stated that his predecessor wanted to hand over. They were looking for a joint solution. As they suited each other, they agreed on reducing the transfer process. Both are still in contact and according to informant 5 the predecessor can appear in the company whenever he wants. Moreover, the statements of the interviewees gave the impression that several successors built largely upon the shortcomings of the predecessors in order to improve the company’s performance and hence increasing the profit potential. Informant 6 declared that the predecessor acted like a “celebrity”. There is no more contact with her. Originally, in the sales contract it had been agreed that the predecessor would offer support at the beginning specifically with regard to the presentation with customers, suppliers and so forth. This aspect was installed as a kind of safety as the successor knew, apart from the figures, only a little regarding the company’s internal affairs. This has not worked.

Buy-Out Initiatives Informant 4 declared that the predecessor wanted to let go and was happy that he was able to do so. The informant declared that he knows the predecessor’s character and had the feeling that it fits. With buy-out initiatives, an emphasis on past shortcomings of the predecessor was observable as well: During the 14 years I have been working for the company, I have seen what could be done better. (case 2)

Analyses The findings suggested that for most successors the owner was mainly perceived as a negotiation partner and not as an asset, which could be valuable for the successful continuity of the firm. This

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represents a clear contrast to previous research underlining the high relevance of the owner’s expertise and so forth regarding company succession (e.g., Bracci & Vagnoni, 2005). This study indicated that the successors attempt to give the firm his/her style of leadership. In doing so, several successors based this new style on deficiencies in the old style as perceived by them. Consequently, the predecessor’s demeanor in view of leadership and management was taken as a platform for improvements. This finding was surprising as it implied that the successors abstained from taking into consideration the owner’s knowledge and thus, the combination of it with their own competencies in order to create the new company standing (Lahaie, 2005). Although the successors mentioned that they wanted to avoid the past mistakes of the predecessors they appeared not to seize the opportunity and to benefit from the good things introduced by these persons. Thus, neglecting the chance to obtain and retain past knowledge the easy way, and thereby reducing the advantage compared to founders of new ventures who, by definition, cannot rely on this possibility. An explanation for this could be that some relationships between successor and predecessor seemed to be quite tense, with the consequence that the predecessor’s role was reduced to a negotiation partner and the successor’s efforts mainly strived for the finalization of the succession process in order to start (case 2 & 6). Another reason could be that the successors underestimated the predecessor’s knowledge and/or expertise (Cabrera-Suárez, De Saá-Pérez, & García-Almeida, 2001) which could be the result of an overestimation of their own abilities and expertise as well as a distinctive self-confidence. With regard to buy-in successors it appeared that they principally hoped for the predecessor’s willingness to cooperate within the succession process, such as providing data. Thus, especially here the owner’s role was primarily reduced to that of a negotiation partner.

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Relevance of Employees Buy-In Initiatives Highly qualified staff with experience represents the basis of the company, thus it was important for me to bind these employees to the company, e.g. by granting them general commercial power. (case 5) This statement confirms the statement by Hofer and Charan (1984) regarding the meaning of employees in SMEs. Despite the high relevance of employees to the successors 5 & 6, the informants 1 and 3 have quite different attitudes to employees. In both the companies a traditional separation between management and production or white collars versus blue collars, respectively, could be observed. It appears that both the successors view employees as a means to an end. Moreover, if this process does not work in the intended way the employee is replaced with a new one, indicating no differences between the workers. This underlines that the pattern of harmonious working relationships cannot be taken for granted in SMEs (Goss, 1991).

Buy-Out Initiatives During my analysis, I realized quickly the firm’s strong dependency on key-employees. The excellent staff holds the business model; our company can be described as a people’s business. Thus as I do not have a technical background it was clear that I need them. Specifically the knowledge and experience of one person (the former technical manager) was essential for me, without him, I did not see any chance of survival. (case 7) Interviewee 2 regards employees as “most important”. Because she already worked in the company before, the term “employee” appears to be strange to her. Instead, she sees herself as a “chief-colleague”. She takes the view that there

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has to be someone who takes the responsibility which in this case she is that person.

Analyses The emphasis on key-employees rather than on normal employees is reasonable as a loss of these persons will have greater implications for the company (Lahaie, 2005). The statements also clarify that several successors were aware of this situation and were looking for measures in order to prevent a loss of them. However, while this relevance is recognized by most of the successors, two successors (buy-in initiatives) regard (key-) employees more as a peril in form of dependency rather than as an asset the company can benefit from.

Relevance of Corporate Culture Buy-In Initiatives Informant 6 knew about an intimidating and tense atmosphere within the firm. She assumed that this would change positively after succession when the new style of management had been introduced. This had the consequence, that some of the employees were resisting to change while others were more than happy to participate. However, according to this informant it works in the meantime. Informant 5 declared that a “culture of trust” was of critical importance to him because he does not have the technical knowledge and is therefore highly dependent on his technical staff and their activities. Based on the analyses of each employee he was convinced that this culture is possible. He provides his employees with freedom, e.g. the employees are free to schedule their working hours. Although, corporate culture is important he tries to prevent interpersonal relations being overvalued, the work denseness must not suffer. This is based on a current observation that some of his employees are fond of discussing private matters thoroughly. This he explained by

a different growth of culture. Informant 1 did not see any specific relevance in the term ´corporate culture` about that matter he stated: Well, we are a manufacturing company, there is not such a great culture compared to another company. Here are workers. Apparently, for this successor the existence of a production company and workers excludes the existence of a culture more or less. Concurrent with this successor informant 3 declared that the aspect of corporate culture was not considered at all.

Buy-Out Initiatives With regard to buy-out initiatives, the word “wellbeing” apparently best describes the situation many successors long for. This is reasonable as people spend a considerable amount of time at work. Informant 2 emphasized the words “atmosphere” and “harmony”. She regards all the members of the firm as team because in her view it is an important requirement in the running of a firm. As the premises of the firm are very small, collaboration is essential and if this is not given the harmony is spoilt and this can be felt by the customers. She explained that nowadays one goes to the hairdresser not only to have a haircut but to have a feeling of well-being, too. Thus, when the firm is not able to offer it, it is doomed to failure. Informant 4 confirmed the high relevance of corporate culture. For him well-being was decisive during his decision-making otherwise, he would have done something else. Informant 8 stated that the good corporate culture given was decisive in conducting the buy-out.

Analyses One can summarize the findings of the relevance of corporate culture as perceived by most successors as very important. In the case of a buy-out, the assessment of the quality of culture seems to

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be the result of feelings built over time. Thereby positive emotions are viewed as being of crucial relevance in the decision process. In the case of a buy-in this time is normally not given, thus these successors have to base their estimations on visible elements as well as trust in one’s own knowledge of human nature. Some successors view shortcomings of the previous corporate culture as a basis for optimization ideally resulting in better company performance. However, in some cases of buy-ins it seemed that the successors did not take into consideration that company culture/climate is built over time and thus it is not, if at all, easy to change. Instead, these successors believed that setting the example of another climate is sufficient to implement the desired changes. Maybe the underestimation of the difficulties linked to culture change is one reason as to why these successors did not decide against the company in focus which would normally be recommendable. Another reason could be that their motives, e.g. independency and self-realization, and/or euphoria are linked to becoming a business owner blurring considerations regarding the feasibility of intended changes (De, 2005).

Relevance of Knowledge Management Buy-In Initiatives In the case of informant 5, knowledge was decisive. On this he declared: This knowledge and experience that have been accumulated throughout the last 18 years since the company’s foundation make up the firm’s capital. All other…engineering and purchase and assembly basically anybody can do. Informant 6 viewed the knowledge of one employee as crucial. The person’s specific knowledge is inextricably linked with the unique selling proposition of the firm. Thus, for the successor it

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was of immense importance to hold this employee in order to keep the quality of the product. On the other hand informant 3 described his firm as not being knowledge-driven, thus this topic was not relevant.

Buy-Out Initiatives To successor 7, knowledge is fundamental. However, so far it is stored within “the heads” of the employees only. That makes the company very vulnerable in terms of employees leaving. The successor is aware of this situation and states that one critical point of his current activities is to find a method for the physical storage of this knowledge. Knowledge is also regarded as being of high importance for informant 8. For him knowledge is closely linked to creativity in order to develop and launch new products and thus serve the customers more appropriately. In this company knowledge is also mainly stored “in the heads” of long-standing employees. With regard to this issue, the successor stated that because of the capital contributions of all the organizational members he is convinced that none of them would leave the company. Due to this, he explained that activities in terms of knowledge storage and transfer are not necessarily required as all critical members have stayed.

Analyses Building on the findings it can be concluded that the term `knowledge´ is primarily regarded as important when the firm’s success mainly depends on it, for example when specific knowledge is needed to conduct an activity. If this is not the fact, little consideration is given to the opportunities offered by knowledge management. This supports previous research findings (Nunes, Annansingh, Eaglestone, & Wakefield, 2006). Furthermore, the findings advert that knowledge is mainly associated with key-employees. Thus, the successors regarded it as particularly critical

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to keep this/these person(s). The knowledge of the predecessor seemed to only play an inferior role. However, this is a logical consequence of the findings related to the factor `owner´. In view of knowledge storage the findings are concurrent with the academic literature which states that the knowledge is mainly in the heads of the persons and not physically stored (Nonaka, 1994); consequently, firstly making the companies very vulnerable to employee loss (Egbu, Hari, & Renukappa, 2005) and secondly prevents the organization itself from benefiting from it (Trevinyo-Rodríguez, & Tàpies, 2006). With regard to this point no differences between buy-in and buy-out initiatives were given. Based on the findings it can be concluded that the concept of knowledge management is too broad. Instead, in the case of company succession it is suggested that successors focus primarily on knowledge retention. Thereby, this term should comprise of the aspects of retention, storage and transfer of knowledge.

Relevance of Organizational Structure Buy-In Initiatives Informant 6 ascertained that the firm was in utter chaos: There was no organizational structure. I considered restructuring as a challenge. According to her the employees could not make head nor tail of it. There were no ergonomic workplaces. Time was wasted on searching for material and so forth. Material was ordered although it was in stock. Consequently, she completely renewed the studio. She put work routines in place and implemented a system facilitating the material search. Informant 5 made use of the analysis of the company’s internal processes. He said that he analyzed the process from production to delivery

based on his managerial knowledge by conducting a target-performance comparison. Furthermore, he scrutinized the software responsible for storing the firm’s knowledge. There he discovered a need for action. On that informant 3 declared that processes were regarded as minor for the moment because he first had to get used to the administrative area.

Buy-Out Initiatives Informant 4 asserted that he very much likes the organization (production) of the firm as their customers mainly determine it. The production facilities are constructed in such a way that based on the orders the needed machines can be easily put together allowing a smooth production process. In consequence, the firm is very flexible. This was a critical point in his company analysis as he assumes that this flexibility is important for future company growth. Informant 7 stated that there was no need for a closer look at the organizational structure. He was satisfied with the balance sheet that is according to him the outcome of the organizational structure. Nevertheless, he evaluated the firm’s workflow management. He did so to get a feeling as to whether the business model was fully “exhausted” or if there was room to expand it.

Analyses In summary, the findings indicated that the organizational structure of the firm was considered by some successors only. This is remarkable, since it is not immediately expected of SMEs of which it is said that their merit lies in their flexibility as a result, amongst others, of simple structures and systems (Carson, Cromie, McGowan, & Hill, 1995). Furthermore, because of the situation that the organizational structure is built around the owner in many SMEs it was surprising that this condition has not been specifically considered. Instead, in terms of company succession, it seemed that these aspects were not recognized or taken

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for granted by some successors. Moreover, this study partly confirms previous research findings by Robbie and Wright (1995) stating that buy-in successors regard deficiencies (here restructuring) as potential/challenge in order to improve a company’s performance but the findings also expand on it by suggesting that buy-out successors are considering this aspect too.

Relevance of Innovative Capability Buy-In Initiatives Informant 1 equated innovation only with products. On the matter, he stated that the firm has neither their own product nor their own development department. Informant 7 said about innovation: We are too small for our own innovations; instead, we are mainly busy with the completion of our orders.

Buy-Out Initiatives For informant 8 the innovative capability of the firm took on a crucial aspect within company succession. He declared that within the industry (printing) permanent innovations are very important. Innovations are initiated by customers, external designers and him. It has to be noted that case 8 is the only company involved in having a R&D department; this would confirm the literature saying that there is a relationship between company size and permanent R&D activities and the institution of a specific department respectively (Spielkamp & Rammer, 2006).

Analyses In summary, aspects related to innovation received little attention in the successors’ decision-making. This describes a contrast regarding the academic literature talking about SMEs´ contributions to innovative activity (e.g., Acs, 2006). However,

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recent activities indicate how important permanent changes are seen by the successors. Patterns of difference between buy-in and buy-out initiatives were not identified.

Relevance of Customers For most successors, customers represented a part considered during their company analysis; however, relationships with them are viewed quite differently. The most commonly used way for customer analysis was ABC ranking. The successors were essentially interested in issues related to customer dependency.

Buy-In Initiatives We still have no order stock. Here things like dependency are present. The company can be described as an extended workshop, the goods are delivered and it is expected that they are finished next week. That’s how it works here. (case 1) Informant 6 stated that to her it was essential to maintain the “fantastic” customers in view of purchase frequency and fashion consciousness.

Buy-Out Initiatives Our company is very customer-driven. We have customers from different areas, fields and countries; offering a high potential. The customers mainly determine the firm’s organizational structure from the production-side as we have orders of different sizes. (case 6) Informant 8 also described the company as very customer-driven and an important notion of the management is the creation of partnerships with their customers. However, the management has already recognized the benefits of partnerships years ago; consequently, this issue was of inferior relevance in the case of company succession.

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Analyses Based on the findings, it can be concluded that the successors viewed customers as relevant and analyzed them during company analysis. However, what emerges from the cases is that customer relations in the form of partnerships, e.g. to acquire new skills, are not given the priority it has in the business literature (e.g., Mouritsen & Bukh, 2005) although some successors talked about customer-driven companies. Instead, the successors mainly emphasized the relevance of a broad customer structure to avoid dependence on a few customers only. This describes a situation given in many SMEs and thence making them vulnerable (Stokes, 2006). Big differences between buy-in and buy-out initiatives could not be observed.

Relevance of Networks Buy-In Initiatives

4 declared that networks were not considered in his decision-making.

Analyses The findings indicated that networks in view of company succession can be said to be far from a high priority issue as indicated in the academic literature (e.g., Inkpen, 1996). Instead, the study’s findings seemed to be concurrent with those of Rammer, Zimmermann, Mueller, Heger, Aschhoff, and Reize (2006) saying that the benefits of networks are mainly seen in high-tech oriented companies. The study revealed that most successors did not consider the benefits related to the continuation of old relationships established by the predecessor. Moreover, the findings suggested that the successors acted more like founders of new ventures standing before the establishment of networks (Wickham, 2004). Differences between buy-in and buy-out successors were not observable.

We need a supplier and they need customers. You want something and I give it to you. I think that’s an easy game. (case 3)

New Intangibles Identified

In this context, Informant 5 explained that suppliers are only interested in turnover. Thus to him relations to them were not considered. Informant 6 said that networks take on a minor role in her decision-making. She explained that the trade she is working in is very small and thus everyone is known.

Buy-In Initiatives Informant 6 said:

Buy-Out Initiatives

Furthermore, the continuity of the brand’s popularity was seen as decisive as the successor himself is not known, however, the brand in focus already has an impact in the market and thus customers are used to it (Case 1). Informant 3 declared that during the succession process he was convinced that the continuation of the brand is critical as he was of the opinion that nowadays

Informant 2 said that aspects such as trust, collaboration and feeling of solidarity are of great importance to her. She found these attributes with the supplier, thus she has continued the cooperation. She thinks highly of him, because he is not that person who talks her into taking something instead he gives advice in a neutral way. Informant

Brand

As the brand name was best-known it was very very important. Without the brand name, the purchase would not have taken place. The brand name is famous beyond the bounds of the city.

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the start of a new venture is pointless due to the given market conditions. Buy-Out Initiatives In the case of a well-established brand name, the continuation of it was regarded as a critical aspect for business development in other countries (Case 7). Informant 2 stated that she slightly changed the name of the company by replacing the predecessor’s first name with hers. She explained that the first name indicates the person who is in charge. Informant 8 said that the brand was important for the continuation of the company. The brand symbolizes quality and service. Analyses One can say that the continuation of the brand name took a critical role in the succession process. Thereby the established brand name was mainly seen as a facilitator for doing business no matter whether or not the successor is new in the industry. This situation is not given or limited only in the case of new ventures. The findings confirm the academic literature referring to the brand name as relevant intangible within the firm (Watters, Jackson, & Russell, 2006).

Partners In the course of analysis, a further asset emerged, which was the partner(s) in view of team succession. This issue concerned only buy-out initiatives. Informant 7 stated that to him the aspects cooperation, perspective, strategy and impression of the partner were of the highest importance. During his analysis, he recognized the firm’s strong dependency on key-employees. One of them represented his partner. The partner has been working for the firm since 1978. In this period, he worked his way up. Thus, the successor viewed the partner’s knowledge and experience as an essential part for the successful continuation of the firm, as “complement”. He is convinced that without the partner the firm would not have survived.

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Informant 4 confirmed the high relevance of the partner. The cooperation with the partner was a decisive factor in his decision-making: I had the feeling that it works at 100 percent. In the case of informant 8, a real close relationship between two partners of the management team could be found. The informant declared that for his decision it was rather decisive that this partner was “on board”. As they have been working together for more than 30 years the existence of a very close relation is in place. They trust each other, they complement each other and they are familiar with the other partner’s way of thinking. Analyses Based on the findings it was concluded that the existence of a suitable `partner´ played a critical role in the context of team succession. Thereby particularly aspects such as “cooperation” and “understanding of each other” seemed to be very relevant. This is reasonable as the quality of cooperation is assumed to have a strong influence on the firm’s success (Pasanen & Laukkanen, 2006). Although the academic literature highlights the benefits of management teams, the aspects discussed are generally considered in view of new venture creations. However, in the case of new venture creation, other aspects can be expected as important, e.g. network creation, recruiting of employees, compared with those relevant in company succession, e.g. restructuring.

Quality Informants 7 and 8 highlighted the relevance of the aspect of quality within company analysis. About which informant 7 declared that he intensively analyzed the quality approach of the company as the firm provides their customers a ten-year right to claim under guarantee. Moreover, in a previous employment he had learned that firms

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offering bad quality are very difficult to improve and need a significant amount of time. Informant 8 stated Quality was very important in the succession process, as the printing sector requires good quality thus symbolizing the fundamentals for achieving success. Analyses According to Hinterhuber and Stadler (2006) good quality is strongly related to a company’s financial performance. Thence, it was surprising that only with these two successors (buy-out initiatives) a devotion to quality apparently exist.

Influence of Intangible Assets on Final Decision The interviewees named a number of different aspects to describe the relevant aspects for decision-making. This is reasonable as decisions are based on different assumptions depending on the industry as well as the successor’s set of criteria. Apart from the anticipated relevance of financial figures, it is notable that for many successors intangibles took on almost the same importance or were even of higher importance.

Buy-In Initiatives The decision was made based on the following aspects: very good industry, relative constant development in turnover, excellent customer structure, many customers, only a few with high portions of the turnover…then committed and loyal employees, and succession due to age. (case 1) The financial figures were decisive for me, they provide me with the platform needed to feel secure. I was rapidly certain of the employees´ knowledge, which is a logical consequence of their experience and long-standing company affiliation and the

existing database which provides the necessary knowledge. (case 5)

Buy-Out Initiatives This salon is a piece of home to me. I completed my training here and have been working for 14 years in this salon. I feel good in the village, I feel good in these premises. Furthermore, the team is most important. Surely, I can put in new personnel, but a team… For me this is essential. (case 2) The cooperation with my partner was decisive. Then, I had the feeling that the way pursued show in the right direction and finally, the corporate culture. Without it, I would have done something else. (case 4)

Analyses The findings imply that business transfer involves more than just financial, legal or tax issues. Additionally, it suggests that the relevance of intangibles runs throughout the whole decision process. Differences between buy-in and buyout initiatives can be found. Especially with the latter the influence on the gut-feeling can be highlighted. These successors did not attempt to justify their decision in a rational way; instead, an intuitive decision-making style seems to predominate (LeMar, 2001). In contrast with buy-in successors, a combination between financial and intangible aspects was more prominent. An explanation for this variance of opinions could be that in terms of buy-out initiatives an emotional relation between the successor him-/herself and the company is given which was built over time. On the other side, this sort of relation cannot be expected with buy-in successors as the company and its environment represent something new to them (Meis, 2000).

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CONCLUSION AND FUTURE RESEARCH DIRECTIONS The purpose of this study was to gain an understanding of the role of intangible assets in external company succession in smaller firms. Thereby a distinction between buy-in and buy-out initiatives was made. The general findings have shown that intangible assets play an important role in external successors’ selection processes. Many different intangible assets were taken into consideration, but apparently five were crucial to reaching the final appraisal of a company’s attractiveness. These are the factor ‘key employees’ and the closely related factor ‘knowledge retention’, together with the factors ‘brand’, ‘partners’ and to a lesser degree ‘corporate culture’. Although these intangibles, with the exception of the aspect ´partner´, can be regarded as critical with buy-in and buy-out successors, the analysis of them revealed different patterns. The former tried to analyse the company on a more or less rational basis in order to evaluate the attractiveness of the company. On the other hand, with buy-out initiatives the impact of gutfeeling became obvious. This can be regarded as a result of the past which facilitated the building of an emotional relationship with the company in focus. Noteworthy is the meaning of the aspect ´partner´ for the buy-out successors. This factor is not company related but is to be taken into consideration independently from the company. During negotiations, the current owner has relatively little influence on this aspect. Moreover, with regard to corporate culture the findings underlined that especially here buy-out initiatives have an advantage over buy-in initiatives in that the former have better insights into the existing culture and the implications of possible changes to it. This research contributes to the literature in several ways. It provides an alternative perspective on external company succession in SMEs as it highlights the intangible assets which make a company attractive to external successors in

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terms of company selection. The findings further suggest that in order to improve our understanding of external company succession researchers need to integrate the concept of intangible assets; thus, it is recommended that the primary focus on financial, legal, and tax issues, that is currently given, should be softened. The research provides evidence that the existing perspective ought to be reconsidered. Finally, the study’s findings provide insights into critical intangible assets in terms of company selection. Regarding the practical implications, the findings support the parties (i.e. successors, incumbents and advisors) involved in their activities during the preparation stage and ideally provides a better understanding of the dynamics of succession. Moreover, the findings may facilitate the formulation of suitable political measures for adequate treatment of external company succession. The findings also shed more light on an alternative way of embarking on entrepreneurship. This understanding is regarded as highly important, as demographic changes and the decline in family succession will lead to an increased need for external successors if SMEs are to survive. In order to obtain a more complete picture about company succession more research in this stream is highly desirable and this study has shown that particularly the intangible asset perspective can contribute to a better understanding of this topic. This study also has limitations. Since the research is based on qualitative research only analytical generalizations (Yin, 2003) and not statistical generalizations can be offered. Thus, the qualitative study of the eight German successors does not allow inferences to be made as to whether the results would also apply to successors in other countries or other sectors not considered. More research activities should follow making use of a larger sample of external successors in order to reach stability and validity regarding the findings. Research on the relevance of intangible assets in company succession in SMEs is in the pioneering phase. A start has been made with this explor-

Intangible Assets and Company Succession

atory study, one in which the focus was on theory building to enhance our understanding in a field with such importance. Hopefully, this research has laid a suitable basis for further investigation.

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Stokes, D. (2006). Innovation and the small business, Enterprise and Small Business. In Carter, S., & Jones-Evans, D. (Eds.), Enterprise and Small Business (2nd ed., pp. 324–337). Harlow: Pearson. Szyperski, N., & Nathusius, K. (1999). Probleme der Unternehmensgründung: eine betriebswirtschaftliche Analyse unternehmerischer Startbedingungen. 2nd ed., Lohmar, Cologne: Josef Eul. Trevinyo-Rodrìguez, R. N., & Tàpies, J. (2006). Effective knowledge transfer in family firms. In Poutziouris, P. Z., Smyrnios, K. X., & Klein, S. B. (Eds.), Handbook of Research on Family Business (pp. 343–357). Cheltenham: Edward Elgar. Watters, J., Jackson, F., & Russell, I. (2006). Capturing intangibles for improved IA management and benchmarking. Journal of Intellectual Capital, 7(4), 549–567. doi:10.1108/14691930610709167 Wickham, P. A. (2004). Strategic Entrepreneurship (3rd ed.). Harlow: Pearson. Wiig, K. (1997). Knowledge Management: An Introduction and Perspective. Journal of Knowledge Management, 1(1), 6–14. doi:10.1108/13673279710800682 Yin, R. (2003). Case Study Research: Design and Methods (3rd ed.). Thousand Oaks, London, New Delhi: Sage.

KEY TERMS AND DEFINITIONS Intangible Assets: The core non-monetary resources, lacking physical substance that are able to contribute to future benefits in SMEs. Company Succession: Is the simultaneous transition of ownership and/or management of a firm from one individual to another. Buy-In Initiative: Involves a single individual or a group of individuals from outside the firm interested in taking over the firm.

Intangible Assets and Company Succession

Buy-Out Initiative: Involves a single buyer or a group of buyers from inside the company. External Succession: A company is taken over by a non-family member. Derivative Corporate Foundation: Corporate foundation happens through the takeover of an already existing company. Original Corporate Foundation: An entirely new firm is established.

ENDNOTE 1

An earlier version of this chapter was presented at the IFKAD 2009, University of Glasgow, Glasgow, Scotland, 17-18 February 2009.

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Section 2

Measuring Intangibles

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Chapter 5

Towards a New Approach for Measuring Innovation: The Innovation-Value Path Josune Sáenz University of Deusto, Spain Nekane Aramburu University of Deusto, Spain

ABSTRACT The aim of this chapter is to provide the foundations of a new measurement system that will help companies to diagnose and manage their innovation performance from a holistic perspective. Adopting a resource-based view of the firm (and more precisely, a dynamic capability approach), the measurement system proposed is intended to show whether the company has the right combination of resources (both tangible and intangible) in order to foster effective and efficient innovation, as well as the degree of mastery achieved in the combination and orchestration of those resources (i.e. capability excellence), the outputs obtained and their influence on value creation and on competitive advantage.

INTRODUCTION Today’s economy is driven by what we could call the “innovation imperative”. As Bessant & Tidd (2007) point out, the logic is very simple: if companies do not change what they offer to the world (products and services) and how they create and deliver them, they risk being overtaken by others who do. In other words, companies cannot be static: they must continually adjust, adapt or redefine themselves (Morris, Kuratko & Kovin,

2008). Actually, it is a matter of survival in a free market economy. Consequently, innovation constitutes a highorder strategic priority for many executives all around the globe. However, just but a few are satisfied with their company’s current innovationmeasurement practices. As the 2008 BCG report on innovation metrics reveals, companies undermeasure, measure the wrong things or, in some cases, do not measure at all, which leads to poorly allocated resources, squandered opportunities and bad decision making. The fact that almost 60% of survey respondents were not satisfied with the

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return of their company’s investment in innovation seems to give clear support to this idea. Measuring innovation has at least two dimensions: measuring individual innovation projects, and measuring the whole innovation system. In this chapter, we focus exclusively on the latter. Specifically, our aim is to contribute to a measurement system that illuminates the path to successful innovation (i.e. to value creation and firm competitiveness). This brings us to the field of strategic performance management, which involves identifying, measuring and managing organizational value drivers (Marr, 2006). From an academic perspective, innovation measurement needs further development. Actually, most of existing proposals in this domain are clearly incomplete (i.e. do not cover all the scope of relevant resources and activities that could lead to successful innovation), show a clear bias towards technological innovation and R&D measurement, and fail to show how different resources are combined in order to enhance the innovation capability of firms. Therefore, the question arises so as to how to design an innovation measurement system that could help managers to have a complete and clearly organized picture of their company’s innovation performance. This is the specific challenge to be addressed in this chapter. In particular, a new measurement system will be proposed (the innovation-value path) whose aim is to overcome the shortcomings of preceding systems. Although still in its conceptual stage (i.e. the new system has not been empirically tested yet), it has been conceived both as an assessment and management tool. In other words, the innovation-value path will provide managers with the foundations for developing and adjusting a sound innovation strategy that would help companies to improve their innovation results. With this idea in mind, the chapter has been organized as follows: The first section within the theoretical background will address the concept and nature of innovation and the second one the logic of value

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creation and competitive advantage. Nowadays, the resource-based view of the firm (Wernefelt, 1984; Rumelt, 1984; Barney, 1991; Grant, 1991; Peteraf, 1993) and its ulterior refinements (as the knowledge-based view of the firm–Kogut & Zander, 1992; Grant, 1996; Spender, 1996)–and the dynamic capability approach–Teece, Pisano & Shuen, 1997; Eisenhardt & Martin, 2000; Teece, 2007, 2009) constitute the predominant paradigm for understanding value creation. The latter will show us the relevance of intangible resources (i.e. intellectual capital) as a basis for generating competitive advantages. Therefore, a third section will be added within the theoretical background that will provide us with a deeper understanding of this type of resource. In view of insights thus gained, a literature review on innovation measurement will be then presented in which two kinds of contribution will be considered. Firstly, those contributions which are specially geared towards the measurement of innovation or specific parts of it, such as R&D, will be analyzed. Afterwards, another set of contributions will be examined, which are much wider in scope (indeed, they are intended to facilitate strategic performance management in general), but which could also be used for innovation measurement. This literature review has allowed us to develop our own measurement system, which tries to overcome the limitations of existing proposals. Adopting a resource-based view of the firm and, more precisely, a dynamic capability approach (Teece, Pisano & Shuen, 1997; Eisenhardt & Martin, 2000; Teece, 2007, 2009), the measurement system proposed is intended to show whether the company has the right combination of resources (financial, physical and intangible) in order to foster effective and efficient innovation. Moreover, the system is aimed at showing the performance level attained in the innovation process, its outputs and its outcomes.

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Once the architecture of the system has been presented, specific guidelines for the content of each measurement block will be provided.

THEORETICAL BACKGROUND The Concept and Nature of Innovation A unique and commonly accepted definition of innovation does not exist, but most of the existing ones agree that innovation implies conceiving and implementing something new. In line with this, Thompson (1965) defined innovation as the generation, acceptance and implementation of new ideas, processes, products or services; Van de Ven (1986) pointed out that innovation is intrinsically about identifying and using opportunities to create new products, services or work practices; and Martins (2000) stated that innovation is about the implementation of a new and possibly problemsolving idea, practice or material artefact (e.g. a product) which is regarded as new by the relevant unit of adoption and through which change is brought about. Of course, the conception of something new implies the creation of new knowledge. In other words, innovation requires new knowledge and new combinations of knowledge (Eisenhardt & Martin, 2000). As a consequence, it could be said that the capacity of an organization to innovate lies in its capacity to generate new knowledge (Nonaka & Takeuchi, 1995; Nonaka, Toyama & Byosière, 2003). This is the point of view of authors such as Subramaniam & Youndt (2005)–who assume that innovation consists of an ongoing pursuit of harnessing new and unique knowledge; Leiponen (2006)–who understands innovation as the generation of novel combinations from existing knowledge; Plessis (2007)–who identifies innovation with the creation of new knowledge and ideas to facilitate new business outcomes; and Lundvall & Nielsen (2007)–who state that “innovation

represents–by definition–something new and therefore adds to existing knowledge” (p. 214). Moreover, it should be noted that the generation of new knowledge is the result of an organizational learning process (Nonaka & Takeuchi, 1995; Lundvall & Nielsen, 2007). “This points to knowledge production as a process of joint production, in which innovation is one kind of output, and the learning and skill enhancement that takes place in the process is another” (Lundvall & Nielsen, 2007, p. 214). This idea of learning as the underlying process of knowledge creation and, hence, of innovation, leads us to another dimension of innovation: that related to its dynamic nature. Actually, innovation lies at the core of what is known as “dynamic capability”. This refers to the particular capacity business enterprises possess to shape, reshape, configure and reconfigure assets so as to respond to changing technologies and markets and escape the zero-profit condition (i.e. a situation that occurs when there are no points of differentiation amongst firms with respect to technology, markets, information or skills, and which involves companies only making just enough to cover their cost of capital) (Teece & Augier, 2009). In particular, innovation allows the resource base of an organization to be shaped or reshaped by the addition of new knowledge embedded in new products, services, processes, technologies or business models. According to Teece (2007, 2009), the “dynamic capability” concept encompasses three first-level (i.e. more simple) capacities. The first one is the capacity to sense and shape opportunities and threats. This can be related to the ideation stage of innovation processes (i.e. the generation of new ideas). The second one is the capacity to seize opportunities. This refers to the selection of the new ideas to be addressed and to their subsequent development and fulfilment. The last one is the capacity to maintain competitiveness through enhancing, combining, protecting, and where necessary, reconfiguring the business enterprise’s tangible and intangible assets. This refers to the

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company’s capacity to reinvent/transform itself and not die because of unfavourable path dependencies generated by past success. This “dynamic capability” concept is considered to be the basis of sustainable competitive advantage and value creation in fast-moving business environments open to global competition and characterized by dispersion in the geographical and organizational sources of innovation and manufacturing (Teece, 2007, 2009). Therefore, the time has come to have a closer look at this issue.

The Foundations of Value Creation and Competitive Advantage As Robert Grant points out (2008), business is about creating value. This refers to the amount of money customers are willing to pay for a good or service. Thus, the challenge for business strategy is, first, to create value for customers and, second, to extract some of that value in the form of profit for the firm. When a company earns (or has the potential to earn) a persistently higher rate of profit than other firms competing on the same market, then it possesses a competitive advantage over its rivals. Over time, two main paradigms have emerged to explain the sources of competitive advantage: the competitive forces approach pioneered by Porter in 1980 and the resource-based view of the firm. The competitive forces approach (or marketbased view) stresses the role of industry’s structure in the generation of superior profits. By examining the principal structural features and their interactions for any particular industry, it would be possible to predict the type of competitive behaviour likely to emerge and the resulting level of profitability (Grant, 2008). In particular, Porter’s “five forces framework” describes competitive imperfections in product markets and how, apparently, these competitive imperfections can be used to create opportunities to earn greater returns (Barney & Clark, 2007). As a consequence, stra-

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tegic management should be concerned primarily with seeking favourable industrial environments, locating attractive segments and strategic groups within industries, and moderating competitive pressures by influencing industry structure and competitors’ behaviour (Grant, 1991). However, empirical research has failed to support the link between industry structure and profitability. For instance, Rumelt (1991) showed that intra-industry differences in profits were greater than inter-industry differences, strongly suggesting the importance of firm-specific factors and the relative unimportance of industry effects (Teece, Pisano & Shuen, 1997). Similar findings have been reported by Roquebert, Phillips & Westfall (1996), McGahan & Porter (1997), Hawawini, Subramaniam & Verdin (2003), and Misangyi, Helms, Greckhamer & Lepine (2006). Hence, establishing competitive advantage through the development and deployment of resources and capabilities, rather than seeking shelter from the storm of competition, should be the primary goal for strategy (Grant, 2008). In particular, the resource-based view of the firm (whose origins could be traced back to Birger Wernefelt’s famous article in 1984) considers that superior profitability is due not to strategic investments that may deter entry of new competitors and raise prices above long-run costs, but to markedly lower costs or to markedly higher quality or product performance (Teece, Pisano & Shuen, 1997). There are four attributes that a specific resource or capability should have in order to be able to generate sustained competitive advantages (Barney & Clark, 2007): (a) it should be valuable (in the sense that it helps to exploit opportunities and/ or to neutralize threats); (b) it should be rare (i.e. scarce); (c) it should be imperfectly imitable; (d) and it should be able to be exploited by a firm’s organizational processes. Company resources could be split up into several categories. Usually, a first distinction is made between tangible (i.e. physical and financial resources) and intangible resources. Overall, the

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greater complexity and singularity of intangible resources over tangible ones makes this type of resource more difficult to imitate and, therefore, a more likely basis for the generation of competitive advantages. However, resources are not productive on their own (Grant, 2008). To perform a task, a combination of resources is needed. This brings us to the concept of “organizational capability”, which refers to “a firm’s capacity to deploy resources for a desired end result” (Grant, 2008, p. 135). Ultimately, organizational capabilities are the cornerstone of competitive advantage, as being unable to properly use available resources would prevent the company from value creation and profit generation (of course, if resources were not available, capabilities could not be deployed either). Indeed, capabilities could be seen as the manifestation of organizational knowledge. As Grant (2008) points out, “from the strategic viewpoint, knowledge is a particularly interesting resource: many types of knowledge are scarce, much of it is difficult to transfer, and complex forms of knowledge may be very difficult to replicate” (p. 159). Subsequently, knowledge is considered to be the most strategically important of a firm’s resources. This has led to what is known as the “knowledge-based view” of the firm, an outgrowth of the resource-based approach (Grant, 1996). According to the knowledge-based view, firms exist due to their effectiveness in creating, assembling and transforming knowledge into goods and services (Kogut & Zander, 1992; Grant, 1996). However, in fast-moving business environments open to global competition, sustainable competitive advantage requires more than the ownership of difficult-to-replicate (knowledge) assets. It also requires unique and difficult-to-imitate dynamic capabilities, which can be harnessed to continuously create, extend, upgrade, protect and keep relevant the enterprise’s unique asset base (Teece, 2007, 2009). This is the dynamic capability approach (the very last refinement of the resource-based view), in whose tradition, “the

essence of strategy involves selecting and developing technologies and business models that build competitive advantage through assembling and orchestrating difficult-to-replicate assets, thereby shaping competition itself” (Teece, 2007, p. 1325). As we have previously stated, innovation lies at the core of the dynamic capability approach.

The Specific Role of Intangible Resources: the Intellectual Capital Perspective In the previous section we have pointed out the strategic role that intangible resources (also known as intellectual capital) could play in facilitating the generation of competitive advantages. In this section, further insight on this topic will be provided. According to Andriessen (2004), there is no consensus on the specific moment the expression “intellectual capital” was used for the first time, but it was at the very beginning of the 1990s when it became extremely popular. In 1991 Thomas A. Stewart published his famous article “Brainpower” in Fortune magazine, and introduced the expression in the popular press. During the same year, Skandia AFS, the Swedish insurance company, appointed Leif Edvinsson as the world’s first director of intellectual capital. Since then, many books and articles have been published on this topic, and some authors even advocate for an intellectual capital-based view of the firm (Reed, Lubatkin & Srinivasan, 2006). During these years, intellectual capital (IC) has been defined in multiple ways. Nevertheless, most of the definitions provided can be grouped into two categories. The first one equates the concept of IC with that of “knowledge capital”. Within this category, IC is considered to be the sum of all knowledge firms utilize for competitive advantage. This is the point of view of authors such as Stewart (1997), Nahapiet & Ghoshal (1998), Sullivan (1998), and Youndt, Subramaniam & Snell (2004).

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Other authors, however, take a broader perspective and consider IC to encompass other intangible resources (not only knowledge) and activities as well. As an example, the European Commission (2006) states that: Intellectual capital is the combination of the human, organizational and relational resources and activities of an organization. It includes the knowledge, skills, experiences and abilities of the employees; the R&D activities, the organizational routines, procedures, systems, databases and intellectual property rights of the company; and all resources linked to the external relationships of the firm with customers, suppliers, R&D partners, etc. (p. 126). Authors such as Roos et alter (1997), Bontis (1999) and Marr (2006) are closer to this second perspective. It should be noticed that the inclusion of “activities” within the concept of IC brings us to its dynamic dimension, as opposed to the static one (Meritum project, 2002; Kianto, 2007). The static view of IC is closer to the “classic” resource-based view of the firm, where the main interest lies in possessing valuable, rare, inimitable and nonsubstitutable resources (Barney, 1991). However, research has shown that the main value creation factor is how resources are exploited and explored, rather than what they are per se (Grant, 2008; Kianto, 2007; Teece, 2007, 2009). Therefore, activities aimed at acquiring or internally producing intangible resources, as well as at sustaining and improving the existing ones, should be analyzed. Regardless of the perspective adopted (limited to knowledge and static, or holistic and dynamic), IC tends to be split up into different categories. Although the specific labels employed may vary, a first distinction is generally drawn between human and structural capital. A second distinction is then drawn within the latter between organizational and social capital–in the case of the knowledge

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perspective–and between internal and external structure–in the case of the holistic one. In both cases (knowledge and holistic perspective), human resources (i.e. human capital) are thought of as the living and thinking part of intangible resources (Marr, 2006). They do not appear on corporate balance sheets because people are not owned: they offer their services under employment contracts (Grant, 2008). In the knowledge perspective, human capital would include the knowledge, skills and abilities residing with and utilized by individuals (Schultz, 1961; Youndt, Subramaniam & Snell, 2004), whereas in the holistic one additional elements such as people attitudes, motivation and commitment (i.e. not only knowledge) would also be included (CIC, 2003; Marr, 2006). Differences between the knowledge and holistic perspective are deeper when it comes to conceptualize structural capital and its two sub-components. In the case of the knowledge perspective, the type of knowledge considered lies at the basis of the distinction made between organizational and social capital (i.e. the two sub-components of structural capital). The former refers to the institutionalized knowledge and codified experience (i.e. “explicit knowledge”) residing within and utilized through databases, patents, manuals, structures, systems and processes (Youndt, Subramaniam & Snell, 2004), whereas social capital is the knowledge embedded within, available through and utilized by interactions among individuals and their networks of interrelationships (Nahapiet & Ghoshal, 1998). Of course, this second definition refers to “tacit knowledge” and it is important to note that the networks and interrelationships mentioned could be both internal and external to the firm. In the case of the holistic perspective of IC, the location of knowledge and other intangible resources and activities lies at the basis of the distinction made between internal and external structure. In accordance with this, internal structure refers to the knowledge and other intangible

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resources that stay within the company when the employees have left and that derive from the organization’s action processes (CIC, 2003). In other words, it encompasses the organization’s essential operating processes, the way it is structured, its information flows and databases, its leadership and management style, its culture and incentive schemes, as well as intellectual property rights (Marr, 2006). External structure refers to all resources and activities linked to the external relationships of the firm with customers, suppliers or R&D partners (Meritum Project, 2002). Those resources could be related to knowledge, but they could refer to other intangible assets as well, such as brand image, customer satisfaction, customer loyalty, negotiating power, etc. In this chapter, the holistic perspective of IC will be adopted, as it is assumed that intangible resources and activities that foster innovation capability go beyond previously accumulated knowledge in different forms (i.e. databases, manuals, procedures, etc.) and encompass other intangible factors too, such as the ones mentioned above. In this holistic perspective, “structural capital” will be the specific label used for internal structure, and “relational capital” will be the one used for external structure. Indeed, although internal and external structure could be considered the original names (Sveiby, 1997), structural and relational capital are now much more widespread (Bontis, 1999; Meritum Project, 2002; CIC, 2003). Nevertheless, it should be noticed that, originally, structural capital encompassed both internal and external structure (Edvinsson & Malone, 1997) and that now it refers mainly to internal structure. Finally, the specific link between intellectual capital and innovation should be highlighted. Indeed, for some authors, innovation is part of the structural capital of the firm (Edvinsson & Malone, 1997; CIC, 2003). For instance, the Intellectus model (CIC, 2003) splits structural capital into two sub-components: organizational capital and technological capital. The latter encompasses the effort carried out by the company in the R&D

and innovation domain, technological endowment, intellectual and industrial property rights, and innovation results. In this chapter, however, innovation will be seen as the result of combining and orchestrating different types of tangible and intangible resources and activities (including previously obtained or acquired intellectual property rights), rather than as a resource category itself, as it is proposed in the Intellectus model.

Innovation Measurement: the State of the Art Specific Contributions to Innovation Measurement The literature review on specific instruments for innovation measurement shows a clear bias towards technological innovation and R&D measurement. In particular, measures for new product development are the most widespread. Undoubtedly, this is a consequence of the evolution of the innovation concept itself. Until very recently, one of the most widespread beliefs has been that innovation is primarily, if not exclusively, about changing technology (Davila, Epstein & Shelton, 2006). However, the outperforming results of many companies that have combined technological advances with changes in their business model (i.e. in the way competition is conceived within a specific industry), has drawn attention to non-technological innovation as well. Dell Computer, Amazon.com and Zara are just but a few of these extremely successful companies which have combined both types of innovation with excellent results. As far as measurement is concerned, the continuous work carried out by the Industrial Research Institute (IRI) in the field of R&D is especially noteworthy (Germeraad, 2003). In the early 1990s, the IRI’s Research-on-Research committee developed a wide set of metrics that could be segmented simultaneously according to different criteria: time (past, present and future metrics), transformation stage (input, output

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and outcome metrics), stakeholder group (staff, internal customers, external customers and society) and purpose (performance tracking, internal productivity improvement and competitor assessment). As can be deduced, the system proposed was a complex one, and although the measures were organized into different groups, no interdependency relationship was established between them. Therefore, interpreting the situation and assessing the impact of different decisions was a difficult task. Later on, the technology value pyramid was proposed (Tipping and Zeffren, 1995), which shows the hierarchical integration of five managerial factors that, according to IRI researchers, describe the innovation capability of the firm: (1) the practice of R&D processes to support innovation, (2) the asset value of technology, (3) alignment of technology strategy with business strategy, (4) portfolio assessment and (5) value creation. From an initial menu of 33 metrics, it was suggested that firms select a small set of them, considering that, at each moment, critical factors are dependent on the particular situation of the company. Thus, this was a clear attempt to reduce the degree of complexity of the measurement system. However, as breakthrough (radical) innovation was becoming increasingly important in the late 1990s, models like the technology value pyramid, as comprehensive as they were, were leaving a gap (Germeraad, 2003). This led to another IRI initiative: the set of metrics for the fuzzy front end (i.e. the set of activities that precedes the development of new products and/or services), as this stage could be critical for identifying breakthrough opportunities. Considering that qualitative aspects were also relevant, a set of 20 anchored scale metrics was developed, which were grouped according to the different phases of the fuzzy front end process: (1) process engine (motivation, communication of strategy, external resource availability and legal hurdles); (2) influencing (public opinion and degree of innovation); (3) opportunity identification

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(number of approaches, knowledge of needs and alignment with business strategy); (4) opportunity analysis (quality of external information, timeliness, number of approaches and effectiveness of processes); (5) idea generation (quality of ideas and effectiveness of the process); (6) idea selection (value of ideas and clarity of value); (7) concept (clarity of path); and (8) output from new concept development. Outside IRI’s contributions, Professor Lev from Stern University in New York proposed in 2001 the innovation value-chain scoreboard. As is the case for the set of metrics for the fuzzy front end, this scoreboard follows a process-based logic to group indicators. Three main stages are distinguished: (1) discovery and learning (which may be the result of internal renewal, external capabilities acquisition, or networking activities with other agents); (2) implementation (which is related to the achievement of technological feasibility of products, services or processes); and (3) commercialisation (where customers are the key, and present and future yield should be measured). In addition to the strong technological focus that can be observed, in this kind of process-based measurement system two main limitations are noteworthy: firstly, cause and effect relationships between groups of measures are not established (except those which could be derived from the sequential logic of the process itself) and secondly, this type of measurement system does not allow one to clearly visualize how different resources are combined in order to develop the innovation capability of the firm. Table 1 summarizes the main conclusions drawn from this section.

General Instruments for Strategic Performance Management Having analyzed specific contributions to innovation measurement, let us now examine another set of contributions, which are much wider in scope (indeed, they are intended to facilitate strategic

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Table 1. Specific contributions to innovation measurement Industrial Research Institute (IRI) The technology value pyramid The set of metrics for the fuzzy front end

Professor Lev The innovation value-chain scoreboard

A process-based logic predominates Indicators are grouped according to the phases of the innovation process. Shortcomings 1) There is a clear bias towards technological innovation and new product development. 2) Cause and effect relationships between groups of measures are not established, except the ones derived from the logic of the process itself. 3) It is not possible to visualize how different resources are combined in order to develop the innovation capability of the firm.

performance management in general), but which could also be used for innovation measurement. The first instrument to be considered is the worldwide-known balanced scorecard (BSC) by Kaplan & Norton. The BSC provides companies with a framework for strategy description and communication, and for subsequent follow-up and control. In this framework, strategy maps (which link the organization strategic objectives in a series of cause and effect relationships) provide the missing link between strategy formulation and strategy execution (Kaplan & Norton, 2004). In their famous book “Strategy maps”, both authors devote a whole chapter to innovation measurement. In quite a similar way as Professor Lev suggests in his innovation value-chain scoreboard, indicators are organized according to the main phases of the innovation process within the internal perspective of the BSC. The phase categories are as follows: (1) identifying opportunities for new products and services; (2) managing the R&D portfolio; (3) designing and developing the new products and services; and (4) bringing the new products and services to the market. However, in addition to the indicators grouped around the different phases of the innovation process (which, among other things, describe the effectiveness and efficiency with which that process is carried out), Kaplan & Norton distinguish a series of indicators referring to those resources of an intangible nature which make the innovation process possible. These resources would appear

to be grouped into three categories: (1) human capital (multidisciplinary skills); (2) information capital (technology to explore, integrate and speed up in marketing); and (3) organizational capital (a culture of creativity and innovation). Furthermore, links are established with objectives belonging to the BSC customer perspective, such as: offering enhanced product/service functionality to customers, being the first-to-market, or extending products/services to new segments. Finally, achieving such objectives from the customer perspective must enable other objectives of a financial nature to be achieved, namely: revenue growth and enhanced margins from new products and services. Although in Kaplan & Norton’s proposal the indicators referring to resources which make the innovation possible are distinguished from those indicators which measure the effectiveness and efficiency of that process, as well as from those which measure the outcomes deriving from it, certain resources of great importance have been left to one side. This is the case of relational capital for innovation (networking activities and cooperation among companies and other institutions), as well as resources of a financial nature. Furthermore, it should be mentioned that Kaplan & Norton focus above all on innovation geared towards the development of new products, without considering other innovation possibilities. On the other hand and following the BSC trail of Kaplan & Norton, numerous authors propose

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indicators for the monitoring of R&D and innovation classified into the four basic perspectives of this tool: financial perspective, customer perspective, internal business process perspective, and learning and growth perspective. This is the case, for instance, with Kerssens-van Drongelen & Cook (1997), of Bremser & Barsky (2004) and of Godener & Söderquist (2004). In the examples above, the different perspectives indiscriminately contain indicators referring to inputs or conditions needed to develop the innovation process, and indicators referring to the degree of success with which that process has been developed. For instance, Kerssens-van Drongelen & Cook (1997) include one indicator referring to the percentage of budget spent internally and externally on basic and applied research, and another one related to the number of patentable discoveries per dollar spent on R&D within the learning and growth perspective. In the same vein, Godener & Söderquist (2004) suggest measuring both market expectations and the degree of success achieved with new products within the customer block. For this to be done (i.e. to measure new product success), the following indicators are proposed: degree of conformity with specifications, the extent to which products are appreciated by customers (added value provided), market share, market penetration and brand image. Likewise, within the internal business process perspective, measurements include development lead-time, engineering productivity and total product quality, as well as motivational and behavioural factors, such as commitment, initiative and leadership of human resources in the R&D process. Therefore, it can be concluded that, in these BSC derivatives, indicators are organized according to the nature of the object being measured (financial, customer-related, process-related or learning- and growth-related), and that resources, outputs and outcomes are mixed up in each perspective, thus preventing users from visualizing

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how different combinations of resources belonging to different categories lead to different types of output and outcome. However, Davila, Epstein & Shelton (2006) recover the spirit of Kaplan & Norton in their strategy maps and, in their balanced scorecard for measuring innovation, they propose an input/ resource-process-output-outcome logic to organize innovation metrics. In this case, the basis for innovation inputs is enlarged, encompassing tangible resources, intangible resources, innovation structure, innovation strategy, external networks and innovation systems. In any case, in recent times, the BSC has been subject to strong criticism on the part of several authors who, perhaps with too narrow an approach to the instrument, consider the BSC to be the “enemy” of the innovation economy (Voelpel, Leibold, Eckhoff & Davenport, 2006). In particular, in the opinion of these authors, its rigidity, static-ism, linear thinking, conception of knowledge and innovation as a routine process and its focus on the individual company renders the BSC an insufficient tool for understanding and dealing with the innovation economy. Table 2 summarizes the main findings regarding the BSC and innovation measurement. A new and promising approach to strategic performance management, which tries to overcome some of the BSC‘s shortcomings (particularly those related to the interconnectivity of resources), is the one intended to highlight the dynamics of value creation by mapping out the influences resources have on each other, according to a resource-based perspective on value creation. Combining this resource-based perspective with recent developments in the field of intellectual capital (IC), Gupta & Roos (2001) proposed the so-called IC navigator. This is an overarching conceptual representation of the rationale employed by decision makers with regard to how resources are deployed. It addresses the following

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Table 2. General instruments for strategic performance management: the BSC Kaplan’ and Norton’s original proposal Types of indicator considered 1) Process indicators – They are organised according to the main phases of the innovation process within the internal perspective of the BSC. They describe the effectiveness and the efficiency of the innovation process. 2) Intangible resource indicators – They are grouped into three categories: human capital, information capital and organizational capital, within the growth and renewal perspective. 3) Result indicators – There are two groups: customer related and financial. Shortcomings 1) Certain resources of a great importance have been left to one side. This is the case of relational capital for innovation, as well as resources of a financial nature. 2) Only new product development is considered. BSC related additional proposals Main characteristics 1) Many authors propose indicators for the monitoring of R&D and innovation classified into the four basic perspectives of the BSC: financial, customer, internal business processes, and learning and growth perspective. 2) The different perspectives indiscriminately contain indicators referring to inputs or conditions needed for the development of the innovation process, and indicators referring to the degree of success achieved. Shortcomings The latter prevents users from visualizing how different combinations of resources belonging to different categories lead to different types of output and outcome.

issues: What tangible and intangible resources are needed to create value in accordance with strategic objectives? How are these resources transformed? How relatively important are the identified resources and their transformations in creating value in accordance with strategic objectives? In the IC navigator, the size of the circles which represent different resources and the size of the arrows linking them are proportional to their perceived importance towards achieving strategic intent. Subsequently, and following the same logic, the IC navigator leaves room for the value creation map (Marr, Schiuma & Neely, 2004; Pike, Roos & Marr, 2005; Marr, 2006). A value creation map is a visual representation of the organizational strategy that includes the most important components that exist within this strategy (namely, stakeholder value proposition, core competencies and key resources) and places them in relationships with each other (Marr, 2006).

TOWARDS A NEW MEASUREMENT SYSTEM: THE INNOVATION-VALUE PATH General Overview The literature review on general instruments for strategic performance management carried out so far has provided us with a mixed picture. Although in the case of the BSC, the measurement of innovation has been specifically addressed by its proponents, the bias towards technological innovation (and more precisely, towards new product development) still remains unsolved, together with the omission of some relevant types of resource for the development of innovation and the lack of sufficient acknowledgement of resource interconnectivity. However, in the case of the IC navigator and the value creation map, the latter (i.e. resource interconnectivity) has been suitably addressed, but innovation measurement has not been specifically analyzed by the authors of these proposals.

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Thus, an effort will be made to propose a system that will try to overcome previous shortcomings and allow companies to diagnose and manage their innovation performance from a holistic perspective. As the system is intended to show the way from innovation to value creation and competitive advantage, let us refer to it as “the innovation-value path”. Considering (according to what has been said above in the theoretical framework) that the generation of sustained competitive advantages depends on the mastery of value creating and difficult-to-imitate capabilities that combine and orchestrate different types of resource (both tangible and intangible), the system proposed will encompass four types of construct: 1.

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Firstly, the mix of resources that make innovation possible will be considered. In accordance with the literature on the resourcebased view and on intellectual capital, five types of resource will be distinguished (Marr, Schiuma & Neely, 2004; Pike, Roos & Marr, 2005; Marr, 2006): financial resources, physical resources (in this case, technological equipment), human capital, structural capital and relational capital. Financial and physical resources belong to the category of tangible resources, whereas human capital, structural capital and relational capital belong to the group of intangible resources (i.e. intellectual capital). As far as each resource category is concerned, and considering both their static and dynamic dimension (Kianto, 2007), the following types of indicator should be included: a. Indicators that show the amount of available resources. b. Indicators that assess the quality of available resources. c. Indicators related to the activities that the company carries out in order to: i. Acquire new resources.

ii.

2.

Augment the quantity of already available resources. iii. Improve the quality of those resources. Secondly, the different dimensions (i.e. firstlevel capacities) making up innovation capability should be added (Teece 2007, 2009). As has been explained above, capabilities refer to a firm’s capacity to deploy resources for a desired end result (Grant, 2008). In the case of innovation, this involves combining and orchestrating available resources to generate new products and/or services, new processes, new methods or new business models that would give rise to a superior value proposition. Following the dynamic capability approach, three first-level capacities should be considered: (1) the capacity to sense and shape opportunities and threats (i.e. the capacity to generate new ideas); (2) the capacity to seize opportunities (i.e. the capacity to select and manage innovation projects); and (3) the capacity to transform or reinvent the company when necessary (i.e. change capacity). For each first-level capacity (and considering once more their static and dynamic dimension – Kianto, 2007), two types of indicator should be included: a. The ones that describe how well the capability is performed (i.e. how good the company is at generating new ideas, or at selecting and executing innovation projects, or at transforming itself where necessary). b. The ones related to the activities that the company carries out in order to improve its performance in each firstlevel capacity.

Moreover, in the case of the second first-level capacity (i.e. the capacity to select and manage innovation projects), a third set of indicators is suggested, which would allow the monitoring of the company’s innovation project portfolio.

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Figure 1. The innovation-value path: system architecture

Having indicators that specifically assess how well the company performs in each capability dimension instead of measuring this indirectly by means of the outputs generated through the innovation process is especially important as, otherwise, it would be very difficult to identify the roots of poor output levels. 3.

Indeed, the third construct category of the measurement system is the one related to the different types of output that innovation could give rise to: new patents (actually, this is an intermediate output), new or improved products and/or services, new or improved processes, new organizing or management methods, new marketing methods, new strategies and new business models. In order to assess the outputs obtained, three aspects should be addressed: a. The amount of output obtained. b. The quality of that output. c. The productivity and efficiency levels achieved in the attainment process.

4.

Finally, the last construct making up the system (i.e. innovation outcomes) will analyze the impact of innovation in value creation and competitive position.

Figure 1 summarizes the architecture of the system proposed. Once the structure of the system has been outlined, specific guidelines will be provided in order to build up the content of each measurement block.

Content Guidelines Financial Resources This is the first resource construct within the measurement system and it refers to the funds needed to develop innovation activities. The indicators included within this section would show the amount of internal funding devoted to innovation, as well as external funds raised (or planned to be raised) to support in-

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novation activities. In both cases (internal and external funding), the total amount of funds could be measured, as well as the percentage that each type of funding represents in the total innovation investment. Likewise, the intensity of the financial effort could be reported, both in terms of company’s turnover and profit obtained (indeed, these are the most typical metrics within this domain). The indicators mentioned so far would show the amount of available resources of a financial nature. In the case of external funding, additional indicators could be added in order to assess the quality of the fund raising process. For instance, the amount of external funds obtained, relative to the total amount requested, could be an easy-toobtain indicator for this purpose. Finally, according to the general overview of the measurement system that was provided in the previous section, a third type of indicator should be added that would allow the monitoring of the activities that the company carries out to acquire new resources, augment the quantity of existing ones or improve their quality. In this case, these activities would relate to the monitoring of innovation funding opportunities (i.e. public funding initiatives), to the active search of innovation partners and to specific action that the company could undertake in order to prepare/develop funding dossiers in a more agile and effective way.

Technological Equipment Technological equipment refers to those resources of a tangible nature that the company uses to perform its innovation activities, namely laboratory equipment, trial banks or specific hardware. Although, strictly speaking, it would be of an intangible nature, innovation software would also be included within this category. Following the content structure proposed for resource constructs, a first set of indicators should be included within this section that relates to the investment made in each type of technological equipment. A second one should then be added to

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assess the quality of available facilities. For this to be achieved, several issues could be considered, such as the average age of existing equipment; the degree of adequacy of existing facilities to innovation requirements; advanced solutions existing on the market for specific types of equipment; and the potential impact of failure to incorporate those solutions in the firm. As regards the set of activities that the company could carry out to improve available resources in this domain (and which would give rise to a new set of indicators aimed at monitoring their degree of implementation), the following ones could be mentioned: monitoring of new market solutions in technological equipment; assessment of the degree of interest and usefulness of those new solutions; activities geared towards extracting the highest yield from available equipment; and activities aimed at facilitating users’ adaptation to new facilities.

Human Capital Human capital is the first set of intangible resources that the company relies on to perform its innovation activities. As has been previously explained, it encompasses the skills and knowledge of employees, their know-how and expertise in specific fields, their aptitudes and attitudes, as well as their degree of motivation and commitment. Traditional metrics used to assess available resources in this domain encompass the amount of people devoted to innovation activities (both in absolute numbers and full-time equivalent), the percentage of people devoted to innovation relative to total headcount (both in absolute numbers and full-time equivalent) and the amount of support/ auxiliary staff per researcher. The educational level attained by innovation staff (for instance, the number of PhD degrees), their previous experience in the field and their degree of diversity in terms of age, gender, original background and previous experience would also help to characterize available resources. This could also be complemented

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by an appraisal of their degree of motivation and commitment that could be obtained by means of specialist surveys in the field of human resource management. Moreover, the awards and distinctions obtained by the research staff of the company, the personal invitations received by staff members to participate in specialist forums and events, the number of publications made and the number of lectures given could be used as good proxies to assess the quality of research staff. In any case, a regular assessment of innovation-related competences (knowledge and skills) would be the best way to obtain an in-depth diagnosis of the quality of innovation human capital. With regard to the activities that the company could carry out in order to augment available resources and improve their quality (and which should lead to the definition of the corresponding set of indicators), the following fields of action should be mentioned: hiring and professional development policies, internal and external training, stay periods in prestigious research centres, conference and fair attendance, and close contact with customers. These quality improvement- oriented activities could be combined with specific action focused on enhancing personal motivation and commitment, as this is a basic condition for ensuring high-level performance.

Structural Capital Structural capital is the second resource category within intellectual capital. As mentioned above, it refers to the knowledge and other intangible resources that stay within the company when the employees have left and that derive from the organization’s action processes (CIC, 2003). In other words, it encompasses the organization’s essential operating processes, the way it is structured, its information flows and databases, its leadership and management style, its culture and incentive schemes, as well as intellectual property rights (Marr, 2006).

Therefore, the first purpose of this section should be to assess the degree of relevant knowledge that the company possesses in order to carry out its innovation activities. By means of a series of scale-anchored indicators, the company could evaluate the extent to which it has a good knowledge (both in terms of quantity and quality) of different topics relevant to innovation, such as: market positioning, competitors’ behaviour, industry trends, customers’ needs, furnishers’ characteristics, distribution networks, technological evolution of complementary product and/ or service providers, relevant legislation, and innovation support offered by different institutions (universities, technology centres, research institutes and industrial associations). The knowledge assessment suggested above would be clearly incomplete without an evaluation of the value associated with the intellectual property rights held by the firm: are the patents owned by the company strategically relevant for its future development? In what ways could they be exploited? What revenues are they expected to generate? Furthermore, is all strategic/relevant knowledge suitably protected? Moreover, considering that innovation involves novel combinations of previously existing knowledge (Eisenhardt & Martin, 2000; Leiponen, 2006), the extent to which the company masters different knowledge-related skills should also be assessed. In particular, the following skills should be considered: the ability to acquire external key knowledge and integrate it within the company’s processes, and the ability to transfer and reuse existing knowledge between different innovation projects and research teams. As regards those activities that could promote the acquisition and generation of new organizational knowledge, a set of indicators could be developed that would address different issues related to knowledge management: the extent to which the company documents relevant knowledge; the extent to which organizational members cooperate in documenting key knowledge; the degree

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of use of the company’s knowledge repositories (databases, on-line project libraries, routines and written procedures); the degree of implementation of different knowledge-sharing initiatives (both personal interaction-based and information and communication technology-based), and the degree of development of different initiatives geared towards the absorption and acquisition of key external knowledge. Another key topic to be considered within structural capital is the one related to organizational culture. In particular, whether the company has an innovation supporting culture and the extent to which different initiatives aimed at promoting such a culture are developed could be assessed. Moreover, structural capital would be the right section in which to assess the company’s innovation strategy and its coherence with business strategy, together with the degree of cognition of such strategy shown by the members of the company and the different activities carried out in order to pass it on.

Relational Capital Relational capital is the last construct within intellectual capital and it encompasses all resources deriving from the relationships the company maintains with different types of external stakeholders. From an innovation perspective, the aim is to assess the extent to which the company has an external innovation network that supports and complements its innovation efforts. Thus, the indicators included would show the amount of partnership agreements that have been established (both permanent and temporary) with different agents, the amount and percentage of innovation projects that have been carried out with external stakeholders, and innovation projects that have been developed on an open innovation basis. However, not only should the size of the innovation network be assessed. As is the case with other resources, the quality of the innovation network should also be monitored. Once more, a set of scale-anchored indicators could be used 102

to assess different quality attributes such as work planning, degree of coordination achieved, work methods employed, degree of commitment of participating agents, communication fluency, network follow-up and overall satisfaction. Additionally, another set of indicators should be included that would monitor the degree of implementation of different activities that the company could undertake in order to enlarge the firm’s innovation network (such as participating in different networking initiatives) and to improve the quality of network functioning.

New Idea Generation This is the first dimension to be assessed within innovation capability. It relates to sensing and shaping opportunities and threats so as to give rise to new innovation possibilities. Company performance in this field could be tracked through the number of new innovation opportunities that have been identified. These opportunities could be classified according to different criteria: the nature of the underlying innovation (product, process, non-technological innovation, or a mix of previous categories) and the extent to which they offer new features (incremental versus radical innovation). Moreover, creative performance could be measured through the number of alternative solutions that the company is capable of identifying in order to solve a specific problem: the higher this number is, the greater the possibilities of choosing the best solution. Additionally, another set of indicators should be added that would show the degree of implementation of specific initiatives that the company would like to undertake in order to improve its creative performance.

Project Management and Selection This is related to the ability of the company to seize opportunities (i.e. the second first-level capacity within innovation capability).

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Not all innovation opportunities previously identified go through the implementation process. Actually, it is a matter of allocating scarce resources. A selection process operates that identifies opportunities with the greatest potential. Therefore, a set of indicators could be developed to analyze the effectiveness of this process: the quality and agility of established procedures to select and approve innovation investments, and the ability of the company to detect the best opportunities. Once an innovation project has been selected, the execution phase begins. Good performance in this domain could be assessed by analyzing the company’s innovation portfolio. Along these lines, two types of project should be considered: finished (and abandoned) projects and in-process ones. In the case of the former, several indicators could help us to assess project management performance: project completion rate (i.e. number or finished projects versus all started ones–finished and abandoned); degree of budget and deadline fit for each project; number of unexpected problems that had had to be addressed during the execution phase; excess costs associated with these problems; and the amount of investment in abandoned projects. This could be complemented with an analysis of the reasons that have led to project abandonment. In the case of in-process projects, providing updated information regarding degree of progress, project feasibility and expected time in which to make it profitable constitutes a basic requirement. Additionally, some of the indicators mentioned for already-finished projects would also apply here. In particular, the ones related to the number of unexpected problems to be addressed during the implementation process and their related excess costs, and the ones referring to budget and deadline fit. Finally, a set of indicators should be added that would help to monitor the degree of implementation of different initiatives geared towards improving company performance in this domain.

For instance, activities aimed at improving investment selection and approval procedures; activities oriented towards improving coordination levels between different departments participating in the development process; specific initiatives that would help to detect problems in early stages of development; or different activities aimed at facilitating organizational learning and reflection from already-developed projects.

Change Capacity This is the last first-level capacity within innovation capability. It refers to the ability the company has to reconfigure and reinvent itself and not die because of unfavourable path dependencies generated by past success. Using a set of scale-anchored indicators, the company could try to assess its self-questioning capacity; its ability to unlearn old beliefs and get rid of old habits; its capacity to overcome resistance to change; to involve people in change processes; to take advantage of unexpected opportunities; to address changes that were not initially planned; and to adopt fast and bold decisions when the situation requires it. Moreover, as was the case with previous capability dimensions, additional indicators should be added to monitor specific initiatives that the firm could undertake in order to improve the situation in this field.

Innovation Outputs Once company performance in each innovation capability dimension has been assessed (i.e. how well the company performs in each first-level innovation capacity), innovation outputs should then be monitored. Along these lines, patents constitute the first innovation output to be considered. Besides tracking the number of patents obtained during a given period and the number of them which are in the process of being granted, the efficiency of

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the patenting process should also be assessed (by calculating the average expenditure per patent), as well as that of the maintenance process. Average revenue per patent would then be an easy way to assess patent productivity, and the number of citations each patent receives could allow the company to assess their level of quality. As far as the rest of innovation outputs are concerned (new or improved products and/or services, new or improved processes, new organizing or management methods, new marketing methods, new strategies and new business models), once more, the number of them that have been introduced within a specific time-period or that are in the process of being introduced should be tracked. In this case, the quality of the innovation being introduced could be measured in terms of the specific problem the innovation was intended to solve, or in terms of the specific challenge it was intended to face (for instance, reducing customer delivery time or improving product functionality). In other words, whether the innovation under analysis fulfils its a priori technical or “content” specifications and the degree of excellence with which it does so could be checked. Finally, measuring efficiency and productivity would only make sense in the case of technological innovation and, more precisely, in the case of product/service innovation. The average innovation expenditure per new product/service and per improved product/service would allow us to assess efficiency; whereas the average revenue per dollar spent (both for new and improved products/services) would allow the company to assess productivity.

Innovation Outcomes This is the very last construct of the measurement system and, as previously explained, it is intended to show the impact of innovation in value creation and competitive advantage. Increased value (via a superior and unique value proposition that better responds to users’

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demands or that addresses unmet needs) should give rise to increased revenues and increased market share. It also could give access to new segments or to entire new markets. Indicators such as the number of new customers accessed, the percentage of revenues due to new or improved products or services, or the revenue increase rate that could be linked to those products or services (or to a new business model) could be used to assess this issue. However, the problem lies in the fact that several types of innovation could be interrelated and that, as a consequence, it may be difficult to separate the influence of each of them. For instance, a new product may be accompanied by a marketing innovation or even by a business model innovation. Where this is the case, the set of interrelated innovations should be treated as a unique innovation, whose specific category would be that of the prevalent (i.e. the most relevant) one. On the other hand, some types of innovation could enhance competitive advantage by making the company more cost-efficient and, therefore, allowing the firm to earn greater profits. In this case, indicators related to the cost savings achieved would be the appropriate ones.

FUTURE RESEARCH DIRECTIONS Until now, the foundations of a new measurement system to diagnose and manage innovation performance have been provided. In particular, the architecture of the system and its underlying logic have been described, and specific guidelines for developing indicators for each measurement block have been suggested. From this starting point, future research should be aimed at developing precise metrics for each of the constructs outlined. These indicators should combine both quantitative and qualitative metrics and, wherever possible, benchmarking opportunities should be added. In this sense, the work carried out by institutions such as the Industrial Research Institute may be of paramount relevance, as this

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institution has traditionally made great efforts to develop very precise metrics. Having precisely defined indicators, without any kind of ambiguity, is something by no means to be neglected, as this is one of the key success factors when implementing a measurement system. Indeed, having poorly-defined metrics (in terms of what should be included or excluded in the calculations and in what way) is a huge waste of time when it comes to data gathering and ratio building, and to interpreting the results obtained. Although some common metrics could be proposed for different companies, custom-designed metrics will also apply. Along these lines, it will be very important to have the chance to test the innovation-value path in different companies, in order to have a set of experiences that could help to refine the system and obtain specific guidelines for different situations. In particular, the empirical test of the measurement system would provide us with an array of already implemented metrics for each construct, whose relevance and usefulness would have already been verified and, thus, could be used as a source of inspiration by other companies aiming to improve their innovation measurement. The implementation test would also allow us to assess the degree of difficulty of setting out such a system, and the amount of resources to be devoted to such an initiative, as well as to identifying the key success factors to be borne in mind and the main pitfalls to be avoided. In short, an implementation guide could be generated that could help companies to navigate through the implementation process. Finally, a set of in-depth case studies could be obtained related to the crafting, assessment and adjustment of innovation strategies through the use of the innovation-value path, which would be the main piece of evidence of the validity of the new system. Additionally, another extremely interesting research path opens up: the set of constructs proposed could constitute the basis for applying structural equation modelling techniques (such

as those based on partial least squares), in order to analyze resource interdependencies and their impact on innovation performance, thus helping to better understand innovation dynamics and to refine the set of relevant innovation metrics to be considered. Some of the macro-propositions that could be tested are as follows: 1.

2.

3.

4.

Technological equipment, human capital, structural capital and relational capital partially mediate the relationship between financial resources and different innovation capability dimensions (i.e. new idea generation, project management and selection, and change capacity). In other words, although they could have a direct impact too, financial resources are believed to influence innovation capability primarily through the impact they have on other resources. Structural capital and relational capital partially mediate the relationship between human capital and different innovation capability dimensions. As is the case with financial resources, human capital could have a direct influence on each innovation capability dimension (and even greater than the one expected for financial resources), but it could also have an indirect one through the influence it exerts on structural and relational capital. However, the reverse could also be true. Structural capital and relational capital partially mediate the relationship between technological equipment and different innovation capability dimensions. In other words, the same logic as the one mentioned in previous propositions applies. Innovation capability (i.e. new idea generation, project management and selection, and change capacity) exert a positive influence on firm competitiveness. This macroproposition would allow us to verify which innovation capability dimension exerts the

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greatest impact on firm competitiveness, or whether all of them exert a balanced one.

CONCLUSION The innovation-value path provides companies with a comprehensive and clearly organized framework in order to assess their innovation performance and its impact on value creation and competitive advantage. Actually, it is much more than an assessment tool: it is also a management tool, as it provides the foundations for developing (and adjusting) a sound strategy that would help companies to improve their innovation results. Following the resource-based view logic of value creation and competitive advantage, the innovation-value path offers a complete journey through all relevant factors to be borne in mind for successful innovation management. It takes care of all types of resource (both tangible and intangible) that are needed to develop innovation; it allows the monitoring of different activities that could be carried out in order to acquire new resources, increase the quantity of existing ones or improve their quality; it shows how well the company performs in each innovation capability dimension (ideation, project management and change management); it allows the follow-up of different initiatives specially geared towards improving performance at each first-level innovation capacity; it shows the different types of output that the innovation process could give rise to, as well as their quality, efficiency and productivity attributes; and finally, it shows the impact of innovation on value creation and competitive advantage. Indeed, the innovation-value path constitutes a complete roadmap for innovation management. In this measurement system, cause and effect relationship between different measurement blocks can be easily established. Resources allow the development of different innovation capability dimensions (i.e. first-level capacities), but it is a matter for each company to decide which specific

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resources are used in each dimension and what the relative relevance of each of them is. Moreover, resources themselves could be interrelated. For instance, relational resources could improve the quality of human capital, and some types of structural resource can influence human capital as well (the reverse could also be true). Having specific blocks for each resource category and for each first-order innovation capacity allows these linkages to be easily reflected. In short, the innovation-value path could allow companies to obtain a clear and systemic picture of their situation in the innovation domain. However, the fact that the innovation-value path is still in its conceptual stage and that no empirical test has been carried out yet should be seen as an important limitation to this claim.

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Grant, R. M. (2008). Contemporary strategy analysis (6th ed.). Oxford, UK: Blackwell Publishing Ltd. Gupta, O., & Roos, G. (2001). Mergers and acquisitions through an intellectual capital perspective. Journal of Intellectual Capital, 2(3), 297–309. doi:10.1108/14691930110400092 Hawawini, G., Subramaniam, V., & Verdin, P. (2003). Is firm’s profitability driven by industry or firm-specific factors? A new look at the evidence. Strategic Management Journal, 24(1), 1–16. doi:10.1002/smj.278 Helfat, E., Finkelstein, S., Mitchell, W., Peteraf, M. A., Singh, H., Teece, D. J., & Winter, S. G. (2007). Dynamic capabilities: Understanding strategic change in organizations. Malden, MA: Blackwell Publishing. Kaplan, R. S., & Norton, D. P. (2004). Strategy maps – Converting intangible assets into tangible outcomes. Boston: Harvard Business School Press. Kerssens-van Drongelen, I. C., & Cook, A. (1997). Design principles for the development of measurement systems for research and development processes. R & D Management, 27(4), 345–357. doi:10.1111/1467-9310.00070 Kianto, A. (2007). What do we really mean by the dynamic dimension of intellectual capital? International Journal of Learning and Intellectual Capital, 4(4), 342–356. doi:10.1504/ IJLIC.2007.016332 Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science, 3(3), 383–397. doi:10.1287/orsc.3.3.383 Leiponen, A. (2006). Managing knowledge for innovation: The case of business-to-business services. Journal of Product Innovation Management, 23(3), 238–258. doi:10.1111/j.15405885.2006.00196.x

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Lev, B. (2001). Intangibles: Management, measurement and reporting. Washington, DC: Brookings Institution Press. Lundvall, B. A., & Nielsen, P. (2007). Knowledge management and innovation performance. International Journal of Manpower, 28(3-4), 207–223. doi:10.1108/01437720710755218 Marr, B. (2006). Strategic performance management – Leveraging and measuring your intangible value drivers. Oxford, UK: ButterworthHeinemann. Marr, B., Schiuma, G., & Neely, A. (2004). The dynamics of value creation: mapping your intellectual performance drivers. Journal of Intellectual Capital, 5(2), 312–325. doi:10.1108/14691930410533722 Martins, E. C. (2000). The influence of organizational culture on creativity and innovation in a university library. Unpublished master’s thesis, Pretoria, South Africa, University of South Africa. McGahan, A. M., & Porter, M. E. (1997). How much does industry matter really? Strategic Management Journal, 18(1), 15–30. doi:10.1002/ (SICI)1097-0266(199707)18:1+3.3.CO;2-T Meritum Project. (2002). Guidelines for managing and reporting on intangibles. Madrid, Spain: Fundación Aitel Móvil. Misangyi, V. F., Elms, H., Greckhamer, T., & Lepine, J. A. (2006). A new perspective on a fundamental debate: a multilevel approach to industry, corporate and business unit effects. Strategic Management Journal, 27(6), 571–590. doi:10.1002/smj.530 Morris, M. H., Kuratko, D. F., & Covin, J. G. (2008). Corporate entrepreneurship and innovation (2nd ed.). Mason, OH: South Western Cengage Learning.

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Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational advantage. Academy of Management Review, 23(2), 242–266. doi:10.2307/259373 Nonaka, I., & Takeuchi, H. (1995). The knowledgecreating company. Oxford: Oxford University Press. Nonaka, I., Toyama, R., & Byosière, P. (2003). A theory of organizational knowledge creation: Understanding the dynamic process of creating knowledge. In Dierkes, M., Berthoin, A., Child, J., & Nonaka, I. (Eds.), Handbook of organizational learning & knowledge (pp. 491–517). Oxford: Oxford University Press. Peteraf, M. A. (1993). The cornerstones of competitive advantage: A resource-based view. Strategic Management Journal, 14(3), 179–191. doi:10.1002/smj.4250140303 Pike, S., Roos, G., & Marr, B. (2005). Strategic management of intangible assets and value drivers in R&D organizations. R & D Management, 35(2), 111–124. doi:10.1111/j.1467-9310.2005.00377.x Plessis, M. (2007). The role of knowledge management in innovation. Journal of Knowledge Management, 11(4), 20–29. doi:10.1108/13673270710762684 Reed, K. K., Lubatkin, M., & Srinivasan, N. (2006). Proposing and testing an intellectual capital-based view of the firm. Journal of Management Studies, 43(4), 867–893. doi:10.1111/j.14676486.2006.00614.x Roos, G., Roos, J., Dragonetti, N., & Edvinsson, L. (1997). Intellectual capital: Navigating in the new business landscape. New York: New York University Press.

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Roquebert, J. A., Phillips, R. L., & Westfall, P. A. (1996). Markets vs. management: what drives profitability? Strategic Management Journal, 17(8), 633–664. doi:10.1002/ (SICI)1097-0266(199610)17:83.0.CO;2-O

Teece, D. J. (2009). The nature and microfoundations of (sustainable) enterprise performance. In Teece, D. J. (Ed.), Dynamic capabilities & strategic management – Organizing for innovation and growth (pp. 3–64). Oxford: Oxford University Press.

Rumelt, R. P. (1984). Towards a strategic theory of the firm. In Lamb, R. B. (Ed.), Competitive strategic management (pp. 556–570). Englewood Cliffs, NJ: Prentice Hall.

Teece, D. J., & Augier, M. (2009). The foundations of dynamic capabilities. In Teece, D. J. (Ed.), Dynamic capabilities & strategic management – Organizing for innovation and growth (pp. 82–112). Oxford: Oxford University Press.

Schultz, T. W. (1961). Investment in human capital. The American Economic Review, 51(1), 1–17. Spender, J.C. (1996). Making knowledge the basis of a dynamic theory of the firm. Strategic Management Journal, 17 (Winter Special Issue), 45-62. Stewart, T. A. (1991): Brainpower. Fortune, June 3 1991. Stewart, T. A. (1997). Intellectual capital: The new wealth of organizations. New York: Doubleday/ Currency. Subramaniam, M., & Youndt, M. A. (2005). The influence of intellectual capital on the types of innovative capabilities. Academy of Management Journal, 48(3), 450–463. Sullivan, P. H. (Ed.). (1998). Profiting from intellectual capital: extracting value from innovation. New York: John Wiley & Sons. Sveiby, K. E. (1997). The new organizational wealth: Managing and measuring knowledgebased assets. San Francisco: Berrett-Koehler Publishers, Inc. Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. doi:10.1002/smj.640

Teece, D. J., Pisano, G., & Shuen,A. (1997). Dynamic capabilities and strategic management. Strategic ManagementJournal,18(7),509–533.doi:10.1002/ (SICI)1097-0266(199708)18:73.0.CO;2-Z The Boston Consulting Group. (2008). Measuring innovation 2008 – Squandered opportunities. Boston, MA: The Boston Consulting Group Inc. Thompson, V. A. (1965). Bureaucracy and innovation. Administrative Science Quarterly, 10(1), 1–20. doi:10.2307/2391646 Tipping, J. W., & Zeffren, E. (1995). Assessing the value of your technology. Research Technology Management, 38(5), 22–39. Van de Ven, A. H. (1986). Central problems in the management of innovation. Management Science, 32(5), 590–607. doi:10.1287/mnsc.32.5.590 Voelpel, S. C., Leibold, M., Eckhoff, R. A., & Davenport, T. H. (2006). The tyranny of the balanced scorecard in the innovation economy. Journal of Intellectual Capital, 7(1), 43–60. doi:10.1108/14691930610639769 Wernefelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171–180. doi:10.1002/smj.4250050207

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ADDITIONAL READING Amabile, T. (1988). A model of creativity and innovation in organizations. In Staw, B., & Cummings, L. (Eds.), Research in Organizational Behavior (Vol. 10, pp. 123–167). Greenwich, CT: JAI Press. Amit, R., & Schoemaker, P. J. H. (1993). Strategic assets and organizational rent. Strategic Management Journal, 14(1), 33–46. doi:10.1002/ smj.4250140105 Brooking, A. (1996). Intellectual capital – Core asset for the third millenium enterprise. London: International Thomson Business Press. Bueno, E., Morcillo, P., & Salmador, M. P. (2006). Dirección Estratégica – Nuevas perspectivas teóricas. Madrid, Spain: Ediciones Pirámide. Christensen, C. M. (1997). The innovator’s dilemma. Boston: Harvard Business School Press. Damanpour, F. (1991). Organizational innovation: A meta-analysis of effect of determinants and moderators. Academy of Management Journal, 34(3), 555–590. doi:10.2307/256406

Fagerberg, J., Mowery, D. C., & Nelson, R. R. (Eds.). (2005). The Oxford handbook of innovation. Oxford: Oxford University Press. Hall, R. (1992). The strategic analysis of intangible resources. Strategic Management Journal, 13(2), 135–144. doi:10.1002/smj.4250130205 Hudson, W. (1993). Intellectual capital: How to build it, enhance it, and use it. New York: John Wiley & Sons. IFAC. (1998). The measurement and management of intellectual capital: an introduction. New York, NY: IFAC. Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Cambridge, MA: Harvard University Press. Nordic Industrial Fund. (2001). A report from the Nordika project: Intellectual capital managing and reporting. www.nordika.net. Prahalad, C. K., & Krishnan, M. S. (2008). The new age of innovation – Driving co-created value through global networks. New York: McGrawHill. Simons, R. (2000). Performance measurement and control systems for implementing strategy. Upper Saddle River, NJ: Prentice Hall. Sullivan, P. H. (2000). Value driven intellectual capital: How to convert intangible corporate assets into market value. New York, NY: John Wiley & Sons.

Danish Agency for Trade and Industry (2000). A guideline for intellectual capital statements – A key to knowledge management.

Teece, D. J. (2000). Managing intellectual capital. Oxford: Oxford University Press.

Danish Ministry of Science, Technology and Innovation (2003). Intellectual capital statements – The new guideline.

Von Stam, B. (2008). Managing innovation, design and creativity (2nd ed.). Chichester, West Sussex, UK: John Wiley & Sons.

Danish Ministry of Science, Technology and Innovation (2003). Analysing intellectual capital statements.

White, M. A., & Bruton, G. (2007). The management of technology and innovation – A strategic approach. Mason, OH: Thomson Higher Education.

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Winter, S. G. (2003). Understanding dynamic capabilities. Strategic Management Journal, 24(10), 991–995. doi:10.1002/smj.318

KEY TERMS AND DEFINITIONS Capability: What the firm can do. It involves deploying organizational resources for a desired end result. Competitive Advantage: A situation in which a company earns (or has the potential to earn) a persistently higher rate of profit than other rivals competing in the same market. Dynamic Capability: A capability that allows the company to create, extend or modify its resource base. Human Capital: It encompasses the skills and knowledge of employees, their know-how and expertise in specific fields, their aptitudes and attitudes, as well as their degree of motivation and commitment.

Innovation: Conceiving and implementing something new (a product and/or service, a process, a management method, a marketing method or a business model). Relational Capital: It encompasses all resources deriving from the relationships the company maintains with different types of external stakeholders. Resource: Any asset the company can use to achieve its aims. Resource-Based View: A strategic paradigm that considers that resources and capabilities are the foundation of sustained competitive advantage. Structural Capital: It refers to the knowledge and other intangible resources that stay within the company when the employees have left and that derive from the organization’s action processes. Value: The amount of money customers are willing to pay for a good or service.

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Chapter 6

Production Cognitive Capital as a Measurement of Intellectual Capital Leonardo P. Lavanderos Sintesys Corporation, Chile Eduardo S. Fiol Sintesys Corporation, Chile

ABSTRACT At present, knowledge plays a key role in the new economy. Nevertheless, its measurement as Intellectual Capital has not been possible from a certainty vision for the states, events and entities, leaving aside the complexity of organizations. This work proposes a paradigmatic shift where the fundamental support is the relational–semiotic condition of human organizations; any deviation from its strategic goals could be explained through the closeness between language and the action emerging from language. Defined as Coherence and Congruity (Sustainability) Management, the process named NETOUT allows increasing both coherence and congruity through co-participating in decisional modeling, and transferring repulsion interactions to organization areas that re-signify the conflict. Configurations arising from Sustainability are Production Cognitive Capital and constitute a measurement of Intellectual Capital.

INTRODUCTION Knowledge Society and Knowledge Economy are concepts coined in the XXth Century to highlight the role of knowledge as key and differentiating element of economic growth. Hence, intellectual capital, defined in the simplest possible terms as knowledge generating value, has become the subject of study in many research works (Petty, Guthrie 2000). However, there exist as many DOI: 10.4018/978-1-60960-054-9.ch006

definitions of Intellectual Capital as there are researchers devoted to the study of this matter. A possible explanation to the above is that Knowledge-based Economy, as a value generation process, is fundamentally characterized by its uncertainty condition. This is based on that knowledge production is the result of organization’s relational dynamics which does not allow locating a productive source in a person but in the network. Under that condition, knowledge generation involves a permanent uncertainty reorganization which we define as crisis. Finally,

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Figure 1. Tangibles and intangibles worlds

to round out these ideas, we call innovation to the art of reorganizing uncertainty or crisis. The above mentioned leads us to the schema shown in Figure 1 (a), where uncertainty, crisis, and innovation are the cornerstones of the intangibles world. By the other hand, object economy, the more traditional view, may be explained through another scheme, shown in Figure 1 (b), whose cornerstones are certainty, power and conservation. Here, certainty is bound to the permanence, to the object itself. Power and Control are a means used to “press” knowing where, when, and how. Finally, the idea of conservation is the appropriation over the capital, leading to richness through consumption. The worlds shown in Figure 1 are not exclusive but may coexist and be integrated, leading to a “better” configuration which is obtained “rotating” the (a) side, superposing both, leading to a virtuous hexagon shown in Figure 2. As shown in Figure 2, there are two apparently opposite “worlds”: tangibles and intangibles, coexisting, and the hexagon is called virtuous because the two opposite worlds are co-active, generating emergence or synergy instead of reduction, as in the ying-yang metaphor, and it is pos-

sible to make a “leap” from one world to the other, depicted in a spiral movement. Thus, we may have, for example, a leap from innovation to uncertainty (in knowledge production), where tangible economy will enforce profit conservation; another example, innovation is always trapped between power/control and conservation. The above leads us obligatorily to a change in our approach, from an objectual-dyadic view to a relational-tryadic one.

BACKGROUND The Relational Approach Relational Theory is an explanatory system basing its operation in the relation as a sense and world generation process. For this theory, the relational unit in cognition is Organism-Entorno, opposite to the classic proposal of organism and environment (Malpartida and Lavanderos, 1995 and 20002000200020002000). The Surroundings of the observer are unique and permanent relational configurations of territoriality (code generation for bonding and belonging) for this one. We spoke of Co-circumstantiality in the distinction of units, implying, as much the definition

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Figure 2. The virtuous tangible/intangible hexagon

of the observer like the definition of the observed unit. The observer constitutes itself in the act of distinction as a unit. If all the unit is a Co-construction, the objectivity principle will have to be applied then to the process by means of which the unit is defined (distinction acts). In this sense, we can define the objectivity of an operating form, like the explanation of the mechanisms of units generation. In the relational process, the objectivity does not talk about the territory or nature (to be experienced), but the process of obtaining the map (reformulation of the experience), that is to say, which are the criteria, explicit rules, alternatives or conventions or implicit statements reporting construction process of models in general and explanations in specific. The relational view compels to think that knowledge constitutes territoriality (Lavanderos and Malpartida, 2005), by way of networks configurations within a process which itself designates as value. This means that the notion of value in the network configuration is located in

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the exchange activity with other networks instead of the network itself.

The Relational Organization Approach Relational Organization Approach (ROA) is a way of studying organizations coming from relational processes –viewing processes, rather than substances, as the basic forms of the universe–. ROA prioritizes change over conservation, novelty over continuity and emergence over reduction. Creativity, change, disruption, and uncertainty are the main topics of a relational view. This approach looks at relationships as fundamental, and does not require the existence of states, events, and entities, but insists on unpacking them as distinctions from culture which emerge as complex processes involved in –set of activities and transactions that take place and contribute to– their constitution. Relational view relies on anti-dualism, i.e. the recognition that everything that is has no sense

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apart from its relationship with other things, and, therefore, long established dualisms such as mind and body, reason and emotion, humanity and nature, tangible and intangible, object and subject, need to be overcome. In a tangible economy, object language (nominalization) has been the condition to overcome intangible world demands. Thus, if we define Organization as a relational system (set of relational processes), semiotically organized from the culture, as a legitimator of the above mentioned, then, knowledge production can be defined as the result of code structuring which generate intentionality to accomplish a determined product/ service development process. Related to the above, we can state that a concept like Intellectual Capital will have no sense if it is bounded to accumulation ideas. So, it is much more appropriate to speak about Production Cognitive Capital instead of Intellectual Capital. Production Cognitive Capital should be defined as a code system (semiosis) intentionally aimed at goods/services production. A first difference between them is that Production Cognitive Capital is sharper, focused on processes. Naturally, as any code needs to be interpreted, this process generates uncertainty, because there is a gap between the code intention and the associated action; a smaller gap means less uncertainty. Production Cognitive Capital is located in the Business Intelligence scope, since it facilitates decision making through the comprehension of current functioning and action anticipation, generating a consistent direction facing complex scenarios. The above definition allows assessing semiotic structure effectiveness in the productive process through closeness evaluation, which is called coherence. This involves a paradigmatic shift in business view and R&I (Research and Innovation) role, which would directly impact the associated strategies development. Because of that, design efforts associated to R&I must be driven from the relationship among those strategies to the form of

knowledge associated to its development, since this one would explain in a better degree the generation of value of use and value of exchange in the new economy scope. Production Cognitive Capital must be understood as the knowledge or configurative process associated to both values, which is a feature only found in the relational process. The above implies that an increase in Production Cognitive Capital is in strict proportion to the relational quality of the network which produces it; in other words, a rapprochement between the argumentative line and the associated action degree (coherence). A desirable consequence of this development would be an increase in network coherence and, hence, in Production Cognitive Capital as company value. In this new scope, knowledge generation would be a natural process aligned with organization’s “emotional state”, which is supported by three cornerstones: Cognition, Semiotics and Interactivity. The present chapter is aimed at looking for alternatives, both theoretical and methodological, to assess Production Cognitive Capital. For that purpose it is divided in three sections: Cognitive Sciences what is knowledge in an economy scope? NEUS method as an approximation to Production Cognitive Capital assessment; and Inventing an organization as relational states structure.

APPROACH Cognitive Sciences: What is Knowledge in an Economy Scope? Knowledge theoretic and scientific analysis in all its dimensions are known as Cognitive Sciences. Information technology is usually the most visible aspect of this huge research and applications field whose main concerns are knowledge, information and communication (Varela, 1998). A first approximation to “Knowing” arises from the symbolic school, which defines cognition

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as information processing in terms of symbolic computations or symbol manipulation based on rules. To this school, symbols must adequately represent an aspect of the real world (Varela 1998). A second approximation comes from the notion of emergent properties and their self-organization (Sun and Alexandre 1997), and since the orientation in the reformulation of cognition is related to connections, this approach was denoted as Connectionism. In this case, the strategy consists not in symbols and rules, but in the connective dynamics among elements. For this School, Cognition is the emergence of global states in a network made of simple components, the validation of which takes place in the relation of a correspondence between the emergent status and the resulting structure for a given cognitive aptitude. The above mentioned schools may be classified as representational, mainly because representation supports cognitive activity according to the definition provided by these schools. In the opposite way, there are the non-representational schools characterized by the Enactive and Relational schools. The Enactive school states that cognitive aptitudes are linked to lived experiences (Varela et al., 1992). Cognition is no longer a device that manipulates representations but makes a world emerge through an effective action: a history of structural coupling that enacts (brings forth) a world. The central idea to enaction is stating common principles to a linkage among sensory motor systems explaining how the action may be perceptually guided in a world that depends on the perceptor (Varela et al. 1992). Finally, the position of the Relational School assumes as irrelevant the existence (ontogeny) of a pre-stated world as a condition for the observer cognition. How we know is explained as the generation of configurations (narrative) whose associative structure is determined by the culture. This way, we go from an ontological approach, whose objects have existence by themselves, to

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an epistemological approach, in which the distinctions are generated on the basis of the observer culture. These configurations (distinction operations) are taken by the observer networks that share meta-configurations organized for bonding guidelines (what one makes a part of himself/herself) and belonging (what one becomes a part of) which is defined as territoriality (Lavanderos and Malpartida 2001). The observer does not exist as an isolated individual but as a component of the cultural network which determines its configuration making approach.

Intellectual Capital Definitions and Their Implications from the Cognitive Schools Perspective From the symbolic school standpoint, if a company’s knowledge is a reality which can be assessed and reduced to symbols, then an observer would have a universal character and could make an invariant narrative with regard to the knowledge given so he/she has the operations which make its representation possible. But, can knowledge be represented on the basis of its physical characteristics? And, if this were so, which should be these physical characteristics to allow their representation, apart from the associated semiotic rules? From this perspective, only objects can be represented which we associate to the quality of knowledge, in fact in most of the works on Intellectual Capital, knowledge is presented as a quality-determined form. This is a distinction which, on the one hand, presents the object in this case and which, on the other hand, accompanies the quality in which it is presented. To put it in other words, it assigns a name to the object that is presented, and on the other hand, it is associated to a sentence to express in what feature such an object is presented. Here the presented object is knowledge, and it is presented in a feature of intangible objects. Let us examine the definition of Intellectual Capital offered by Stewart (1998), “it is the sum

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of everything everybody in a company knows that gives a competitive edge”. This clearly shows that knowledge is conceived as an object so that it may fulfill the condition of the sum. This same conception is repeated when researchers divide intellectual capital into human, structural and relational capital. Another way in which knowledge manifests is as an object state, though expressed through a series of intangibles (Andriessen, 2001). This author selects a set of distinctions allowing describing knowledge as a way of accumulation. By way of an example, we can quote the idea of managing information, patents, manuals, etc. The latter implies a reifying of the knowledge process. From this perspective, knowledge is homologous to socially useful work as it is conceived as the root cause for the generation of wealth. Seen from this perspective, knowledge acts as the causal cumulative force, a sort of “stock” which the members of an organization possess. Therefore, the amount of knowledge should be proportional to the amount of individuals who make up the company, which, by reduction to the absurd, would imply that companies with a greater intellectual capital should be mega-companies. At this point, we could well sum up that Intellectual Capital is conceived in most cases as a concept that can be represented on the basis of producing objects which are associated to the quality of knowledge. This allows us to create an illusion of measure, since we can quantify the number of objects associated to that quality, reflected in believing that the observer’s universality condition and its descriptive invariance which makes it possible to speak of human resources accounting and financial balances. Likewise, the objectual idea of representation leads us to the design of multiple indicators, all of which are different, even those from companies belonging to the same industry. This has a direct impact on the impossibility of establishing distinctions for each indicator as to how much is good and how much is bad, low level of interpretation by the

investors and what is more important, not being able to establish how the indicator and the creation of value are related. On the stated above, we may say that issues arising from the multiplicity of Intellectual Capital measurement and management approaches derive from the fact that knowledge is conceived as arising from symbolic representation, or as an object. If the symbols must leave the scene, which one of the cognitive schools would enable us to establish the groundwork for an epistemology of Intellectual Capital?

Towards an Epistemology of Production Cognitive Capital From the above it is deduced that, as a general rule, intellectual capital definitions are seen as a symbolic conception, which brings as a consequence a multiplicity of models and indexes. Because of it, at this point we analyze the constitutive to build the intellectual capital from the schools: connectionist, enactive and relational. From the connectionist or internal representation school view, the interest is in the processing rules that respect the semiotics of internally represented knowledge that generates value. In this domain, knowledge would be an emergent of the communication process, an interpretation which an observer makes of the interaction between two observers. Hence, knowledge would be a representation of a relation between oneself and some other party. This implies establishing equivalences determined by language, in terms of number, and by culture in relation to the diversity of knowledge which generates value. The latter could explain the problem that indexes are all different, even those in companies that belong to the same industry, in addition to the ambiguity in the investors’ interpretation. The basic issue of this approach is the notion that relationships can be represented as internal computations of entities and instants (Von Foerster, 1972). Hence, intellectual capital would be seen as a final representation, in the

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physical sense, a product of a relational element, the company, whose internal structure (the specific organization), is an internal representation of that Intellectual Capital. Even though this approach eliminates a series of obliged assumptions such as objectivity, their main limitation is the access to the internal structure or the form in which the relational process acquires sense and meaning, not for an individual, but for the network. And, hence, Intellectual Capital would be “computable” in terms of operations of the representation which this structure makes, something very close to the vision of the company, operations which have shown very little effectiveness in their application. Knowledge is neither a thing nor the property of a thing, because it primarily addresses to a process; it cannot be localized independently from the network that generates it. Hence, it follows that it is not possible to represent knowledge as an object. Knowledge accounts for relational aspects, which implies that it is not possible to describe a relational element that generates knowledge as an internal representation of a knowledge structure. Because of this, there is a burst of indicators, under the form of companies that determine them and, due to dissatisfaction; these indexes are changed by the same companies within a given timeframe. Based on the above, if we cannot represent knowledge, we must give up this idea seeking refuge in non representation. Next, we shall analyze the possibilities that non representational cognitive schools offer us facing the operationalization of intellectual capital definition. If knowledge emerges as action in the world (Varela, 1998), that is, if knowledge that generates value makes emerging a world of meaning, then, Intellectual Capital is a set of actions accepted as such. There would exist, then, an operational closure or autonomy, which in the organization context would allow making the distinction in that set of actions which constitute and are networkgenerative, making possible its emergence as such. Even though this approach, designated as enactment, allows us to remit ourselves to the

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process and not to the object, it does not enable us to assess Intellectual Capital in an operational manner. This statement is based on the following sentence: “knowledge is at an interface among mind, society and culture, and not in one or even in all of these elements. Knowledge does not pre-exist in any form or place, but is enacted under particular conditions” (Varela et al. 1992). The question arising immediately is: what are the particular conditions allowing knowledge enactment which generate value? Due to this same situation, under certain particular conditions the computation of these depends on the observer describing them and on the network for which they are described. In other words, what has been enacted will be an enacted translation made by a narrator, a return to subjectivity but, at the same time, without any representation. Upon the above stated, it would seem that the computations associated with Intellectual Capital should already operate from the observer, though in the art of narrative, the latter understood as a configuration, a product of operating in culture, the fruit of organizing the relations as semiosis, that is, highly significant networks generating value for the organization. In order to achieve the latter, we shall take the relational vision, which compels us to think that knowledge constitutes territoriality (Lavanderos and Malpartida, 2005), that is to say, code generation for bonding and belonging, by way of configurations of the networks within a process which itself designates as value. The latter means that the notion of value in the network configuration is located in the exchange activity with other networks. Relational theory establishes that transactional activities across different networks allow territorial value equivalence associated with the object as located configuration in the relation and not in the object as such. It is then the network activity and the structure supporting it what constitutes Intellectual Capital. Thus, knowledge definition in intellectual capital

Production Cognitive Capital as a Measurement of Intellectual Capital

becomes located then in the exchange mode and in the configuration type in which it makes sense to be exchanged. Therefore, we may talk of intellectual capital assessment as the structural expression of the relations which culturally determine those configurations in terms of the value notion which generate territoriality. From this perspective, knowledge as an object disappears, and what is accumulated is the relational strategy for the production of value configurations that allow, due to the high degree of semiotic equivalence, their ability to transact with other networks. Upon this basis, what must be assessed is the network relational structure to an extent such that, for different contexts, the intellectual capital value is the consistency of the configurations which have sustained the organization of the network, allowing its conservation. As can be inferred from the above, talking about knowledge and value is to make a reference to the relation as a process. Consequently, it is not possible to generate universal rules for building a unique semiotic structure. Is in this sense that cognitive sciences contribute with an appropriate guide as regards the implications of including intellectual capital in the representation and non representation domain. The first makes possible to recover external elements and project internal ones, thereby rendering intellectual capital non-viable as a process and inevitably reducing it to object accounting. In the second case, enactment is incomplete because it maintains the observer/setting duality, which makes unviable to understand intellectual capital as a network relational process. Finally, the relational school allows the design of basic computations of Intellectual Capital, as it locates the process as emerging from decisional history made up of the relational form or network structure, determined by the culture and conservation of territoriality. The latter, then, permits modeling the decisional process and the interactive and relational structure of the network in relation to its semiotic production of value exchange. It is at this

instance where intellectual capital indexes become structural descriptors of the decisional process. Summing up, epistemological foundations that better interpret the Intellectual Capital spirit in the XXIst century are in relational theory. This allows the development of a new vision of Intellectual Capital, which emphasizes the organization of the relations determined by a culture. If we look deeply inside through this point of view, we can define a company as a process of relationships determined by its culture and organized according to the exchanges of bonding and belonging codes among people which guide the decision-making process for value generation. Therefore, Intellectual Capital–as knowledge which generates value–emerges from the consistency of the relational process between the structure of the organization and the decisional process within that structure. In conclusion, Intellectual Capital measured as coherence and congruity is defined as Production Cognitive Capital. From relational epistemology we can deduce that intellectual capital cannot be conceived as an object, but as a process, the Production Cognitive Capital.

The Management Process: Description, Explanation and Tautology If we define the management process as a system of actions towards achieving a goal, then the success of the last one depends fundamentally, from the relational vision, on the coherence among what is described, the associated explanation and the legitimacy of the tautology to the relational network. The description of the actions does not endure any logic, as Bateson points out (Bateson, 1980) it is a series of facts about which we do not know how they get interconnected. By the same, the explanation will not supply any information more than the already owned by the description. It is then the tautology or connective form applied to the description which allows connecting

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the actions generating sense to the series of facts contained in the description for a certain context. Then, when we refer to the tautology legitimacy, what we state is that for a command relational network, an instruction not necessarily achieves an explanation generating decisional coherence, this means that the narrative should match what it is going to be done with what finally is done. This way, the Production Cognitive Capital (PCC) references the legitimacy degree of the tautologies used in the productive management process. This is, the greater the tautological legitimacy the greater the coherence in the management process which will have as consequence a relational network highly co-organized (semiotic production), cohesive (use value), coordinated, decentralized and with high power of exchange or congruity (change value). Then, it is a matter of understanding PCC as the semiotic-aesthetic effective exchange which allows the network to act cohesively to achieve a goal. We understand semiotic-aesthetics as relational configurations generating effective and affective belonging and bonding. The above could be exemplified in the following way: it is not enough that the leader generates orientation in the actions with high explanatory value, fruit of the applied tautology, but also it shall be legitimized in the subordinates affections or confidence. On this base we have developed the NEUS method which is shown next.

NEUS METHOD AS AN APPROXIMATION TO PRODUCTION COGNITIVE CAPITAL ASSESSMENT Introduction The Production Cognitive Capital evaluation process is named NEUS (Network Evaluation for Unbalanced Systems), which is aimed at reducing incoherence and incongruity through a joint participation in the decisional modeling, managing

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difficult interactions by means of reconfiguring the relationships, improving this way both coherence and congruity. NEUS is focused in explaining and evaluating network linking state on configuration exchange basis (narrative) and action schemes or interactivity (behavior) that have meaning for this network context. Production Cognitive Capital arises from relational processes sustainability between the organization structure and its decisional process, and its evaluation can be supported by two parallel processes: meaning exchange (semiotic configurations) and interactivity. Semiotic configuration exchange is the process that generates meaning equivalence from the used narrative. Narrative arises from the cognitive type that generates it, linked to semiotic recursive circuits’ presence, and the possible meaning in the exchange process, named Structural and Semiotic Equivalence respectively. On the other hand, interactivity is related to organization’s behavioral dynamics, which is understood as the approach or rejection process among stakeholders, when a decisional process occurs. Then: PCC = ƒ (NCA, NSA, NIA) Where: • • • •

PCC is the Production Cognitive Capital NCA is the Network Cognitive Affinity NSA is the Network Semiotic Affinity NIA is the Network Interactivity Affinity

Network Cognitive Affinity in the Decisional Process (NCA) In essence, human activity is based on semiotic operations, particularly language; thus the base of distinctions as cognitive operation generates connective structures in the reformulation speech with regard to a question. These structures arise from connection type and number among concepts used in an explanatory process. Semiotic

Production Cognitive Capital as a Measurement of Intellectual Capital

relationships relate to terms or words presence in any series as the paradigmatic ones are to joining terms or words without specifying a particular way. Speech paradigmatic axis translates essential, stable, accepted, and implicit relationships for a certain network. From this, an analogy is established among the axes of the speech, the distinctions and the used relationality, in the following way: • • •

Speech Syntagma (distinctions from a base question). Thinking Paradigm (connective network of distinctions). Type of used associations or terminological relationships: associative or causal.

The following are some rules or outlines that allow connecting the syntagmas: •

•

Attainment: Concepts in which the presence of one affects other, the connection is temporary. The simplest scheme is causality. Association: Concepts overlapping their meanings in the relationship.

From the above, it is established that the discursive process, from its base of distinctions, generates a configuration of concepts by means of consecutive and associative connectors. In the case of a network, for every member the type of configuration expresses the affinity degree among them when building the explanations. The specific methodology for this kind of modeling is based on the Cognitive Map concept (Ackerman et al. 1995), a system that charts the reasoning line of the observer as concepts and connections (Figure 3), where rectangles Si represent the semiotic line, connectors the paradigmatic line, arrow connectors the attainment, and simple connectors the association; P1 is the question that rules the context and S5 is the potential attractor. From this structure, it is possible to carry out different

types of analysis, for example: speech attractors, terminal elements, opening elements, and concept centrality. With this, it is possible to find out that some reasoning concepts centralize and rule the connectivity of ideas and concepts, so that they allow characterizing the cognitive speech type. The cognitive map accounts for the paradigm from where the observer builds its observation. This technique allows to structure, analyze and generate meaning for different problem types. Cognitive mapping can be developed directly in an interview, allowing the observer to build and argue, as the problem arises. The narrative structure is generated as a cognitive map, from concepts within the scope of decision-making problems inside the organization, as well as their connections. Maps are compared, trying to establish significant differences among speech structures. The criteria used to evaluate if there are differences among speeches is focused, on one hand, in the conservation of the “attractors” of the generated structures, and in the presence of semiotic circuits. An attractor is a concept that guides and centralizes the construction of explanatory ways or argumentation; it is obtained from the calculation of centrality of the elements that compose the cognitive map. On the other hand, the comparison of every context discursive structures is focused on observing the presence or absence of circuits, specifically the presence of “recursive semiotic circuits”.

Figure 3. Example of a cognitive map

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These criteria allow to explicit explanatory routes (sequences of concepts that generate meaning), from which the generative mechanism of the explanation is shown. Types of analysis used and their aim are: •

•

Centrality Analysis. Prioritizes the connective density around syntagmas and their connectivity domain. The aim is to show the presence of centrality elements ruling the reformulation ways. Circuit Analysis. Extracts circuits generated by semiotic model concepts. If there is recursion (process a procedure goes through when one of the steps of the procedure involves rerunning the procedure), the complexity of the explanation structure and its way of association with other processes are predicated. When a closed circuit is formed, it generates a complex chain of argumentation.

From this, the Cognitive Type Affinity (NCA) is a function of the narrative type generated by the connectivity (attainment and association) and the recursion degree or number of present circuits when the centralizer is compared with the rest of the network. With this, the Cognitive Type Affinity (NCA) equals to: f (CRTj versus CRTi) Where CRT is the connective-recursive type which allows getting the structural equivalence degree, when the members of the network are compared with their boss or centralizer. CRT is obtained from the predominant Connective Type (CT) and from the circuits presence or Recursion Degree (RD) by means of a matrix arrangement of both.

CONNECTIVE TYPE (CT) CT is calculated from the affinity/closeness among the connective types of the centralizer (CTj) and

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the rest of network members (CTi). This way, CTj is equivalent to the number of Dominating Connection divided by the number of Total Connections, whose values are ranked in categories of Dominating Connective type (causal or associative). Once CTj is calculated, the connective dominance is calculated for the rest of the nodes of the network (CTi). If the connective type of a node is inversely proportional to CTj dominance type then it takes the category of opposite. For any purpose, the subscript j is assigned to the Centralizer and the subscript i to the rest of the network.

RECURSION LEVEL (RL) RL is calculated from the rate of circuits over a heuristic value of 10. This way, RLj (centralizer) corresponds to the Number of circuits of the centralizer divided by 10. This operation iterates for all network members. As previously mentioned, CRT is obtained from the matrix arrangement: CRTji where j equals to (CT-RL)j and i equals to (CT-RL)i, values taken by the matrix are qualitatively determined. This way, when comparing the Centralizer j and the Collaborator i the Cognitive Type on the recursion is prioritized. In the case that CT of both is Associative; the distinction is determined by the recursion. Once CRTji is obtained, it is ranked in five categories depending on the closeness obtained after comparing CT-RL of the centralizer to each member of the network. Two individuals have a high NCA inside the network if their cognitive maps are close. This means that, facing any question, people making up the network structure the solution in a similar way; therefore, they have close structures or forms, allowing them a better possibility of coherence, scenario that propitiates Production Cognitive Capital generation.

Production Cognitive Capital as a Measurement of Intellectual Capital

NETWORK COGNITIVE AFFINITY (NCA)

The relationship established among attractors is named “relata”, forming the following typology:

The type of feature associated to the description of the Network Cognitive Affinity is based on the idea of building an “organizational mesh” from the communication process. For the same reason, an organization is compatible if the cognitive structure (way of establishing an explanation in a context) is common for its members. In other words, they obey the same paradigmatic type. See Table 1.

•

Network Semiotic Affinity in the Decisional Process (NSA) A second step in the development of NEUS is to evaluate speech closeness, according to its content; i.e. to evaluate the semiotics associated to Cognitive Map structures. An indicator of this is the speech attractor and, as defined previously, it is the one that centralizes the connections in relation to the universe of concepts composing the map. According to Bateson (1980), it is an explanatory principle. The attractor can be understood as the concept that rules the meaning of the speech. Semiotic equivalence is calculated from this base, which implies to establish the closeness among the attractor of the boss and the attractor of every member of the network. Semiotic equivalence from the attractor is calculated from certainty and similarity conditions.

Table 1. Cognitive affinity categories in the scope of the network Value

Category

0.75 < NCA ≤ 1.00

Cognitively Homogeneous

0.50 < NCA ≤ 0.75

Cognitively Allied

0.25 < NCA ≤ 0.50

Cognitively Loose

0.00 < NCA ≤ 0.25

Cognitively Heterogeneous

• • •

Hyperrelata: Context shared by all, is equivalent to the base question. Hyporelata: Vertical concepts, different natures, there is no relationship. Holorelata: Member-class concepts, coincidence of constituent parts, equal idea. Merorelata: Member-class concepts, horizontal, establish inclusion.

When comparing the meanings of the attractors, there are two big categories arising to which these can ascribe, concepts whose relationship with the centralizer’s attractor are of different nature, for example: “Corporate image” versus “Create internal learning cycles”, where the first one comes from a strategic scope and the second one from an operational scope, i.e. in spite of being under the same hyperrelata or context, the explanatory principles that support the centralizer speeches versus its collaborator are in different hierarchical levels, which qualifies as hyporelata. Another big category refers to holo-merorelata types, which explains the equivalence degree in terms of meaning. Of both, the holorelata is where the biggest resemblance is established. As an example, the attractors “Corporate Image” and “Institutional Prestige” correspond to the same relata. Likewise, the category of merorelata is established when there is a smaller equivalence degree between two concepts from an inclusive relationship (one is part of the other). Example, in the scope of planning “to define roles” is part of “Corporate image”. On the other hand, the concept of certainty is related to the possibilities of interpretation associated to the attractor, in a given context. This means that an attractor allowing a wide scale of meanings is classified as of low certainty, this impacts negatively on the execution of the decision-making process. For example, “Corporate Image” gener-

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Table 2. Network types after the loyalty degree of decisional speech reproduction Type

Status

Definition

Tuned up

0.75 < NSA ≤ 1.00

Decisional process is completely reproduced by the network

Convergent

0.50 < NSA ≤ 0.75

Decisional process is partially reproduced by the network

Divergent

0.25 < NSA ≤ 0.50

Decisional process is inadequately reproduced by the network

Discordant

0.00 < NSA ≤ 0.25

Decisional process is not reproduced by the network

ates a wide scale of meanings, which diversifies and allows high degree of freedom in how it must be understood inside the network. In the Cognitive Map scope, certainty is conveyed in the attractor’s environment structure. This way, there are concentrating (incoming connections) and dissipating (outgoing connections) attractors. Because these connections are causal and associative, they can be classified according to their dominance, forming three categories: Incoming Causal, Outgoing Causal and Stationary (equal number of inputs and outputs or associative dominance). Relationship coherence is analyzed between the certainty level of the attractor and its structure. This way, an outgoing causal low certainty attractor is highly coherent, but not when it is incoming causal. This is established from that a wide meaning concept (low certainty) needs to be explained by a high number of concepts (dissipates) to be able to give content. Then, based on similarity and certainty, a matrix array in the form SCji where j corresponds to relata-entornoj and i to relata-entornoi is developed, values taken by the matrix are qualitatively determined. This way, when comparing the Centralizer j and the Collaborator i, the type of similarity (Relata) is prioritized over certainty. In the specific case of Hiporelatas, it is not possible to compare them, since by definition there is no relationship. When comparing Merorelatas, values are determined by the certainty generated by the attraction and dissipation structure. Once SCji is obtained, the viability of certainty and similarity types generated by the crossover between the centralizer and each member of the

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network compared is analyzed. Table 2 shows the classification. From the Classification, it is possible to explain the differences, in the scope of action, between a control structure that designs an action scheme and the design implementation responsible team.

Network Interactivity Affinity (NIA) Production Cognitive capital is constituted from success in narrative reproduction of company’s management associated to the executing relational structure (command). Relationships are not measurable, since they belong to the information scope (Bateson 1973, 1980; Von Foerster, 1974). A methodological possibility is to deduce them from value judgments made by persons about their own colleagues in an organization. These judgments allow establishing action schemes which are translated to attraction or repulsion processes inside the network. Action schemes determining cohesion or disintegration are called network interactivity. Establishing the organizational network configuration, from interactivity, is aimed to deduce relationship types allowing the organization to be carried out as a process. This network is constructed according to the affective-relative position of every actor inside the organization. Its construction is performed from what every member connotes in relation to other participants, from the company’s daily activities. The interactivity type of the stakeholder towards the question: how do you evaluate actor k

Production Cognitive Capital as a Measurement of Intellectual Capital

competence against actor i to carry out a decision making process? This interactivity process can change in time, generating a recurrent pattern, which is analyzed, evaluating if it is stable and sustainable as structure base. The stability, as interactivity type, is initially evaluated locally, this is from every actor towards the network and, later, local values are integrated into a global indicator. NIA calculation is developed from the answers of network members to interviews. As an example, in the calculation of NIA between A and B, there are 3 “participants”: A, B and R, being R the remaining members (neither A nor B) of the network. Every participant expresses simple judgments (declaration), which are grouped according to: 1. 2. 3. 4. 5.

A declares about (A versus B) B declares about (A versus B) B declares about (A versus R) A declares about (B versus R) R declares about (A versus B) From the above, 3 values are calculated:

D1) Relative difference between 1. and 2. D2) Relative difference between 3. and 4. D3) Average of the relative differences between (5. and 1.) and (5. and 2.) Every difference between A and B is calcu(A + B ) lated as: A − B × 2 Values are weighted (by pi) and added, and the result is multiplied by a heuristic correction factor (k), being p1 greater than p2 and p3. In short, the Network Interactivity Affinity index between A and B (NIAAB) is obtained from: k å pi Di The resulting values are compound judgments of simple judgment comparison.

NIA values range between 0 (high level of repulsion) and 1 (high level of attraction), which is classified according to Table 3. When having a network classified as reciprocal it is said that the dominating relationships regulate the differences among persons in such a way that, in case of divergence, these are lowered through coexistence quality. In case of a dealer network, the dominating relationships force to look for agreements to normalize coexistence. Finally, complementary and symmetrical relationships generate division and rupture; the complementary because of subjection to hierarchy and the symmetrical because of direct amplification of the discrepancy.

Production Cognitive Capital (PCC) Calculation Finally, when relating the three indicators previously described, a quantification of PCC is obtained. It is important to emphasize PCC value in leading the network coherence state, this is essential as soon as it moves away from the idea of “reification” or objectualization of this intangible. PCC analysis leads to a triadic interpretation of Cognitive Affinity (NCA), Semiotic Affinity (NSA) and Interactivity Affinity (NIA). It is necessary to emphasize that this process is complex, and reductionism shall be avoided in the interpretation. Quantitatively, PCC =

3

NCAxNSAxNIA

PCC takes values between 0 and 1 which are classified in Table 4.

Table 3. Relationality of NIA values Type

Status

Reciprocal

0.75 < NIA ≤ 1.00

Dealer

0.50 < NIA ≤ 0.75

Complementary

0.25 < NIA ≤ 0.50

Symmetrical

0.00 < NIA ≤ 0.25

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Table 4. Organization’s coherence classification Type

Range

Definition

Cohesive

0.8 < PCC ≤ 1.0

High coherence

United

0.5 < PCC ≤ 0.8

Medium coherence

Untied

0.2 < PCC ≤ 0.5

Low coherence

Disperse

0.0 < PCC ≤ 0.2

Very low coherence

This way, a decisional network which has compatible cognitive types of sintonic speech reproduction and a reciprocal NIA, classifies as cohesive. As said above, the way from prescriptive to postcriptive logic in relation to what is understood as knowledge production, implies locating the creation of value (as PCC) in the decisional process coherence, which can be configured as: distinction-explanation-decision-action between objectives and goals, between actions and programs, i.e. to look for the alignment according to the narrative and action axes. Incoherencies produced are fundamentally due to insufficiency of communication support to control the difference between both axes, so difference amplification is generated, by cognitive type incompatibility or low certainty speech generation in decision making, or because in the daily affective ambience a symmetrical relationship freezes any possibility of network cohesion. Network state is dynamic. Although cognitive type is the variable showing the least possibility of change, this does not define network state by itself. This way the coherence can be improved managing speech decisional certainty and re-configuring interactivity, by generating participation in decisional modeling and modifying repulsive type interactions to non conflicting areas. Coherence management evaluates decisionaction configurations the network can take, as bending and stressing which occurs from the triadic cognition-semiotics-interactivity. These configurations are organization’s PCC because they are legible not only to the own network but

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also to the external ones, with which they have decided to establish or to cut off relationships. NEUS goal is the evaluation analysis and management of productive processes decisional coherence, generating a communicationally sustainable connective network, proposing configurations to manage the difference between the narratives to do and the doing of an organization.

Inventing an Organization as Relational States Structure In the current context, the value generation process roots in understanding the strategic role of intangibles, especially when speaking about knowledge. Obligatorily, this statement involves a paradigmatic change in the vision of business and the role of R&I (Research and Innovation) what would directly affect the development of innovation strategies. Taking the above as a basis, design efforts associated with R&I must be driven from the relationship between innovation strategies and the knowledge form associated with its development, since value generation would be explained in better degree in the new economy scope. One feature constituting this relationship is expressed in the coherence degree, which is the closeness between the narrative of decisionmaking and the actions actually made. Therefore, a small gap leads to a high organizational coherence degree. Under this scheme, management is stated so that its results change, from a certainty vision to a confidence one. This separation from certainty responds to the fact that organizations must be understood as communicating networks,

Production Cognitive Capital as a Measurement of Intellectual Capital

where transactions are organized and directed from culture–language relationship, so any operation–action is always an interpretation. This uncertainty condition in the interpretation allows to venture, then, that the center of attention is not goal fulfillment, but coherence. By the same, it is through coherence that value generation might be explained, in a better degree, in the economy of knowledge scope. Managing coherence implies designing a strategy to reduce the gap between the narrative and the actions derived from the decision making so that, a lower gap drives to organization higher coherence degree. Value will arise in every step of the production process assembly relationships as it controls the difference between the saying and doing scopes. But, where is this difference located?, what determines the difference between the narrative and the action scopes?. A possible answer is to explain it by means of two concurrent processes: • •

Meanings exchange (effectiveness in command reproduction), and Network interactivity (behavioral process of rapprochement or rejection among actors, when carrying out a decisional process).

In other words, the network has a way of thinking and doing, fruit of its history of decisions, which is conservative through shielding or closure operations facing external agents. This means that a person joining a network to work for the first time will not understand the network working codes, although the words are the same he/she handles. Simultaneously, the persons who make up the network do not necessarily understand what the boss pretends in decision making, what will generate uncertainty and actions will be far from the wished. These processes generate differences between saying and doing and are responsible of

effectiveness and efficiency loss facing strategic operations. An organization can be defined as a “constituted relational structure, from its culture, from narrative and behavior configurations for decision making in contexts of certain meanings”; then, the coherence concept binds closely to code and meaning notions as base operation. This leads us to reconsidering management, going from a certainty belief to a confidence sensation inside uncertainty or complexity. From the above, if we consider organizations as complex systems (since their operations are fundamentally processes organized in the language, which introduces the uncertainty condition), it does not turn out to be strange to observe, in practice, the low correspondence between strategic programs and their fulfillment actions. Analogous to Network concept, we have defined Rel or Relational system, which allows locating organizational problems in the relations that emerge on its daily dynamics; the above implies that relational methods evaluate persons as entities in regard to others. If we take that into account, the low correspondence would be explained as a specific state of the relational structure associated to decision making. Both narrative and interactivity are expressed in the relational structure quality which is defined from its co-organization, cohesion, conduction and coordination, named Co4. The Strategic Alignment process to improve business coherence and congruity has been named Co4 System Configuration (the whole –inside and outside– relational system). One of the strategic results obtained from interactivity (NIA) and semiosis is the connective structure of the network. From that it is possible to derive the key players (Everett, M. G., and Borgatti, S. P. 1999). The key player problem is compounded of two related but different questions about a social network.

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Type 1, KPP-1, or KPP-Neg. ◦ It is the minimum set of k nodes which, if deleted, generate maximum perturbation or disconnection (augments the number of components or the mean distance) in the network, resulting in a residual network with minimum cohesion. They connect in high degree, allowing establishing “bridges” among all the actors; without their presence, the network fragments. ◦ Quantifies network fragmentation after deleting nodes in non-directed and non-weighed networks. ◦ To solve the problem, Fragmentation (F) and Distance (FD) are measured in the network (graph).

•

Fragmentation: F = 1 −

Distance: F D = 1 −

∑ s (s i

i

i

− 1)

N (N − 1) 1 2∑ d i> j ij

N (N − 1)

Type 2, KPP-2, or KPP-Pos. ◦ It is the minimum set of k nodes, which is maximally connected to the rest of the network. Is used to assess the “transmission” or “dinamization” of ideas. ◦ One approach is the distance-weighed Reach (RD), considering differences among individual routes. ◦ To solve the problem, the amount of connections among a set and the rest of the network (graph) is directly measured (cohesion among sets).

•

1

Reach: R D =

128

∑d j

N

Mj

The state of the whole organizational Rel settles from four concomitant processes: • •

•

•

Co-organization. Code production to maintain the organization. Cohesion. Robustness of the resulting structure from the reciprocal relations determined by interactivity and Semiosis. This way, as more reciprocal connections, more network cohesion. Conduction. Is the ruling form associated to command, which goes from highly centralized systems (hierarchies) to decentralized systems (heterarchies). Coordination. Propagation quality (reach) among members of the network facing an event.

Which we have named Co4 relational structure, as shown in Figure 4. In turn, Co4 is defined according to three generative conditions: • • •

Cognitive type or Knowledge (C), Semiotic process or narrative (S), and Interactivity or confidence (I).

Figure 4. Co4 relational structure

Production Cognitive Capital as a Measurement of Intellectual Capital

The qualitative expression of the four processes would be: • • • •

Co-organization = ƒ (C, S) Cohesion = ƒ (S, I) Conduction = ƒ (I, S) Coordination = ƒ (S, C)

Note: C, S and I factors appear in different order, reflecting this way the difference in their relative importance (weighing) for every case. Based on the above, if in an organization, considering its form of knowledge, they are not verbalizing the business key concepts and, at the same time, suspicion exists among actors; the possible result is a low sustainability between strategic programs and their fulfillment actions. The expression of that, in Co4 jargon, will be: low cohesion, low coordination, high centralization (in conduction), and low co-organization. Once obtained the PCC (Cohesive, United, Untied, and Disperse), types are directly related to Co4 structure, which takes a value ranging between Highly Hierarchized (low cohesion, high centralization, low coordination, and low co-organization) to Highly Heterarchized (high cohesion, low centralization, high coordination, and high co-organization).

Netout Process The art or process of Co4 improvement or reconfiguration, aiming to diminish the gap between the narrative of the decision making and their actual actions (coherence), consists of reproducing the conditions under which Co4 is generated, making business generative networks to emerge and reconfiguring those which do not contribute. This process has been defined as Netout. Depending on its location, it implies managing coherence (inside the network) and congruity (relating to other networks). The process consists of finding the network which generates business knowledge, consolidating it through the generation of semiot-

ics or specific language, coordinated with action and change lines in repulsion interactivity types or dissociative behaviors. As a result from the above, the strategic alignment degree emerges from the language generation process and its harmonic spreading inside the network. Coherence management through Netout can be done designing and implementing devices controlling the proper narrative field of the guidelines and the environment of the supporting relationships, strategically aligning to the managing decision making network. This alignment is translated to cohesion improvement, conduction decentralization, augmenting the relational system coordination and co-organization through integrated and configured communication channels, so that they are sustainable and, by means of which, the strategic lineaments are reinforced in the organization’s day by day. There is a set of tools which allow assessing the decisional process quality from the way of thinking (cognitive), the guidelines understanding degree (semiotic quality) and the environment or climate where the process develops (interactivity). This evaluation is established, on one hand, from the command strategic distinctions regarding those of their collaborators (cognitive maps, decision making programmable models) and on the other hand, of the interactivity state inside the team. Co4 System Configuration allows, from results obtained in the diagnosis to establish a strategy for improving management sustainability. This process aims to elaborate narrative, by building a Strategic Scenario (S2). S2 is built by configuring four general criteria: Political, Economic, Social and Technical and twelve sub-criteria resulting from the combination of these. Every criterion and sub criterion generates a meaning context which allows comparing a set of business solution alternatives. The building process comprehends from cohesion up to congruity, organizing the team constituting a high link quality unit, both in narrative and interactivity.

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FUTURE RESEARCH DIRECTIONS In accordance with the results achieved when applying NETOUT in different organizations, future research lines are in two scopes: •

•

The development of models related to relational stability of Co4 configurations obtained from semiotic and interactivity variables. It is trying to use the molecular stability concept from quantum chemistry in connectivity configurations of way of obtaining viable stages that increase the Production Cognitive Capital. Associated to the intervention methodology of the relational systems. In this one, the research goal is developing techniques and methods which allow improving the Co4 state as regards the semiotic and interactivity structures obtained from the simulations which produce the wished change states.

The methods used at present by our team have resulted in high effectiveness, nevertheless, although we have achieved changes of state in the organizations, they respond more to a casual drift than to a strategy aimed to one or a set of states.

CONCLUSION Today, there must be a paradigm shift, from object to relation. Relations constitute complexity, and result from Rel’s culture. This way, uncertainty as a condition is added. The strategy to aboard this kind of system is understanding how territory narratives are produced and interchanged to generate meaning and action equivalence among relational systems. The above invites us to correct certainty-based criteria, specifically in what is named value creation through knowledge production. As stated early in the development of this chapter, knowledge is configurative, which

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translates into organizational structures highly dependent of their interactivity and semiosis; by the same, intellectual capital production is partially located at those organizational structures which are highly coherent; i.e. decision making, as an action, is very close to the proposed narrative corresponding to that action; strictly speaking, is where decision making supporting knowledge process is produced. From a relational epistemology view, we can deduce that intellectual capital cannot be conceived as an object, but as a process, the Production Cognitive Capital. Organization’s Production Cognitive Capital shall be understood as the knowledge which generates the value of use and the value of exchange. •

•

The value of use is a function of organizational coherence; hence its generation depends directly on the cognitive type, semiotic quality, and trust. The value of Exchange is a function of congruity, which implies that the three factors (Cognitive type, Semiotic quality and Trust) go legitimated in an exchange relationship between two Rels.

Productive Cognitive Capital is the one that generates sustainability as a value generation process, so that its measurement shows the Rel’s effectiveness in producing the organization’s Intellectual Capital. Finally, wealth generation in the new economy will depend on relationships quality which should structurally overcome hierarchies, moving to heterarchies.

REFERENCES Ackerman, F., Eden, C., & Cropper, S. (1995). Getting Started with Cognitive Mapping. Glasglow, UK: University of Strathclyde.

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Andriessen, D. (2001). Weightless wealth: four modifications to standard Intellectual Capital theory. Journal of Intellectual Capital, 2(3), 204–214. doi:10.1108/14691930110399941

Von Foerster, H. (1974). Notes pour un Épistémologie des objets vivants. In Morin, E., & Piatelli-Palmerini, M. (Eds.), L’unité de L’homme (pp. 401–417). Paris: Editions du Seuil.

Bateson, G. (1973). Steps to an Ecology of Mind. Boulder, Co: Paladin Books.

Von Foerster, H. (1974). Cybernetics of Cybernetics. Biological Computer Laboratory, Department of Electricity. University of Illinois.

Bateson, G. (1980). Mind and Nature - A Necessary Unity. New York: Bantam Books. Everett, M. G., & Borgatti, S. P. (1999). The centrality of groups and classes. The Journal of Mathematical Sociology, 23(3), 181–201. doi:1 0.1080/0022250X.1999.9990219 Lavanderos, L., & Malpartida, A. (2001). Cognición y Territorio. Santiago: Editorial Universitaria UTEM. Lavanderos, L., & Malpartida, A. (2005). Teoría relacional de la comunicación como proceso eco semio autopoiético. Complexus, 1(2), 45–86. http://www.sintesys.cl/complexus/revista2/articulos2/complexus2.pdf. Malpartida, A., & Lavanderos, L. (2000). Ecotomo: A nature or society-nature relationship? (Vol. 48). Actha Biotheoretica. Petty, R., & Guthrie, J. (2000). Intellectual capital literature review. Measurement, reporting and management. Journal of Intellectual Capital, 1(2), 155–176. doi:10.1108/14691930010348731 Stewart, T. (1998). Intellectual Capital: The New Wealth of Organizations. New York: Broadway Business. Sun, R., & Alexandre, F. (Eds.). (1997). Connectionist symbolic integration. London: LEA. Varela, F. (1998). Conocer. Spain: Editorial Gedisa. Varela, F., Thompson, E., & Rosch, E. (1992). The Embodied Mind: Cognitive Science and Human Experience. Boston: MIT Press.

ADDITIONAL READING Alle V. (1999). New tools for new Economy. Perspectives on Business and Global Change. 13(4). World Business Academy. Alle, V. (2000). The value evolution, Addressing larger implications of an intellectual capital and intangibles perspective. Journal of Intellectual Capital, 1(1), 17–32. doi:10.1108/14691930010371627 Andriessen, D. (2003). Making sense of Intellectual Capital. Butterworth Heinemann. Arenas, T., & Lavanderos, L. (2008). Intellectual Capital: object or process? Journal of Intellectual Capital, I(1). Bontis, N. (1998). Intellectual Capital: an Exploratory Study That Developments measures and Models. Management Decision, 36(2), 63–67. doi:10.1108/00251749810204142 Borgatti, S. (2002). The Key Player Problem. Available at SSRN: http://ssrn.com/abstract=1149843. Brooking, A. (1996). Intellectual Capital: Core asset for the third millennium. International Thomson Business Press. Club Intelect. (1998). Medición del Capital Intelectual, Modelo Intelect. Madrid: Instituto Universitario Euroforum Escorial. Edvinsson, L., & Malone, M. S. (1999). El Capital Intelectual: Cómo identificar y calcular el valor de los recursos intangibles de su empresa. Barcelona. Gestion, 2000.

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Kaufmann, L., & Schneider, Y. (2004). Intangibles, a synthesis of current research. Journal of Intellectual Capital, 5(3), 366–388. doi:10.1108/14691930410550354 Lahitte, H. B. (1981). Representación y registro en antropología. Cuadernos LARDA, III, 8. Lavanderos, L. (2002). Culture-Nature Systems organization, Unpublished Doctoral Thesis, Science Faculty, University of Chile, Santiago, Chile. Lev, B. (2001). Intangibles: Management, Measurement and Reporting. Washington, DC: Brooking Institution Press. Mantilla, B. (1999). Intellectual Capital and Knowledge Accounting. Bogotá: Ecoe Ediciones. Marr, B. (2005). Perspectives on Intellectual Capital: Multidisciplinary Insights into Management, Measurement, and Reporting. Elsevier Butterworth-Heinemann. Mokyr, J. (2002). The Gifts of Athena: Historical Origins of the Knowledge Economy. Princeton, Oxford: Princeton University Press. Roos, G., & Roos, J. (1997). Measuring your company’s intellectual performance. Long Range Planning, 30(3), 413–426. doi:10.1016/S00246301(97)90260-0 Roos, J., Roos, G., Edvinsson, L., & Dragonetti, N. C. (2001). Capital Intelectual: El valor intangible de la empresa. Barcelona: Paidós. Sullivan, P. H. (2000). Value-Driven Intellectual Capital. How to convert intangible corporate assets into market value. New York: John Wiley and Sons, Inc. Sveiby, K. (1997). The New Organizational Wealth: Managing and Measuring KnowledgeBased Assets. New York: Berrett-Koehler Publishers.

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KEY TERMS AND DEFINITIONS Cognitive Sciences: Theories and scientific analyses of knowledge in all its dimensions. Coherence: Closeness between narrative and its corresponding actions. Congruity: Emergent feature of the relationship among the command team and other networks inside and outside the organization. Intangibles: Use value configurations which, in the exchange process (exchange value), are transformed into assets. Key Players: Given a social network, there are members who play different roles, one kind, if removed, would maximally disrupt communication among the remaining members, and the other, who are maximally connected to all other members. Knowledge: Territoriality configurations, i.e., generation of bonding and belonging codes, by way of configurations of the networks within a process, which the network designates as value. Production Cognitive Capital: Knowledge generating use and exchange value in a productive context. Relational Approach: Epistemology which supports the knowledge process in any relationship, which configures as culture-determined effective and affective distinctions. Sustainability: Organization’s conservative strategy, as a relational system, from structural or configurational changes in the relationships, determined from the culture. Uncertainty: Time-space location impossibility of extracting the difference between two objects.

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Chapter 7

Making Sense of Knowledge Productivity Christiaan D. Stam INHolland University of Applied Sciences, The Netherlands

ABSTRACT In the knowledge economy knowledge productivity is the main source of competitive advantage and thus the biggest management challenge. Based on a review of the concept from two distinct perspectives, knowledge productivity is defined as the process of knowledge-creation that leads to incremental and radical innovation. The two main elements in this definition are ‘the process of knowledge creation’ and ‘incremental and radical innovation’. The main aim of this chapter is to contribute to a better understanding of the concept of knowledge productivity in order to support management in designing policies for knowledge productivity enhancement. After elaborating on the concept of knowledge productivity, the two main elements are combined in a conceptual framework: the knowledge productivity flywheel. This framework appeared to be an effective model for supporting initiatives that aim for enhancing knowledge productivity.

INTRODUCTION Our economy has changed from an industrial into a knowledge economy (Drucker, 1993; Toffler, 1981), in which the competitive advantage of organizations is based on the ability to exploit knowledge resources. The increased importance of knowledge as an economic resource has been reviewed from many perspectives, resulting DOI: 10.4018/978-1-60960-054-9.ch007

in slightly different denotations, each usually emphasizing a different but related aspect of the same phenomenon. Some examples of this are the “knowledge society” (Toffler, 1981), “knowhow society” (Sveiby & Lloyd, 1988), “information society” (Giddens, 1994), “information economy” (Shapiro & Varian, 2003), “learning society”, “learning economy” (Harrison & Kessels, 2004), “network society” (Castells, 1996), “intangible economy” (Andriessen, 2004) and the “creative economy” (Florida, 2002). Within the different

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Making Sense of Knowledge Productivity

denotations of the new reality, we see that authors are either referring to society as a whole, or to the economy. However, as Jacobs (1999) argues, the term knowledge society is a tautology–a needless repetition–as society and mankind have always been dependent on the interpretation of knowledge. Yet, the knowledge economy, in which knowledge has become the main factor of competitive advantage, is a new phenomenon. The transition to the knowledge economy is about the increase in scale of knowledge as a production factor. Knowledge is not a new production factor, but the relative importance of knowledge, related to land, labour and capital, has substantially increased during the past few decades (Castells, 1996; Weggeman, 2000). In line with this reasoning, Stewart reminds us that, “not for nothing are we homo sapiens, thinking man” (Stewart, 1997, p.5, italics in original). Inspired by Stewart (2002) and Drucker (1999), the essence of the knowledge economy can be summarized in three characteristics. First, in the knowledge economy, knowledge is what we buy, sell, and do. Second, intellectual capital (IC) is the new wealth. Third, knowledge productivity (KP) is the biggest challenge (Stam, 2007). Whereas the first and second characteristic of the knowledge economy are extensively elaborated on, the third remains relatively unexplored. If we accept as true that knowledge productivity is the main source of competitive advantage and the biggest management challenge in the knowledge economy (Drucker, 1999), this might threaten organizational effectiveness. Therefore, the aim of this chapter is to contribute to a better understanding of the concept of knowledge productivity in order to increase management effectiveness to develop policies that aim at enhancing knowledge productivity. This chapter is based on the findings in a case-based research related to knowledge productivity (Stam, 2007).

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KNOWLEDGE PRODUCTIVITY IS… Although KP is a relatively new concept, the combination of the concepts of knowledge and productivity is not new. The awareness that knowledge and productivity are closely related already goes back for many decades. Some would argue that it goes back for several centuries (Warsh, 2006). In a sense, the importance of knowledge as an economic factor has always been the core of the economic sciences. The famous story about the pin factory in The Wealth of Nations (Smith, 2000, original publication 1776), already stressed the importance of knowledge accumulation (through specialization). However, as mathematics started to dominate the economic sciences, and as knowledge was hard to quantify, knowledge was long considered to be a side-effect, a spill-over, or residual. The acknowledgement of knowledge as an important wealth creating factor, has been an “underground river” that came to the surface every now and then, but only recently started to get accepted in mainstream economics and management sciences (Warsh, 2006). In a sense, it was in The production and distribution of knowledge in the United States that Machlup (1972, original publication in 1962) rediscovered the importance of knowledge as a product. In his recalculation of the national product of the United States, Machlup discovered that “total knowledge production in 1958 already accounted for almost 29 per cent of adjusted GNP” (p.362). In addition, the “knowledge-industry” was not only the largest industry, but also grew faster than the traditional industries. These conclusions led to the observation that there should be some relationship between knowledge, value creation and economic growth. It was Drucker (1981; 1993) who realized that the increased importance of knowledge as a source of production, had to be followed by a revision of the concept of productivity. As he realized that not only the main source of production (knowledge), but also the tools of production (brains) are owned

Making Sense of Knowledge Productivity

Figure 1.

by the employees, he concluded that the biggest challenge in the knowledge economy was the productivity of the knowledge worker. Therefore, he proclaimed knowledge-worker productivity to be the biggest of the 21st-century management challenges. The most important, and indeed the truly unique, contribution of management in the 20th century was the fifty-fold increase in the productivity of the manual worker in manufacturing. The most important contribution management needs to make in the 21st century is similarly to increase the productivity of knowledge work and knowledge workers. The most valuable assets of a 20th-century company was its production equipment. The most valuable asset of a 21st-century institution (whether business or non-business) will be its knowledge workers and their productivity. (Drucker, 1999, p.79) In The post-capitalist society, Drucker (1993) stressed the importance of the development of a new economic theory that puts knowledge in the centre of the wealth creating process. In Knowledge worker productivity: The biggest challenge (Drucker, 1999) he elaborates on this new economic theory and describes a set of management guidelines for knowledge-worker productivity. According to Drucker, knowledge-worker productivity is primarily a management responsibility and it is “the biggest of the 21st-century management challenges” (Drucker, 1999, p.92). The two interpretations of the concepts of knowledge related to productivity by Machlup

and Drucker illustrate the two main approaches of the concept of knowledge productivity. Whereas Machlup, based on economic theories, interpreted knowledge productivity as a result, Drucker, based on management theories, interpreted knowledge productivity as an organizational ability. Whereas Machlup predominantly aims at explaining, Drucker predominantly aims at improving the knowledge-based production process. Similarly, in recent literature we see two different interpretations of the concept of knowledge productivity (Figure 1), of which one uses knowledge as a starting point, whereas the other uses productivity as a starting point (Stam, Evers, Leenheers, De Man, & Van der Spek, 2004). Although distinct approaches, they are related in the sense that they both search for more appropriate instruments to reveal and improve knowledge-related performance. The main concern of the first approach is to identify the sources, or conditions for knowledge productivity. The hypothesis is that improvement of the conditions will obviously lead to better performance. The concepts of knowledge management (Nonaka & Takeuchi, 1995; Weggeman, 1997), the Corporate Curriculum (Kessels, 1996; Van Lakerveld, 2005), and knowledge productivity (Kessels, 2001), are examples of this first approach in the sense that they present theories and methods to improve the conditions for knowledge creation. Core to the second approach is the quest for indicators that can measure and value the output of knowledge-based work. The hypothesis is that these measures will lead the way towards improving conditions. Some examples of this approach

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are intellectual capital measurement (Andriessen, 2004; Edvinsson & Malone, 1997; Stewart, 1997; Sveiby, 1997) and the productivity of knowledge as interpreted by Zegveld et al. (2000; 2002; 2007; 2004) in the sense that they provide methods to calculate knowledge-based performance. Considering the nature of these two approaches to the concept of knowledge productivity, the first approach, aiming at improving the process of knowledge creation, is labeled knowledge management. The second approach, aiming at measuring the effects of the knowledge creation process, is labeled intellectual capital measurement. Both knowledge management and intellectual capital measurement aim at improving knowledge-based performance or knowledge productivity. Furthermore, both knowledge management and intellectual capital measurement can enhance each other in the sense that increased awareness about the knowledge-based performance will improve the ability to develop policies for improvement (Bontis, 2002; Marr, Gupta, Pike, & Roos, 2003; Mouritsen, Bukh, Larsen, & Johansen, 2002; Roos, Roos, Dragonetti, & Edvinsson, 1997; Stam et al., 2004; Wiig, 1997). Based on a review from both the perspective of knowledge management and the perspective of intellectual capital measurement, knowledge productivity is defined as the process of knowledgecreation that leads to incremental and radical innovation (Stam, 2007). The two main elements in this definition are ‘the process of knowledge creation’ and ‘incremental and radical innovation’. These two elements are inherently bound together, because innovation (new knowledge) is the unavoidable result of the process of knowledge creation (Nonaka & Takeuchi, 1995). In both knowledge management and intellectual capital literature the concepts of “learning” and “knowledge creation” are frequently used interchangeably. Although both concepts refer to the process in which knowledge is developed, the concept of knowledge creation was chosen because it gives better expression to the combination of

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organizational knowledge processes as defined in the KM literature. Similarly, the concepts of “innovation” and “knowledge” are frequently used interchangeably. Although both concepts refer to the result of the knowledge creation process, the concept of innovation was chosen, because it better expresses the results of the knowledge creation process at an organizational level. In this sense we can make a distinction between knowledge as a personal ability to perform a task, and innovation as an organizational ability to create value. The following sections further elaborate on the two main elements of the above definition of knowledge productivity.

…THE PROCESS OF KNOWLEDGE CREATION… Closely related to Drucker (1993), Kessels (1996; 2001) introduced the concept knowledge productivity. “Knowledge productivity concerns the way in which individuals, teams and units across an organization achieve knowledge-based improvements and innovations” (Harrison & Kessels, 2004, p.145). Whereas Drucker (1999) interpreted knowledge worker productivity as a management challenge, Kessels puts the individual in the centre of his theory. The assumptions of Kessels’ work are that: The character of labour is changing: routine work is more and more taken over by machines and computers. The work that remains requires independent decision-making and creative thinking; the physical activities of employees are being replaced by mental and social activities. In the economic context the value added to products and services is mainly due to the capability of applying knowledge. Constant incremental improvement and radical innovation are becoming critical in the endeavour of staying ahead or keeping up with competitors.

Making Sense of Knowledge Productivity

As this change of the character of labour takes place, it is inevitable that the workplace turns into a learning environment. New work is to be described in terms of learning and work processes take the characteristics of learning processes. This implies not only to managerial work, but also to almost all work of every individual in the company. This transition is not only dependent on theoretical knowledge and formal schooling, but it is also based on adequate day-to-day learning in the social work environment. The conditions for good work become similar to the conditions for good learning. (Kessels & Van der Werff, 2002, p.20) As a consequence knowledge productivity requires a good learning environment. In order to help organizations improve their knowledge productivity, Kessels introduced the Corporate Curriculum: “the plan for learning to increase knowledge productivity, leading to constant improvement and radical innovation, and ultimately to economic advantage” (Kessels & Van der Werff, 2002, p.23). The Corporate Curriculum consists of all the intended and unintended conditions that affect the learning processes among workers in organizations (Van Lakerveld, Van den Berg, de Brabander, & Kessels, 2000) and identifies seven critical learning functions (Kessels, 1996). These learning functions are critical in the sense that their quality determines the effectiveness of the process of knowledge creation.

Learning Function 1: Subject Matter Expertise The first learning function has been defined as acquiring subject matter expertise and professional knowledge directly related to the organization’s business and core competencies (Kessels, 1996; Keursten, Verdonschot, Kessels, & Kwakman, 2006). In a sense, the first learning function covers the main part of the concept of knowledge management (Davenport & Prusak, 1998; Nonaka &

Takeuchi, 1995; Stam, 2004; Weggeman, 1997). Subject matter expertise stresses the importance of “strategic grounding” (Stam, 2004) as it is about knowledge which is directly related to the main work processes and work-related objectives (Keursten, 2001; Keursten et al., 2006; Van Lakerveld, 2005). Furthermore, subject matter expertise is both about tacit and explicit knowledge (Kessels, 2002; Polanyi, 1974), and it is about the way knowledge is developed, shared and codified throughout the organization (Kessels & Keursten, 2001; Keursten, 2001). Subject matter expertise refers to the strategic grounding and processing of knowledge and therefore asks for knowledge-based strategies and the support of the knowledge processes.

Learning Function 2: Solving Problems The second learning function has been defined as learning to identify and deal with new problems using the acquired subject matter expertise (Kessels, 1996; Keursten et al., 2006). From a knowledge management perspective, solving problems refers to the process of applying (Davenport & Prusak, 1998; Weggeman, 1997), combining (Nonaka & Takeuchi, 1995; Van der Spek & Spijkervet, 1994) or exploiting (Sprenger, van Eijsden, ten Have, & Ossel, 1995) knowledge. Within these processes, which are at the “end” of the knowledge value chain, knowledge is put into use, or in other words “made productive”. In this respect, all other knowledge processes support this second learning function. The distinguishing characteristic of this learning function is that it stresses the gap between existing subject-matter expertise (as a result of the first learning function) and the knowledge that is needed in order to find solutions for new challenges. Solving problems is the competency with which this gap can be closed. However, the gap will never be closed entirely. New situations always require new interpretations of existing

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knowledge, therefore the need for the ability to solve problems will remain. Solving problems is a personal capacity and cannot be separated from its context (Sveiby, 1997). If the context is complex and dynamic, the professional’s work should be based on a body of knowledge which has to be interpreted and applied depending on the specific circumstances (Weggeman, 1997). Solving problems refers to the ability to renew and stretch expertise and therefore asks for creativity and room for experimenting with new ways of working.

Learning Function 3: Reflective Skills and Meta-Cognitions The third learning function has been defined as cultivating reflective skills and meta-cognitions to find ways to locate, acquire and apply new knowledge (Kessels, 1996; Keursten et al., 2006). The main message of this learning function is that we should not only learn how to develop, share and apply knowledge (first two learning functions), but also reflect on the effectiveness of these processes (Kessels & Keursten, 2001). Meta-learning reflects an organisation’s attempts to learn about (and improve) its ability to learn (Argyris & Schön, 1978). The main questions related to this learning function are: Why are we good in solving problem A, and why is it that we do not know how to handle problem B? What can we learn from our experiences and can we do it better? Reflective skills are necessary in order to learn from past processes (Van Lakerveld, 2005). This learning function enables organizations, teams and individuals to manage their own learning processes. “How can we improve our ability to develop, share and utilise knowledge in the workplace, and help others to do so” (Harrison & Kessels, 2004, p.156). From a knowledge management perspective, this learning function refers to the process of evaluation (Stam, 2004; Weggeman, 1997). In addition, this process makes the connection to the concept of the learning

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organization (Senge, 1992). Reflection stresses the idea that the output of the process also serves as input for a new (production) cycle (Nonaka & Takeuchi, 1995; Zack, 1998). Reflective skills are of vital importance for the development of meta-cognitions. Important preconditions for the development of reflective skills are open communication, constructive feedback and creating time and space to look backward (Kessels & Keursten, 2001).

Learning Function 4: Communication Skills The fourth learning function of the Corporate Curriculum has been described as acquiring communicative and social skills that help people access the knowledge network of others, participate in communities of practice and make learning at the workplace more productive (Kessels, 1996; Keursten et al., 2006). Communication skills stresses that knowledge is processed through people. More and more research is being done to identify the critical skills of the knowledge worker (A. Abell & Ward, 2000; Sprenger et al., 1995; Tissen, Andriessen, & Lekanne Deprez, 1998). Some important skills are the ability to communicate and collaborate, as it is through communication and collaboration that knowledge is developed and shared. Another aspect of this learning function is the extent to which the environment supports knowledge sharing. From a knowledge management perspective, this aspect refers to the preconditions for knowledge management in terms of structure and culture, as these aspects have an important impact on the knowledge processes and the knowledge friendliness of the company (Stam, 2004; Weggeman, 1997). Communication skills refers to the ability to communicate and collaborate and the knowledge friendliness of the organization in terms of structure and culture.

Making Sense of Knowledge Productivity

Learning Function 5: SelfRegulation of Motivation

supported by providing space for personal entrepreneurship.

This fifth learning function has been defined as acquiring skills to regulate motivation, affinities, emotions and affections concerning working and learning (Kessels, 1996; Keursten et al., 2006). This learning function, also at the heart of the Corporate Curriculum, is the most implicit learning function (Keursten et al., 2006) and refers to the importance for knowledge workers to identify personal themes and ways to develop these. It is about skills that give meaning to learning and enhance commitment (Kessels, 1996), because “in a knowledge economy it is useless when a manager says: Be smarter, or show more creativity! Being smart and creative depend heavily on personal interest” (Kessels & Van der Werff, 2002, p.22). People are only smart if they want to be (Harrison & Kessels, 2004). Personal interest is closely related to the process of inspiration, passion or motivation and sense-making (Leenheers, 2004). In their reconstruction study, Keursten et al. (2004) conclude that “personal motivation and affinity with a particular topic was the driving force behind innovations and improvements” (p.167, translation CS). A positive correlation has been found between attention to intrinsic motivation and the performance of individuals in the learning process (Van Lakerveld, 2005; Vansteenkiste, Simons, Lens, Sheldon, & Deci, 2004). Self-regulation of motivation puts the locus-of-control with the individual, because it implies that the extent to which organisational objectives are achieved, heavily depends on personal entrepreneurship. “A personal entrepreneur works from an intrinsic passion and primarily strives for personal interest. He has the ability to organize his work in such a way that it suits his personal preferences. He sees himself as a firm, although he is an employee” (Rondeel & Wagenaar, 2002, p.123, translation CS). Although motivation cannot be “managed” in the sense that it can be controlled, it can be

Learning Function 6: Peace and Stability The sixth learning function has been described as promoting peace and stability to enable exploration, coherence, synergy, and integration (Kessels, 1996; Keursten et al., 2006). This learning function refers to the need for incremental improvements through further specialization (Ansoff & Sullivan, 1993; Harrison & Kessels, 2004). Peace and stability gives employees the opportunity to explore existing knowledge and search for possibilities to apply this knowledge into their daily practice. Peace and stability also refers to the need for time for reflection, learning and knowledge sharing. Time and peace provide the opportunity to reflect on the efficiency and effectiveness of processes, products and services. Peace and stability provides a context in which people can experiment, without direct consequences. Peace and stability provides the certainty and the time which is necessary for specialization and improvement (Van Lakerveld, 2005). From a knowledge management point of view, this learning function refers to the organizational need for a certain degree of redundancy in creating knowledge. Redundancy means that the knowledge level within the organization exceeds the minimum level of knowledge needed to perform the necessary tasks (Nonaka & Takeuchi, 1995). “Lack of redundancy and time to reflect exploit existing (intellectual) resources, and consume these without generating new knowledge. Lack of peace and stability results in impoverishment of intellectual assets” (Kessels & Van der Werff, 2002, pp.22-23). However, the drawback of this learning function is that “too much peace and stability might bring about overly one-sided specialization and an excessive internal focus” (Kessels, 2001; Keursten et al., 2006). In this sense, Sveiby

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(1997) argued that stability should be seen as a counter balance of growth and renewal. Peace and stability is an important precondition for knowledge productivity in general and incremental innovation in particular. Important elements of this learning function are specialization, time to reflect and redundancy.

Learning Function 7: Creative Turmoil The seventh learning function has been described as causing creative turmoil, which leads to radical innovation (Kessels, 1996; Keursten et al., 2006). Creative turmoil refers to the need for creativity as a driver of innovation and improvement (Shapero, 1985). The cause of the turmoil is often “an existential threat: a matter of winning or losing, surviving or going under, being in or out of the game” (Harrison, 2004, p.156). Although Van Lakerveld (2005) found a positive relationship between work-pressure and learning, not all pressure is creative turmoil. Creative turmoil is mainly recognized by pressure which is caused by “the importance that is attached to the outcome of the process or because people themselves feel a strong urge to solve a particular problem” (Keursten et al., 2004, p.168). Although described variously, many authors refer to the need for creative turmoil when they stress the necessity of a certain degree of “strategic ambiguity” (Nonaka & Takeuchi, 1995), “strategic imbalance” (Itami, 1991), “strategic distance” (Senge, 1992), “strategic confusion” (Stacey, 1995) or “strategic disorder” (Levy, 1994). According to Senge (1992) distance between vision and reality is the source of creative tension as distance makes it necessary to take action in order to come closer to the objective. Similar reasoning can be found in Itami (1991) and Nonaka and Takeuchi (1995). A certain degree of chaos, disorder or even failure may prevent complacency, and could stimulate organizations to stretch beyond their strategic focus. Creative chaos can stimulate

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individuals to fundamentally change their ways of thinking and create new knowledge. Keursten et al. (2006) argue that external pressure is important to make a difference in daily work. However, not all unrest is creative turmoil and too much creative turmoil may yield many new ideas but leaves little opportunity to elaborate on them, thus limiting innovation. Creative turmoil without the time to reflect will lead to “destructive chaos” (Schon, 1983). This implies that the sixth and the seventh learning function should be in balance. Creative turmoil is seen as a precondition for creating radical innovation. The main prerequisite for this learning function is strategic ambiguity. The policy and activities that an organization develops to promote these seven learning functions form its Corporate Curriculum. At the end of the day, the quality of this ‘Corporate Curriculum’, or ‘plan for learning’ determines the effectiveness of the process of knowledge creation that leads to innovation.

…THAT LEADS TO INNOVATION Considering the pivotal role of innovation with regard to the concept of knowledge creation, it is striking to notice that only so little has been written about this concept in the knowledge management literature. Although (or maybe because), continuous innovation of products, services, and processes is generally accepted as the ultimate goal of knowledge creation, the concept is hardly elaborated upon. Elements of agreement seem to be that (1) today’s competitive environment requires continuous innovation, (2) innovation is the result of the process of knowledge creation, and (3) a distinction can be made between incremental and radical innovation (Stam, 2007). First, it seems to be generally accepted that in today’s competitive environment, continuous innovation is a necessary precondition. Therefore, many authors, implicitly or explicitly equate

Making Sense of Knowledge Productivity

Figure 2.

the ability to innovate with competitive advantage (Davenport & Prusak, 1998; Dixon, 2000; Drucker, 1993; Jacobs, 1999; Leonard-Barton, 1995; Nonaka & Takeuchi, 1995; Weggeman, 1997). So, knowledge creation and knowledge management are not a goal in itself, but support the economic goal of continuous innovation as a decisive factor of competitive advantage. Second, innovation is acknowledged as the result of the process of knowledge creation. Therefore, Amidon (2003) defines innovation as “knowledge in action”. According to Nonaka and Takeuchi (1995), innovation is the result of the combination of the ontological and epistemological knowledge spirals. Furthermore, innovation is the ability of organizations to connect internal and external knowledge: the process in which knowledge flows from the market into the company and back again in the form of new products and services. Within this process, both problems and solutions are redefined, in order to adapt to the changing environment. In line with Nonaka and Takeuchi and other knowledge management sources, Leonard-Barton (1995) also considers innovation to be the core capability of today’s organization, and therefore stresses the importance

of encouraging and combining knowledge creating and –diffusing activities. “It is this process that enables innovation, and it is this combination that managers manage” (Leonard-Barton, 1995, p.8). Third, distinction can be made between incremental and radical innovation. Based on the paradigm of the punctuated equilibrium (Eldredge & Gould, 1972) and Kuhn’s (1996) scientific revolutions, distinction is made between incremental improvements of existing practice and radical changes (Zegveld, 2000). Inspired by evolutionary biology, innovation is not seen as a process of gradual change, but as a process of intermitted change (Figure 2). Relative long periods of relative stability are altered with relative short periods of radical change. This implies that we can make a distinction between two types of innovation: incremental and radical. This distinction is closely related to Hamel and Prahalad’s (1993; 1994) distinction between “stretch” and “leverage”. Stretch can be defined as “doing the impossible” or where ambition outpaces resources. It requires a total commitment to achieve the desired goal which is communicated to and accepted by the whole workforce. Leverage is about getting the most out of re-

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sources. The distinction between incremental and radical innovation can also be related to the exploitation/exploration dilemma (March, 1991; Zack, 1999). This dilemma represents the two strategic options a company has: exploitation of old certainties or exploration of new possibilities. Von Krogh et al. (1994) distinguish between an organization’s need to survive (maintain its position in its current environment) and its need to advance (forge ahead in an emerging new environment). Abell (1999) summarizes these innovation strategies as “competing today while preparing for tomorrow”. Based on Walz and Bertels (1995), Kessels (2001) makes a distinction between gradual improvements and radical innovation. Gradual improvement (involving adaptive learning) elaborates on what is already present and leads to additional refinement and specialization. Radical innovation (involving investigative and reflexive learning) involves breaking with the past and creating new opportunities by deviating from tradition. (Harrison & Kessels, 2004, p.157) According to Leonard-Barton (1995), these two types of innovations are the essence of the core capabilities of the firm, because they can be either “competence-enhancing”, or “competencedestroying”. The former refers to possibilities to be combined into current products, the latter refers to, what she calls, innovations that “may wash away the technical foundation of the company” (Leonard-Barton, 1995, p.145). Similarly, Christensen (2005) makes a distinction between “sustaining”1 and “disruptive” technologies. According to Boisot (1998) the two types of innovation can be explained in Kuhn’s (1996) terms of shifting paradigms. The distinction between “cumulative” and “disruptive” knowledge evolution is that the latter involves a paradigm shift; a destruction of existing knowledge assets and the building up of new ones on different foundations. He describes disruptive knowledge evolution as

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an “edge of chaos” phenomenon out of which new knowledge structures suddenly emerge. The distinction between incremental and radical innovation also stresses the close relationship between the concepts of innovation and learning as many distinct types of learning can be compared to these two types of innovation. Examples of this are “first order” and “second order” learning (Bateson, 1972), “single-loop” and “double-loop” learning (Argyris & Schön, 1978), “lower-level” and “higher-level” learning (Hedberg, 1981); “adaptive” and “reflexive” learning (Guile and Young [1999] in Harrison & Kessels, 2004). All of these distinctions refer to incremental improvements to existing practice on the one hand, and radical rethinking of basic goals, norms, and paradigms on the other. In the knowledge economy, to be successful, organizations should continuously improve their processes, products and services, and radically renew from time to time (Drucker, 1993; Nonaka & Takeuchi, 1995). This distinction between incremental and radical innovation is a first step in the operationalization of the concept of innovation in the definition of knowledge productivity. These two types of innovation, together with the seven learning functions of the corporate curriculum as described in the previous section, serve as a starting point for a framework for making sense of knowledge productivity in the next section.

THE KNOWLEDGE PRODUCTIVITY FLYWHEEL This chapter is based on a case-based research in which a method was designed and tested to make sense of knowledge productivity in order to help management to design policies that aim at enhancing knowledge-based performance (Stam, 2007). As the aim of this research was to combine elements from both knowledge management and intellectual capital measurement, the method that was tested consisted of elements from both

Making Sense of Knowledge Productivity

disciplines2. Some of the elements appeared to be more effective than others. This section presents a framework that appeared to be helpful to make sense of the concept of knowledge productivity and to support management in designing policies for knowledge productivity enhancement. This framework–the knowledge productivity flywheel–is based on the seven learning functions of the Corporate Curriculum (Kessels, 1996) and the two types of innovation, as described in the previous sections. According to Van Lakerveld et al. (2000), a distinction can be made between those learning functions that directly refer to the learning processes (1 to 5) and those that refer to the conditions of learning (6 and 7). Within the five functions that refer to the learning processes we can make another distinction between those that predominantly refer to the knowledge processes (1-3), and those functions predominantly referring to the knowledge workers (4 and 5). The result is that we can make a distinction between three different types of learning functions: those related to the individual (competences and motivation),

those related to the knowledge processes (subject matter expertise, solve problems, reflection), and those related to the organizational environment or conditions (calm and stability, creative turmoil). Together they can be visualized in a circle with three layers (Figure 3). These three circles represent the “process of knowledge creation” and try to pay respect to the human-centred definition of knowledge of Kessels (1996). Therefore the inner circle represents the learning functions that are predominantly related to the individual. The outer circle represents the learning functions that are predominantly related to the organizational environment. The circle in between represents a combination of the inner and the outer circle and refers to the learning functions which are predominantly related to the knowledge processes as defined by the knowledge management literature. These knowledge processes are both related to the people and the organization. They are both human and structural capital. In his research on the Corporate Curriculum, Van Lakerveld (2005) finds evidence for the positive relationship between the learning func-

Figure 3.

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tions of the Corporate Curriculum on the one hand and quality improvements and innovative potential on the other. In a large reconstruction research of sixteen case studies, Keursten et al. (2006) found a positive relationship between the quality of the Corporate Curriculum and successful innovation processes. Based on these findings, we could represent the relationship between the Corporate Curriculum and incremental and radical innovation as a flywheel (Figure 3). The better the wheel (the process of knowledge creation) functions, the stronger the ability to generate incremental and radical innovation. The knowledge productivity flywheel appeared to be an effective instrument for making sense of knowledge productivity and designing policies that aim at enhancing knowledge productivity (Stam, 2007). Presupposition of the framework is that enhancement of the seven learning functions of the Corporate Curriculum leads to an improved ability to produce incremental and radical innovation, which eventually leads to improved organizational performance. Important to note is that the relationship between knowledge productivity and organizational performance remains implicit. In this respect, further research is needed (Harrison & Kessels, 2004; Keursten et al., 2004; Stam, 2007; Van Lakerveld, 2005; Weggeman, 1997).

THE KNOWLEDGE PRODUCTIVITY ENHANCER Based on the KP we designed a method for diagnosing knowledge productivity and planning for enhancement (Stam, 2007). This section briefly introduces the KP-enhancer and summarizes the main findings of seven case studies in which this method was tested. Before elaborating on the method, it has to be noted that the KP-enhancer is not a solution, but a solution concept. This implies that this method is not a standardized solution, but should be translated to the specific context of application.

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Therefore, this method is not developed for the layman, but for the (knowledge management) professional. In order to be able to successfully apply the KP-enhancer, the person applying the method should: •

• •

Be familiar with the main concepts of the method (knowledge productivity, Corporate Curriculum, incremental and radical innovation, knowledge management); Be familiar with the organization in which the method is applied; Not only be able to technically understand and apply the method, but also have the ability to facilitate the process.

Context of Application The KP-enhancer can be applied to mid-sized (50-250 employees) knowledge-intensive organizations, or knowledge-intensive departments (50-250 employees) within large organizations. These can be private or public organizations, profit or not-for-profit.

Design of the KP-Enhancer The KP-enhancer consists of three phases (Figure 4). The method starts with defining the problem at hand, goes through a phase of diagnosing the current situation, and finishes with formulating a plan (KP-statement) for improvement. The implementation of the initiatives that are mentioned in the KP-statement are not part of this method. The KP-enhancer consists of a web-based questionnaire (with a response time of ±25 minutes) for all employees within the organization (or department), three workshops of three hours with a representative selection of the employees, an interview (two hours) with the client at the beginning of the project, and a presentation to the client or MT at the end of the project. The leadtime of the method is between four and six months.

Making Sense of Knowledge Productivity

Figure 4.

The remainder of this section provides a broad description of the three phases of the KP-enhancer. The description focuses on the aim of each phase, the main elements and the result of each phase. Phase 1: Problem Definition The aim of the first phase of the method is to determine the scope of application, define and verify the problem at hand, and check the necessary preconditions for applying the method. Some important questions that have to be answered in this phase are: •

•

•

Does the organization fit into the class of contexts (see context above) for which the method has been designed? Does the problem fit into the class of problems (diagnosing and planning KM initiatives) for which the method has been designed? Does the organization (and the persons involved) meet the necessary preconditions for successful application of the method?

The result of this phase is the verification that the method suits the situation, a validated problem statement, and a concrete planning for applying the method. Phase 2: Diagnose Current Situation The aim of the second phase of the method is to diagnose the current situation with regard to

knowledge productivity, and come to an agreement about possibilities for improvement. The main elements of this phase are a survey among all employees within the scope of application and a workshop for a representative selection of the employees. The survey consists of two parts. The first part consists of about seventy items related to the quality of the seven learning functions of the Corporate Curriculum. The second part consists of a set of ten items related to the innovation profile of employees in terms of incremental and radical innovation. The aim of the workshop is to introduce the main concepts, present and discuss the outcome of the survey, and collect and formulate shared findings with regard to possibilities for improvement. One important element in the workshop is a KP board game based on the KPflywheel, in which participants are literally asked to match their cards to the learning functions that, according to them, should be improved. The result of this phase is a set of possibilities for improvement of the current situation with regard to knowledge productivity. Phase 3: Formulate KP-Statement The aim of the third phase of the method is to formulate a plan for knowledge management objectified in a KP-statement. This phase consists of two workshops and a presentation of the final product to the client.

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Based on the New Guideline for Intellectual Capital Statements (STI, 2003), the main elements of a KP-statement are the Knowledge Strategy, KPchallenges, KM initiatives and a set of indicators. A Knowledge Strategy is a way of expressing the organizational objectives in knowledge terms. The KP-challenges describe the challenges (related to KP) the organization has to face in order to be able to realize its strategic objectives. The KM initiatives describe the actions that follow from the challenges and the indicators help organizations to monitor the progress of the KM initiatives. As internal consistency is an important element of the (communicative) strength of the method, the consistency of the statement is continuously tested. A final check is performed within this phase to guarantee the quality of the final product. The result of this phase is a completed KPstatement (See Case 1). The KP-statement tells us which initiatives have to be put in place in order to improve the current situation from a knowledge perspective.

Effects of the KP-Enhancer Based on the implementation of the method in seven organizations (Stam, 2007), the KPenhancer can be characterized as a method that helps to plan KM initiatives through creating awareness about and assessing the quality of the process of knowledge creation. The method can be used either to translate organizational strategy into KM initiatives, or to connect existing KM initiatives to strategic objectives. The result of the method (the KP-statement) helps to improve communication about KM initiatives. In terms of effects, applying the KP-enhancer contributes to: 1. 2. 3.

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Creating awareness about the importance of KP; Assessing the quality of the process of knowledge creation; Developing a plan for KM;

4. 5.

Aligning (existing) KM initiatives with strategic objectives; Improving (internal) communication about KM.

Although the method is called KP-enhancer, applying the method does not lead to KP enhancement directly. The final product of applying the method is a plan for enhancement (plan for knowledge management). Therefore, the method could also be called a “KP enhancement planner”. KP enhancement comes from implementing the plan for KM.

APPLYING THE KPENHANCER AT DE BAAK De Baak is the Management Centre related to the Dutch federation of industries (www.debaak. nl) and employs 150 people. The core activity of de Baak is learning to learn, among other things through training, coaching and events. De Baak is a typical professional service firm, which is reflected in its mission statement: “De Baak is a special company that takes itself and others forward through learning”.

Problem Definition The main reason for de Baak to implement the KP-enhancer was because it had recently gone through a process of strategic reorientation. This process caused doubts about the quality of the available knowledge in the organization. The main question was: do we have the right knowledge to execute our new strategy? The main aim of implementing the KP-enhancer was to add a knowledge perspective to the strategy statement. Another aim of applying the KP-enhancer was to develop an alternative language (alternative to the traditional financial language of the management) in order to facilitate strengthening of the knowledge focus.

Making Sense of Knowledge Productivity

Diagnosing KP Based on a questionnaire, the quality of the seven learning functions of the Corporate Curriculum were diagnosed (Figure 5) together with a representative selection of employees. Important finding was that the concern of the management with regard to the quality of the available knowledge appeared to be justified. After interpreting the data, the employees concluded that the following learning functions needed to be strengthened: Subject matter expertise (learning function 1), Communicative and collaborative skills (learning function 4) and Peace and stability (learning function 6).

Formulating a KP-Statement In a second workshop, the strategy statement and the outcome of the diagnosis of the lerning functions were translated into a knowledge strategy, a set of challenges needed to realize this strategy and a set of initiatives needed to face these challenges. Finally, a set of indicators were defined to measure the progress of the initiatives. The result of this workshop was summarized in a KPstatement (Figure 6). Based on this KP statement,

the management decided to implement almost all initiatives as proposed.

FUTURE RESEARCH Based on the issues raised in this chapter, this section suggests several directions for further research. First, intellectual capital measurement and knowledge management are often treated as distinct concepts. Based on the experiences in this research, an interesting direction for further research would be to further explore to what extent these concepts are complementary and to what extent these concepts enhance each other. Second, the concept of knowledge productivity includes both conditions (learning functions) and results (incremental and radical innovation). However, hardly any research has been done to proof the relationship between these two elements (Keursten et al., 2006). In addition, hardly any research has been done to proof the relationship between knowledge productivity and organizational performance. Therefore, a relevant direction for further research would be to test these supposed relationships between conditions and

Figure 5.

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Figure 6.

results and between knowledge productivity and organizational performance. Third, the use of the word ‘productivity’ in relation to the process of knowledge creation suggests that the effectiveness of this process can be measured in terms of the ratio between input and output. In our research we found out that this suggestion is based on the logic of the industrial era and arouses wrong expectations. Therefore we need new concepts that help us better understand the essence of the knowledge-based production process. Future research could focus on the discovery of better metaphors for phenomena such as knowledge productivity, knowledge management and intellectual capital. This research could build on the work by Andriessen & Van den Boom (Andriessen, 2006; Andriessen & Van den Boom, 2007, 2009). Fourth, in our research we also experienced the importance of aligning knowledge management initiatives with corporate epistemology (Venzin, Krogh, & Roos, 1998). It can be questioned whether the conceptual framework presented in this chapter fits all different corporate epistemologies. Therefore interesting direction for further

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research would be to make sense of knowledge productivity from an epistemological perspective.

CONCLUSION In the knowledge economy knowledge productivity is the main source of competitive advantage and thus the biggest management challenge. Despite the pivotal role of knowledge productivity, this concept remains relatively unexplored, which might threaten organizational effectiveness. Therefore, this chapter attempts to make sense of the concept of knowledge productivity in order to contribute to the management ability to develop policies that aim at enhancing knowledge productivity. In recent literature we see two different interpretations of the concept of knowledge productivity, of which one uses knowledge, and the other uses productivity as a starting point. Although distinct approaches, they are related in the sense that they both search for more appropriate instruments to reveal and improve knowledge-related performance. Based on a review of the concept

Making Sense of Knowledge Productivity

from both perspectives, knowledge productivity is defined as the process of knowledge-creation that leads to incremental and radical innovation. The two main elements in this definition are ‘the process of knowledge creation’ and ‘incremental and radical innovation’. These two elements are inherently bound together, because innovation (new knowledge) is the unavoidable result of the process of knowledge creation. Today’s production process is a process of knowledge creation. This process is closely related to the process of learning and therefore, the conditions for a productive environment are similar to the conditions for good learning. Based on this reasoning Kessels (1996) identified seven critical learning functions: subject matter expertise, ability to solve problems, ability to reflect, communicative and social skills, self-regulation of motivation, stability and peace, and creative turmoil. These learning functions are critical in the sense that their quality determines the effectiveness of the process of knowledge creation. In the knowledge economy, to be successful, organizations should continuously improve their processes, products and services, and radically renew from time to time. Therefore, incremental and radical innovation is the second main element in the definition of knowledge productivity. Based on the paradigm of the punctuated equilibrium, innovation should not be seen as a process of gradual change, but as a process of intermitted change. Relative long periods of relative stability are altered with relative short periods of radical change. Incremental innovation refers to improvements of existing practice (doing things better). Radical innovation refers to radical changes that deviate from existing practice (doing better things). In order to make sense of the concept of knowledge productivity and to support management in designing policies for knowledge productivity enhancement, the seven learning functions and the two types of innovation were combined in a knowledge productivity flywheel. Underlying logic of this conceptual framework is that en-

hancement of the seven learning functions of the Corporate Curriculum leads to an improved ability to produce incremental and radical innovation, which eventually leads to improved organizational performance. In other words, the better the wheel (the process of knowledge creation) functions, the stronger the ability to generate incremental and radical innovation. Based on the knowledge productivity flywheel we designed a method for diagnosing knowledge productivity and planning for enhancement. This so called KP-enhancer was tested in a case-based research and appeared to be an effective instrument for making sense of knowledge productivity and designing policies that aim at enhancing knowledge productivity.

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Mouritsen, J., Bukh, P. N., Larsen, H. T., & Johansen, M. R. (2002). Developing and managing knowledge through intellectual capital statements. Journal of Intellectual Capital, 3(1), 10–29. doi:10.1108/14691930210412818 Nonaka, I., & Takeuchi, H. (1995). The knowledge creating company. New York: Oxford University Press. Polanyi, M. (1974). Personal knowledge. Chicago: University of Chicago Press. Rondeel, M., & Wagenaar, S. (2002). Ondernemer zijn van je eigen talenten. [Being an entrepreneur of your own talents] In Rondeel, M., & Wagenaar, S. (Eds.), Kennis maken, leren in gezelschap. Schiedam: Scriptum. Roos, J., Roos, G., Dragonetti, N. C., & Edvinsson, L. (1997). Intellectual capital: navigating in the new business landscape. New York: New York University Press. Schon, D. A. (1983). The reflective practitioner. London: Temple Smith. Senge, P. M. (1992). De vijfde discipline [The fifth discipline]. Schiedam: Scriptum. Shapero, A. (1985). Managing professional people: understanding creative performance. New York: The Free Press. Shapiro, C., & Varian, H. R. (2003). The information economy. In Hand, J., & Lev, B. (Eds.), Intangible assets. Values, measures, and risks (pp. 48–62). Oxford: Oxford University Press. Smith, A. (2000). The wealth of nations. New York: The Modern Library. Sprenger, C. C., van Eijsden, C. H., ten Have, S., & Ossel, F. (1995). De vier competenties van de lerende organisatie [The four competences of the learning organization]. ‘s-Gravenhage: Delwel Berenschot Fundatie.

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Stacey, R. D. (1995). The science of complexity: an alternative perspective for strategic change processes. Strategic Management Journal, 16(6), 477–495. doi:10.1002/smj.4250160606 Stam, C. D. (2004). Kennismanagement: van theorie naar de praktijk van kennisproductiviteit. [Knowledge management: from theory to the practice of knowledge productivity] In C. D. Stam (Ed.), Productiviteit van de kenniswerker (pp. 9-22). Noordwijk: de Baak. Stam, C. D. (2007). Knowledge productivity. Designing and testing a method to diagnose knowledge productivity and plan for enhancement. Ph.D. thesis, Universiteit Twente, Enschede. Stam, C. D., Evers, A., Leenheers, P., De Man, A., & Van der Spek, R. (Eds.). (2004). Kennisproductiviteit: het effect van investeren in mensen, kennis en leren [Knowledge productivity: the effect of investing in people, knowledge and learning]. Amsterdam: Pearson Education. Stewart, T. A. (1997). Intellectual capital. The new wealth of organizations. New York: Doubleday. Stewart, T. A. (2002). The wealth of knowledge. Intellectual capital and the 21st century organization. London: Nicholas Brealey Publishing. STI. (2003). Intellectual capital statements - The new guideline. Copenhagen: Ministry of Science Technology and Innovation. Sveiby, K. E. (1997). The new organizational wealth. Managing & measuring knowledge-based assets. San Fransisco: Berret-Koehler Publishers Inc. Sveiby, K. E., & Lloyd, T. (1988). Managing knowhow. Increase profits by harnessing the creativity in your company. London: Bloomsbury. Tissen, R., Andriessen, D. G., & Lekanne Deprez, F. (1998). Value-based knowledge management. Amsterdam: Addison Wesley Longman.

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Toffler, A. (1981). De derde golf [The third wave]. Utrecht/Antwerpen: Veen Uitgevers. Van der Spek, R., & Spijkervet, A. (1994). Kennismanagement: intelligent omgaan met kennis [Knowledge management: dealing intelligently with knowledge]. Utrecht: CIBIT. Van Lakerveld, J. (2005). Het Corporate Curriculum: Onderzoek naar werk-leeromstandigheden in instellingen voor zorg en welzijn. [The corporate curriculum] Phd thesis, Universiteit Twente, Enschede. Van Lakerveld, J., Van den Berg, J., de Brabander, K., & Kessels, J. W. M. (2000). The Corporate Curriculum: A working-learning environment, Paper presented at the AHRD Conference (Symposium 13), Raleigh-Durham, NC, March 8-12. Vansteenkiste, M., Simons, J., Lens, W., Sheldon, K. M., & Deci, E. L. (2004). Personality processes and individual differences: Motivating learning, performance, and persistence: The synergistic effects of intrinsic goal contents and autonomysupportive context. Journal of personality and social prychology, 2, 246-260. Venzin, M., Krogh, G. v., & Roos, J. (1998). Future research into knowledge management. In Krogh, G. v., Roos, J., & Kleine, D. (Eds.), Knowing in firms: understanding, managing and measuring knowledge (pp. 26–66). London: SAGE. Von Krogh, G., Roos, G., & Slocum, K. (1994). An essay on corporate epistemology. Strategic Management Journal, 15 (Summer Special Issue), 53-71. Walz, & Bertels. (1995). Das intelligente unternehmen. Schneller lernen als der wettbewerb [Intelligent enterprises. Learning faster than the competition]. Landsberg/Lech: Verl. Moderne Industrie.

Weggeman, M. (1997). Kennismanagement; inrichting en besturing van kennisintensieve organisaties [Knowledge management; design and management of knowledge intensive organizations]. Schiedam: Scriptum. Weggeman, M. (2000). Kennismanagement: de praktijk [Knowledge management in practice]. Schiedam: Scriptum. Wiig, K. M. (1997). Integrating intellectual capital and knowledge management. Long Range Planning, 30(3), 399–405. doi:10.1016/S00246301(97)90256-9 Zack, M. H. (1998). Managing codified knowledge. Sloan Management Review, 40(4), 45–58. Zack, M. H. (1999). Developing a knowledge strategy. California Management Review, 41(3), 125–145. Zegveld, M. A. (2000). Competing with dual innovation strategies. A framework to analyse the balance between operational value creation and the develpment of resources. Phd thesis, Katholieke Universiteit Brabant, Tilburg. Zegveld, M. A., Berger, L., Van Asseldonk, A. G. M., & Den Hartigh, E. (2002). Turning knowledge into cash-flow. Governing knowledge-based productivity. Veldhoven: TVA developments bv. Zegveld, M. A., & den Hartigh, E. (2007). De winst van productiviteit [The return on productivity]. Den Haag: Van Gorcum. Zegveld, M. A., Zegveld, W. C. L., & den Hartigh, E. (2004). Sturen op productiviteit in de kenniseconomie [Steering at productivity in the knowledge economy]. Den Haag: Stichting Maatschappij en Onderneming.

Warsh, D. (2006). Knowledge and the Wealth of Nations. A story of economic discovery. New York: Norton.

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ADDITIONAL READING 1Drucker, P. F. (1993). Post-capitalist society. New York: HarperCollins Publishers. 2Drucker, P. F. (1999). Knowledge-worker productivity: the biggest challenge. California Management Review, 41(2), 79–94. 3Garvey, R., & Williamson, B. (2002). Beyond knowledge management, dialogue, creativity and the corporate curriculum: FT. Upper Saddle River, NJ: Prentice Hall.

11Verdonschot, S. (2009). Learning to innovate. A series of studies to explore and enable learning in innovation practices. Published doctoral dissertation, Twente University, Enschede. Available at: www.narcis.info (section “Doctoral e-theses”). 12Von Krogh, G., Ichijo, K., & Nonaka, I. (2000). Enabling knowledge creation. Oxford: Oxford University Press. 13Warsh, D. (2006). Knowledge and the Wealth of Nations. A story of economic discovery. New York: Norton.

4Harrison, R., & Kessels, J. W. M. (2004). Human Resource Development in a knowledge economy. An organisational view. New York: Palgrave Macmillan.

14Zegveld, M. A., Berger, L., Van Asseldonk, A. G. M., & Den Hartigh, E. (2002). Turning knowledge into cash-flow. Governing knowledge-based productivity. Veldhoven: TVA developments bv.

5Machlup, F. (1972). The production and distribution of knowledge in the United States. Princeton, New Jersey: Princeton University Press.

KEY TERMS AND DEFINITIONS

6Nonaka, I., & Takeuchi, H. (1995). The knowledge creating company. New York: Oxford University Press. 7Stam, C. D. (2007). Knowledge productivity. Designing and testing a method to diagnose knowledge productivity and plan for enhancement. Published doctoral dissertation, Twente University, Enschede. Available at: www.narcis. info (section “Doctoral e-theses”). 8STI. (2003). Intellectual capital statements - The new guideline. Copenhagen: Ministry of Science Technology and Innovation. 9Van Aken, T., & Engers, T. v. (Eds.). (2002). Beyond Knowledge Productivity: report of a quest (Vol. 1). Utrecht: LEMMA. 10Van Lakerveld, J. (2005). Het Corporate Curriculum: Onderzoek naar werk-leeromstandigheden in instellingen voor zorg en welzijn. [The Corporate Curriculum] Published doctoral dissertation, Twente University, Enschede. Available at: www. narcis.info (section “Doctoral e-theses”).

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Knowledge Productivity: The process of knowledge creation that leads to incremental and radical innovation. Corporate Curriculum: The plan for learning to increase knowledge productivity, leading to innovation and ultimately to economic advantage. Knowledge Management: Deliberate initiatives that aim at enhancing knowledge productivity. Intellectual Capital Measurement: The discipline that identifies and measures intangibles. Knowledge: The product of learning. Intellectual Capital: All intangible resources that are available to an organization, that give a relative advantage, and that in combination are able to produce future benefits. Learning: The process in which knowledge is created. Innovation: The product of learning (knowledge) can be either incremental or radical. Incremental Innovation: Incremental improvements to existing practice (doing things better).

Making Sense of Knowledge Productivity

Radical Innovation: Knowledge that breaks with the past and opens new opportunities (doing better things).

ENDNOTES 1

According to Christensen (2005), sustaining technologies can be either radical or incremental, however, what they all have in common is that they improve the performance of established products. Therefore, within this

2

context sustaining refers to incremental, and disruptive refers to radical innovation. The complete method was called the “Knowledge Productivity Enhancer”. Other main elements of this method were based on the Quantitative Framework (Zegveld, 2000) and the Danish Guideline for IC statements (STI, 2003). More about the effectiveness of the complete method and the individual elements can be found in Knowledge Productivity (Stam, 2007).

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Chapter 8

Measuring Intangible Assets: Assessing the Impact of Knowledge Management in the S&T Fight against Terrorism Kimiz Dalkir McGill University, Canada Susan McIntyre Defence Research and Development Canada – Centre for Security Science, Canada

ABSTRACT At present, there are no standards for assessing the value of intangible assets or intellectual capital. Historically, a number of frameworks have evolved, each with a different focus and a different assessment methodology. In order to assess that knowledge management initiatives contributed to the fight against terrorism in Canada, a results-based framework was selected, customized and applied to CRTI (a networked science and technology program to counter terrorism threats). This chapter describes the step by step process of how the results-based framework was applied to measure the value contributed by knowledge-based assets. A combination of qualitative, quantitative and anecdotal assessment techniques was used and a map was employed to visualize the evaluation results. The strengths and weaknesses of this particular approach are discussed and specific examples from CRTI are presented to illustrate how other organizations can use this method to assess the value-added to innovation and research and development using a results-based framework.

INTRODUCTION Executives and managers would be hard-pressed to argue against the theoretical foundations, goals and intended results of the discipline of Knowledge Management (KM)ii as a potential benefit to their organizations. It is self-evident, even trite, to state DOI: 10.4018/978-1-60960-054-9.ch008

that a successful organization must manage its intellectual capital well to achieve competitive advantage, be more innovative and enhance its value. The challenge lies not in accepting these maxims but in the practical application of knowledge management activities and subsequently to be able to demonstrate whether these same activities actually do contribute to the enhancement of the organization and its goals.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Measuring Intangible Assets

This was the challenge faced by Defense Research and Development Canada’s Centre for Security Science. When its flagship program, the Chemical, Biological, Radiological, Nuclear (CBRN) Research and Technology Initiative (CRTI)iii was originally created in 2002 in response to the changing North American domestic security environment, the need for a KM approach was obvious. This Canadian Government program intended to bring together multiple federal departments and agencies to strengthen a national science and technology (S&T) capability to counter the terrorist threats from chemical, biological and radiological or nuclear agents. Only a KM approach would be able to build communities, collaboration, knowledge sharing and creation where such a domain had not existed previously. A robust KM program was created through consultation with stakeholders and the program thrived, becoming a model for others. Yet, did the KM program contribute to the success of the program? Did it impact the federal laboratories’ capability and capacity to respond to CBRN incidents or contribute to focused expertise, knowledge and capabilities of Canadian CBRN S&T performers in the short-term? Did it assist in any way in engaging the Canadian Innovation System in CBRN counter-terrorism or help the creation of industrial products, technologies and knowledge for CBRN counter-measures in the medium term? Finally, would the KM program contribute to the long-term goals of building the Canadian S&T capacity and capability to prepare for, prevent and respond to CBRN attacks, or enhance the communication, cooperation, collaboration, and interoperability amongst Canadian and international CBRN counter-terrorism communities, or eventually to a effectively positioned Canadian S&T innovation system that contributed to national and international security? In order to answer these questions at some stage, it would be necessary to develop a meaningful measurement tool and process that would provide qualitative data that would be useful

during an evaluation. Measurement tools often tend toward the quantitative side, measuring occurrences of activities. This is helpful to determine pull on services, increased (or decreased) need for resources and to measure trends. But can such measurement processes indicate whether the KM activities themselves actually contribute to the attainment of organizational objectives? The question for the KM Team was: do the KM activities contribute in a meaningful way to the mission and outcomes of the CRTI and can that be measured? This chapter focuses on the search for a method of meaningful value measurement. The main goal will be to illustrate the use of a result-based management accountability framework (RMAF) as a tool to measure the impact of knowledge management activities on the intangible assets of an S&T counter-terrorism organization. The key alignment of the measurement framework and the strategic importance of the knowledge-based assets will be presented as a recommended best practice. The case study will also describe the general approach to identifying the intangible assets and investigating and selecting the best approach to assessing these assets.

BACKGROUND In the early 2000s the CRTI was born within a cultural milieu that recognized the need for breaking down knowledge stovepipes and the advantages of working collaboratively for common aims. KM authors were expounding the virtues of collaboration and the need to leverage knowledge in order to gain the “knowledge advantage.” (Prusak, 1996) The Government’s Standing Committee on Industry, Science and Technology had just released A Canadian Innovation Agenda for the TwentyFirst Centuryiv in which it indicated the need for “more coordination of intramural S&T activities among federal agencies, as well as greater collaboration on major horizontal issues–those that

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cut across departmental and agency boundaries.” Clearly, the time was ripe for a collaborative approach for finding new solutions to existing and emerging challenges. Initially CRTI had 14 government departments and agencies participating in what was considered to be the first “horizontal” delivery model for S&T in the Canadian federal government. Knowledgebased in itself, the initiative partners determined investment priorities according to a consolidated risk assessment with both scientific and intelligence input. Clusters, or de facto communities of practice, in each of the chemical, biological, radiological-nuclear domains worked together to identify target investments and action plans for devising S&T in support of the prevention of and response to terrorist attacks. The program sought to deliver S&T solutions to the responder community and other operational authorities, as well as to enhance the national capability in research and development (R&D) and scientific support in these risk areas. The CRTI KM Team began its strategic approach by consulting with the primary stake-

holders, primarily federal scientists and science managers who were involved in the scientific Clusters. Intuitively understanding KM for what it could and must do for the growth of a new national community, they were able to articulate a number of approaches and requirements for the new program. In turn, the KM Team developed a strategic action plan that would support the community in their objectives. From the beginning, it was a holistic approach that would facilitate the creation of both tacit and explicit knowledge, use tools and techniques for capturing and sharing knowledge, and disseminate this knowledge to various stakeholders (including scientists, endusers and the citizens of Canada) in various and appropriate ways. Figure 1 illustrates the highlevel KM model that was used to guide the KM initiatives within CRTI. The KM Team knew that facilitating intellectual capital growth involved the three elements of human, relational, and structural capital. It was evident that expertise was going to be developed on the individual and team levels because the CRTI was encouraging experts in multiple disci-

Figure 1. High-level model of KM processes (adapted from Dalkir, 2005)

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plines to apply their existing knowledge in new areas and that, in order to accomplish this, they were going to learn from each other. Initially, the KM focus was not expressly on facilitating the human aspect of intellectual capital, although it was fully anticipated and hoped that this would happen. Indeed, without this outcome, the initiative would not succeed in achieving its mission. It was determined, however, that without focusing on the other types of intellectual capital, the development of human intellectual capital would not happen to the required extent. On the structural capital side, for example, it was evident that there was a need for a technology platform that participants could use to exchange, collect and disseminate their existing knowledge and knowledge that was being created as the initiative took hold and evolved. Within 6 months, the InfoPort, a collaborative Community of Practice application was launched. There was also a need for a publication mechanism which would permit the creation of knowledge products that could not be published elsewhere given that there were multiple partners working together under this umbrella initiative. Soon the CRTI Secretariat was disseminating various types of documents primarily in electronic, but also paper, formats. And finally, structures, processes and tools, which would often be behind the scenes, were required to support the overall functions. However, building relationships and developing community would surely be the most important first step. The Team knew that without a concentrated effort in facilitating opportunities where new relationships could be built or trust could be developed, there would be no possibility of moving ahead in innovative ways to create S&T knowledge and products that would ensure Canadian public security and resiliency. Therefore, the KM approach dedicated a great deal of effort to planning and hosting knowledge exchange events, workshops dedicated to finding novel solutions, exercises for experiential and shared learning, and communications to multiple stakeholders. It

was after two years of the program, that the KM Team acknowledged that it was time to measure the impact of these activities on the CRTI program and went to work on a research project with McGill University’s School of Information Studies to find a mechanism to do this.

INTANGIBLE MEASUREMENT MODEL AND METHOD In order to select the best fit measurement model and method, the research team first undertook an extensive literature review. This review looked at both scholarly and practitioner publications in order to identify the major measurement models in use, the most widely applied measurement methods and to compare the strengths and weaknesses of each. All types of assessments were included in this review: quantitative, qualitative and anecdotal. Following the review, a recommendation was made as to the most compatible measurement approach to use for CRTI, together with suggested adaptations to best accommodate the CRTI assessment objectives. Next, the specific performance indicators were established and a data collection method was developed. Once the design was complete, all that remained was to collect the data and analyze the results. The literature review surveyed intangible metrics best practices and discussed the pros and cons of quantitative, qualitative and anecdotal measurement approaches.

Literature Survey of Models to Measure Intangibles Intangibles are 96% of the value of the world’s most successful company: Microsoft. The rest is book value (Nash, 2004). The history of the measurement of intangible assets has run the gamut from extensions of purely financial methods from the accounting discipline to more value-based assessment frameworks

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(Bouteiller, 2002). In the disciplines of accounting and finance, intangibles or “intangible assets” are generally referred to claims (or assets) which have the potential to deliver future benefits (Lev, 2001). Intangible assets are more commonly referred to as intellectual capital (IC) and the terms have become synonymous. Intangible assets do not have a physical or concrete form but they are still capable of contributing to the value produced by the company (revenues or profit in commercial companies, innovations, service in non-profit companies). Contractor (2001) lists examples of IC such as reputation, brands, goodwill, customer loyalty, expertise, or a unique corporate culture. Intangibles which have direct influence on monetary gains are known as commercial intangibles e.g., copyrights, brands, patents, franchises, product quality and value, reputation, R&D, and so on. The rest fall into the category of “other” intangibles like creative employees, innovative workers, highly motivated staff, enhanced morale, etc. Alternatively, generative and commercial intangibles can be divided into individual and structural intangibles. Individual intangibles are qualities linked directly to individuals such as specialized knowledge and skills, customer loyalty and supplier loyalty. On the other hand, structural intangibles are assets that are attributed to interpersonal and inter-group relations rather than attached to individuals. The most apparent examples of these intangibles are team work and corporate culture, (Bouteiller, 2001). One of the first IC models, the Skandia model, categorized intangible assets as human capital, customer capital and organizational capital (Edvinsson and Sullivan, 1999). Three types of IC were defined in this model, namely: 1.

2.

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Human capital: includes employee brainpower, competence, skills, experience and knowledge; Customer capital: includes relations and networks with partners, suppliers, distributors, and customers. It also includes the image

3.

of the organization in the market, its social identity, and brand equity; Structural capital: covers every intellectual capital that can be owned by the organization including routines, business processes, practices, databases, systems and intellectual property.

The Skandia model of intellectual capital has subsequently evolved, notably the term “social” or “relationship” capital has gained popularity over customer capital in order to include noncommercial organizations. Sveiby (1997) described three types of “invisible” capital that are quite similar to the Skandia categories: 1.

2.

3.

Internal structure: includes all the systems, databases, processes and routines that support an organization’s operations and employees (corresponds to structural capital); External structure: includes all external relations and networks that support the organization’s operations, including support and administrative staff (corresponds to customer capital); Competence: includes individual experience, knowledge, competence, skills and ideas (corresponds to human capital).

Both Sveiby and Edvinsson distinguish the different types of IC based on where they are located or where they can be found. In contrast, Andreissen and Tissen (2001) proposed that intellectual capital be viewed through the contextual lens of organizational competencies. They define the role of IC to essentially be to strengthen the core competencies that an organization would have typically already identified. Al-Ali (2003) argues that this may be too myopic a model for IC and notes that it tends to focus on what the organization is supposed to do now as opposed to looking at innovation, the capacity for the organization to learn and adapt (referred to as absorptive capacity

Measuring Intangible Assets

by Cohen and Levinthal (1990). She describes the case of Xerox that decided not to invest in the PC leaving the field to Steve Jobs: Xerox focused on its identified core competencies and failed to recognize an exceptional opportunity to go beyond these core competencies. Al-Ali (2003) goes a bit further with her CICM model (Comprehensive Intellectual Capital Model). She argues that although being able to define, recognize and appreciate intellectual capital is of great value, management needs a comprehensive guide as to how to develop and leverage intellectual capital. The CICM identifies not only the location but the function that the various different types of IC perform. In particular, the function of IC in an operational production process is quite different from that of an innovation process. Al-Ali (2003) further advocates further grouping the various forms of intellectual capital into resources, processes, and products where the relation between them is made clear for the purposes of management, something that the older existing IC models do not do. The CICM classifies IC as: 1.

2.

Knowledge resources: the human or organizational knowledge that goes into the making of products and services of the organization and which supports the critical business processes and operations. This is a mix of human and organizational capital. Innovation processes: the processes and networks that an organization needs to en-

3.

able its innovation process and convert ideas and knowledge resources into marketable products and services. For non-commercial organizations, this encompasses the critical decision making. This is a mix of social and organizational capital. Intellectual property: the technologies, products, processes, methods, software, publications and other works that the organization has protected legally and can commercialize independently as an intellectual product to maximize value. This is a mix of social and organizational capital.

Based on this classification the CICM model manages the various groups of intellectual capital under three stages of knowledge management, innovation management and intellectual property management. Table 1 summarizes the key features of these IC models. For the CRTI assessment, all of the models are compatible and the one that was used was a combination of the Skandia and CICM models. One of the major goals of CRTI is to promote innovation in the S&T sector in the fight against terrorism. Also, the notion of knowledge resources fit quite well with the knowledge products that were to be assessed as stemming from the KM projects. Bouteiller (2001) notes that the meaning and perception of value depends very much on whose perspective is being taken. Abdul-Samad and MacMillan (2004) argue that:

Table 1. Comparison of features of major intellectual capital models Skandia Model (Edvinsson and Sullivan)

Sveiby Model

Andriessen and Thissen Model

CICM model (Al-Ali)

Human capital

Internal structure

Core competencies (organizational)

Knowledge resources

Structural capital

External structure

Innovation process

Customer capital

Competence (individual)

Intellectual property

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In the field of accounting and finance, numerous studies have been carried out linking intangible assets to company performance and profit margin. Several empirical examples demonstrate that improvement in key intangible drivers translates into increased market value of commercial entities. These findings also suggested that intangible assets in organizations have both direct and indirect influences over a company’s value. For instance, customer loyalty affects other factors of business operational functions like brands, marketing, services, communications, and so on. Through identification of critical success factors, intangibles can also be used to drive and enhance organizational business performance. In short, intangible assets are gaining widespread recognition as key value drivers of business performance. (p. 3) Al-Ali also notes that a different perspective may be required to assess intangible assets. The confusion about intellectual capital management as a field is due to a great extent to its multidisciplinary nature. This is because intellectual capital includes disparate types of resources and assets that are human based (brainpower, competence and skills) information and data based (databases, software and hardware), innovation based (R&D routines, processes and practices) and legally defined property rights (patents, trade secrets, trademarks/brands, and copyright). As such it is a field of interest and application to human resources professionals, IT, R&D, business and marketing managers, IP lawyers and business

consultants. (available at: http://www.ipmall.fplc. edu/hosted_resources/al-ali/IC_main.htm). A combination of quantitative, qualitative and anecdotal measures must therefore be included as components of a comprehensive IC measurement model. The complexity extends to the specific indicators to be measured in this comprehensive IC model. A good guideline can be taken from the European Commission’s (1999) MEANS framework. MEANS is a program used by the European Commission to classify indicators for the evaluation of socio-economic programs based on their level of objectives. The indicators are classified into five categories: resource, output, result, specific impact and global impact, which closely resemble the results-based evaluation framework. One type of indicator that is defined is a sustainable specific objective, as shown in Table 2. The MEANS framework includes indicators for sustainable objectives and effects which is an excellent fit for the objectives and expected effects of the CRTI program. All KM initiatives should include an objective and an indicator of sustainability. In the case of CRTI, the objectives were: • • • •

To create clusters of S&T labs; To create an S&T fund to build S&T capability; To accelerate technology development into the hands of the first responder community; To address any gaps in S&T capacity; and,

Table 2. An extract from the European Commission (1999, p. 29) definitions of indicators (MEANS) Level of objective Sustainable specific objective (e.g. to create and maintain a high-level of collaboration between different units)

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Type of objective Specific impact (as opposed to more general outcomes)

Definition Sustainable effect as consequence beyond its direct short-term effect - an objective that is expected to maintained as opposed to being a one-shot endeavor

Example Results of output – a measurable result that attests to the attainment of the sustainable objective (e.g. number of new customers through phone calls over a period of x months, increased throughput per employee for the next 3 years, etc.)

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•

To increase horizontal coordination across Government departments and agencies.

The subsequent CRTI KM objectives consisted of: •

• •

•

Building relationships and organizational structure, processes and opportunities to encourage a knowledge culture; Collecting and organizing programmatic and scientific knowledge; Facilitating the creation and dissemination of CBRN knowledge including methods for data, information and knowledge exchange; Proving, evaluating and adjusting KM activities.

Both the overall CRTI and the KM objectives address sustainable goals. This in turn means sustainability needs to be assessed. The MEANS framework is an example of an assessment framework that looks at sustainability of goals as well as one-shot goals and more short-term goals. Finally, the assessment of intellectual capital in R&D or innovation projects was surveyed in order to identify the best possible framework and indicators (Subramaniam and Youndt, 2005). In R&D, human capital plays a significant role in triggering innovation and performance. Satisfied and highly educated knowledge workers tend to improve organizational capital (e.g., process, culture, and brand value) which is owned by organizations and accumulated for a long-time. Human capital is only “rented” by the organization. Thus, organizations’ efforts focus on encouraging employees to concentrate on their job as well as on providing them satisfaction to prevent them from leaving the organization or retiring (Pike et al, 2005). In addition to reviewing the major measurement models, the literature review also surveyed the major measurement methods such as EVA (Economic Value Added), value chain scorecard,

Balanced Scorecard, Skandia navigator, Intangible Assets Monitor, and the Results-Based Management and Accountability approach. Each model was reviewed and assessed according to its applicability to this specific KM approach and to the objectives of the impact assessment. Financial models were rejected as they were difficult to adapt to a government context. Others were too theoretical and did not really provide a means of measuring concrete outcomes. The literature review suggested that an assessment framework rather than a model would better serve to evaluate the CRTI KM objectives. A measurement framework is a combination of individual metrics that are used for comparative purposes (Yates-Mercer and Bawden, 2002). Frameworks are superior to lists of metrics because they show the effects and relationships between specific KM activities. A measurement framework is also useful for ensuring that metrics are aligned with the goals of a KM initiative (Smith, 2001). Frameworks are usually depicted as one of three visual representation types: flow diagrams, matrices and causal diagrams. A flow framework is excellent for showing the direct benefits of KM activities on those involved in or affected by the initiative. The drawback of a flow framework in such a case is that it does not show the meeting’s effect on the broader KM strategy (Smith, 2001). A matrix framework is useful for illustrating the reasons for prioritization and selection amongst various KM projects. “Matrices are effective for condensing many interdependent factors into a readable format. For example one matrix can show the relationship among KM activities, Points of Contact, expected results, measures used, actual results, stakeholders, and resource costs” (Smith, 2001, 15). Causal loop diagrams, which are usually associated with the field of systems thinking, are effective frameworks for showing relationships within an organization that do not immediately appear to be a cause and effect relationship. One of the benefits of causal loop diagrams is that they not only show positive

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relationships between activities and outcomes but can also demonstrate negative relationships. This will allow management to identify areas that need improvement quickly and efficiently (Smith, 2001). Most initial frameworks represented rudimentary efforts to visually represent (in numbers or other means) the intellectual capital of an organization. However, there remains a rather wide gap between what should be measured and what can be measured. One form of the causal diagram is found in applications of the results-based assessment framework. Results based management is a management philosophy that emphasizes defining realistic expected results and provides a framework to both monitor and report contributions made towards these results. The RMAF guidelines provided by the Treasury Board of Canada represent broad outlines of how to implement this philosophy in assessment activities and consists of a planning and high-level program evaluation tool. In fact, the antecedents of this framework date back to Shalock (1995), subsequently updated in 2000, who introduced the notions of a form of program evaluation that uses objective person-referenced outcomes (measuring the functional level and social roles of program clients) to analyze effectiveness, impact, or costs and benefits. Fujitsu Consulting (formerly, DMR Consulting) adapted a form of results-based assessment, named Results ChainTM, that represented one of the earlier implementations of this framework. As a form of causal diagram, this approach did not necessarily represent measurable causality but rather a form of “contributes to” type of relationship was visually depicted between inter-related objectives and initiatives.

Selection of the ResultsBased Measurement Model Following this review the Results-based Management Accountability Framework (RMAF)

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was selected (Treasury Board Secretariat, 2005). RMAF is a tool designed specifically to help track, manage and measure the outputs and outcomes of an organizational initiative and is ideally suited for ensuring that strategy is continuously aligned with the objectives and goals of the organization. The selected RMAF evaluation was chosen because of its heavy focus on the cause and effect relationships. Using the RMAF helped to characterize the impact that the two major CRTI KM initiatives, collaboration support and the InfoPort, were having on CRTI’s broader goals of increasing its preparedness, capability and capacity to respond to CBRN terrorist attacks. The results chain assessment framework, together with the set of specific metrics or indicators, was developed specifically for the CRTI context. While the RMAF philosophy was followed, there was no pre-existing “recipe” to implement in order to assess the CRTI KM objectives. The logic model was developed to capture the KM initiatives and their intended effects on KM outcomes (both short-term and long-term outcomes). The specific performance indicators were then developed in order to identify measurable outcomes which would then indicate progress made towards the expected outcomes. The creation of the logic model was the critical component that allows the RMAF to maintain accurate and measurable links between the activities and their outcomes. The logic model graphically shows the chain of results between the activities and the final outcomes and identifies the steps in between that must occur for the achievement of the final outcomes. Designing the logic model was an iterative process, the results of which represent a shared understanding between management, stakeholders, and the eventual evaluators of the initiative’s activities, outputs and most importantly, their outcomes. Designing these metrics then, consisted of moving step by step through the various levels of the logic model and identifying the most appropriate and relevant measurements for each output/outcome. Figure 2 shows the logic

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model developed for one of the KM objectives, the knowledge repository “InfoPort.” Next the team was able to identify metrics that would measure each output and outcome. In this step, the final or ultimate CRTI objectives were identified (what impact is expected on the organization?) together with the activities and outputs and outcomes. The flow diagram can be derived forwards from outputs or backwards from final objectives. Plot models were used to verify the logic. Finally, indicators were chosen for each outcome level.

DATA COLLECTION AND ANALYSIS A combination of quantitative, qualitative and anecdotal data was then collected. As Mouritsen (2001) argues, “intellectual capital is “no ordinary accounting concept” (p. 760). He strongly advocated a mixed approach to measuring and reporting IC, one in which

…intellectual capital activities (need to) be related to narratives of innovation, the information society and ‘we-live-from-knowledge claims…to create a persuasive intellectual capital statement… (that) consists not merely of numbers, but also of stories/ narratives and visualization/sketches that allows a series of translations to take place. (pp. 760-761). The RMAF measurement was as comprehensive as possible (Dalkir, et al., 2007). Quantitative, qualitative and anecdotal data were integrated in order to provide a more complete portrait of how well KM objectives were met. Quantitative measures were included as typical multiple choice questions, ranging from yes/no answers to Likert-scale multiple choices to assess attitudes. Qualitative data was gathered both in the interviews and in the short-answer section of the data collection survey. Anecdotes were also included as data. In their book Working Knowledge (1998) Davenport and Prusak discuss a concept that they have coined as “serious anecdote management.”

Figure 2. Logic model of CRTI InfoPort

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This concept refers to knowledge that is often passed through stories. A “serious anecdote” is a story that has a “lessons learned” punch line. These anecdotes are excellent for capturing the context of a valuable piece of knowledge and providing a memorable medium in which to transfer that knowledge. Often, the punch line carries a quantitative measurement that can then have more meaning for employees because it is delivered within the context of a story that they can easily relate to. Serious anecdotes also illustrate the value of KM and as such are useful metrics to include when evaluating certain initiatives (Wiseman, et al., 2005). Next, a series of stakeholder interviews and auditing of internal documentation was completed in order to clearly identify the expected outcomes for the CRTI assessment. In consultation with the KM Team, it was determined that five activities would be selected for the measurement framework: •

•

•

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Collaboration support: to support existing and new communities of practice in achieving their objectives; to enable effective collaboration within the community, with other communities, and with the wider CRTI community; to create synergy and trust; to create opportunities for learning and creating knowledge from CRTI experiences; and to encourage reciprocal dissemination of knowledge between CRTI stakeholders and First Responders; Intranet portal solutions (IPS): to offer centralized online access to CRTI information, CBRN expertise and knowledge, and to encourage virtual exchange by creating tools and methods for sharing knowledge; Knowledge products: to capture, create and disseminate knowledge products that apply CBRN knowledge to ensure that this knowledge is synthesized and packaged in a manner that meets the needs of each target audience;

•

•

Knowledge and information management structures: to create and implement processes and tools to assure CBRN and CRTI knowledge are accessible now and in the future, and Communications and media relations: to establish guidelines to help ensure effective communications among CRTI stakeholder groups and with the media; to develop and maintain positive relationships; to increase CRTI’s visibility; and to help promote CRTI as a legitimate and credible voice on CBRN issues.vi

The CRTI objectives address both social and organizational capital. Although the value added to human capital was not directly measured, it was expected that increased value in structural and relationship capital would, in turn, contribute to enhanced human capital (Nahapiet and Ghoshal, 1998). The next step involved the formulation of an appropriate data-gathering strategy. It was decided that the most added-value method would be to design an electronic qualitative survey, using Likert scales and open ended questions, which would be distributed to CRTI members through CRTI’s InfoPort and then followed up with interviews to gather additional anecdotal information on the impact that KM has had on CRTI’s ultimate goals. The survey was run for two months in order to maximize the response rate as much as possible. It consisted of 32 questions, half of which were multiple-choice (Likert scale) and the other half were open-ended questions. In addition, structured interviews were conducted with members of the CRTI KM team and volunteers in order to validate the RMAF logic models, the metrics to be used and the questionnaire design. The survey was initially emailed to 213 members. Of these, 129 were actually reached and 26 completed the survey, resulting in a response rate of 20%.

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RESULTS The results obtained using the RMAF model and method served to “quantify” and render more concrete the value of the not-so-visible knowledge assets. For example, the team was able to show that CRTI KM initiatives aimed at supporting collaboration were highly successful. A majority of respondents felt that CRTI workshops, conferences and exercises had increased the communication of CRTI CBRN information and documentation, permitted them to expand their personal network or partners and afforded valuable learning opportunities for them. A number of new ideas, project or papers were the direct result of CRTI collaboration support activities and suggestions for improvement included encouraging greater audience participation and providing more cluster-specific follow up activities. There was strong indication

that participants preferred face-to-face contact with their internal and external (i.e. outside the CRTI) colleagues. This finding reinforced the high evaluation accorded to events such as symposia that brought people together. These indicators thus indicate that CRTI collaboration support has helped increase trust and synergy within the CRTI community, helped ensure more valuable knowledge was exploited, that collaboration and communication had increased and the organizational memory has grownv. The results for the CRTI InfoPort were more mixed, showing a combination of positive and negative indicator values. Table 3 shows the RMAF used for the InfoPort. The activity column represents the list of KM initiatives that were implemented by CRTI. The output column represents the expected deliverables of each activity in specific tangible terms (e.g. reports, systems,

Table 3. InfoPort RMAF Activity

Output

Immediate outcomes

Intermediate outcomes

Final outcomes (impact)

Develop, manage and facilitate usage of virtual workspace for exchange of knowledge between cluster members, across clusters and with other CRTI stakeholders

■ CRTI InfoPort ■ Portal maintenance, training, etc. ■ Automated profiles, Push/pull dissemination, filtering and alerting (automatic and bulletins)

Collect and disseminate CRTI documentation (internally generated) electronically

■ Electronic repository of internal documentation

Create an repository of relevant external knowledge products

■ Electronic repository of external CBRN documents and sources

Develop expertise locator system

■ Expertise directory (see also C4: competency map) ■ Expertise locator system

Centralized and timely access to CBRN S&T knowledge is provided (on demand, as needed) Available CBRN knowledge is current and vetted Communication and dissemination of CRTI information and documentation is enhanced Virtual knowledge sharing and collaboration is enabled Assistance in connecting members with experts is provided Input to the creation of a secure, collective lab management system is provided

Exploitation of valuable knowledge is increased ■ Increased use/ re-use of available knowledge Awareness of existing knowledge and expertise at CRTI is increased Exchange of knowledge (tacit and explicit) is increased Collaboration and communication within and between clusters is increased Organizational memory is preserved Knowledge base is perceived as complete

CBRN S&T knowledge and expertise in support of operations is developed, managed and leveraged Horizontal capability, links within CRTI communities are built Capability and capacity to respond is increased (operational readiness) ■ Performance is improved ■ Improved skills or competencies ■ Improved decisionmaking ■ Enhanced S&T advice and services provided

Develop dynamic lessons learned system

■ Lessons learned database

Explore secure web processing options

■ Options for secure web processing for clusters (PSEPC pilot)

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training sessions etc.). Immediate outcomes are short-term expected results of each activity. A set of activities, each with their own outputs, are expected to contribute to at least one immediate outcome. A set of immediate outcomes are then expected to contribute to one or more intermediate outcomes, which are medium term expected results. Finally, intermediate outcomes are expected to contribute to final outcomes, which tend to be very high-level mission statements, vision statements or long-term expected results. As one moves from left to right, there should be fewer and fewer entries to form the inverted pyramid of the logic model (shown in Figure 2). Once the RMAP information has been collected and represented, both as a logic model and as a table of components, the specific indicators need to be identified: what can and will be measured in order to assess how well each activity has contributed to the expected outcomes? Table 4 shows an extract of the indicators used for the InfoPortal activities.

Finally, an extract of some of the results obtained from the survey is presented in Table 5. The survey instrument was a combination of multiple choice and short answer questions. The questions were designed to provide the measurements for the RMAF framework. The data collected from both the interviews and the survey were analyzed and used to instantiate the RMAF indicators that were developed for each of the CRTI KM activities. In summary, with respect to the InfoPort, three-fourths of participants had filled out their online user profile but others stated they were reticent due to issues with unsolicited emails and general privacy. While the majority felt the content was easy to access and up to date, there were a number of qualifiers to be found in the qualitative data collected, both in the survey short answers and in the interviews. Participants expressed some concerns with the user-friendliness of the system and the fact that the content needed to be much more complete and updated more frequently in

Table 4. Performance indicators: extract from CRTI InfoPort RMAF Quantitative) ■ Frequency of use ■ Time to access information/knowledge ■ Number of unique visitors, percentage of total using system (trend) ■ Number of hits, downloads on portal, dwell time (trend) ■ Searching precision and recall / time to find object ■ Number of experts in directory / domains covered ■ Time required to find expert ■ Number of contacts / relationships made through portal (directly or indirectly) ■ Number of referrals made ■ Number of contributions made to the portal / knowledge base ■ Number / range of lessons learned in database ■ Number of alerts sent out ■ Number of alerts made use of ■ Frequency of contributions / Contribution rate increase / decrease ■ Perception of time or cost avoided by leveraging expert knowledge or knowledge base (also, reduced learning curve, reduced training)

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(Qualitative)

Anecdotal

■ Perceived value of portal and knowledge objects ■ Improvement in awareness of available information ■ End-user satisfaction / increased ability to work ■ Perception of confidence

■ Illustration of examples of occurrences where access to knowledge from portal, expertise directory or other databases resulted in acquiring a new skill or competence, an improvement in quality or efficiency, or solving a problem

Measuring Intangible Assets

order to serve a more operational support function. The greatest gap was that not a single person reported locating an expert through the InfoPort. This appears to be due to the large number of competing information sources, most of which are more mature, better maintained due to more abundant resources and almost all were external links (international). Another issue with expertise location was that the level of specificity was not sufficient to enable users to locate the specialized, technical expertise they would require to fulfill their responsibilities. The top content picks for the InfoPort were • • •

Lessons learned reports from exercises; Descriptions of cluster activities and projects in progress; and, “As was said” summaries.

It is interesting to note that these three types of content represent value added content: that is

to say, they were not simply cut and pasted into the InfoPort as is from another source but the CRTI team had contributed to the creation of new knowledge and to the contextualization of existing knowledge. There is clearly a value perceived by the InfoPort clients when the original content has been enhanced in this way. The major suggestions to improve the InfoPort included: • • •

•

•

Make it easier and more efficient to use Organize the content better; Make information with a short shelf life available much sooner while posting more scientific content at a later date; Broaden the scope of content: more technical, more international, more connected to external sites; and, The expert directory and its objectives need to be revisited.

Table 5. InfoPort questions and results (extracted from survey) Question Text

Response

Are you able to find information on the InfoPort in a reasonable amount of time?

68% Yes

Does the InfoPort’s search function meet your needs?

68% Yes

Do you feel you have centralized access to CBRN S&T knowledge through the InfoPort?

68% Yes

Have you accessed workshop and conference proceeding summaries from the InfoPort?

32% Yes

Do you feel the information available on the InfoPort is up to date?

79.2% Yes

Have you filled in your user profile on the CRTI InfoPort?

73.9% Yes

Can you locate an expert through the InfoPort when you need one?

65% Yes

Have you ever been contacted as an expert through your profile on the InfoPort?

0% Yes

Has the CRTI InfoPort helped increase your awareness of CRTI expertise, projects, gaps and successes?

52.2% Yes

How often do you use the information/knowledge available through the InfoPort to accomplish a task?

18.2% never 54.5% rarely 27.3% sometimes

The information on the CRTI InfoPort represents the best/most complete information that you need for your job.

4.5% strongly disagree 27.3% disagree 59.1% no opinion 9.1% agree

How frequently do you look at “alerts” or notices of new additions to InfoPort?

9.1% never 36.4% rarely 22.7% sometimes 22.7% often 9.1% very often

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As with the collaboration support project, the InfoPort also contributes to the final or impact outcomes of developing, managing and leveraging CBRN S&T knowledge in support of operations, building horizontal capability and links with the CRTI communities. Only anecdotal data was collected to directly support the final outcome of increased capability and capacity to respond: • • • •

Improved performance; Improved skills or competencies; Improved decision-making; Enhanced S&T advice and services provided..

It is not unusual to have limited direct measures for the final outcome in an RMAF context. By definition, the final outcome is the raison d’être of the entire organization. Missions, visions and high-level goals tend to be assessed indirectly. In the case of CRTI, the existence of anecdotal evidence indicates that there is a positive loop and that additional research can be conducted to develop more intermediate outcome measures, both quantitative and qualitative, to further strengthen the contribution that can be shown to the ultimate or final outcome.

SOLUTIONS AND RECOMMENDATIONS The overriding result from the InfoPort evaluation was that there was a strong preference for personto-person contact within and between CRTI clusters, as well as with external networks. This result is not surprising given the effort devoted to creating the clusters as vibrant communities of practice in the first place. However, the results do indicate that technology still falls short in providing a virtual conduit for knowledge sharing interactions. The measurement model and method selected proved to be a good fit for the CRTI objectives as there

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was a strong case for “contribution” if not outright causality. What is even more important, however, was the development of the correct indicators. As is the case with every intangible assessment approach, it is crucial to customize the metrics to fit the organizational objectives at hand. The research team found that at least three months were required for the stakeholder interviews, determination of measurable objectives and the customization of the RMAF method. While this is a substantial up-front cost in terms of time and effort, the end results are certainly worth it. In the case of CRTI, the RMAF represented a summative assessment of specific KM contributions to CRTI goals at the five-year stage of the CRTI program. What is interesting is that now the model and the indicators have been developed and validated, the approach is easily re-applied as future formative measures (for example, on a yearly basis) to better monitor progress towards organizational goals. At the same time, the measurement model can be used to ensure a continually re-aligned model with respect to evolving organizational goals. This particular approach should be easily applicable to other research-based organizations, S&T government programs and any organization that needs to address a sustainable innovation goal as part of its core mission. Figure 3 illustrates the major steps implemented in the CRTI intangible asset measurement framework.

FUTURE RESEARCH DIRECTIONS The focus in this assessment was on organizational and social capital. It would be interesting to extend the RMAF to include indicators of the value added to the human capital that can be harnessed in the S&T domain to fight terrorism. These indicators could be applied over time to assess whether the expected increase in the human expertise and competencies that can be harnessed to provide a response to terrorist threats would

Measuring Intangible Assets

Figure 3.

be a strong addition to this measurement model. Some of the data collection questions could, for example, include: •

•

•

Do you feel you are able to locate the appropriate person to help you out more easily than 5 years ago? Do you feel you have a good overview of the type of human resources available to you through the CRTI clusters? Has this overview improved over the last 5 years? Do you feel that you have increased your knowledge or research capacity through your association the CRTI? How? vii

Additional indicators could also be added to measure innovation and research productivity, such as number of patents over the 5 years studied, number of publications and citation index measures (Mouritsen, et al, 2004). Further work is also needed to triangulate the results obtained using the RMAF model. Given that causality cannot be rigorously demonstrated, the use of other valuation methods such as the Balanced Scorecard (Kaplan and Norton, 1997)

would serve to strengthen the validity and reliability of the results obtained. Finally, a future research direction would be to extend the results-based management accountability framework used to assess individual CRTI intellectual assets to a more holistic or systemsbased evaluation framework. Chen et al (2004) notes that there is a need to understand the causal relationship among the intangibles to be able to measure and monitor them so as to steer them towards the firm’s success. The systems thinking approach (Richmond, 2001; Sternman, 2000) is also an excellent means of visualizing the entirety of intellectual assets and valuing not only each individual one but also assessing the value of their interactions. Recently, value maps have been advocated as holistic measures of the value added by knowledge assets that are visualized as “unbreakable” wholes or a gestalt (Jhunjhunwala, 2009). In value maps, the performance of each intangible is linked to others. Bygdas et al. (2004) describe an activitybased value map approach for measuring IC that consists of three phases: modeling, measuring and action. The modeling phase begins with a mapping and description of the company’s critical value

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processes, the activities in those and a description of how they are interrelated. In the measuring phase, the resources required for each activity is mapped. For each activity there is a mapping of what intellectual (critical and necessary) resources are needed to give sufficient quality and frequency of the activities. This approach is quite compatible with the result-based assessment that was carried out on CRTI KM activities and would prove a useful extension to the existing framework.

CONCLUSION Without the existence of generally accepted KM measurement tools, in general, and those which would address impact on outcomes, in specific, the CRTI KM Team solicited expert KM advice and research to find a novel solution. While many models exist for measuring or evaluating intellectual capital, most were exceedingly complex for a small organization or directed at for-profit enterprises. The RMAF approach afforded the ability to evaluate the impact of KM activities on outputs and outcomes. The final evaluation has permitted the CRTI to acknowledge the relative success of its program and to adjust future efforts according to these results. The RMAF also provides a broader KM opportunity in that by using it as a dynamic planning and management tool from the onset, the measurement process will be facilitated and will result in a natural measurement and evaluation output. In future efforts, there will be a need to address all types of intellectual capital with a focus on the impact of KM activities on human capital creation, development, and outcomes. The current analysis indicates that KM activities do have an impact and it will be essential to include a holistic approach in measuring intangible assets on a continuing basis. The RMAF measurement model has also proven to be very compatible with the major models (and types) of intellectual capital. The

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comprehensive, integrated assessment framework and guide presented here should prove to be useful to researchers and practitioners in the assessment of intangible value.

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Leitner, K.-H., & Warden, C. (2004). Managing and reporting knowledge-based resources and processes in research organisations: specifics, lessons learned and perspectives. Management Accounting Research, 15(1), 33–51. doi:10.1016/j. mar.2003.10.005 Lev, B. (2001). Intangibles: Management, measurement and reporting. Washington, D.C.: Brookings Institution Press. Lev, B. (2004). Sharpening the intangible edge. Boston. Harvard Business Review, (June): 109–116. Liebowitz, J., & Suen, C. (2005). Developing knowledge management metrics for intellectual capital. Journal of Intellectual Capital, 1(1), 54–67. doi:10.1108/14691930010324160 Marr, B., & Chatzel, J. (2004). Intellectual capital at the crossroads: managing, measuring and reporting of IC. Journal of Intellectual Capital, 5(2), 224–229. doi:10.1108/14691930410533650 Marr, B., & Spender, J.-C. (2004). Measuring knowledge assets--implications of the knowledge economy for performance measurement. Measuring Business Excellence, 8(1), 18–27. doi:10.1108/13683040410524702 Martin, W. (2000). Approaches to the measurement of the impact of knowledge management programmes. Journal of Information Science, 26(1), 21–27. doi:10.1177/016555150002600102 MERITUM. (2001). Meritum Measuring Intangibles to understand and improve innovation management. European Commission: Target Socio-Economic Research. Roos, J., Roos, G., Dragonetti, N., & Edvinsson, L. (1997). Intellectual Capital. London: MacMillan Business. Shalock, R. (1995). Outcome-based evaluation (1st ed.). New York: Kluwer Academic.

Shalock, R. (2000). Outcome-based evaluation (2nd ed.). New York: Kluwer Academic. Stewart, T. (1997). Intellectual Capital. New York: Doubleday. Sveiby, K. (1997). The new organizational wealth. San Francisco, CA: Berrett-Koehler Publishers.

KEY TERMS AND DEFINITIONS Intangible Asset: Intangible assets are claims to future benefits (e.g., cost savings, increased revenues) that do not have a physical (e.g., factory) or financial (e.g., a stock or a bond) embodiment (Lev, 2001). Knowledge Management: Knowledge Management is the discipline that systematizes the capture, codification, sharing and dissemination of knowledge in order to leverage individual, group and organizational intellectual capital for increased innovation, value and productivity. (CRTI McGill KM Team) Innovation Management: Innovation management is a term used to refer to new practices and tools improve the organization’s ability to innovate by creating the right culture (e.g. soliciting and encouraging employees’ submission of ideas, and developing new products and solutions). (Al-Ali). Intellectual Asset: Intellectual assets is the term preferred by intellectual property lawyers and professionals to refer to intellectual property (particularly patents, trademarks and copyrights) since their value can be more accurately perceived or evaluated - hence the word “asset”. (Al-Ali). Intellectual Capital: Intellectual capital is the part of a country’s or a firm’s capital or an individual’s human capital that consists of ideas rather than something more physical. It can often be protected through patents or other intellectual property laws. (The Economist). Human Capital: Human capital is defined as the knowledge that employees bring and take with

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them when they join or leave the firm. It includes the knowledge, skills, experiences and abilities of people. (Grasenik & Low, 2004) Structural Capital: Structural capital is defined as the pool of knowledge that remains with the firm at the end of work, after employees have left (Stewart, 1997). It comprises the organizational routines, procedures, systems, cultures, databases, etc. Some of this may be intellectual property. (Grasenik & Low, 2004) Relational Capital: Relational capital is defined as all resources linked to the external relationships of the firm such as customers, suppliers or R&D partners. It comprises that part of human and structural capital affecting the firm’s relations with stakeholders (investors, creditors, customers, suppliers, etc.) plus the perceptions that are held about the firm (brand, reputation, etc.). (Grasenik & Low, 2004) Outcome Based Evaluation: Outcomes Based Evaluation is an approach to measuring the effects of a project or an institution’s services and activities on the target audience that these programs seek to benefit or serve. (Publishers Bindings Online). Qualitative Measurement: Qualitative measurement attempts to provide context and value to notions that are either difficult or irrelevant to quantify, such as the value an individual employee gains from being a member of a community of practice (Smith, 2001). Quantitative Measurement: Quantitative measurement means assigning a numerical value to an observable phenomenon, such as the number of times an employee visits a specific KM web portal. This type of measurement would be described as a usage metric (Hall, 2000).

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Results-Based Evaluation: Results-based management is a life-cycle approach to management that integrates strategy, people, resources, processes and measurements to improve decisionmaking, transparency, and accountability. The approach focuses on achieving outcomes, implementing performance measurement, learning and changing, and reporting performance. (Treasury Board of Canada).

ENDNOTES i

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The Chemical, Biological, Radiological, Nuclear and Explosives (CBRNE) Research and Technology Initiative Science) Defined as: the discipline that systematizes the capture, codification, sharing and dissemination of knowledge in order to leverage individual, group and organizational intellectual capital for increased innovation, value and productivity. The Initiative originally covered only CBRN. Explosive threats were added in 2006. http://www2.parl.gc.ca/content/hoc/Committee/371/INST/Reports/RP1032098/ indurp05/indurp05-e.pdf Excerpt from Dalkir, Kimiz, Erica Wiseman and Michael Shula. CRTI Knowledge Management Metrics Project Report: a survey evaluation of major knowledge management objectives. Submitted February 2007. While generally not within the scope of KM programs, the early CRTI strategically used these methods for knowledge transfer to target stakeholders. With contributions from Dr. Albert Simard.

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Chapter 9

Visualising the Hidden Value of Higher Education Institutions: How to Manage Intangibles in Knowledge-Intensive Organisations

Susana Elena Pérez Institute for Prospective Technological Studies (IPTS) - Joint Research Centre, Spain Campbell Warden University of La Laguna, Spain

ABSTRACT European universities are immersed in an intensive transformation process to order to transform themselves into more autonomous and competitive organisations. Adapting to the new demands implies the introduction of management systems, traditionally used by firms, in order to govern universities according to criteria of efficiency and effectiveness. In recent years, the idea of managing and reporting on intangibles and Intellectual Capital in universities has been acquiring progressive importance in Europe. The Chapter provides a comparative analysis of the most significant European experiences in managing and reporting Intellectual Capital in higher education institutions addressing two main issues: the identification of the benefits and obstacles of implementing IC frameworks in these particular institutions and reflect on the necessary degree of standardisation of indicators to allow comparability. To this purpose, three types of initiatives are analysed: the case of Austrian universities, which are compelled by law to report annually on their IC; five initiatives developed by individual institutions on a voluntary basis, and an attempt to build a homogeneous IC framework for European universities.

INTRODUCTION In the knowledge-based economy, intangible assets and investments are seen as key drivers in the value creation processes in companies and, hence, in economic growth. Since the second

half of the 20th century the main economic and strategic management theories have recognised, in one way or another, the importance of intangible elements as part of the economic growth (Solow, 1957; Shultz, 1961; Denison, 1962; Arrow, 1962; Kendrick, 1974; Becker, 1975; Nelson & Winter, 1982; Nonaka & Takeuchi, 1995; Gorey & Dobat,

DOI: 10.4018/978-1-60960-054-9.ch009

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Visualising the Hidden Value of Higher Education Institutions

1996; OECD, 1996; Freeman & Soete, 1997; European Commission, 2000). New ideas about managing and reporting on intangibles and IC have been acquiring progressive importance, not only among academics but also among governments, regulators, enterprises, investors and other stakeholders, which has been reflected in the variety of guidelines and reference documents: MERITUM Guidelines (2002) 1 , Danish Guidelines (2003)2Japanese Guidelines (2004) 3, Australian Guiding Principles (2005) 4 and RICARDIS (2006) 5, the InCaS (Intellectual Capital Statement for Europe) Project (2007)6. Although most of the analysis on identifying, measuring and reporting on intangibles and IC refers to firms, during the last two decades the interest has extended from private organisations to public ones, such as hospitals (Vagnoni & Castelleni, 2005; Habersam & Piber, 2003), cultural organisations (Donato, 2005), local governments, cities and nations (Viedma, 2003; Pasher, 1999; Remble, 1999; Bontis, 2004; Andriessen & Stam, 2004) and universities and research centres (Sánchez & Elena, 2006; Sánchez et al., 2006a,b and c). These latter institutions are the main focus of this Chapter since they are particularly active in implementing IC approaches because their main goals (production and dissemination of knowledge) and inputs (human resources) are intangibles. Higher Education Institutions (HEIs) exist to create and share knowledge. However, few of these organisations have institutionalized processes that leverage knowledge to spur innovation or maximize operational efficiency and effectiveness. In many HEIs there is no organized knowledge system, or even understanding of this kind of system. Such an oversight is striking. This Chapter provides a comparative analysis of the most significant European experiences in managing and reporting IC in universities and research centres and addresses two main research questions: (a) what are the benefits and obstacles of implementing IC frameworks in these particular organisations? And (b) shall policy makers encour-

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age the development of voluntary IC models in universities or promote laws to make the management and reporting on IC mandatory? To this purpose, Section 2 discusses the rationale for implementing IC management models in universities and research centres. In the following three sections different types of initiatives are analysed. The case of the Austrian public universities, which are obliged to report their IC annually, is analysed in Section 3; Section 4 describes five different IC models developed by individual institutions across Europe on a voluntary basis; and Section 5 presents the attempt to build a homogeneous IC framework for European universities developed by the Observatory of the European University Project. Finally, the lessons learnt from the analysis of these experiences regarding the two main research questions and future research lines are presented in Section 6.

2. WHY INTELLECTUAL CAPITAL MANAGEMENT FOR UNIVERSITIES?7 Universities are a key element of the ‘knowledge triangle’ that links research, innovation and education. The important role that they play as research performers in national innovation systems has been translated into increased demands to improve their capabilities in teaching, research and knowledge transfer and their overall role in socio-economic development. In a context of financial and social pressures, universities are being transformed into more autonomous and competitive organisations in order to face, among others, the challenges of the knowledge-based economy. Adapting to the new requirements and demands implies the introduction of management systems, traditionally used by private companies, in order to govern universities according to the criteria of efficiency and effectiveness. In fact, some HEIs are now making important efforts to

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become increasingly professional in their strategic management of change. The first step towards better management and governing systems was the establishment of a general framework of quality assurance procedures during the second half of the 90s. In accordance with this, national accreditation agencies were created and evaluation methods, procedures and indicators were defined. Furthermore, and following the Council Recommendation (1998) on European Cooperation in Quality Assurance in Higher Education, the quality assurance system should be based on common features (European Network for Quality Assurance in Higher Education, 2003): the creation of an autonomous body, objective internal and external aspects of quality assurance, the participation and real commitment of stakeholders, and, the diffusion of the results. The appearance of quality as a relevant issue in university discourse shows an initial, but important, awareness with respect to managing and publishing information about intangibles. In accordance with this, some universities in Europe seem willing to embrace business concepts–such as strategic planning or quality control and assessment mechanisms–following the patterns in the business world (Wissel, 2004). However, several obstacles to such a change management programme can arise. For instance, the specific character of these organisations and their prevailing academic culture with its unique traditions. As a result “business thinking”, in the sense of profit-oriented companies, as a model in steering an HEI towards a successful future has been resisted, if not rejected out of hand, by some leading academics. Something that has complicated the acceptability of a change process even more has been the “tendency to borrow management and governance models from the private sector without any change to their design or use” (Meister-Scheytt & Scheytt, 2006, pp.23). In order to avoid potential pitfalls, universities, as a specific type of organisation, should adapt the IC models to their own characteristics and

context and address the cultural challenges first, so as to gain general acceptance among the key stakeholders, before imposing major reforms or implementing new managerial tools. Even though assessing a university’s outputs and inputs is not a completely new idea, the implementation of IC approaches within HEIs means going at least one-step further. The concept of IC goes far beyond a limited understanding of individual knowledge, but covers multiple aspects of an organisation: Human Capital as the knowledge and experience of the individual actors, Structural Capital as knowledge inherent in structure, processes, and culture; and Relational Capital as relationships beyond the borders of the organisation. IC management approaches could become significant management and reporting tools for the following reasons: •

•

•

The identification and management of intangibles are especially significant in HEIs, since a university’s main inputs and outputs are basically intangibles (mostly knowledge and human resources). However, only a small part of these are identified and very limited instruments exist to measure and manage them (Cañibano & Sánchez, 2004). Because of the new demands for accountability in public institutions, universities and research centers are forced to be more transparent and to disseminate more information to stakeholders (students, funding bodies, the labour market, and society as a whole). As asserted by the European Commission (2003, pp.13) “universities have a duty to their stakeholders to maximise the social return of the investment”. European HEIs are being provided with more autonomy to manage their own affairs, not only academic but also financial, to redefine their own internal structures, which necessarily requires new manage-

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•

•

ment and reporting systems (ERAWATCH, 2009). Universities are also becoming aware of this increasingly competitive environment in the higher education system, and this competition appears to increase in the future (Cañibano & Sánchez, 2004). The globalisation processes, the increasingly competitive environment and the creation of the European Higher Educational (EHEA) and the European Research Area (ERA) are forcing universities to improve their attractiveness in order to get the best students, researchers and professors, and to compete successfully for public and private funds. Finally, the increasing cooperation between universities and firms has resulted in the demand for similar processes of evaluation for both players. Accordingly, universities and research organisations would have to implement new management and reporting systems, which necessarily incorporate intangibles.

In summary, and as pointed out by EARMA8, those academic and research organisations that are able to develop both the culture and the capacity of their staff, to value, manage and report on their IC, will be advantageously placed in the HE scenario. Moreover the emphasis on people, their value and their networks, may make such an approach more acceptable to academics than those approaches that are blatantly cost-control or for-profit oriented One concrete tool successfully applied in different sectors is the so-called Intellectual Capital Statement or Report (from now on: ICR), which is used to identify and deliver information on strategy, aims, visions, activities and resources, based on indicators (financial and non-financial). The benefits of using the ICR fall into two categories (European Commission, 2006b; Marr, 2005):

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One category is its potential to function as a management tool to help to allocate resources, define a strategy, prioritise challenges, monitor performance, and facilitate decision-making. The other category is its potential to function as a communication device to link the institution to its main stakeholders and to attract resources: financial, human and technological.

Therefore, IC information is conceived to complement financial management information (internally) and the financial report (externally). Moreover, this external information can facilitate benchmarking across institutions. In recent years these ideas about managing and reporting on intangibles and IC in universities and research centres have been acquiring progressive importance worldwide and, especially, in the European Union. Our analysis will mainly focus on the most relevant experiences at the European level. Accordingly, the next sections describe the most significant initiatives that are being developed across Europe with the aim of giving new insights into the potential benefits and drawbacks of the co-existing different models and indicators, and into the voluntary approach/ mandatory basis debate.

3. INTELLECTUAL CAPITAL REPORT AS A LEGAL OBLIGATION: THE CASE OF AUSTRIAN UNIVERSITIES For more than a decade the Austrian HE system has been under radical reform processes in order to provide more institutional autonomy to universities. This reform, however, is not the only attempt in the world9, or even in Europe10, to change governance structures and introduce managerial mechanisms in universities, although it could be considered a unique initiative regarding the introduction of ICR.

Visualising the Hidden Value of Higher Education Institutions

This section analyses Austrian endeavours to reshape university internal organisation, mainly focusing on the development of ICRs. The Austrian HE system is a legacy of the Humboldtian tradition. Its most outstanding characteristics are the unity of teaching and research, state funding, civil servants and almost free access to all studies and programmes for everybody. Until the 90s, Austrian universities were characterised, at institutional level, as over-bureaucratised organisations where top management (Deans and Vice-chancellors) lacked the professional experience or capabilities to govern (Meister-Scheytt & Scheytt, 2005). Changes implemented in Austrian universities came as a result of a mandatory legal reform whose main objective was to make universities more competitive, efficient and autonomous in order to face the challenges of the new globalised HE context. The models followed as reference were those of Anglo-Saxon tradition, mainly, the UK, Australia and the Netherlands (Meister-Scheytt & Scheytt, 2006). In general terms, it can be affirmed that the Austrian university reform clearly follows New Management Principles, focusing on autonomy, output orientation and performancebased funding (Titscher et al., 2000)11. At the beginning of the 90s, an ambitious reform towards more autonomy12 and, consequently, more accountability started. Indeed, it is in 1990 when the State first allowed universities to attract private funds by selling their services to the market. The first relevant step in that direction was the University Act 199313 (from now on UOG93). UOG93´s main objective was, in fact, to provide universities with more institutional autonomy regarding the design of their internal organisational structures, a mechanism for personnel recruitment and the management of financial affairs, mostly related to the allocation of resources. The Act clearly attempted to introduce businesslike management and service-oriented views in order to better use their resources following efficiency, quality and cost effectiveness criteria (Austrian Ministry of

Education, Science and Culture, 2001). This new conceptualisation of the university meant redefining the relationship between the Federal State and HE institutions, which implied, simultaneously, a significant increase in decision-making power and the independence of the university governing bodies14as well as a reduction of governmental influence. UOG93 represented the first clear step towards new managerial ideas in the governance of Austrian universities. However, when implementing the law, universities were faced with some problems since the flexibility to manage resources remained restricted while “the general regulations for employment, payment and budget of the federal government continued to apply” (Beerkens, 2003, pp.36). Therefore, some years later, the University Organisation and Studies Act15 (from now UG200216) converted universities into “legal entities in public law” (UG2002, art. 4), in practice this has meant that since October 1st, 2004 they are “largely free to run their own affairs” (Höllinger, 2004, pp.1), although the Federal Government still has the legal responsibility to fund them (UG2002, art. 12). This has probably constituted the most important move towards a real reform in university governance and management structures. The main ambitions of the UG2002 are increasing autonomy and, consequently, accountability, as well as introducing new mechanisms for funding allocation (linking public funds with performance). One of its main implications 17 has been the introduction of ICRs. As a consequence of the reform, Austrian public universities are the first HEIs in the world that are obliged to produce and diffuse ICRs (called Wissensbilanz). The UG2002, in article 13, established the obligation and the general framework for developing the ICR. By doing so, the Austrian Ministry for Education, Science and Culture recognises that the “the efficient use of IC is essential for universities’ performance” (Leitner et al., 2005). The first ICR should have been published in 2005, however,

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the ministerial Order18 with the selected indicators was, in fact, issued on February 15th, 2006 (Alterburger & Schaffhauser-Linzatti, 2006). It was therefore mandatory by 2007 and Austrian universities have to submit an ICR annually, following the calendar year. The introduction of ICR in the Austrian HE system represents a crucial internal dynamic in universities towards identifying, measuring, managing and disclosing core intangible assets and activities as well as the value creation process. Publication of the ICR has to be parallel to the preparation of the performance report, the financial statements and other reports as part of the new reporting system (Alterburger & SchaffhauserLinzatti, 2008). However, they should not overlap. On the contrary, “while the performance report only deals with the topics addressed within the performance contract, the idea behind IC reports is to give universities the opportunity to report on their full range of activities without restrictions” (Leitner, 2004, pp. 132). Like other relevant university documents, ICR should be publicly available (UG2002, art. 20.6). According to the UG2002, the ICR has a twofold objective: to identify and measure intangibles for management purposes and to provide information to stakeholders. It has to fulfil a minimum set of points: • • •

University activities, social goals and selfimposed objectives and strategies. Its Intellectual Capital, broken down into human, structural and relationship capital. The processes set out in the performance agreement, including outputs and impacts.

Each university has to report on its input, output, and performance indicators for teaching, research, and third mission activities. The Ministry detailed the structure and contents of the Report. The ICR should be prepared for the whole institution and each university is free to publish ICR for other sub-levels, like departments, or faculties (Leitner et al., 2005) 182

The ICR is based on the model and principles developed in the Austrian Institute of Technology (AIT)19, the pioneer European research institution in applying IC models to manage its intangibles and in reporting that information. The characteristics and main foundation of the AIT Model will be explained in the next subsection. The model (see Figure 1) starts by considering the contextual conditions of the institution, analysing its strategic objectives and mission, and incorporating the three categories of IC: Human Capital, Structural Capital and Relational capital. The core of the model is the performance processes: research, education, training, commercialising of research, and knowledge transfer, that can be enlarged or reduced depending on the university profile (obviously, colleges of art, technological universities or business schools have different configurations and strategic objectives and processes) (Leitner, 2004). Finally, the impact on different stakeholders (academic community, government, industry, etc.) is included in the analysis. A set of indicators complete the model based on (Leirner, 2004): (a) the set of measures used in the past in Austrian universities; (b) proposed indicators in the IC literature, and (c) the findings of the research evaluation. Considering the main missions and activities of universities, the majority of them will be non-financial, so the descriptive elements become crucial to contextualise and comprehend the information provided by the figures. The Federal Ministry, in collaboration with the Conference of Rectors, selected the final set of indicators in the Order published in 15th February 2006 (Federal Ministry of Education, Science and Culture, 2006). The new Order specifies the structure of the ICR, the way of presenting the information and the indicators to be published. It is very extensive and comprises 13 sections and two appendices (Altenburger & SchaffhauserLinzatti, 2006). Even though, as mentioned before, universities were obliged to report their IC by 2007, some leading universities worked on it in advance. This

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Figure 1. Model IC Reporting for Austrian Universities (Source: Leitner (2004))

was the case of the Chair of Financial Accounting and the Department of Pastoral Theology in the University of Vienna, which has been developing trial ICRs for their subdivision. Altenburger & Novotny-Farkas (2005) analysed their experience with the implementation process, focusing on the problems and opportunities at external (regarding stakeholders) and internal level (for the university). As the authors pointed out, despite the fact that the ICR should refer to the university as a whole and this experience only covers particular units or departments, the trials produced interesting conclusions. The main difficulties and advantages identified by the authors are summarised in Table 120. Although, it is clear that the Austrian university reform made a significant move towards more managerial universities considering the implementation of management tools–particularly the ICR–as a valid way to increase efficiency and effectiveness in HEIs. In particular, the new resource allocation rationale forces Austrian universities to provide the Ministry with more and better information on their performance in teaching and research. In this context, the ICR could be considered a robust tool to provide improved information and help the decision-making process

in budget negotiations. Furthermore, the development of a framework for valuing, visualising and reporting IC in universities at national level might be helpful for further diffusion and comparison and mean a significant step in spreading these initiatives. However, a thorough analysis reveals that the following implications should be considered in further developments of ICR for universities: •

•

There is a risk of divergence between external and internal reporting. The ICR should be a model that sheds light on the internal value creation process within the organisation and, in addition, a tool to disclose information to stakeholders. However, as in private companies, it is important to achieve a balance between the information used by the institution for internal purposes and that released to the public. For this to happen, auditing processes seem to be crucial to consolidate the process and to avoid potential information manipulation. There is great danger in reporting a set of indicators imposed by law without descriptive elements. As argued in the specialized literature and by practitioners of

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Internal

External

Table 1. Intellectual Capital Report in Austrian Universities: Opportunities and Difficulties (Source: Based on Altenburger & Novotny-Farkas (2005))

•

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Opportunities

Difficulties

To improve their reporting to the Ministry on their performance in teaching and research (crucial under the new resource allocation rationale). To provide a more objective decision-making basis (for the Ministry to decide on the allocation of funds). To present interpretable and marketable information (important to attract funds not only from the State but also from third parties). To provide information to other stakeholders such as students, firms, non-profit making organisations, and society as a whole. To provide a basis for benchmarking.

Very focused on quantitative data: lack of a holistic idea of the value creation process. Every ICR follows the same instructions (previously defined by law): and the same indicator could have different meanings and interpretations. The ICR model has been developed for a small research unit: its implementation in a large and heterogeneous university could be unworkable.

To define the university mission statement, strategic objectives, academic priorities and university profile. To understand the value creation process of the university, identifying structural and personal strengths and weaknesses. To support the development of new strategies and actions, that can be used as a benchmark with other universities. To monitor university performance, including incentives and sanctions systems. To communicate the university objectives and performance to employees, increasing the link between institutional and personal interests.

this topic, the system of indicators is not self-explanatory since each indicator can denote or imply different things depending on the agent who receives the information. Consequently, the descriptive elements become crucial to contextualize and better understand the information provided by the indicators. So, if universities limit their commitment to nothing more than a battery of indicators missing out the narrative elements that should complement the quantitative information, there is a risk of reporting a set of meaningless indicators. Moreover, as we explained in this sub-section, the number of indicators to be published is so excessive that the data could be more confusing than useful. We believe that the ICR should be designed around the specific characteristics of each organisation to capture its idiosyncrasies and to reflect the specific situation and problems. Indeed, the Austrian ICR fol-

•

Universities will intensify those activities which improve the indicators considered crucial in the Order. Important specific processes and aspects could be disregarded. ICR could be used more as a controlling instrument than a motivating system. ICR gives a lot of leeway in interpreting the indicators provided, and subjective influence on ICR results are likely. University reporting model is based on the calendar year while university activities are organised in academic years.

lows the AIT model and the UG2002 law established the definition of the university’s strategy and corporate goals as one of the minimum requirements. Nonetheless, the selection of indicators has been made in general terms to allow comparability among Austrian universities so there is no direct link between the set of indicators and the university’s strategic plan. Indicators might reflect the strategic priorities, but the generally expected situation is an uncoupling of both elements in the process. Indeed, in the medium and long run, universities may redefine their strategic objectives and goals taking into account the indicators that they have to fulfil. This potential situation could bias the main objective of the whole process. When designing the implementation process within the institution, it is extremely important that from the beginning there is a high degree of participation in the aca-

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•

demic community and real involvement of the university top managers to accomplish the real objective of the ICR. Merely applying the law as an obligation to build a few indicators will not result in a learning process. Finally, it is important to mention that the law itself does not guarantee a real transformation of university governing bodies and structures. Indeed, fundamental shifts in university cultural behaviour may be necessary to really achieve the effects intended by the law. The way the reform is implemented in each university, the composition of the governing committee and the adaptation of the management mechanism to the specific characteristics of HE institutions are the key aspects to generating real change (Meister-Scheytt & Scheytt, 2006). At the same time, it could be also considered that a cultural and organisational change could be provoked as a consequence of the practice and implementation of the law.

The process of applying ICR in Austrian universities has to be followed up by analysing its real impact on university management and reporting systems in the coming years.

4. LEARNING FROM VOLUNTARY INITIATIVES Besides the paradigmatic case of Austrian universities, the majority of IC models developed by universities and research centres are based on voluntary approaches. As a consequence, different models for measuring, managing and reporting IC have emerged, which has, at the same time, increased the interest in the relevance of intangibles within HE and the research sector, but also hindered the comparative analysis among institutions.

Although, it is not our objective to explain in detail all the initiatives taking place to measure and manage IC in HEIs across Europe, the following sub-section outlines some of the most outstanding experiences in this field: • • • • •

Austrian Institute of Technology (AIT). Innovation and Knowledge Management Institute (INGENIO) Intellectual Capital in HEROs PCI Project Managing scientific and technical knowledge in the University of the Basque Country

4.1. Austrian Institute of Technology (AIT) The Austrian Institute of Technology (AIT) is Austria’s largest non-university research institute. It is a limited liability company, whose shareholders are the Republic of Austria (through the Federal Ministry for Transport, Innovation and Technology) with a share of 50.46% and the Federation of Austrian Industries which owns 49.54%. It was founded at the end of the 50´s as the Austrian Research Centre (ARS) and has grown and diversified its research portfolio since then. In the course of the year 2008, ARC underwent the most fundamental change processes in its corporate history, transforming itself into the current AIT. AIT has an important function as an interface between the basic research developed within universities, at national and international level, and the applied research carried out by private companies. In concrete terms, it transfers academic knowledge to practical application, provides an infrastructure for cooperative research projects, addresses the need for information and concepts that benefit society as a whole, and assumes the risk of innovative research in the early stage (AIT, 2007). Among others, its portfolio includes outstanding research activities in different fields,

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such as energy, health, environment, safety and security. This Austrian research organisation has carried out intensive research on intangibles and IC, covering the theoretical approach and practical results. In 1999, the AIT was the first European research organisation to publish an ICR. As explained by the Institute, the main reasons argued to develop an ICR were: •

•

•

•

As a public funded organisation, it is crucial to maintain transparency in the use of public resources, and the ICR helps to illustrate the value creation process. The communication internal policy is considered a priority in the organisation. However, the research activity is “not selfexplanatory: its benefits must be interpreted and communicated in a comprehensible way” (ARC, 2000, pp.3). Accordingly, the ICR became crucial to provide relevant information about their performance and to contextualize and explain it. AIT is aware of the importance of knowledge management and intangible assets for the value creation process. The model developed to identify, measure and manage its intangibles should help the organisation to illustrate the development of intangible assets and to point out future areas of performance. The ICR is conceived as a new instrument to measure those intangibles that are not reflected in its annual report and is a crucial component in their corporate strategy. By producing this, all the stakeholders,–including public and private owners, customers and suppliers, business partners, and the staff,–“can see the whole picture” (ARC, 2000, pp.4).

The ICR is not merely conceived as an instrument to diffuse information to external and internal agents, but also, and even more importantly, to

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improve strategic management and to understand and learn about their internal process of value creation. As stated by Rudoplh & Leitner (2002, pp. 4), the IC report “reflects the knowledge production within a research organisation”. It is based on a model which intends to show the flows of knowledge within the company over time, supported by a set of indicators. Despite the importance of the quantitative measurements, descriptive elements are included in order to help the reader to analyse the information supplied by the indicator, which should be understood taking into account the context and circumstances of the organisation. This instrument aims to monitor and express the value of the organisation’s intangible assets. The process of implementation of the ICR has provided the opportunity to have an internal discussion of goals and strategies, trade-offs, human resource policy, etc. Moreover, it is itself a learning process. The Report is being published annually21, and it has been modified and improved reflecting the latest aims, strategic objectives and changes in the company. Although, changes in the organisation could mean that some indicators cannot be directly compared to those from the previous year due to restructuring measures in the company, the organisation has made every effort to build the indicator for each period, compare them over the course of time, assess the ongoing goal achievement and to establish the general trend of the indicators for the next period. Hence, the figures, commentaries and interpretations of the goal achievements represent an important strategic controlling tool. A total number of 57 indicators are organised followed the three main pillars of IC: Human Capital, Structural Capital and Relational Capital and also indicators about core processes (independent and contract research) and results (both commercial and research outcomes) (AIT, 2007, pp.54-55) Having analysed and reflected on this experience carried out for almost a decade, it is argued

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that the model can be a suitable system to make the knowledge creation process visible in an organization, and would be useful for other institutions with similar goals and missions. Indeed, it has been accepted as a model by other knowledgebased companies and research centers and has also become established internationally as a reference. For instance, one year after AIT´s first Report, in 2000, the German Aerospace Research Center and Space Agency (DRL) started to publish its own ICR based on the same principles and approaches although adapting the model to the special needs and context of DRL. Even more importantly, is the impact of this experience on the Austrian university sector. As explained in the previous section, this model was the main foundation of the ICR for Austrian universities.

4.2. The Innovation and Knowledge Management Institute (INGENIO) The Innovation and Knowledge Management Institute was created in 1999 as a joint initiative between the Spanish Research Council (CSIC) and the Polytechnic University of Valencia. It took shape as a center for reflection and action, open to learning, and with three strategic research lines: Innovation Systems, Organisational Innovation, and Knowledge Management. The latter research field works on new approaches, methodologies and tools to analyse and generate models that enable them to explain knowledge processes inside organisations. In the framework of the Second National Plan for Assessment of Quality in Universities in 2002 an interesting research project was developed on the use of knowledge management technologies to improve quality management in Spanish universities (INGENIO, 2002). The project aims to build a “Knowledge Portal” for Spanish universities. This tool should facilitate knowledge management through a set of “follow-up” indicators, identify good practices

and disseminate them. The process developed allows the research group to understand the most important support elements and the main barriers against knowledge management systems within the Spanish HE system. Accordingly, appropriate strategies can be defined, improving the quality of universities in a broad sense.

4.3. Intellectual Capital in HEROs HEROs (Intellectual Capital in Higher Education Institutions and Research Organisations), was an initiative led by the members of the European Association of Research Managers and Administrators (EARMA) in collaboration with the European Center for the Strategic Management of Universities (ESMU) that started in 2002. Based on the IC experiences in the private sector, the main goal of this initiative is “to raise awareness and disseminate good practice in the fields of managing and reporting intellectual capital among universities and research organisations” (Leitner & Warden, 2004). Its underlying idea is that HEIs and research centers have become increasingly familiar with the concept of intangibles in a knowledge-based economy where the economic impact of R&D activities is becoming ever more relevant (Warden, 2003). Accordingly, it intends to bring together those people sharing interests in the topic, allowing them to exchange information and build a network; develop ‘standards’ for valuing and reporting IC by HEROs (considering their different contexts) to facilitate benchmarking analysis; and define a common set of indicators, from which individual HEROs can select those most appropriate for their needs, context, strategies. On the basis that an organisation’s intellectual assets are specific to the organization and their value and relevance depend on their potential contribution to the institution’s key objectives, a necessary starting point would normally be the definition and diffusion of the mission and strategic

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goals of the specific organisation. However, it is recognised that not all HEROs are at the same stage in the formulation of their Mission Statement and this could hinder the adoption of the proposed Intellectual Capital approach. A Working Group on this topic – called “VIMaK in HEROs” (Valuing Intangibles and Managing Knowledge in Higher Education and Research Organisations) - was launched at the EARMA Annual Conference in Budapest in June 2002. The leaders of the W.G. are actively exploring the establishment of initiatives to continue to drive forward the research into this topic, as well as the identification and diffusion of ‘good practices’ across and beyond Europe. One of these was the proposal that an Equity investor in a startup developed by a HERO would use an IC based checklist that would concentrate on questions such as those included in the “FIVE*” acronym: •

•

•

•

•

Fit: The investors fit with the academic and proposed management team and feel that they will be able to work well together. IP: The existence of Intellectual Property (IP) and prospects for the generation of new IP and protected income streams arising from Intellectual Property Rights (IPR). Value: The size of, and timeframe for, the market opportunity, the upside potential of the investment proposition and the scope to leverage the opportunity based on investing. Exit: Exit options and the ability to groom the organisation to ensure that the exit value fully reflects the value creating potential of the business. 5* Finally, is this a 5 STAR opportunity, or a mediocre one?

A key aspect of this proposal is that before addressing the search for equity the HERO should carry out IC audit of the innovative product/ service, the Human Resources it can offer and

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the Relational Capital it can bring to the table. This in turn should lead to an evaluation of what complementary intangibles are needed and only then should the issue of additional financial resources be addressed.

4.4. PCI Project The PCI Project (2000-2003) developed an Intellectual Capital Indicators Programme applied to the research activity in universities and research centers in the Madrid Community in which four universities22 and two research institutes participated23. This Project aimed at studying how these organisations manage their knowledge in order to improve their processes and their relationships with other social agents, and how this information is disclosed to stakeholders in order to improve transparency (Comunidad de Madrid, 2002). The starting methodological framework was the Intellect Model (Instituto Universitario EUROFORUM, 1998), which was adapted to the features of the research institutions, defining the variables that define each of the three categories of intellectual capital (see next figure). The model attempts to (1) establish the general characteristics of the research processes in these organisations, (2) root cause-effect relationships between inputs and outputs within the research process, and, finally, (3) suggest how to manage intellectual capital inputs to improve research outputs in universities and research centers.

4.5. Managing Scientific and Technical Knowledge in the University of the Basque Country The University of the Basque Country (UPV/EHU) has developed a knowledge management casestudy project considering a key and strategic crossorganisational process: “Research-DevelopmentTransfer (R&D&T) of scientific knowledge”24. To reach their objective, a multidisciplinary working

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Figure 2. Intellectual Capital Structure linked to research activity in universities and research organizations in the Madrid Community (Source: Rodriguez Pomeda et al. (2003))

group was created to diagnose the current state of the management of the R&D&T process (identifying its main strength and weaknesses) and to design a new model to improve the process (Rodríguez et al., 2004). Accordingly, the project was developed in the following stages: • • • • •

Analysis of the existing models in similar institutions Identification of the key knowledge Diagnosis of the current situation Building a model for the process selected. This phase was the core of the project. Implementation of the model.

From the analytical point of view, the knowledge management in universities is described as all the processes undertaken to increase the IC of the institution. It was therefore created with the conviction that the effective management of knowledge in universities is crucial as a response to the new challenges that they have to face (Rodriguez et al., 2004): defending their leadership position in the field of creation and diffusion of knowledge in an increasingly competitive context, searching for new sources of funding, etc. The project identified knowledge types as drivers of the of R&D&T capital at the University

of the Basque country that need to be considered for the management of this process of research development and transfer of the scientific and technical knowledge (see Table 2). Like most of the experiences reviewed, this project covered the broadest IC taxonomy taking in the three categories already mentioned: Human, Structural and Relational capital Some of the lessons learnt in this project have been incorporated in the Knowledge Network, named “UNIKNOW”, which brings together the efforts of researchers at the university to implement the new management model for the R&D&T process.

5. TOWARDS A HOMOGENEOUS IC FRAMEWORK FOR EUROPEAN UNIVERSITIES25 5.1. The Observatory of European Universities The Observatory of the European University (OEU) was developed within the PRIME Network of Excellence and supported by the VI Framework Programme from June 2004 to December 2006. Its main aim was to improve the understanding of

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Table 2. Main driver of IC (Source: Based on Rodriguez et al (2004)) IC pillars

Main drivers identified H1. Scientific and technical knowledge H2. Specific skills and knowledge concerning the actual process of research

Human capital

H3. Knowledge and acceptance of the need for applied and basic research H4. Knowledge and acceptance of the need to share knowledge with other areas H5. Knowledge and acceptance by university researchers of the image of the university S1. Knowledge shared by researchers concerning the experience of knowledge transfer at the university S2. Knowledge of the creation and maintenance of value chains of scientific and technical knowledge

Structural capital

S3. Knowledge shared and accepted by university researchers concerning quality indicators for applied research S4. Knowledge of the management of the scientific and technical knowledge transfer process S5. Knowledge of the administrative management of projects and contracts R1. Knowledge of the needs in this area of companies, organizations and society R2. Knowledge of the ways in which companies and non-university bodies which engage in applied research meet the needs in this area of businesses and institutions, in terms of both methods and prices

Relational capital

R3. Knowledge of the ways of dealing with the private sector to form strategic alliances and co-operative projects R4. Knowledge of methods to develop the image of the institution as a producer of transferable scientific and technical knowledge R5. Knowledge by companies of the possibilities offered by the university as a supplier of scientific and technical knowledge

the importance of managing intangibles in public universities and in turn apply this in the efforts to improve their quality and competitiveness. However, being aware of the existence of different evaluation systems across Europe, as well as other endeavours to assess research activity, the Project did not try to build another assessment exercise, but to provide universities and research centres with the necessary tools and instruments for the strategic governance of research activities. In other words, to build a framework useful for university managers oriented, not towards accounting, but to facilitating strategic decisions. Sixteen universities and research institutes from eight different European countries worked together to develop a common framework of analysis and a battery of indicators to measure and compare the intangible elements related to research activities. Conscious of the complexity and multi-functions that characterise contempo-

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rary universities, the Observatory decided to start with the research activity, even though the general aim would be to extend the Project to teaching and other activities in the future. In order to provide a comprehensive and shared structure, and as a result of the joint work done by the research team and the participant universities, an analytical bi-dimensional framework was created. It is organised to encompass five thematic dimensions and five transversal questions that reflect the key or strategic questions related to the management of research activity. The “Thematic Dimensions” selected were: • • •

Funding: all budget elements, analysing revenues and expenses. Human Resources: administrative staff, researchers/teachers and PhDs. Academic Production: results from research activities in all the fields: articles,

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•

•

academic publications, non-written results, and the knowledge embodied in PhDs. Third Mission: all the activities and relations between university and non-academic partners: firms, non-profit organisations, public authorities, local government, and society as a whole. Governance: the process by which the university converts its inputs (funding and human resources) into research outputs (academic outcomes and third mission). Given its qualitative profile, it should be approached mainly as a characterization issue. The “Transversal Issues” considered were:

•

•

•

•

•

Autonomy: the university margin for manoeuvre is analysed. In other words, the degree of freedom the university has to allocate resources or to use funds. Strategic Capabilities: the university’s real ability to implement its own strategic choices. Attractiveness: the university’s capacity to attract resources (finances, people, equipment, collaboration, etc.) within a context of scarcity. Differentiation Profile: the main features of a university which distinguishes it from the other strategic actors. Territorial Embedding: geographical distribution of university involvement, contacts, collaborations, etc.

Thus, the so called ‘Strategic Matrix’ (see Table 326) is the result of the interactions of the aforementioned issues and dimensions. Each cell of the Matrix contains various key questions and a set of indicators. It was designed to be a valid instrument to characterise research activities in European universities, facilitate a common framework to compare them, identify the best performing universities and good practices, facilitate benchmarking

analysis, and help university managers to assess the strategic strengths and weaknesses of the organisation over time. Notice that, in line with the main goals of the OEU, the Strategic Matrix and the battery of indicators proposed have been designed for management purposes and are not stakeholder-oriented. In other words, the indicators have been conceived to aid strategic management of research activities. The Observatory was also able to define the indicators and to test the interest in those proposed and their feasibility. To cope with the need to diffuse more information to external agents and increase transparency, an “Intellectual Capital Report for Universities” 27 (ICU Report hereafter) was developed in parallel. While the ‘Strategic Matrix’ aimed to improve internal management, the ICU Report focused on improving transparency and helping to diffuse IC indicators in a homogeneous way. It was developed in the conviction that disclosure is the natural step after management, and the use of the IC approach and terminology will provide the greatest potential impact at the political and organisational level (Sánchez et al.., 2006a). The ICU Report has three different parts which in one way or another depict the logical movement from internal strategy (design of the institution’s vision and goals) and management to a system of indicators for disclosure (OEU, 2006): •

•

Vision of the institution (strategic objectives, strategic capabilities and key intangible resources) presenting the institution’s main objectives and strategy and the key drivers (or critical intangibles) to reach these objectives, Summary of intangible resources and activities. This part focuses on the intangible resources the institution can mobilize and the different activities undertaken to increase the value of those resources. The goal is to highlight the knowledge resources that need to be strengthened and to list

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Human Resources

Academic Outcomes

Third Mission

Governance

Autonomy

What is the degree of freedom of universities in the use of government funding? How significant is the portion of nongovernmental funding that goes to research?

What freedom is there to: • create new positions? • recruit and allocate staff? • specify staff duties?

Does the research portfolio reflect the university’s strategic choices of scientific fields or does it result mainly from national or European Framework Programmes?

What are the university structures in charge of the management of relations with non academic partners (transfer office, etc.)?

How much autonomy does the university have in defining and implementing its Strategic Plan?

Strategic Capabilities

What is the amount of resources devoted to research activity? How diverse is the funding basis for research?

What freedom is there to: • create new positions? • recruit and allocate staff? • specify staff duties?

What leverage does the university have to set scientific agendas in the various fields in which it is active?

How is the third mission presented in the Strategic plan? What us does the research staff make of the transfer office?

To what extent does the university have the ability to make strategic decisions and resource allocations according to the strategic plan?

Attractiveness

What is the fundraising capacity of the university? Which kind of external sources does the university attract?

How attractive is the institution for future and for qualified researchers? and for students?

What scientific partnerships appear in the university’s co-publications networks? What scientific partnership patterns appear in the university’s portfolio of participation in and coordination of international research programs?

What laboratories of non-academic actors are located on the university premises?

What is the structure of the budget by scientific fields and by type? What is the structure of the university’s own resources?

Is the institution clearly specialized in training of PhDs? What profile does the university choose for recruitment?

In which field does the university publish the major part of its scientific articles? What are the main instances of academic recognition that have been awarded to university researchers?

What are the main focal points of non academic collaboration for the university, in terms of industrial, cultural, and social relations?

Does the content of its strategic plan distinguish the university from other institutions?

What is the geographical origin of research funding?

Are there mobilityenhancing activities? Is there regional support for training and recruiting researchers?

What are the main geographical levels of scientific cooperation for the university?

What are the main geographical levels of the university’s industrial relation?

What are the degrees of participation of the different actors at different territorial levels of negotiation and influence?

Territorial Embedding

Funding

Differentiation Profile

Table 3. Analytical Framework of the Observatory of European Universities: Strategic Matrix (Source: Observatory of the European University (2006))

•

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the initiatives that have been taken, are ongoing or planned, to improve these resources; and finally, A system of indicators, to allow the members of the university and external parties

to see what the University is like. The system is organised following the general taxonomy of IC in three subcategories: Human, Organisational and Relational Capital (MERITUM, 2002).

Visualising the Hidden Value of Higher Education Institutions

5.2. Main Shortcomings A critical analysis of the strengths and weaknesses of the work done by the OEU shows a number of methodological shortcomings that should be taken into consideration when undertaking further development of IC models and indicators for HEIs in Europe. From a general perspective, the high degree of specificity of each university illustrates the enormous difficulty in providing a homogeneous analytical framework which is both useful and significant in the whole European HE context. Indeed, it can be said that there is as such no homogenous European HE sector in itself, despite the on-going endeavours to create the European Higher Education Area and the European Research Area. Hence, universities’ internal structures and governing modes rely on national, or even regional, conditions which make it extremely complex to analyse research activity comprehensively. When talking about universities, one of the first concerns that emerges is how to define the boundaries of the institution. Quite often, universities have research units located on campus and in hospitals, technological parks and hybrid centres associated with the institution. For this reason, the definition of the perimeter of the university is one of the starting points that should be addressed in order to account for and manage research activities in universities. Another important issue that the Observatory tackled is the level of data desegregation/aggregation. How much desegregation is useful for benchmarking analysis? How much is possible and cost-efficient? Is it feasible to break down data regarding an institution, faculties, departments, research groups, at an individual level? Although some indicators in the ICU Report refer to the institutional level, faculties have been taken as minimum unit of analysis. Breaking down indicators regarding research groups, labs or research centers has been considered extremely difficult (if at all possible), extremely costly and worthless

for comparative analysis. Indeed, this information is unavailable in most universities. Another difficulty when building the indicators selected was that most of the needed data were scattered throughout the university in different departments, institutes, administrative offices, etc. The process of gathering information may vary significantly from one to another. As a result, on the one hand, comparability between areas and issues is not always possible, and, on the other, managers have only a partial notion of the university’s activities. In order to solve this problem, some universities are starting to integrate all the databases in a common data warehouse, which will include economic data, human resources, teaching activities, research results, information about all students, etc. One of the main recommendations of the OEU was to encourage the development of integrated databases to facilitate internal management and benchmarking with other institutions in the future. As explained before, the strategic framework was built as a tool to improve internal management of research in universities across Europe and to increase their level of transparency. However, there is a risk of using the matrix only as a set of meaningless indicators without descriptive elements or narrative to contextualize the university’s profile. It is important to note that the proposed framework and its indicators were not conceived to create another ranking of universities. Obviously, this comparability exercise assumes the potential diffusion of the indicators. Although the figures were not considered, in principle, as confidential, some universities showed reticence or scepticism about the benefits that can be obtained from divulging this information. On the same lines, some university managers are specifically concerned about the misleading way in which data could be presented. Last but not least, one of the main drawbacks of the Project has been the lack of activity-related indicators. According to the classification provided by the MERITUM project (2002), we can distin-

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guish between intangible resources and activities. From the static point of view, intangible resources are defined as the stock or current value of a given intangible at a certain moment in time. They may or may not be expressed in financial terms. The resources can be both inputs (researchers, for instance) or outputs (publications, patents, spinoffs). From the dynamic point of view, intangible activities refer to the allocation of resources aimed at developing internally or acquiring new intangible resources, increasing the value of existing ones, or evaluating and monitoring the results of the two former activities. These are crucial in order to understand the institution, not today but in the medium-long term. They give revealing insights into the expected evolution of the organisation’s IC linked with its strategic objectives. In the particular case of HEIs, we can study, for instance the mechanisms to encourage researchers to produce academic outputs. This concrete activity reveals university research strategy and provides some hints about its future prospects. Neither the Strategic Matrix nor the ICU Report includes activity-related indicators. As argued by Sanchez et al. (2006), the main reason for this is that the OEU project built indicators mainly in accordance with the availability of data. Furthermore, this limitation is also related to one of the main goals of this exercise: creating a list of indicators that that could serve as a basis for comparability among institutions. For benchmarking analysis it may advisable to use resource indicators. Finally, only these resource indicators were selected bearing in mind the possible reluctance of university administrators to publish strategic initiatives. In our view, how a university is planning to improve a certain situation throws invaluable light on the strategic decisions that have been made internally. It is for these reasons that indicators on activities to be disclosed should be selected more carefully. Nonetheless, we do consider that building indicators that provide information on activities is paramount in future steps in ICU Report development.

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5.3. Testing the ICU Report: Lessons from the Spanish Case28 In order to check the applicability of the ICU Report, 30 interviews were held in two different Spanish universities aiming to get some insights about the perception of university managers about two main issues: (a) the usefulness of the ICU Report for management purposes, and (b) the willingness to disclose the indicators proposed. Two public universities were chosen: the Autonomous University of Madrid (UAM) and Pablo de Olavide University (UPO). The interviews were carried out during the period July 2006 to January 2007 with the main decision-makers and other positions that were considered strategically important due to the amount of information they receive and their decision-making. Among others, the Rector, Vice-rectors, Deans, Directors of Departments, members of the transfer units, Head of foundations, Director of Human Resources department, and professors from different disciplines who would all have different views of the university. The interviewees were first approached via personalised letters sent by e-mail. The correspondence provided an introduction to the researchers, the university affiliation, a synopsis of the research and a template with the selected topics to be discussed. Interviews lasted between about one and two hours (the shortest being 45 minutes and the longest two and a half hours), depending on the previous experience, background and involvement of the respondent. On most occasions, the interview was held in the respondent’s usual workplace. When it was necessary, follow-up questions and clarifications of issues discussed during the interview were pursued through e-mail exchanges or telephone conversations. The main conclusions from the interviews are the following: The perceived usefulness of the indicators was so high that no indicator was rejected. It was a very positive exercise in general since it clearly

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shows the acknowledged importance of this kind of information: interviewees appear to be aware that IC information is playing an even larger part in managing the research activity comprehensively. The general willingness to disclose IC information shows that the interviewees are aware of the need for transparency required by the European Union, and that they are no longer party to the traditional opacity in universities regarding funding distribution. Moreover, there is a growing interest in seeing the university taking on measurable objectives, which subsequently shows a commitment to society. In other words, agreeing on the disclosure of a list of indicators means that the university is willing to accept the commitment to transparency and accounting, which is a very positive signal. Although the empirical work has been done only in a limited number of cases, we do consider that the results could be applicable to other Spanish universities, and even to other European universities, with similar structures and governing modes.

6. CONCLUSION AND WAYS FORWARD Like other kinds of organisations moving in the knowledge-based economy, contemporary universities are immersed in far-reaching transformation processes that shape their behaviour, culture, internal structures and management systems. There is general consensus in the specialised literature about the idea that adapting to the new requirements implies the introduction of management systems, traditionally used by private companies, in order to govern universities according to the criteria of efficiency and effectiveness. As described in this Chapter, the challenge of measuring and managing intangibles and IC in universities and research centres has become a major issue since the 90s, not only for academics but also for practitioners and politicians. This growing interest is being translated into different initiatives with

different implications and impacts on university governing structures and transparency. The analysis of the most significant experiences of the IC models developed in (or for) European universities presented in this Chapter, shows that there is a clear need of new methods of measuring and managing the different activities of the European HEIs, particularly their research activity. Our argument is that IC framework approaches seem to be a potential answer for universities to deal not only with the new managerial needs but also with the transparency requirements. This idea coincides with the approach presented in the PRIME position paper: “IC Reports answer a growing need for accountability and especially fulfil the need for the transparency and competitiveness required in the Bologna process” (Schoen, et al., 2007, pp.2). The European Commission recommends the IC reporting in universities and research organisations as a way to improve their internal management and their transparency level (European Commission, 2006). However introducing new managerial tools in these complex institutions requires a deeper understanding of their governing mode and their margin of manoeuvre to define a strategic approach and include new managerial mechanisms. For this reason the way that these organisations manage their own “mixture”–their educational schemes, research intensiveness and profile, and their third mission activities–is a key element of their competitive position in the HE sector now, and will be even more so in the future. In our opinion, the organisations’ management perspective will lead to differentiation. This idea is widely acknowledged in the case of private firms, whose managerial approach and tools are considered crucial to sustain their competitive advantage in the market and attract professionals. In public HEIs, it can be expected that a good and transparent governing mode will positively affect researcher and student mobility, inter-institutional cooperation, recognition of the institution, and, in

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general, the excellence, attractiveness and competitiveness level of the organisation. Furthermore, we agree with the idea that for policy makers “the introduction of management structures and managerial forms of decisionmaking will make it possible to provide high quality education to more people and create more relevant research output at the same or even lower cost” (Schoen at al. 2007, pp.4). It is important to highlight again that the IC framework must be regarded as having a two-fold objective (European Commission, 2006 and Marr, 2005). It is used both as a management tool to help develop and allocate resources as well as a communication device outside the institution to attract resources and develop relationships. This process depicts the logical movement from the identification of the elements that are linked to the organisation’s value creation and internal strategy, to the measurement and management of the critical intangibles that have been identified and the disclosure of a battery of indicators. Although the ultimate goal of identifying and measuring IC is to improve internal management, disclosure seems to be the logical conclusion of the IC management process: communicating to stakeholders the university’s abilities, resources and commitments in relation to its strategy. The analysis of the existing experiences aimed at shedding some light about two main questions: (a) What are the benefits and obstacles of implementing an ICR in HE institutions? And (b) Shall we base the introduction of IC models and reports on a voluntary or mandatory basis? Regarding the first research question, the experiences described in this Chapter prove that the IC approaches are useful for both internal and external communication. When applying them, the most important positive effects or benefits that universities can obtain are similar to those obtained in private companies. The most significant are: A.

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Improving Internal Management:

HE institutions will obtain the following benefits: •

•

•

Defining and updating the mission statements of HE institutions and helping to identify priorities in terms of research and teaching activities, and when possible, defining the organisation’s profile more clearly. Interestingly, the analysis of the existing experiences has revealed the crucial importance of governing research activity and the lack of managerial instruments to do this effectively. There is a strong need to include instruments and indicators for managing research activity in the long run and from a broader and more comprehensive perspective. It would therefore appear that universities are aware of the need for a managerial tool that could present all the information homogeneously and incorporate a dynamic perspective. The ICR can help to identify the strengths and weaknesses of the research activity in each institution and define actions to reinforce or sustain it that can be decided and implemented for the particular institution (for instance, investment in equipment and infrastructure, creation of new research posts, promotion of emerging groups with younger researchers, or inter-institutional collaboration frames). Therefore, when research priorities cannot be explicitly defined, the ICR could be a mechanism used to reflect the knowledge creation process, present the research projects or fields in which the organisation is already working, and highlight the actions to be implemented in order to consolidate and extend the research activity. Thus, it promotes an internal process of learning about the institution’s structure and performance. Linking strategic objectives to long-term targets and annual budgets. As argued when analysing IC models at firm level,

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•

the starting point to implement a research measurement system, is the discussion and definition of corporate goals and strategies, because “the process of acquiring, applying and exploiting knowledge starts with the definition of specific goals” (Leitner & Warden, 2004, pp.8). Nowadays as a consequence of increasing autonomy and accountability in universities and research centers and the growing importance of the performance agreements, these organisations are forced to define Mission Statements or Strategic Plans. These processes are expected to promote an internal process of learning about the institution’s structure and performance, facilitate strategic discussions among the members of the organisation and discussion on the intangible value drivers and success factors, which are also objectives of the IC approaches. Universities should improve their performance and the way they manage their activities in order to differentiate themselves in a more competitive environment and attract more and better students and research and teaching staff (Shoen et al., 2007). We consider that introducing IC approaches into the governance of our HE institutions will improve internal management and hence be an important competitive advantage for the pioneer universities.

B.

Improving transparency and relationships with stakeholders:

•

The growing importance of accountability and transparency for the public sector in general, and for public HE institutions in particular, is pressuring universities to report more information to their stakeholders which are mainly governments and accreditation agencies. In our view, IC approaches facilitate the monitoring of the

•

•

•

achievement of goals, assess the organisation’s performance over the course of time and increase the level of transparency. This issue is not only an internal priority for universities but has become crucially important since other external agencies and governments are supervising the academic outputs and linking public funding to research results through new performance and funding agreements. According to the previous point, the ICR provides comprehensive and valuable information to stakeholders: students, teaching personnel and researchers, Ministries, funding organisations, businesses, and society as a whole. In the case of the Austrian university reform, the ICR is explicitly recognised as a communication tool with the Federal Ministry. It can enhance competitiveness. For instance, when a university needs to renew a grant or attract additional funds for research, assessing performance is of crucial importance. Accordingly, the ICR can facilitate the presentation of results, which could contribute to attracting funds to the detriment of other lower-performing competitors. However, if the university is deteriorating, disclosure may prejudice the chances of getting future grants. Since the private firms are standardizing their reporting practices on intangibles through IC models and reports, the use of the same language or terminology could be a good mechanism for improving the communication between both spheres. Therefore, implementing IC Reports to diffuse information could have a positive impact on university-industry collaborations and third mission activities.

However, adopting management systems from the sphere of private companies is not always easy given the traditional behaviour of universities. For

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instance, although the aim of the implementation of the ICR is to improve internal management and provide more detailed information to stakeholders, some universities can limit their commitment to only publishing a set of indicators without really learning about their knowledge creation value or using them in the definition of their strategic priorities. Indeed, there is a high risk of using the ICR only as a mechanism for funding allocation, as may have happened in the Austrian case. Defining a mission statement and strategic objectives, the basic premise for any profit-making organisation, is still a novelty for many universities. So even though it is not a direct goal of the reform to encourage universities to define their research priorities and strategic lines, the process of implementing ICR forces HE institutions to go one step back and start identifying their mission, vision, and key processes. Otherwise, the final result will be a set of meaningless indicators that do not provide comprehensive information about the institution. Among the obstacles or critical aspects hindering the management of IC in universities, we can highlight: •

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The diversity and heterogeneity of fields, areas of knowledge and disciplines, even within the same University, which makes it difficult to have a unique ICR for the whole institution. The aggregation of indicators on the organisational level is problematic if the organisational units are heterogeneous, and could lead to a pointless report unable to present a real picture of the institution. Although there is a general trend within HE institutions to define and develop strategic plans and mission statements, they are not all at the same stage of formulation. Since the ICU Report should be based on the vision and strategic objective of the organisation, universities lagging behind in this process would have serious difficulties to benefit from the learning process of the

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definition and implementation of such a model. Use of only a set of indicators, missing out the descriptive elements. In our opinion, the interpretation of the indicators is crucial, and contingent on the context and aims of the organisation/unit. The possible manipulation of data, as pointed out by Altengurger & Scahffhauser (2006) for the Austrian case, could be a risk that should be compensated with the introduction of auditing and control mechanisms. The performance agreement developed between universities and ministries or local governments (as in the Spanish, Norwegian and French cases) is a funding allocation mechanism. Thus, it can be considered a ‘zero sum game’, which means that if one university gets more funds because of its better performance, it implies that another will get less, and there is, at least, a temptation to manipulate data to get better results, and, thus, obtain more funds. In order to prevent this manipulation which could distort the system, external auditing of data is crucial. Finally, another issue that becomes especially relevant when talking about reporting IC information in universities is timing. In all the experiences analysed, the ICR is published annually following the financial year. However, in Europe, the academic year does not correspond with the financial year. Furthermore, research activity is often, if not always, long term. Both situations make it difficult for the data collection process and the presentation of information in an ICR to be made every year.

This chapter has presented some of the most outstanding experiences across Europe, clearly distinguishing between the case of Austrian universities that have to implement ICR by law and other successful initiatives developed in different

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HE institutions on a voluntary-basis. By doing so, we reflect on the second research question trying to give some insights into the ongoing debate “voluntary approach versus mandatory basis”. The Austrian experience shows that it is possible to create a radical shift within the university sector through changes in legislation. UOA1993 and UG2002 aimed to increase universities’ institutional autonomy creating new governing bodies and introducing new management systems. Making it a legal obligation for universities to submit an ICR every year is a crucial step in the proliferation of IC models world-wide, not only for management purposes but for disclosing information to stakeholders. Having homogeneous ICRs throughout the country will facilitate benchmarking analysis and comparative studies which will help the decision-making process and improve the articulation of public policies. However, as the trials developed in the University of Vienna show, the law cannot prevent problems, difficulties and conflicts of interest in the implementation process. For this reason, a cultural change in the academic community is required in order not only to accept changes in the governing structures, but new ways of working, new assessment processes, new labour positions, and new accountability at all levels. In other words, accepting a new conceptualisation of the university will require more than a top-down reform. On the other hand, the increasing awareness of the importance of measuring, managing and reporting on intangibles has led some universities and research institutions to build their own model voluntarily. The models presented in this Chapter are a good example of the endeavours that some leading organisations are making towards better management and more transparency. Since it is a self-imposed initiative in these cases, it is not expected that the implementation process of the IC model will represent a problem and the institution will really learn from the process. However, the proliferation of different models with different approaches and different sets of indicators will

not mitigate the problem of comparability among institutions. Regarding the debate, “unique and mandatory model versus different models based on a voluntary approach”, it is not so simple to adopt a clear position. On the one hand, the obligation to report on IC with a common battery of indicators facilitates comparative analysis among faculties and universities, increasing transparency in the whole HE system. Moreover, the possibility of publishing additional indicators will benefit external agents, mainly funding agencies, in their decision-making processes. On the other hand, it is crucial to understand that IC assets are context specific. Accordingly, each institution should identify their own key intangibles according to the contribution to the value creation process and taking into account the strategic objectives. This could lead us to think that it would be better to build specific models for each organisation, which only could be done with voluntary initiatives. As argued by the OCDE (2001, pp.18), ICRs “should be prepared in line with the specific features of each organisation. There is no one-size fits-all formula”. If IC Reports are designed around the specific characteristics of each organisation, standardization is difficult and comparative analysis is limited. Besides the clear advancements in managing and reporting on intangibles and IC achieved by universities and research centres in the last decade, more efforts should be made regarding the issue of standardisation of the indicators. The RICARDIS document (European Commission 2006) explains how important it is to achieve general and homogeneous standardization to help comparability, interpretability and information credibility. When referring to companies, it proposes three levels of indicators: organisation-specific indicators; sector-specific indicators; and general indicators. Regarding this proposal for standardization, we should consider the basic set of indicators as those that are mandatory for all organisations and institutions, for instance, related to funding. Using

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the same thinking, there would be a set of sectorspecific indicators (only mandatory for them), as for instance, degrees of autonomy, which would be of more interest to universities than private companies. Lastly, organisation-specific indicators can be chosen by each company or institution taking in individual considerations.

7. REFERENCES Altenburger, O., & Novotny-Farkas, Z. (2005, October). Intellectual Capital Reports for Universities – A Trial Intellectual Capital Report at the University of Vienna. Paper presented at the EIASM Workshop on Visualising, Measuring, and Managing Intangibles and Intellectual Capital. Ferrara, October 18-20. Alternburger, O., & Schaffhauser-Linzatti, M. (2006, October). Controlling Universities´ Intellectual Capital: Are the Recently Implemented Austrian Instruments Adequate? Paper presented at the EIASM Workshop on Visualising, Measuring, and Managing Intangibles and Intellectual Capital, Maastricht, October 25-27. Andriessen, D. G., & Stam, C. D. (2004). The Intellectual capital of the European Union. Diedem, The Netherlands: Centre for Research in Intellectual Capital, Inholland University of Professional Education. Arrow, K. J. (1962). The Economic Implication of Learning by Doing. The Review of Economic Studies, 29(June), 155–173. doi:10.2307/2295952 Austrian Institute of Technology. (2007). ARC Intellectual Capital Report. Retrieved November 20, from http://www.ait.ac.at/downloads/about/2007_ ARC_Intellectual_Capital_Report_en.pdf. Austrian Research Center. (2000). Intellectual Capital Report 1999. Austrian Research Centers, Seibersdorf.

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Becker, G. (1975). Human Capital (2nd ed.). Chicago: Chicago University Press. Beerkens, E. (2003). Higher Education in Austria. Country Report. CHEPS Report, Center for Higher Education Policy Studies. Bontis, N. (2004). National Intellectual Capital Index. The Benchmarking of Arab Countries. Journal of Intellectual Capital, 5(1), 13–39. doi:10.1108/14691930410512905 Cañibano, L., & Sánchez, P. (2004). Measurement, Management and Reporting on Intangibles. State of the Art. In Cañibano, L., & Sánchez, P. (Eds.), Reading on Intangibles and Intellectual Capital (pp. 81–113). Madrid: AECA. Comunidad de Madrid. (2002). Capital Intelectual y Producción Científica. Dirección general de Investigación. Madrid, Spain: Consejería de Educación, Comunidad de Madrid. Danish Trade and Industry Development Council. (2003). Intellectual Capital Statements. The New Guidelines. The Danish Trade and Industry Development Council. Denison, E. F. (1962). The Sources of Economic Growth in the United States and the Alternatives Before Us. New York: Committee for Economic Development. Donato, F. (2005, October). The orientation towards the management and the reporting of the intangibles in cultural organisations: a starting point or an escape route? Paper presented at 1st Workshop on Visualising, Measuring and Managing, Ferrara, Italy, October 18-20. Elena (2007). Governing the University of the 21st Century: Intellectual Capital as a Tool for Strategic Management. Lessons from the European Experience. Unpublished doctoral dissertation, Autonomous University of Madrid, Spain.

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ERAWATCH. (2009). Activities of EU Member States with Regard to the Reform of the Public Research Base. ERAWATCH Network ASBL. Retrieved December 7, from http://cordis.europa. eu/erawatch/index.cfm?fuseaction=reports.home European Commission (2000). Innovation Policy in a Knowledge-Based Economy. A Merit Study Commissioned by the European Commission Enterprise Directorate General EUR 17023. European Commission. (2003). The role of the Universities in the Europe of Knowledge. Brussels 05/02/2003, COM (2003) 58 Final. European Commission (2006). Reporting Intellectual Capital to Augment Research, Development and Innovation in SME´s (RICARDIS). European Commission, Directorate General for Research. EUR 22095 European Network for Quality Assurance in Higher Education. (2003). Quality Procedures in European Higher Education. ENQA Occasional Papers, Nº 5, Multiprint, Helsinki, Finland. Federal Ministry of Education, Science and Culture (2002). University Organisation and Studies Act – University Act 2002. Nº 120/2002. Federal Ministry of Education, Science and Culture (2006). Verordnung ueber die Wiessenbilanz (Wissensbilanz-Verordnung-WBV), BGB1, II Nr.63/2006. Freeman, C., & Soete, L. (1997). The Economics of Industrial Innovation (3rd ed.). London, New York: The MIT Press. Gorey, R. M., & Dovat, D. R. (1996), Managing on the Knowledge Era, New York Habersam, M., & Piber, M. (2003). Exploring Intellectual Capital in Hospitals: two Qualitative cases in Italy and Austria. European Accounting Review, 12(3), 753–779. doi:10.1080/09638180 310001628455

Instituto de la Gestión de la Innovación y del Conocimiento (INGENIO) (2002). Portal de Conocimiento del II Plan de la Calidad de las Universidades. Instituto Universitario Euroforum El Escorial. (1998). Medición del Capital Intelectual, Ed. Instituto Euroforum El Escorial, Madrid. Japanese Ministry of Economy, Trade and Industry (2005). Guidelines for Disclosure of Intellectual Assets Based Management. METI, October, 2005. Kendrick, J. W. (1974). The Accounting of Human Investment and Capital. Review of Income and Wealth, 20(December). Leitner, K. H. (2004). Intellectual Capital reporting for universities: conceptual background and application for Austrian Universities. Research Evaluation, 13(2), 129–140. doi:10.3152/147154404781776464 Leitner, K. H., Schaffhauser-Linzatti, M., Stowasser, R., & Wagner, K. (2005). Data Envelopment Analysis Method for Evaluating Intellectual Capital. Journal of Intellectual Capital, 6(4), 528–543. doi:10.1108/14691930510628807 Leitner, K. H., & Warden, C. (2004). Managing and Reporting Knowledge-based Resources and Processes in Research Organisations: Specifics. Lessons Learned and Perspectives Journal of Management Accounting Research, 15(1), 33–51. Marr, B. (Ed.). (2005). Perspectives on Intellectual Capital. Multidisciplinary insights into Management, Measurement and Reporting. Amsterdam: Elsevier Inc. Meister-Scheytt, C., & Scheytt, T. (2005). The Complexity of Change in Universities. Higher Education Quarterly, 59(1), 76–99. doi:10.1111/ j.1468-2273.2005.00282.x

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Meister-Scheytt, C., & Scheytt, T. (2006, July). Boards at Work: Enacting Governance in the ‘New’ Austrian University. Paper presented at International Research Conference, Lancaster, United Kingdom, 24-26th July, 2006. MERITUM. (2002). Guidelines for Managing and Reporting on Intangibles (Intellectual Capital Statements). Madrid: Vodafone Foundation. Nelson, R., & Winter, S. G. (1982). An Evolutionary Theory for Economic Change. Cambridge, MA: Harvard University Press. Nonaka, I., & Takeuchi, H. (1995). The Knowledge-Creating Company. New York: Oxford University Press. Observatory of the European University. (2006). Methodological Guide. Final Report of the Observatory of the European University. PRIME Project. OECD (1996).The Knowledge-Based Economy. Paris, OECD. OCDE/GD(96)102. OECD (2001). Knowledge Management: learning-by-comparing experiences from private firms and public organisation. Centre for Educational Research and Innovation Governing Board, CERI/ CD(2001)2, 2 July 2001. Pasher, E. (1999). The Intellectual Capital of the State of Israel. Herzlia Pituach, Edna Pasher PhD and Associates. Remble, A. (1999). Invest in Sweden: Report 1999. Stockholm: Halls Offset AB. Rodríguez Castellanos, A., Landeta Rodríguez & Ranguelov. (2004). University R&D&T capital. What types of Knowledge drive it? Journal of Intellectual Capital, 5(3), 478–499. doi:10.1108/14691930410550417 Rodríguez Pomeda, J., Villar Mártil, L., Murcia Rivera, C., & Merino Moreno, C. (2003). Indicadores de Capital Intelectual en las Universidades y Organismos Públicos de Investigación en la Comunidad de Madrid. Madrid, Spain.

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Rudolph, B., & Leitner, K. (2002, November). Research Organisations discover their intellectual capital: Experiences of the organisations DLR and ARC and the potential for comparing IC measures. Paper prepared for the Conference The Transparent Enterprise. The Value of Intangibles, Autonomous University of Madrid and Ministry of Economy, November 25-26, 2002, Madrid, Spain. Sánchez, P., & Elena, S. (2006). Intellectual Capital in Universities. Improving Transparency and Internal Management. Journal of Intellectual Capital, 7(4), 529–548. doi:10.1108/14691930610709158 Sánchez, P., Elena, S., & Castrillo, R. (2006a). The Intellectual Capital Report of Universities. Guidelines for disclosing IC information. In PRIME-OEU Methodological Guide (pp. 223– 251). Observatory of the European University. Sánchez, P., Elena, S., & Castrillo, R. (2006b, October). Intellectual Capital Management and Reporting for Universities: The case study of the Autonomous University of Madrid. Paper presented at the 2nd Workshop on Visualising, Measuring and Managing Intangibles and Intellectual Capital, Maastricht, The Netherlands, 25-27 October 2006. Sánchez, P., Elena, S., & Castrillo, R. (2006c, November). Intellectual Capital Management and Reporting for Universities: Usefulness, Comparability and Diffusion. Paper presented at the International Conference on Science, Technology and Innovation Indicators. History and New Perspectives. Lugano, Switzerland, 15-17 November 2006. Schoen, A., Laredo, P., & Bellon, B. &Sánchez, P. (2007)PRIME Position Paper Working Paper. Shultz, T. (1961). Investment in Human Capital. American Economic Review. Papers and Procedures, 51, 1–17.

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Society for Knowledge Economy. (2005). Australian Guiding Principles on Extended Performance Management. A Guide for Better Managing, Measuring and Reporting Knowledge Intensive Organisational Resources. GAP Congress on Knowledge Capital, Society for Knowledge Economy, Melbourne. Solow, R. (1957). Technical Change and the Aggregate Production Function. The Review of Economics and Statistics, 39, 312–320. doi:10.2307/1926047 Sporn, B. (1999). Adaptative University Structures. An analysis of adaptation to socioeconomic environments of US and European Universities. Higher Education Policy Series 54. London, Philadelphia: Jessica Kigsley Publishers. Titscher, (Eds.). (2000). Universitäten im Wettbewerb (München). In Leitner (2004) Intellectual Capital reporting for universities: conceptual background and application for Austrian Universities. Research Evaluation, 13(2), 129–140. Vagnoni, E., & Castellini, M. (2005, October). Designing Intellectual Capital Report: A Study on Health Care Organisations. Paper presented at 1st Workshop on Visualising, Measuring, and Managing Intangibles and Intellectual Capital”, Ferrara, Italy, 18-20 October 2005 Viedma, J. M. (2003, January). A Methodology and a Framework for measuring and managing Intellectual capital in Cities. A Practical Application. Paper presented to the 6th World Congress on the Management of Intellectual Capital and Innovation, Hamilton, Canada, 15-17 January, 2003. Warden, C. (2003). Managing and Reporting Intellectual Capital: New Strategic Challenges for HEROs. IP Helpdesk Bulletin, 8, April-May.

Wissel, C. V. (2004, November). New Modes of Self-description: University’s Reaction in a Changing Environment”. Paper presented at the Workshop Towards a multiversity? Universities between national traditions and global trends in higher education, November 11-13, 2004, Bielefed, Germany.

8. ADDITIONAL READING Allen, D. K. (2003). Organisational climate and strategic change in higher education: Organisational insecurity. Higher Education, 46, 61–92. doi:10.1023/A:1024445024385 Almqvist, R., & Skoog, M. (2007). Colliding discourses? New Public Management from an Intellectual Capital Perspective. In Chaminade, C., & Catasus, B. (Eds.), Intellectual Capital Revisited: Paradoxes in the knowledge-intensive organization. Cheltenham, UK: Edward Elgar. Amaral, A., Jones, G., & Karseth, B. (Eds.). (2002). Governing higher education: National Perspectives on Institutional Governance. The Netherlands: Kluwer Academic Publishers. Amaral, A., Meek, V. L., & Larse, I. M. (Eds.). (2003). The Higher Education Managerial Revolution. The Netherlands: Kluwer Academic Publishers. Arviran, N. K. (2006). Modelling knowledge production performance of research centres with a focus on triple bottom line benchmarking. International Journal Business Performance Management, 8(4), 307–327. doi:10.1504/ IJBPM.2006.009611 Basic, M. (2005, October). Model of Evaluation and Measurement of Intangibles Assets/Intellectual Capital in Bosnia and Herzegovina. Paper presented at the 1st Workshop on Visualizing, Measuring, and Managing Intangibles and Intellectual Capital, Ferrara, Italia, 18-20 October.

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Boer, H. Enders, J. & Leisyte. (2007). Public sector reform in Dutch higher: education: the organizational transformation of the University. Public Administration, 85(1), 27–46. doi:10.1111/j.14679299.2007.00632.x Bonaccorsi, A., & Daraio, C. (2007). Universities and Strategic Knowledge Creation: Specialization and Performance in Europe. Edward Elgar. Bossi, A., Fuentes, Y., & Serrano, C. (2005). Reflexiones en Torno a la Aplicación del Capital Intelectual en el Sector Público. Revista Española de Financiación y Contabilidad, XXXIV(124), 211–245. Bounfour, A. (2005). Modelling Intangibles, transaction regime versus Community regime. In Bounfour, A., and Edvinsson (Ed.), Intellectual Capital for Communities, Nations, Regions and Cities (Chapter 1). Boston: Elsevier ButtwerworthHeinemann. Cañibano, L., & Sánchez, P. (2004). Measurement, Management and Reporting on Intangibles. State of the Art. In Cañibano, L., & Sánchez, P. (Eds.), Reading on Intangibles and Intellectual Capital (pp. 81–113). Madrid: AECA. Clark, R. B. (2004). Sustaining Change in Universities. Continuities in Case Studies and Concepts. Bershire, England: SRHE and Open Press Imprint. Croatian Chamber of Economy. (2002). Intellectual Capital. Efficiency on National and Company Level. Croatia: Croatian Chamber of Economy and Deloitte. Elena, S. (2008). Hacia un nuevo concepto de gestión de la universidad pública: la introducción de modelos de capital intelectual. In Cañibano, (Eds.), Economía del Conocimiento y la Innovación. Nuevas aproximaciones a una relación compleja (pp. 197–224). Madrid: E. Pirámide.

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Elena, S. (2009). Intellectual Capital approaches within Higher Education Institutions: Lessons from the Autonomous University of Madrid. In Value Added Partnering in a Changing World, International Series on Technology Policy and Innovation. Purdue University Press. European Commission. (2003). Study on the Measurement of Intangibles Assets and Associated Reporting Practices. Enterprise DirectorateGeneral. Brussels, April, ENTR/01/054. Guthrie, J., Carlin, T., & Yongvanich, K. (2004). Public Sector Performance Reporting: The Intellectual Capital Question? MGSM Working Papers in Management. Macquarie Graduate School of Management Huisman, J., & Currie, J. (2004). Accountability in higher education: Bridge over troubled water? Higher Education, 48, 529–551. doi:10.1023/ B:HIGH.0000046725.16936.4c Jacobs, B., & Ploeg, F. (2006). Guide to reform of higher education: a European perspective. Economic Policy, (July): 535–592. doi:10.1111/ j.1468-0327.2006.00166.x Leitner, K.-H. (2007). Intellectual capital reporting and evaluation in Austrian universities: relationships and complementarities. In Platform Research and Technology Policy Evaluation and Austrian Council for Research and Technology Development (Ed.), pp. 97-105. Austria. Lev, B. (2000). Intangibles: Management, Measurement and Reporting. Washington, DC: Brookings Institution Press. Molas-Gallart, J. (2005). Defining, measuring and funding the Third Mission: a debate on the future of the university. Coneixement i Societat, 7(January-April), 6–27.

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Mouritsen, J., Thorbjornsen, S., Bukh, P. N., & Johansen, M. R. (2005). Intellectual capital and the discourses of love and entrepreneurship in New Public Management. Financial Accountability and Management, 21(3), 279–290. doi:10.1111/ j.0267-4424.2005.00221.x Musselin, C. (2005). Change and Continuity in Higher Education Governance? Lessons drawn from Twenty years of National Reforms in European Countries. In Bleiklie, I., & Henkel, M. (Eds.), Governing Knowledge. A study of Continuity and Change in Higher Education (pp. 65–80). Dordrecht: Springer. Sánchez, P. & Elena, S. (2007). La Gestión Estratégica de la Universidad Contemporánea: Reflexiones sobre la Potencialidad de los Modelos de Capital Intelectual. Madrid I+D, Revista de Investigación en Gestión de la Innovación y Tecnología, 42(May-June).

9. KEY TERMS AND DEFINITIONS Intellectual Capital: The term Intellectual Capital can be defined, in a broad perspective, as the resources on which the organisation relies, including the human capital resources (their skills, capacities, knowledge, experience, training, etc.), those of the organisation itself (the routines, processes, organisational culture, etc.) and its external relations (with customers, distributors, suppliers, the public administration, competition, etc.) and how the organisation is perceived (its image, attractiveness, reliability, solvency, etc.) Intellectual Capital Report: Intellectual Capital Report is a tool for visualizing organisations´ inputs, outputs and processes comprehensively. It is used to identify and deliver information on strategy, aims, visions, and tangibles and intangible activities and resources (based on indicators) of an organisation.

Higher Education Institutions: Higher education institutions are formal organisations, public or private, which act in post-secondary education and research areas. Institutions of higher education include colleges and universities and professional schools in such fields as law, medicine, business, and art. Management: The term management comprises of the interlocking functions of formulating corporate-policy and organizing, planning, controlling, and directing the organisation’s resources to achieve the strategic objectives Accountability: Accountability could be defined as the obligation to report to others, explain, justify, and answer questions about how resources have been used and to what effect Transparency: Transparency in the field of business management and public administration could be understood as a minimum degree of disclosure to which agreements, dealings, practices, and transactions are open to all for verification. Transparency implies openness, communication, and accountability. Transparent procedures include open meetings, financial disclosure statements, the freedom of information legislation, budgetary review, audits, etc. Institutional Autonomy of Universities: Institutional autonomy may be defined as the power to make decisions within universities. It refers, among other things, to the power to appoint the heads of different units (Rector, Dean, Head of Department, etc.), the define priorities and strategic objective, manage human resources and deal with internal political conflicts.

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European Commission (2006) http://www.incas-europe.eu/ Section mainly based on: Sanchez & Elena (2006), and Sánchez, Elena & Castrillo (2006b, c) European Association of Research Managers and Administrators For experiences in Australia and USA see (Coaldrake et al., 2003) For other examples In Europe: Netherlands (de Boer, 1999), Scotland, (Sizer & Cannon, 1999) England and Wales (Bennett, 2002; Knight, 2002; Middlehurst, 2004; Salter & Tapper, 2002; Shattock, 1999, 2002), UK (Coaldrake et al., 2003) In Leitner (2004; p. 132) In this context we are referring to the concept of institutional autonomy meaning the margin of manoeuvre to make strategic decisions, not in the Humbodltian sense of academic freedom of professors and researchers The implementation process started in 1994/95 academic year (Sporn, 1999) over a period of 6 years, and was fully implemented in 1999 when the undertaking was completed by the three biggest Austrian universities: Vienna, Innsbruck and Graz. In general terms, a University Organisation Act (UOA) is the legal mechanism used by the Federal Government through which the general mission of the universities is determined, as well as the guiding principles for teaching and research, the university’s location, the competences of the governing bodies and the internal procedure. In practical terms, this meant that the Rector and the dean became “much more powerful than previously” (Pechar, 2003; p.7). University Organisation Amendment Act and Universities of the Act Organisation Amendment Act, Nº 120/2002 / 9th August, 2002. For more information see http://www. bmbwk.gv.at

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See Federal Ministry of Education, Science and Culture (2002). Other relevant implications are, among others, the implementation of performance agreements, introduction of quality management systems and evaluation procedures. Federal Ministry of Education, Science and Culture (2006). Federal Ministry of Education, Science and Culture (2006). Formerly called the Austrian Research Center (ARC). See Sub-section 4.1. Notice that the trials were developed before the final definitions of the battery of indicators The last ICR available in English and German refers to the year 2007. See: http://www.ait. ac.at/downloads/about/2007_ARC_Intellectual_Capital_Report_en.pdf Autonomous University of Madrid, Carlos III University, Polytechnic University and Rey Juan Carlos University., National Center of Biotechnology (CBN) and Energy, Environmental and Technology Research Center (CIEMAT) Autonomous University of Madrid, Carlos III University, Polytechnic University and Rey Juan Carlos University., National Center of Biotechnology (CBN) and Energy, Environmental and Technology Research Center (CIEMAT) The research project “Knowledge management at a public university: the process if research, development and Transfer of Scientific and technical knowledge” was set up in 2000 at the UPV/EHV with a four year time frame. This Section is based on the discussions held in the different OEU International Meetings: Pisa (July, 2004), Manchester (January, 2005), Lausanne (February, 2005), Madrid (April, 2005), Budapest (December 2005), Paris (June, 2006) and Lugano (November, 2006) and in the final OEU document (2006)

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“Methodological Guide”.1 June 2004-December 2006 Table xx includes some of the key questions for each dimension To see the ICU Report and the set of indicators see “The Intellectual Capital Report

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for Universities”, Chapter VII of the Methodological Guide of the Observatory of European Universities This Section is part of the empirical work developed by Elena (2007) in her PhD Dissertation.

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Chapter 10

The Complex Issue of Measuring KM Performance: Lessons from the Practice Enrico Scarso University of Padua, Italy Ettore Bolisani University of Padua, Italy Antonella Padova Ernst & Young, Italy

ABSTRACT Most companies that are deeply investing in Knowledge Management (KM) initiatives encounter substantial difficulties in assessing the effectiveness of these programmes. Actually, measuring the impact of KM projects is still a puzzling problem both at the conceptual and operative level. However, measuring their performance is necessary for monitoring their progress and for successfully managing and allocating resources, as well as to maintain the support and commitment by the top management. Although several KM performance evaluation approaches have been proposed in literature, they are still far from becoming an established practice. The chapter aims at discussing this issue by placing it in a business context. First, the literature on KM performance evaluation is briefly reviewed, and the main methods currently used are classified. Then, the practical experience of a multinational company is discussed, with the purpose to describe the problems that practitioners face in their daily experience, and provide insights into the possible improvements of KM performance measurement.

INTRODUCTION An often-cited maxim says that you can’t manage what you can’t measure. This sentence well represents one of today’s greatest challenges to knowledge managers. While more and more

companies are deeply investing in Knowledge Management (KM) initiatives, driven by alleged promises or actual business advantages (Chua & Goh, 2008), they generally encounter considerable difficulties in measuring the effectiveness of these programmes. As the current practice clearly shows, how KM outcomes can be effectively measured

DOI: 10.4018/978-1-60960-054-9.ch010

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The Complex Issue of Measuring KM Performance

is still a puzzling problem (Kim, 2006; Zboralski & Gemünden, 2006) both at the conceptual and operational level. This can be related to the substantially intangible nature of knowledge (Bose, 2004) and of KM projects (del-Rey-Chamorro et al., 2003), as well as the long-term horizons of their impacts. Nevertheless, trying to measure the performance of KM programmes is absolutely necessary, not only for monitoring the effectiveness of KM-related activities but also for successfully managing and allocating the resources that are needed. Measurement is also required to demonstrate the results achieved, which is essential to maintain the support and commitment by the top management. It is indeed common that the executives who feel that KM investments do not pay off tend to prematurely cut those initiatives (Bose, 2004; Desouza & Raider, 2006). In short, as affirmed by Chong & Chong (2009), without proper measurement even a successful initiative may be abandoned or, conversely, an unsuccessful program may be continued without corrections. This is the reason why there has been an increasing interest in the area of KM performance measurement. Even though several methods and approaches have been proposed in the literature (Chen & Chen, 2006; Grossman, 2006), they are still far from becoming an established practice with standardised methodologies (Kim, 2006; Chua & Goh, 2008). In particular, they are very heterogeneous. They normally derive from techniques formerly developed for other goals (for instance: traditional accounting, capital budgeting, decision making, etc.), and combine general and specific “ad hoc” elements. In addition, they are often poor in usability, and some of them are very complex and involve different kinds of metrics. In point of fact, at present there is no consensus on a standard approach (Grossman, 2006; Kim, 2006). Open questions are therefore the following: is there a “best method” to measure KM performance? Why it is so difficult to find one? And

what implications can this have for the research and practice of KM? The purpose of this chapter is not to give an ultimate answer to these questions (which, considering the current state of knowledge, may appear pretentious), but rather, to contribute to the discussion by shedding light onto some central aspects. In particular, we debate such issues by placing them in a business context, and propose the findings of a case-study. Indeed, the controversial picture of measurement methods has not discouraged companies from attempting to measure KM performance, as testified by the experiences mentioned by the literature. Consequently, the analysis of the current practice can help us to identify and discuss the main issues that companies face for choosing and using appropriate measurement approach. The case of a multinational consulting corporation (Ernst & Young), which is one of the pioneers in KM, is presented with the purpose: a) to illustrate the variety of methods that have been considered necessary by managers to measure the KM initiatives even in the same organisations and for measuring similar aspects; b) to discuss pros and cons of each method implemented and, by doing this, to formulate hypotheses about the reasons that led knowledge managers to develop and use different methods; c) based on the previous points, to draw useful lessons on the managerial issues of KM measurement and, more generally, on the actual problems of developing standard KM performance evaluation methods in business. The chapter is organised as follows. The next section offers a brief review of the literature on KM performance measurement and on the main methods that are currently used, with the twofold aim of stressing their key features, and showing that there is no clear consensus on the superiority of a specific technique or approach. In the third section, the practical experience of Ernst & Young is examined and discussed. The last section highlights the main findings and lessons which emerged from the study, and provides insights

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into the possible future developments of KM performance measurement.

BACKGROUND Before proceeding, it is useful to discuss and clarify some concepts and terms that are used in this chapter. First, despite the growing interest that academicians and practitioners have deserved to KM in the recent years, there isn’t a commonly shared definition on what it is. For our purposes, we adopt here the definition of Wong & Aspinwall (2004): KM can be viewed as the systematic management of knowledge-related activities and processes (i.e. creating, organizing, sharing and using knowledge) in order to create value for an organisation. It is therefore possible to speak about KM initiatives when they refer to deliberate actions intended to enhance the distinctive capability of the organisations through a systematic approach to generating, capturing, disseminating and exploiting knowledge (Chua & Goh, 2008). Such interventions can involve managerial practices, organisational solutions, human resources, technological tools and so on. In view of that, KM performance concerns activities, resources, results obtained from the implementation of a KM initiative. Accordingly, measuring KM performance consists in measuring the effectiveness of the KM initiative in terms of its relevant activities, resources or results. It is also worth clarifying that the chapter essentially focuses on ex-post measurement, i.e. the measurement of the outcomes or effects of a KM programme. Therefore, it will not deal with the evaluation of a new KM project in advance, although there is awareness that this is a crucial matter and raises complex questions as well. The purpose of this section is twofold. First, we present a brief survey of the current literature on KM performance measurement and illustrate the features of the main methods proposed. Secondly, we will show that, despite the huge

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efforts of researchers and practitioners, the issue of KM performance measurement is puzzling and controversial. In point of fact, it is still the least developed aspect of KM (Bose, 2004; Grossman, 2006; Chong & Chong, 2009), although attempts to “measuring the value of KM” and evaluating “KM performance” date back to the beginnings of the discipline (Chen & Chen, 2006). Clearly, managers are very interested in this question (Tseng, 2008), since measuring KM performance can serve various goals including: allocating funds, setting targets, giving feedback for implementation, drawing lessons for future initiatives, and so on (Kankanhalli & Tan, 2004). More precisely, many are the reasons why the performance of KM initiatives should be measured (Chua & Goh, 2008). On the economic front, measurement helps to ascertain if the outcomes produced by a KM project are in line with the efforts and the resources spent on it. On the strategic front, it allows to refine the existing practice and to provide guidelines for the future developments. On the political front, it acts as a “signal” for an organization to declare its commitment to KM. In short, as underlined by de Visser (2008), it is essential to measure the effect of KM activities in some way to avoid taking KM as another “management fad”. In the last years, various approaches for measuring the performance of KM initiatives have been proposed both by scholars and practitioners. There are collections of individual measures (i.e. performance indicators) without weights or priority level, and also techniques that elaborate several measures and synthesize them in a single indicator. To have a picture of the current state of the art, a brief review of some significant studies is provided here. First of all, it must be noted that the various authors generally use different terms (e.g., KM performance measurement, KM performance assessment, KM measurement, KM metrics, KM evaluation, valuation of KM practices, and so on) to indicate the performance measurement of a KM initiative or project. What was stated

The Complex Issue of Measuring KM Performance

by Andriessen (2004) in reference to intellectual capital (IC), can be repeated for KM: there seems to be some confusion between valuation (which makes use of a value scale that reflects usefulness or desiderability) and measurement (which makes use of a metric scale that does not include a criterion of value), and this contributes to make things more complicated. The mention to intellectual capital is not irrelevant. As underlined by Grossman (2006), an approach often proposed to measuring KM performance consists in measuring IC, because it can be seen as both an input and an outcome of KM initiatives. A vast review and classification of the literature about IC measurement is in Kannan & Aulbur (2004), where the main limitations of the single methods are discussed. The numerous techniques of IC measurement developed in the past years can be classified into distinct categories (Sveiby, 2007): (1) the models based on the direct value of Intellectual Capital estimate the economic value of its various components (human, structural and relational) and sub-components (e.g., people, documents, and so on), which are then evaluated either singly or together (examples of these methods are the Technology Broker, the Value Explorer, and the Intellectual Asset Valuation); (2) the models based on market capitalization compute Intellectual Capital as the difference between the firm’s market capitalization and the stockholder equity (examples include Tobin’s q, Investors Assigned Market Value, and similar), (3) the models based on return on assets build on traditional accounting concepts (examples are Economic Value Added, Human Resource Costing and Accounting, and Knowledge Capital Earnings); (4) the models based on scorecards identify the key components of Intellectual Capital and then report relevant indicators in scorecards or graphs (examples include the Modified Balanced Scorecard, the Intangible Assets Monitor, the Skandia Navigator, and the Intellectual Capital Index). The recent literature also proposes methods to measure the IC of a nation (see e.g., Chen & Dahlman, 2005; Yeh-Yun

Lin & Edvinsson, 2008), but they are beyond the scope of this chapter. Many of the IC measurement approaches mentioned before are also proposed for KM, at least with some adaptation. Indeed, it should be recalled that, when they are applied to KM, they are all indirect ways to measure KM performance. Their rationale bases on the assumption that any effective KM initiative increases the value of IC of an organisation. Of course, measuring IC is not the only possible approach to KM measurement. Many other methods have been proposed, each of them focusing on specific elements or contents. A study of Rao (2005) identifies five types of indicators for measuring KM: the technology (e.g., number of emails, usage of online forums, web site traffic, number of search queries); the processes (response time to queries, international certification standards, etc.); the knowledge items produced (number of employees’ ideas submitted, best practices created, active CoPs); the employees’ organizational involvement (degree of bonding with colleagues, peer validation, feeling of empowerment); the business outcomes (reduced costs, greater market share, improved productivity), and others. In addition, the kind of measurement can vary in relation to the distinct organizational units or levels that are involved. For instance, Kankanhalli & Tan (2004) identify 22 different KM measurement criteria focusing on the users and their motivation to use the KMS, or the features of the particular KMS, or the individual KM initiative, or the organization as a whole. Each organisational level may thus require different techniques (Hanley & Malafsky, 2003): a system metrics can be adopted to monitor the usefulness and responsiveness of the supporting technology tool, while an output metrics can be more appropriate to measure specific project characteristics, and an outcome metrics can be necessary for considering the whole organization and measuring large scale characteristics, such as increased productivity.

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In KM performance measurement, the connection with business outcomes is clearly essential, but again, this connection can be developed in many different ways. From a vast survey of the literature between the years 1992 and 2002 Loermans & Fink (2005) were able to single out several business-related evaluation factors: some concerning financial aspects, others business processes, or customers, or human capital. For these factors a great number of metrics has been developed, and there is lack of a common standard. Similarly based on an extensive survey of the literature, Choy et al. (2006) identified almost 40 performance outcomes from KM implementation that can be adopted as possible indicators for measuring KM efforts in organizations. As their empirical analysis proves, different companies make use of different sets of indicators according to the specific application context. Distinct approaches are also introduced even to measure the same object, which may depend on various factors e.g., the specific purpose of measurement, the capability of the assessor, the data sources available, etc. To give order to this complex picture, attempts to classify all the different approaches have been made but, again, they have led to different classifications. For example, Teruya (2004) distinguishes among: (1) internal measurements that evaluate how well KM strategies are implemented and suggest ways to improve performance; (2) external measurements that involve numerical or financial analysis about the results obtained by the organisation in implementing KM; (3) inferred measurements that are based on speculation, hypothesis and conjecture and include anecdotal benefits. Chua & Goh (2008) identify four different elements related to a KM initiative that could be measured, and the different tools that are suitable for each of them: a) KM activities/processes (i.e. measuring specific forms of KM activity: like putting a document in a knowledge depository, attending a meeting, etc.); b) knowledge assets (i.e. the pool of knowledge possessed by a

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company and manipulated or produced in a KM programme, that can be measured with methods such as Skandia Navigator, Intellectual Capital Index, or Intangible Asset Monitor); c) impact on organizational processes (i.e. measuring the effect of KM on organizational variables, with methods like the Knowledge Management Assessment Tool–KMAT–developed by the American Productivity and Quality Center, or the so-called “KM maturity” models); and d) the impact on business objectives (i.e. measuring the impact of KM on business goals such as profit generation, cost savings, time-to-market, improved customer’s satisfaction and so on, possibly with established managerial indicators such as ROI). Another classification proposed by Chen & Chen (2006), that essentially bases on the kind of operational method used to conduct the measure, leads to these categories (Table 1): •

•

Qualitative analysis, which generally includes typical non-quantitative techniques (e.g., questionnaires, expert interviews, etc.), and is usually proposed as a way to “measure” the tacit components of knowledge; Quantitative analysis that, when applicable, avoids the drawbacks of qualitative analysis and subjective judgment. It includes: a) traditional financial indicators (e.g., ROI, NPV, Tobin’s q, etc.); b) non-financial indicators (frequency of logins to a company’s knowledge repository, number of topics or enquiries submitted to a discussion forum, etc.), which are generally related to behavioural factors and system usage levels. Quantitative analysis is also used to measure the amount of “countable” elements of knowledge (for instance, the number of “knowledge items” or documents stored in a database). The quantitative methods include simple measures and more complex approaches, such as the Analytic Hierarchy Process (AHP) or

The Complex Issue of Measuring KM Performance

Table 1. A review of KM performance evaluation methods (adapted from: Chen & Chen, 2006) Category

Method

Qualitative analysis

Questionnaires Expert interviews Critical success factors

Quantitative analysis

•

•

Financial indicator analysis

Return on investment Net present value Tobin’s q Options

Non-financial indicator analysis

Communities of practice performance evaluation Individual, context, content and process knowledge assessment

Internal performance analysis

Balanced scorecard Activity-based evaluation

External performance analysis

Benchmarking Best practices

Project-orientated analysis

Social patterns KM project management model

Organizational-orientated analysis

Intellectual Capital

its revised version (the Analytic Network Process - ANP), recently proposed by some authors (Ngai & Chan, 2005; Wei & Bi, 2009; Wen, 2009) for evaluating KM performance. Another complex quantitative measure is the knowledge management performance index (KMPI) introduced by Lee et al. (2005). KMPI assesses the performance of a firm in its KM at a point in time on the basis of the progress obtained in its knowledge circulation processes; Internal performance analysis, which focuses on process and goal achievement efficiency, and evaluates the performance of KM processes by measuring the gap between targets and actual values. Methods proposed include: (balanced) scorecards, performance-based evaluation, activitybased evaluation, etc; External performance analysis, where a firm’s KM performance is compared with benchmark companies, primary competitors, or industry average. With benchmarking or best practices methodologies, companies are able to analyze their own KM

•

•

performance and contrast themselves with their competitors, thus taking appropriate actions. Recently, the ANP has been proposed by Huang et al. (2007) and Chen et al. (2009) as a tool to compare the KM program of a company with those of its major competitors; Project-orientated analysis, aiming at measuring the outcomes of individual KM projects seen as a means towards the solution of specific business problems; Organizational-orientated analysis, which considers the entire organization and focuses on its multi-dimensional aspects. The primary objective is to measure the KM performance at the various level of the whole organization. Such analysis is generally accomplished by means of the various approaches for measuring the Intellectual Capital.

All these classifications are somewhat similar, but not completely consistent, and they also diverge in some points. As a result, despite these

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precious efforts to provide a clear framework, the overall picture remains confusing. Given that there are so many methods to measure KM performance, it has been proposed to select a “good” set of indicators and to combine them into a single measure. For example, the Danish Guideline to Intellectual Capital Statements (Danish Ministry of Science, Technology and Innovation, 2003) identifies three groups of indicators: the first concerning resources, the second activities and the third effects. Another way to identify a significant set of key performance indicators is suggested by Minonne & Turner (2009): these indicators are associated to four possible strategic objectives of KM (i.e. linked to the cultural, organisational, methodical and procedural integration) and targeted to the evaluation of the overall “Knowledge Management Maturity” achieved by the company. A similar approach is suggested by del-Rey-Chamorro et al. (2003) who developed an eight-step framework for defining a set of indicators to measure the alignment of a KM solution to the business strategy of the company. Another popular way to integrate different indicators is the application of a variation of the balanced scorecard approach (Arora, 2002; Hanley & Malafsky, 2003; Lee & Lai, 2007). All the cases mentioned here have some similarities. First, these methods propose lists of indicators to integrate in a single measure, but these lists aren’t meant to be “exhaustive” but, rather, they represent a guideline for managers. Secondly, the selected “good indicators” are generally those that measure the effectiveness of KM strategies and their alignment with business strategy. Thirdly, both qualitative and quantitative measures are proposed. In summary, these important efforts of measurement integration are useful but do not resolve the problem in an ultimate way. To sum up, this review of the literature highlights some important points. In particular, the toolbox of the KM performance measurement approaches seems to be well equipped. There are many methods, each one of them deals with

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the issue from a specific perspective and resorts to a different set of qualitative or quantitative indicators. But each method shows its own advantages and limitations that may restrict its field of application, and raises the problem of selection of the approach. There is therefore the need to investigate how managers choose their KM performance indicators, and why they use these methods.

THE CASE STUDY Method The case study presented here focuses on the experience of Ernst & Young in KM. The approach is grounded on the specialised literature on casestudy methodology and in particular Yin (2003). In order to provide the reader with information about the method used, this subsection describes the main elements of the research strategy followed for designing and conducting the case-study.

Purpose and Questions Using Yin’s (2003) framework, the purpose of the case study is a combination of a “how” and (partly) a “why” question: how knowledge managers try to measure KM performance (in particular, what methods they use), and why they use these methods. The first aim is essentially descriptive, and is intended to show that even in a single organisation and for the same issue different measurement methods are actually adopted. The second question is somewhat exploratory, and investigates the possible reasons that may lead to this situation by analysing the points of strength and weakness of each method as they appear to managers. It is however worth to note that, in this chapter, while the first question was fully treated, the second question was partially considered, and mainly with the aim to identify critical issues for future research.

The Complex Issue of Measuring KM Performance

Unit of Analysis and Selection of the Case Study The unit of analysis is represented by the KM measurement approach adopted by the KM department in Ernst & Young. Since this comprises several distinct measurement programmes adopted in different times, this can be intended as an “embedded case study” using Yin’s (2003) definition, which implied the analysis of both the single programmes and the entire measurement approach. The company (Ernst & Young) was selected for various reasons. First, this company has one of the most significant KM programmes around the world, and therefore the case study was expected to provide interesting insights into the issues analysed. The second important reason is a question of opportunity: it was possible to have direct access to relevant data thanks to the participation of a key knowledge manager officer to this research. Generally speaking, this may raise a problem of bias, which was considered as described below.

Single Case-Study and Generalisation The reason for using a single case approach is mainly the descriptive and exploratory nature of its purposes. Also, this can be seen as a revelatory case in Yin’s (2003) terminology, because this study offers the opportunity for an in-depth investigation of internal managerial aspects that are generally less accessible to researchers. As regards the generalisation of findings, the idea is not to draw conclusions of general validity, but rather to formulate hypotheses about the future practical prospects of KM performance measurement. These can provide suggestions for further analysis.

company sources were used, including key informants, internal documentation, websites and databases, etc. The direct contact with those that defined and used the various KM measurement methods allowed revealing aspects that are often difficult to discover from an external researcher. The potential bias in the analysis (due to the fact that one of the authors is directly involved in the company) was mitigated by separating the “collection” phase from the “analysis” (which was mainly conducted by the other researchers).

KM in Ernst &Young: Overview This subsection allows to understand the context where the KM measurement methods have been developed, and to provide explanations of the decisions that were taken with regard to this. Ernst & Young is a global leader in accounting and auditing, tax reporting and operations, business and technology risk services, business advisory and transaction advisory services. It has over 144,000 employees throughout 140 countries, and annual revenues of more than $21 billion in FY 2009 (July 2008-June 2009). Ernst & Young services are based entirely on the knowledge and ideas of its people. The organization’s knowledge strategy is to enhance the capacity to create and share that intellectual capital globally, whether in its service lines, in the 25 focused industry sectors, or in individual client accounts. In a company of this nature and scale, the activity of KM is thus critical and challenging, for several reasons: • •

Data Collection

•

The data were collected in 2008 and 2009, but refer to several years of experience. Different

•

Information and knowledge has to be shared among all the 743 offices worldwide; It is necessary to retain valuable tacit knowledge that resides in the minds of employees; Reusing knowledge, to deliver more value to more clients faster, is critical; There is the need to operate in a variety of sectors, and with diverse business units.

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A pioneer in KM, Ernst & Young began integrating formal practices into its culture, processes, and infrastructure more than ten years ago. In 1993, the Ernst & Young Center for Business KnowledgeTM (CBK) was established, to make KM a reality and bolster the capacity to create and share knowledge on a global scale. CBK consists of a worldwide network of content, people and knowledge programs that allows professionals accessing the latest information on companies, industry trends, benchmarking studies, and leading practices. Regional and global teams can also source business insights from different locations around the world to generate timely proprietary research. CBK drives integration of KM into the daily responsibilities of professionals, thus ensuring that the company delivers significant value to clients by capturing, reshaping, and transferring knowledge. The knowledge generated and managed by CBK is stored and delivered through the firm’s Intranet system (the KnowledgeWeb - KWeb). Ernst & Young is aware that KM can be worthwhile only if it produces value for the company, and this value has to be measured. So, the research for good proofs that the KM programs work never stops. Some answers come from the fact that, for 10 times, Ernst & Young has been awarded of the Most Admired Knowledge Enterprise (MAKE) award, and it is recognized as being among the top performer companies on KM. But this is not enough: on a regular basis, knowledge managers are requested to demonstrate the goodness and benefits of KM activities, and their alignment with the company’s strategic goals.

Measurement Tools and Techniques The process pursued by Ernst & Young to ensure the alignment of KM to the firm’s strategic objectives, consists of: •

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Linking knowledge and business strategies. The company deems critical to have

•

•

•

good understanding of the organizational business strategy in order to provide a context within which a knowledge sharing culture can be developed; Gaining acceptance from the business, and reaching agreement on knowledge goals. Hence, knowledge goals need to be made explicit and to derive from the overall strategic aims of the company. Goals should be specific and measurable; Implementing plans. The company identifies a range of activities both for “quick wins” and for longer term implementation to allow the service line understand how knowledge can be built into the design and execution of core business processes; Measuring success and achievement of knowledge goals. Specific quantitative and qualitative measures are used to justify the value of KM investments and monitor the changing culture.

To monitor the effectiveness of KM activities in relation to these goals, a large amount of statistics and data is gathered from a range of sources. In detail: •

•

•

Knowledge plans outline the services and tools, the estimated budget to provide these, and the associated action required on behalf of the business units or communities to effectively utilise the KM services. The plans represent the grounds on which the execution of KM activities is tracked and monitored; Internal database metrics are collected to measure the usage of KWeb databases and portals accessed using a browser, via Lotus Notes, or the KWeb Search Engine. The metrics include number of accesses, documents opened, and similar; Submission rates are recorded to demonstrate knowledge-sharing at work. Submission rates are reported as part of the

The Complex Issue of Measuring KM Performance

•

•

•

•

Service Level Agreement (SLA) review process: SLAs are established for the various services provided by CBK; Reporting about electronic resources includes the usage of external content sources accessed via the KWeb and other systems. The usage measurement is generally broken down by practice area and country. Since usage data are provided by external vendors, the metrics for some sources have limitations, and the report details vary depending on what is provided and when; Satisfaction surveys and user profiling are also deployed, quarterly for large business research and analysis requests, and monthly for core knowledge training; As regards the Ernst & Young Intranet, its access is profiled for each user. Detailed usage measures of all the intranet sections are collected, which allows adding or removing contents based on usage level and feedbacks from users; As regards the Ernst & Young Online (extranet), there are reports of usage by registered clients for Ernst & Young Online which collect data about registered users, actual users, accesses, sessions, top content, and page views in a moving twelve month trend chart, broken down by country. These statistics are reported via a stakeholders report, and form part of the SLA review process.

All the above-mentioned data and statistics are the input of several specific tools, employed to evaluate KM activities as described in the following sections.

Scorecards Scorecards are used to measure broader organizational performance, by integrating non-financial and financial measures and results. It is one the most known business performance evaluation tool.

Kaplan & Norton’s (1996) popular version – the “balanced scorecard” - links an organization’s strategy and goals to specific measures focusing on four different areas: finance, customers, internal business process, and learning and growth. For each area, specific objectives and relevant indicators have to be developed that are aligned with the overall business strategy of the company. The balance scorecard allows to track the progress achieved by the organization in its business goals. Given its features, it is a very flexible tool that can be applied to the particular situation of the individual firm. Hence, it can be tailored to a KM initiative, as stated by Hanley & Malafsky (2003) and confirmed by various authors (Arora, 2002; Lee & Lai, 2007). The crucial issue here concerns the formulation of the parameters and the index used to monitor the progress of the KM program. Lee & Lai (2007) propose a list of possible KM performance indicators, and relevant weights, under the different areas. The scorecard used by Ernst & Young does not conform to the balance scorecard original version: it was indeed recognised that specific customisations were necessary to the specific application context. In particular, the scorecard does not consider the four areas previously recalled, but instead, it tries to measure the degree of attainment of some KM goals concerning the resources devoted to the KM activities, the availability and the creation of knowledge content, the cognitive services provided and the training activities supplied. Also, the experience suggested some modifications to improve its measurement capability. The first version of the scorecard was made a few years ago, and centred on measuring the adoption level of KM processes and tools by the different Ernst & Young communities. Based on targets agreed with the top management, the tool regularly measured the achievements of this target by the single KM initiative, by each community, and also at an overall level. In particular, the scorecard measures the variance from the target in percentage (Figure 1), and

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the gross total denotes the degree of improvement. This approach is especially suitable when the target is quite challenging, and it thus needs a continuous effort for reaching it. The quantitative dimension gives a clear explanation of the issues involved, and allows corrective actions. For example, a negative “delta” for submissions is a clear indicator that few people contribute to the knowledge system, which calls for actions to promote knowledge sharing culture and reinforce the importance of contributing to the knowledge environment. In short, this tool has the advantage of being intuitive and exhaustive, provides quantitative measurements, and allows singling out the critical issues immediately. Two are the main disadvantages: first, it is too much detailed, which makes a consistent interpretation of all data Figure 1. KM scorecard

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somewhat difficult; secondly, the target levels are affected by a certain degree of subjectivity.

Qualitative and Quantitative Dashboard Not very different from the scoreboard, the dashboard was introduced to evaluate the level of implementation/adoption of different KM activities by a particular community in the company. It mixes quantitative and qualitative measures: numbers are given a meaning depending on the organization’s objectives. This tool is not new in the context of KM: for instance, it is quite similar to the KM metrics chart used by Hewlett&Packard to compare the progress of KM programmes in different countries (Hagi, 2004).

The Complex Issue of Measuring KM Performance

Quantitative measures provide insights into the maturity and effectiveness of the KM systems. These data are simple to collect: once the IT department has developed the audit mechanism, they are put under the direct control of the knowledge manager. The advantage of such measures is that they are simple to read, and deliver a powerful message. The downside is that quantitative measures are susceptible to multiple interpretations and often require the support of “softer” qualitative information to give them some meaning. Qualitative indicators are based on feedbacks or collected through surveys, and can provide an indication of the way KM efforts are being perceived, and of the reputation gained by the KM system in the company. They are associated to the quantitative measures, thus providing a context to interpret the otherwise too hard quantitative data. In short, the major effort here is to combine quantitative metrics with more qualitative ones (represented by colours: Figure 2). For each KM service/tools (in rows), the degree of implementation/adoption by the target community (in columns) is measured; in doing this, the Ernst &

Young management can push each community to perform better. Quantitative measures can also be assigned to the different rows, and the results can be compared with targets agreed with the community knowledge champions. Measures can vary: for example, in the case of the “knowledge repository”, it is measured how many knowledge databases have been created; while the “business researches” metrics measure how many business researches have been done by the KM team. As can be noted, the totals reported in the table are not always consistent. The reason is that they may represent a yearly cumulative total in some cases, while the table measures monthly achievements for the different products/ services. The weight average is measured in the following way: for each community and for all the services, each service provided is multiplied by a number representing the degree of implementation (depicted in different colours, where light gray = 0, white = 1; black = 2; gray = 3) and then the total is divided by the total services / products provided to that community.

Figure 2. KM dashboard

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The usefulness ascribed to this tool is generally related to its intuitiveness and self-explanatory feature. Also, since it combines qualitative and quantitative data, it can be used to benchmark one community against another, and can thus promote the adoption of best practices throughout the company. The coloured table gives a lot of information at first glance about the performance reached on various activities by the different communities, which is clearly seen positively by managers. Unfortunately, the translation of quantitative measure into qualitative dimensions is quite subjective.

Knowledge Activity Tracker Since several years, Ernst & Young has been using a tracker tool, where all KM team members register their activities on a daily/weekly basis (Figure 3). Measuring activities is one of the typical ways to monitor the functioning of a specific KM programme (Chua & Goh, 2008). Since the KM system also services the high level/strategic activities with a “help desk”, the possibility to Figure 3. Knowledge activity tracking

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regularly monitor the work done has a double effect: on the one hand, it avoids duplications of activities (similar requests or inquiries from managers can be treated in the same way, and the results can be reused), on the other hand it enables a tangible and quantitative measure of the effectiveness of the services provided. In a well-managed and proactive team, the tendency is to exploit all the Full Time Equivalent (FTE), which results in a total amount of hours over the year (the total team FTE). However, when the ratio between business professionals and KM people is of several hundred, KM people may be asked to justify what they have done for each single community of professionals in the company. With such a tool, the calculation is dynamic and the statistical data are always updated. The main dimensions used to track KM activities are: • • • • •

Status of a request, Date of request; Requester name and organizational data; Description of the request; Type of service required;

The Complex Issue of Measuring KM Performance

• •

Person assigned to the request in the KM team; Amount of time dedicated to the request.

By processing the single data, several statistics concerning the KM activity can be obtained, and can be subdivided (Figure 4): • • • •

By requester; By sector (of clients); By service line (the Ernst & Young service line asking for support); By service type (the KM service provided).

This tool is considered particularly useful because, when data collection is well structured, it can allow tracking the requests served at every organizational level. However, the system calls for some time to be populated, and thus requires the commitment by the KM team. Also, it can be perceived as a way to control the team’s work, and is often seen as an additional burden.

Top Management Reporting This is a periodical report of KM activities and services for the top management (Figure 5). It collates quantitative data extracted by the tracker and qualitative comments to the main work streams and KM projects done by the team. It also links these activities to the firm’s strategy, when applicable. The report is structured with a one page executive summary with quantitative indicators and highlevel project details, and is declined by activity and service type using visual bar charts and pies. The example proposed in Figure 5 is a bi-yearly report for the top management, with reference to the S&K department (until July 2009) - at that time, the KM department merged the supporting sales in order to accelerate the “go to market” process. The top achievements considered are the top services/products/processes provided or supported by the team. The top figures are macro quantitative data (on the left side) and the population served with indication of the internal rank of the services requesters (on the right side).

Figure 4. Knowledge activities statistics

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The data below represent the top projects done in the period. The resulting picture is synthetic and intuitive. However, it does not properly emphasize the efforts made by the KM team, as well as the issues and problems encountered in the daily work.

Knowledge Survey Another means that has been used in the last 5 years is the Global Knowledge Survey (GKS), which provides a standard tool and process to assess the internal customer satisfaction related to the delivery of knowledge services, by means of questions submitted to key users. This is a typical non-quantitative technique used to collect information that cannot be expressed or synthesized by means of quantitative indicators. The set of questions to be submitted is customized for the specific country/area. More standardized sets of Figure 5. Top management reporting

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questions would make the comparison among different contexts and geographic areas easier, and for this reason Ernst & Young is currently investigating the possibility to re-define it as a “truly global” standard tool to be adopted by the different areas in a more coherent and consistent way. The survey is sent to a rotating sample of people at the different levels of the organization and in different communities of practice, and assesses transversal and specific tools, internal and external knowledge resources, knowledge sharing culture, and awareness. The typical questions submitted attempt to “measure” key dimensions such as: • • • •

Time saved by using knowledge; “Win work” by using knowledge; Awareness and value of business research & analysis; Awareness and value of IT systems;

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• •

Use of key external content; Awareness of copyright policy.

The results of this survey are used to make recommendations for the business, and for further CBK actions. The survey also allows the CBK team to provide feedback to other relevant Ernst & Young groups as concerns issues that are outside the control of the CBK (e.g., marketing publications or IT issues). The main advantages of this survey are that the people are given the possibility to express their needs, complaints, and expectations. On the other hand, it increases user expectations mainly at the bottom level of the organization; some actions cannot be taken without the support and the sponsorship of the top management, but sometimes the different needs are not part of the organization balance scorecard. For example, if the people want more training on knowledge tools and top management wants them to be more present on client projects, this is clearly an issue that cannot be easily managed.

ROI-Like Measures Return on investment is probably the most known and used of the traditional financial indicators. Its use in the field of KM appears to be quite complex, given the intangible nature of related costs and benefits. Notwithstanding, many companies have tried to assess the financial return of their KM investments, especially to justify the amount (sometimes relevant) of money spent on those initiatives. In the case of Ernst & Young, the “return on investment” in KM activities has been estimated through the calculation of how much it would have cost to deliver the same knowledge services that are provided by the KM teams, by means of other organizational units. For example: how much would have cost to the organization, if the professionals had been compelled to devote part of their time to make all the business research directly?

Based on this approach, and using standard hour fees, it was found that the central KM team produces, in one year, services and deliverables whose value is ten times the cost of the team itself. This can be an important way to prove that the KM team is not only a cost centre but also a profit centre. This measurement is a way to justify the investment, and since it is a sort of ROI, usually the top management easily understands it. A disadvantage is that its application can become difficult or inappropriate when considering that many people involved in KM activities can have a double role. For instance, professionals can say that their contribution to KM activities is, for them, extra-work that does not result in the ROI calculation and, what’s more, is not recognized as part of their working hours.

Success Stories Success stories are a very qualitative way of considering measurement; nevertheless, if properly packaged and communicated, they can have a valuable impact on the perception of KM activities within the organization. Storytelling is in fact one of the most popular ways to communicate qualitative measures (Hanley & Malafsky, 2003). Since 2004, the Italian KM team of Ernst & Young has been collecting positive feedbacks from the people who have worked in it, or have exploited its services. The feedbacks (Figure 6) include: name of the requester (who may be local of from another Ernst & Young practice), date of the request, details about the request, date of answer, person of the KM team responsible for the activity, and the feedback itself usually provided by email. It should be noted that, normally, the feedback is not requested but, rather, sent voluntarily by the requester. The entire testimonials package is then published in the knowledge team database, and becomes visible to everybody. Another way to talk about success stories is to publish some quick articles or news on the

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Figure 6. Examples of success stories

Intranet to show how knowledge has helped on specific projects and which kind of support has been provided by the knowledge team. The advantage of this tool is that all feedbacks are very much focused on KM intensive business activities, thus resulting in a very tangible and concrete way of evaluating the KM results and their impact on the sale and delivery of services to the clients and, hence, on the profits of the company. Also, stories allow to capture the context, which gives them meaning and makes them powerful. The main disadvantage is that feedbacks depend too much on the personal relationship that the KM team establishes with the single requester, thus resulting in a somewhat subjective (and selfreferential) way of evaluating the KM services.

CONCLUSION AND FUTURE RESEARCH DIRECTIONS This concluding section is divided in two parts. First, the findings of the case-study are presented

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with reference to the two main questions: a) how KM performance is measured in Ernst & Young and what features characterise the different methods adopted; b) what can explain this variety of methods adopted in Ernst & Young. The second part proposes some more general remarks that can be derived from the case-study, remarks that represent useful lessons and food for thought for managers on the one hand, and can also provide elements of reflection on the future research and practice on KM measurement.

Case-Study Findings As mentioned, a particular feature of the casestudy is the extreme variety of methods used for measuring KM performances. It can be of use giving a summarizing picture of the methods employed by Ernst & Young, classified according to two dimensions drawn from the literature (Hanley & Malafsky, 2003; Chen & Chen, 2006; Chua & Goh, 2008): the object of evaluation, and the kind of measurement.

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Figure 7. A summarizing picture of KM measures at Ernst & Young

The object of evaluation can range from a specific KM activity or tool (e.g., accessing a knowledge portal, putting a document in a repository, calling the help desk, answering requests, etc.), to the whole KM program (e.g., degree of progress, perceived usefulness, time to find experts, etc.), to the impact of KM on the business goals (e.g., time and/or cost savings, reduced cost of training, increasing performance, etc.). The kind of measurement can be quantitative, qualitative, or a combination of both. The result of this concise description is showed in Figure 7. Also, it has to be recalled that all the methods are used just with the aim of conducting an internal performance analysis, i.e. focused on outcomes expected by the program (so far, Ernst & Young has made no effort to compare its KM performance with benchmark companies or competitors, which may probably call for new approaches to comparing data of other organisations). Figure 7 clearly points out that Ernst & Young has to employ a wide set of KM evaluation methods, which are quite dissimilar from one another. As mentioned before, it is interesting to note that

different approaches are also used to measure the same object. This can be explained by the fact that each method is not completely appropriate for measuring all the aspects at stake. For instance, the amount of the consulting activities delivered by the KM team does not give any indication about their actual usefulness, and this is the reason why the company makes use of both the activities tracker (that register the activities of the team) and the success stories (that collect the positive feedbacks of the services delivered). Since developing a measurement system and feeding it with appropriate data is a costly activity, it can be concluded that the application of different indicators means that a single way to measure KM performance in a completely satisfactory way has not been found yet. Another lesson from the investigated case is that the immediate application of methods derived from the literature can’t be taken for granted. The company had to implement specific methods, or to adapt those proposed in the literature. In general, the approaches used can be seen as “heterodox” and “ad hoc” versions of traditional

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methods, as testified by the scorecard and the dashboard: these techniques make reference to popular methods (such as the balance scorecard) but are implemented with substantial changes. This means that, when it comes to KM, the particular nature of the elements that need to be monitored can call for the design and implementation of specific methods. This process of customisation can be very costly. Indeed, the practical problems of KM measurement in Ernst & Young implied a difficult “trial-and-error” approach, where managers struggled to find suitable ways to measure the key aspects of KM performance. The lack of consistent theoretical and applicative models to represent these aspects appears a central problem. An additional point to make is that, in spite of their popularity, Ernst & Young preferred not using techniques for measuring intellectual capital; these are often perceived as too complicated. Similarly, complex tools recently proposed in the literature (like AHP, ANP, KMPI and option-based evaluation) are not used for the same reason. The company seems to prefer ease-of-use and intelligible techniques rather than sophisticated approaches. As Figure 7 shows, the company makes a great use of quantitative or semi-quantitative methods. As the practical experience clearly proves, managers feel more comfortable with numbers, even when their real significance is not completely clear. This does not mean that qualitative techniques are not employed, but their utility is clearly limited. This is an important lesson for the future development of KM measurement: the design of quantitative methods is essential. Another interesting finding is that, in Ernst & Young, the direct effect of KM on business goals is not measured yet. The implicit conjecture here is that performing a vast amount of knowledge activities, meeting the target of the KM program, having satisfied users are all good indicators that the organization is drawing benefits from its KM initiatives. Therefore there is no effort to develop a KM metric that involves the overall organization

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and measures large scale outcomes. As underlined by the literature, is not easy to establish a direct causality between KM projects and business objectives and this can induce companies not to do it. Nevertheless, there is a continuous engagement in refining and improving the used tools, also in accordance with the evolution of the KM programme.

Practical Lessons and Prospects of KM Research This case-study makes it possible to draw some general conclusions on the implementation issues of KM measurement systems. First, the case of this important company, that has a vast experience of KM, shows that, despite the great efforts of the KM team, a clear “one best approach” does not emerge. It confirms the perceptions of other studies (cf. de Visser, 2007) that show that many tools seem to be considered more or less appropriate depending on the specific situations. This has an important implication: large KM teams (like at Ernst & Young) may still need to spend part of their time to continuously work on new ways to measure and “economically justify” their activities. All this is associated to the nature itself of the KM activities, that are multidimensional and difficult to define, and can have an impact on the performance of the whole organization and on its single parts. Also, most of KM activities have to do with people’s behaviours and attitudes, which are, by their intrinsic nature, difficult to measure in an objective way. Hence, the implementation of KM systems appears to strongly depend on the specific organizational context of application, which makes even harder to adopt standard measures. Secondly, several methods can be used and need to be used even in the same organization. Also, a long-lasting experience in KM does not immediately result in the adoption of standardized measures. On the contrary, it may be argued that the more articulated is the KM system, the more

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complex become the measurement problems. Hence, a company has to be careful with choosing a method, since applying it to the wrong situation could be counterproductive. Lastly, every method shows specific advantages and limits which, again, increase the difficulty for KM managers, that have to choose the proper measurement system depending on several factors: the object of measure, the available data, and also the people to whom measurements will be delivered. Generally speaking, quantitative methods seem more suitable to evaluate specific and delimited aspects of a KM initiative, while qualitative methods allow appraising vast and complex KM programs. Mixed approaches are often a solution that is considered to take into account the different factors into play, but they increase the problem of integrating different sources of data in a consistent way. The costs of a particular measurement tool is important, as well as the resources that can be used for implementing it. Having said that, the case study provides food for thought about the future of KM measurement systems. As a matter of fact, the way this field will develop may take two directions that are strongly influenced by the behaviour of firms and by the advancements of the conceptual models of KM. A first possibility is that the companies involved in KM, aware of the difficulty to measure this activity in a standard way, adopt a “best of breed” approach, depending on the specific goals and objects. In other words, taking for granted that, in the end, not everything is measurable, nor needs to be measured, companies can choose their methods to KM measurement based on their experience, on the experience of others, and/or on any kind of suggestion that can come from external sources (e.g., KM literature, consultants, IT vendors, etc.). Actually, the literature abounds of single indicators that can are recommended to measure some specific aspects of a KM project, in many cases without an overall vision of the entire program. This “practical” approach is somewhat sensible,

but it may lead to a highly heterogeneous environment that can hinder the development of common KM practices. Also, the lack of a comprehensive evaluation could hinder the proper improvement of the existing practice and guide to wrong future developments. A second possibility is that a general standard way to measure KM activities is found. This would be very important for allowing comparisons between KM and other managerial activities, as well as between KM programmes of different companies. Budgeting, allocation of resources, and managerial control would become easier. The possibility to achieve these results is strongly based on the advancements in the conceptual modelling of KM activities and processes. Indeed, the foundations of any measurement system rest on robust conceptual representations of the reality that has to be measured. Other managerial branches (from accounting to production) are based on their own formal models, with which the measurement process becomes possible, and meaningful for the current practice. This is the real challenge for both scholars and practitioners involved in KM.

ACKNOWLEDGMENT This paper contributes to the FIRB 2003 project “Knowledge management in the Extended Enterprise: new organizational models in the digital age”. The authors are grateful to the Book Editors and the anonymous reviewers for their supportive suggestions.

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KEY TERMS AND DEFINITIONS Knowledge Management (KM): the systematic management of knowledge-related activities and processes in order to create value for an organisation. KM Initiative: any deliberate action intended to enhance the distinctive capability of an organisation to generating, capturing, disseminating, and exploiting knowledge. KM Performance: any activity, resource, and result obtained from the implementation of a KM initiative.

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KM Performance Measurement: measuring the effectiveness of a KM initiative in terms of relevant activities, resources or results. Intellectual Capital: the pool of knowledgebased intangible resources, owned by a firm, that determine its value and competitiveness. Balanced Scorecard: a performance measurement framework that adds strategic non-financial performance measures to traditional financial metrics. Dashboard: a performance measurement tool that is designed to be easy to read. Success Stories: a very qualitative way of communicating qualitative measures.

Section 3

Financial Valuation of Intangibles

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Chapter 11

Engineering Business Reasoning, Analytics and Intelligence Network (E-BRAIN): A New Approach to Intangible Asset Valuation Based on Einstein’s Perspective Annie Green George Washington University, Institute of Knowledge and Innovation (IKI), USA

ABSTRACT “Any intelligent fool can make things bigger, more complex, and more violent. It takes a touch of genius: and a lot of courage–to move in the opposite direction.”…Albert Einstein. This chapter details a performance-based theoretical model of intangible asset valuation: Engineering–Business Reasoning, Analytics and Intelligence Network (E-BRAIN). E-BRAIN’s origin started with the construction of a validated taxonomy of intangible asset value drivers: Framework of Intangible Valuation Areas (FIVA) (Green 2008). E-BRAIN is a culmination of research and practice and offers valuable insights into the emerging discipline and field of intangible assets. Using systems engineering and organization memory (cognition) as the foundation for its structure, the model identifies the path from intangible key performance indicators to performance measurement. This chapter introduces E-BRAIN as a systemic and holistic approach to intangible asset valuation that starts with a set of metrics by which business leaders can account for intangible or non-financial factors that affect value creation in the knowledge era business.

BACKGROUND: THE EVOLUTION OF E-BRAIN In 2004, Annie Green conducted research to identify and define intangible valuation areas for the business enterprise. Green’s (2004) empirical research provides significant evidence that when DOI: 10.4018/978-1-60960-054-9.ch011

a corporation uses standard and consistent intangible asset taxonomy to define and develop their intangible asset valuation models, it increases the firm’s ability to identify, measure, account for, and validate more intangible assets. The Framework of Intangible Valuation Areas (FIVA), the result of this research, is an intangible asset taxonomy that represents a validated set of business value drivers.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Engineering Business Reasoning, Analytics and Intelligence Network (E-BRAIN)

FIVA establishes a path to determine the importance of interactions between intangible mechanisms, processes, representation and goals that compose the basic concepts of organizational memory within a business. It sets the foundation to capture intangible measures and indicators that align with the business enterprise value drivers. Additional empirical research conducted by Andreas Andreou (Andreou, Green and Stankosky 2007) defines some of the antecedents of FIVA. This empirical research presents a concept that allows a business to identify and link performance measurements/indicators to its intangible value drivers and subsequently capture measures to monitor and evaluate leading and lagging intangible indicators. It identifies performance focus areas and their respective critical success factors resulting from the interaction of the employee value driver with other value drivers (i.e., customers, competitors, partners, information, technology, processes and products/services). Expanding on her original research and the research conducted by Andreou, Green evolves FIVA into a cognitive intangible asset valuation model termed: Business Reasoning, Analytics and Intelligence Network (BRAIN) (Green, 2009). BRAIN is one of many models as there are numerous efforts by researchers and individual companies to develop methods and tools to account for intangible assets ((Sveiby 2001) (Bontis 2000) (Hurwitz et. al. 2002) (Shand 1999) (Edvinsson & Malone, 1999) (Sullivan 1998) (Lev 2001). What differentiates BRAIN is that it starts with intangible value drivers that support the capability of business leaders to tailoring their models to a specific industry as opposed to being case-based or limited to one industry or organization. Figure 1, the BRAIN concept, depicts the BRAIN’s valuation components and their relationship to each other. BRAIN links business value drivers (such as employee, customer, technology…etc.) and their intangible indicators to business operational and historical data. The business value drivers are

correlated to determine the strength of the relationship between them. The correlation of the value drivers, intangible indicators and operational and historical data, provides intelligence into understanding the importance of interactions between intangible assets and business operations. This intelligence can be used to make decisions surrounding the development of improvement initiatives targeted at more efficient and effective operations of businesses. BRAIN begins with operational and historical data and transcends through the cognitive layers (intelligence, knowledge, learning, change, and performance measurement) of the business mind. Below is an example that walks-through a single business thought using the BRAIN concept. This is a simple example that focuses on two business value drivers: Customer and Employee. It also focuses on one intangible asset for each of these value drivers. An intangible indicator for customer is their Satisfaction with the services and/or products of the providing organization. An intangible indicator for an employee is the Training they have received and apply in the performance of their work. The cross-pollination of Customer/Satisfaction with Employee/Training indicators linked to operational and historical data presents the following results: Relational Intelligence: •

•

•

Operational Data Indicators:Customers with a high satisfaction rating are aligned with employees that have successfully completed specific training requirements. Operational Data Indicators:Customers with a low satisfaction rating are aligned with employees that have not successfully completed specific training requirements. Historical Data Indicators: Prior to implementing Employee Training Requirements, Customer Satisfaction ratings were low and Customer Longevity (an

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Figure 1. Business Reasoning, Analytics and Intelligence Network (BRAIN) concept

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intangible asset) was low: customers were leaving. Historical Data Indicators: After implementing Employee Training Requirements, Customer Satisfaction ratings increased substantially

•

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Knowledge: •

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Employees that have completed a specific Training are directly related to Customer Satisfaction and Customer Satisfaction is directly related to revenue from current customers.

Question: Based on historical trends, what is the financial impact of employees not being trained to support customers? Action: Identify pilot group of employees and train employees aligned with current customers that have a low satisfaction rating

Learning: •

Monitor the results of the action to determine if the impact is favorable.

Engineering Business Reasoning, Analytics and Intelligence Network (E-BRAIN)

Change: •

If favorable, implement a policy to ensure all employees receive and complete the required training. Performance Measurement:

•

The revenue gains from satisfied customers due to the increase longevity (customers not leaving) of current customers.

As demonstrated in the above example, BRAIN provides attributes that demonstrate recall and intellectual skills. This facilitates regression analysis, enabling a business leader to be cognizant of past practices and obstacles (lessons learned). In addition, it supports the dynamic capture of current transactions for continuous monitoring to support gut instinct (Gladwell, 2005) and future forecasting (Davenport, 2007). BRAIN is the mind of the organization and the mind requires a physical existence. BRAIN is further develops into a system or tool that facilitates its analytics and outputs. This adds the engineering component establishing Engineering Business Reasoning, Analytics and Intelligence Network or E-BRAIN (Figure 2). E-BRAIN provides a scientific enterprise system to identify solutions focused on innovation and improvements to business problems and inefficiencies. It identifies relationships between specific intangible value drivers, measures and indicators to uncover trends and patterns providing insights, opportunities and solutions. Being cognizant of thought patterns leverages businesses in creating models that simulate business intelligence and knowledge. Such a system enhances business leaders understanding and cognition of best practices and lessons learned that have successfully contributed or not contributed to achievement of strategic objectives and goals.

UNDERSTANDING THE ELEMENTS OF E-BRAIN E-BRAIN (Figure 2) takes a systems approach that requires businesses to think about and define a language, for describing and understanding, the forces and inter-relationships that shape the behavior of intangible asset valuation (Senge, 1990). This approach helps businesses to see how to change systems more effectively, and to act more in tune with the larger processes of the natural and economic world or business environment (Senge, 1990). E-BRAIN is built using a systems engineering/architecting discipline. The overall goals and objectives are to: emulate business intelligence & knowledge and provide a view of organizational performance based on changes attributable to knowledge functions. E-BRAIN provides a systemic and holistic approach to intangible asset valuation through the integration of valuation components, characteristics, value drivers and technology-based components. It establishes a common language of valuation in the context of the business decisionmaking processes. Its approach establishes elements that promote cognitive learning from the data, information, intelligence and knowledge stored in operational and historical repositories. E-BRAIN (Figure 2) is a cognitive theoretical performance based valuation system that integrates: 1.

Seven valuation components ◦ System: constructs a map that details the coherent picture of the value components that drive the performance of a business. ◦ Cognitive: identifies business value drivers which support the understanding and construction of mental models that make sense of the business and what guides the decisions and actions of the decision-makers.

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Figure 2. E-BRAIN theoretical performance-based intangible asset valuation system

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Intelligence: provides a view of negative and positive impacts on an organization’s performance based on a body of information from which knowledge can be obtained to questions by inquiries, predictions, explanations and prescriptions for control. Knowledge: builds formal models based on rules or principles prescribing a particular course of action. Learning: accumulates and analyzes information in the form of knowledge aligned with activities that may be well or badly performed. Change: supports inquiry that results in thinking and acting that yields profound inner shifts in people’s values, aspirations, and behaviors and outer

2.

shifts in processes, strategies, practices, and systems. ◦ Performance Measurement: measures success factors from different perspectives, as well as perspectives of past, current, and future performance. Nine characteristics: ◦ Concept: building blocks that depict the components of valuation within a business. ◦ Structure: definition of relationships and orderings that exist between and amongst business concepts and the language and symbols which represent them. ◦ Language: internal representation of data and external communication of value.

Engineering Business Reasoning, Analytics and Intelligence Network (E-BRAIN)

Data Retrieval: interfaces to external and internal data of the business enterprise. ◦ Logic Models: transformation of business functions/conditions into a form that represents easily recognizable sets of objects in the real business world. ◦ Pattern recognition: identification of similarities among differences and differences among similarities. ◦ Knowledge Representation: transformation of knowledge into a business fact. ◦ Hypotheses: formulation of facts from relationships and orderings that are used for further investigation. ◦ Evidence: assertion of validity into the simulation of business knowledge. Eight intangible asset value drivers: ◦ Customer: economic value that results from the associations (e.g., loyalty, satisfaction, longevity) an enterprise has built with consumers of its goods and services. ◦ Competitor: economic value that results from the position (e.g., reputation, market share, name recognition, image) an enterprise has built in the business market place. ◦ Employee: economic value that results from the collective capabilities (e.g., knowledge, skill, competence, know-how) of an enterprise’s employees. ◦ Information: economic value that results from an enterprise’s ability to collect and disseminate its information and knowledge in the right form and content to the right people at the right time. ◦ Partner: economic value that results from associations (e.g., financial, strategic, authority, power) an enter◦

3.

4.

prise has established with external individuals and organizations (e.g., consultants, customers, suppliers, allies, competitors) in pursuit of advantageous outcomes. ◦ Process: economic value that results from an enterprise’s ability (e.g., policies, procedures, methodologies, techniques) to leverage the ways in which the enterprise operates and creates value for its employees and customers. ◦ Product/Service: economic value that results from an enterprise’s ability to develop and deliver its offerings (i.e., products and services) in a timely manner that reflects an understanding of market and customer(s) requirements, expectations and desires. ◦ Technology: economic value that results from the hardware and software an enterprise has invested in to support its operations, management and future renewal. Six technology-based components: ◦ Indicators & Measures (Database): facilitates the capture of indicators and measures, known as capacity, to form a body of information. ◦ Intelligence Models (Logic): uses the body of information to crosspollinate value drivers and construct a body of intelligence. ◦ Knowledge Models (Logic): uses the body of intelligence to identify events that can be symbolized and manipulated to determine a course of action. ◦ Search & Retrieval: the use of classification technologies to retrieve, filter, and manage information in computers.

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◦

◦

Security (Access and Rights): ensuring intelligence and knowledge has adequate protection from intrusion. Interface (Reports, Scorecards, and Dashboards): visualization software that helps organizations view information, intelligence and knowledge in graphical form and perform analytics.

THE CRITICAL COMPONENTS OF E-BRAIN BRAIN defines the cognitive layers of a business. Cognition in the context of valuation is designed in the structure, functions, and elements of the system. It facilitates the appropriate behavior within the business enterprise structure. A cognitive theoretical model, such as BRAIN, expresses attributes of the mind, to include: 1) perception: the ability to influence, 2) awareness: the abilFigure 3. List of intangible assets

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ity to distinguish between events, 3) attention: the ability to be selective amongst percepts and thoughts, 4) learning: the ability to store and recall experiences (Newell, 1990). The core of E-BRAIN is BRAIN and the critical functions of BRAIN drive the success of E-BRAIN. The sections that follow will discuss these six critical functions of E-BRAIN: 1. 2. 3. 4. 5. 6.

Naming Intangible Assets Using Operational Data to Drive Intelligence Evolving Intelligence into Knowledge Learning from what is Known Driving Change from what is Learned Measuring the Performance Contributions of Intangible Assets.

Naming Intangible Assets There are lots of existing models that clearly define the names of intangible assets. However, naming intangibles is a work in progress. Figure 3

Engineering Business Reasoning, Analytics and Intelligence Network (E-BRAIN)

provides a list of intangible assets compiled from existing models created by Kaplan and Norton (1996), Sveiby (1997), Sullivan (1998, 2000) and Edvinsson and Malone (1997). Although not inclusive, the list in figure 3 provides the name information that establishes the “What” a business needs to capture to build its indicators and measures of intangible assets. The value of an intangible asset is the economic measure of the utility it brings to the business enterprise (Sullivan 2000). The value of intangible assets is tightly coupled to the business enterprise’s ability to transform intangible assets into financial returns (Edvinsson & Malone 1997). Table 1 aligns the intangible assets with business value drivers and defines a value question for each. The key to capturing the correct business information is to ask the right questions and to know what is needed to answer the questions. Each intangible asset introduces key questions that align with performance. This primitive layer of E-BRAIN positions the business to construct intelligence and subsequently knowledge to act on.

Using Operational Data to Drive Intelligence

Today, there is so much data and information available to industry. This increased availability is a benefit, as industry has access to knowing more than in the past. The question is: How does man effectively and efficiently use this data and information? Certainly, it is captured in context and provides a view of the past, the impacts of the results of the past on the present and the forecasts of the future based on the present. Can organizations extract relevant information knowing its degree of importance and relegate the noise of “additional information” until it has a useful purpose. E-BRAIN’s initial layer contains measures and indicators or the “capacity” of the business. This “capacity is the value-creating ability of an organization” (McNair and Vangermeersch, 1998, 1). It is represented in four categories of diagnostic information (Drucker, 1998): 1.

2.

“Not everything that counts can be counted, and not everything that can be counted counts.” … Albert Einstein Operational data provides a wealth of information that serves as a source of understanding and addressing the problems that face a business in achieving its goals. The right set of operational data grows intelligence, which is the ability to comprehend, understand and profit from experience (Wiig, 1994). The challenge is to be able to identify within a business domain and in common language, what measurements and indicators the business needs to gain knowledge from its intangible assets.

3.

4.

Foundation Information: Routine measures such as cash-flow and liquidity projections which when normal, basically do not tell much, however, when not normal, indicate a problem that needs to be identified. Productivity Information: Measures that deal with performance of key resources. These measures must also include the totalfactor productivity, which means that they should provide the value add of all costs, including the cost of capital. These measures incorporate such tools as economic-valueadded analysis (EVA) and benchmarking to measure and manager total-factor productivity. Competence Information: Measures associated with core competencies that link market or customer value with special skills and abilities of the producer or supplier of products and services. Resource-allocation Information: Measures associated with the allocation of scarce resources, such as capital and performing people.

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Table 1. What a business needs to know from value drivers ((Kaplan & Norton, 1996) (Sveiby, 1997) (Sveiby, 2001) (Sullivan, 1998) (Sullivan, 2000) (Edvinnson & Malone, 1997)) Information (Meta Data)

Value Driver Customer

Competitor

Employee

Information

Partner

Value Question

Acquisition

What is the ratio of new customers?

Satisfaction

What is the ratio of satisfied customers?

Longevity

What is the ratio of loyal customers?

Profitability

What is the ratio of profitable customers?

New Markets

What are new potential markets?

Market Share

What is the company market share?

Branding

What is the company branding index?

Image

What is the company image index?

Reputation

What is the company reputation index?

Assignments

What is the ratio of employee assignments?

Competencies

What is the ratio of competent employees?

Education

What is the ratio of educated employees?

Experience

What is the ratio of experienced employees?

Hiring

What is the ratio of new employees?

Longevity

What is the ratio of loyal employees?

Position

What positions do employees occupy?

Productivity

What is the ratio of productive employees?

Profitability

What is the ratio of profitable employees?

Reporting Relationship

What is the employee to supervisor ratio?

Satisfaction

Who is the ratio of satisfied employees?

Training

What is the ratio of trained employees?

Turnover

What is the ratio of employee leaving the company?

Benchmarks

What is the benchmark availability ratio?

External Availability

What is the external information availability ratio?

Internal Availability

What is the internal information availability ratio?

Market Studies

What is the market studies availability ratio?

Trend Studies

What is the Trend Study availability ratio?

Acquisition

What is the partner acquisition ratio?

Assignments

What is the ratio of partner assignments?

Competencies

What is the ratio of competent partners?

Education

What is the ratio of educated partners?

Experience

What is the ratio of experienced partners?

Longevity

What is the ratio of loyal partners?

Merger

What is the partner merger ratio?

Partnering Agreements

What is the ratio of new Partners?

Position

What positions do partners occupy?

Productivity

What is the ratio of productive partners?

Profitability

What is the ratio of profitable partners?

continued on following page

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Table 1. continued Information (Meta Data)

Value Driver

Process

Product/Service

Technology

Value Question

Reporting Relationship

What is the partner to employee ratio?

Satisfaction

What is the ratio of satisfied partners?

Training

What is the ratio of trained partners?

Turnover

What is the ratio of partners terminating partner agreements?

Business Activities

What are the business activities?

Business Procedures

What are the business procedures?

External Processes

What are the external processes?

Internal Processes

What are the internal processes?

Training

What are the training?

Work Methods

What are the work methods?

Work Techniques

What are the work techniques?

Convoyed Sales

What is the convoyed sales ratio?

Copyrights

What are the copyrights?

Patents

What are the patents?

Products

What are the profitable products?

Repeat Sales

What is the repeat sales ratio?

Sales

What is the ratio of sales?

Services

What are the profitable services?

Trade Secret

What are the trade secrets?

Trademarks

What are the trademarks?

Win/Loss Ratio

What is the win-to-loss ratio?

Databases

What is the database?

Hardware

What is the hardware?

Software

What is the software?

Drucker (1998) identifies the four categories of information to provide results that inform and direct business tactics and strategy development. E-BRAIN incorporates the basic economics of business within its structure by identifying a core set of intangible asset activities. It provides attributes that have a direct or indirect relationship with the identification of profit and value-adding activities on resources. Each activity and its attributes are mapped to the expense structure of the business and changes are reflected in the associated expense as activities are affected in operations. This aids decision making in effective management of intelligence and the ability to identify

value and non value-adding activities to achieve an optimum level of organizational performance. Capacity is critical to the creation of a “body of information”. Capacity is defined for every intangible asset. The union of the eight value drivers (i.e., customer, competitor, employee, information, partner, process, product/service, and technology), intangible assets and capacity (Figure 4) creates the “body of information” to engineer business intelligence. This body of information is constructed in the form of a valuation repository that is used to create an intelligence data base (IDB). The “body of information” provides a common language for discussing and measuring capacity

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Engineering Business Reasoning, Analytics and Intelligence Network (E-BRAIN)

Figure 4. Body of information

utilization. It provides a comprehensive approach to the engineering of business intelligence and supports a valuation system to measure the achievements of the business. The engineering of intelligence seeks to identify the major independent components of intellectual behavior and determine the importance of and interactions between value drivers such that the degree of intelligence can be measured or evaluated. Structure and function are identified and defined as the conduit by which intelligence is achieved. Business measures and indicators are extracted from operational data and transformed into business intelligence aligned with objectives and goals. The objective of E-BRAIN is to provide the business with a view of negative and positive impacts on performance based on intangible assets. Business Intelligence is extrapolated from the body of information to form the IDB. The

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IDB contains a “body of intelligence” logically constructed from the cross-pollination of the value drivers (Green 2007a, Green 2007b). Business intelligence is the second layer of E-BRAIN and it supports business reasoning, problem-solving, decision-making and learning. Walter Fritiz (1997) in his e-book, Intelligent Systems and their Societies, defines intelligence as the system’s level of performance in reaching its objectives. This definition supports the creation of intelligence from the eight value drivers, which align with intangible assets and their strategic decision-making capacity. The cross-pollination of the value drivers provide multi-dimensional data that contributes to the diagnostics of productivity, competence and resource-allocation information. The crosspollination of business value drivers (Table 2) constructs the following three categories of intelligence:

Engineering Business Reasoning, Analytics and Intelligence Network (E-BRAIN)

Table 2. Cross-pollination of value drivers

1.

2.

3.

Relationship Intelligence: understanding of how the interactions between knowledge workers influence organization performance. Competence Intelligence: understanding of how the abilities/proficiency of knowledge workers influences organization performance. Structure Intelligence: understanding of how the organization’s infrastructure environment influences organization performance.

The combination of relationship, competence and structure intelligence provides a “body of intelligence” to create knowledge. The modeling of business intelligence introduces a new level of abstraction that allows the conventional naming of these combinations and

the elevation of them into strategic, tactical, and operational business models.

Evolving Intelligence into Knowledge The “body of Intelligence” is further manipulated and summarized to identify, uncover, and model business knowledge. This once again introduces a new level of abstraction that allows visibility into the value contributions of intangible assets and the elevation of them into strategic, tactical, and operational knowledge models. Knowledge models are engineered on the identification of business events that can be symbolized and manipulated to achieve expected business results. Business knowledge models utilize the routine and special statistical, financial, forecasting, management science, and other quantitative models that provide analysis capabilities (Turban & Aronson, 1998).

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E-BRAIN components support the business levels of abstraction through the development of semantic networks and frames. Semantic networks and frames provide a simple and intuitive way of representing facts about objects (Stanfill & Waltz, 1986). Both schemes allow the representation of categories of objects and relationships between objects, and draw simple inferences based on intelligence. Semantic networks are used in the definition of complex interrelationships in a knowledge base. Semantic networks provide the foundation of a sophisticated inference system. Frames emulate the mental recall of images of a particular object and its related attributes by human thought. How does a business gain knowledge from the cross-pollination of business information and intelligence? Table 3 provides an example of the questions that result from the hybrid mix of customer and competitor value drivers. Knowledge models establish a foundation to plan for the future. The business knowledge that results from

the cross-pollination establishes reason and basis for actions. This new level of abstraction provides a “body of knowledge” to serve as the basis of the business leader’s decisions.

Learning from what is Known “Learning may signify either a product (i.e., something learned) or the process that yields such a product. In the first sense, we might ask, ”What have we learned?” referring to an accumulation of information in the form of knowledge or skill; in the second sense, ”How do we learn?” referring to a learning activity that may be well or badly performed” (Argyris & Schön, 1996). An organization learns when it adds to its store of information or body of knowledge through organizational inquiry (Argyris & Schoön, 1996). The business enterprise is an ever changing large and complex venture that has an overabundance of internal and external information to

Table 3. Cross-pollination of customer and competitor Relationship Intelligence Customer Indicators 1. Acquisition 2. Satisfaction 3. Longevity 4. Profitability

Customer-to-Competitor 1. Does the acquisition of new customers introduce new markets? 2. Does the acquisition of new customers’ impact market share? 3. Does the acquisition of new customers impact company image? 4. Does the acquisition of new customers impact company reputation? 5. Does the acquisition of new customers’ impact company branding? 6. Does customer satisfaction impact new markets? 7. Does customer satisfaction impact market share? 8. Does customer satisfaction impact company image? 9. Does customer satisfaction impact reputation? 10. Does customer satisfaction impact branding? 11. Does customer longevity impact new markets? 12. Does customer longevity impact market share? 13. Does customer longevity impact company image? 14. Does customer longevity impact reputation? 15. Does customer longevity impact branding? 16. Does customer profitability impact new markets? 17. Does customer profitability impact market share? 18. Does customer profitability impact company image? 19. Does customer profitability impact reputation? 20. Does customer profitability impact branding?

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Competitor Indicators 1. New Markets 2. Market Share 3. Image 4. Reputation 5. Branding

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gain insights and learn from. These insights and learning contribute to the business leader’s improved decision-making surrounding operational efficiencies. Business leaders learn from operational and historical data through the comprehension of information, organization of ideas, analysis and synthesis of data, forecasting, application of knowledge, and the ability to choose amongst alternative in problem solving. In particular, forecasting provides a business how a situation will turn out, or how a situation would have turned out had they followed another approach. E-BRAIN’s capturing of intangible asset information, intelligence and knowledge follows the practice of econometrics. Econometrics is the developing and applying of quantitative or statistical methods to the principles of economics (Johnston & DiNardo, 1997). Econometrics focuses on applying analyses to time-series, cross-sectional, panel & multidimensional panel data. Time-series data contains observations over time; cross-sectional data contains observations at a single point in time; panel data contains both time-series and cross-sectional observations, multi-dimensional panel data contain observations across time, cross-sectional, and across some third dimension. Econometrics combines economic theory and statistics to analyze and test economic relationships. Econometric methods use statistical procedures to estimate relationships for models defined on the basis of theory, prior studies, and domain knowledge. It combines numerical forecast output with subjective or judgmental input to refine forecast findings. Quantitative statistics supporting econometrics and forecasting in E-BRAIN are: •

Extrapolation: the use of historical data to forecast. Exponential smoothing applies the principle that recent data is weighted more heavily and averages cyclical fluctuations to forecast the trend from the

•

•

•

data and to derive a forecast (Makridakis, Wheelwright & Hyndman 1998). Quantitative Analogies: the identification of situations that are analogous to a target situation to be used to extrapolate the outcome of a target situation. Causal Models: cause and effect models developed on the basis of prior knowledge and theory. Time-series and cross-sectional regression used to estimate model parameters or coefficients. Defining of causal variables using theory and prior knowledge. The key is to identify important variables, the direction of their effects, and any constraints (Allen and Fildes 2001). Segmentation: identifying important causal variables, creating segments and their priorities to make forecasts.

Decisions in the knowledge economy require content and analysis to learn from and promote the future growth and renewal of a business. With the complexity of so much information and analysis, visualization software serves the purpose of measuring change for learning. A visualization interface helps the business to leverage information and perform analytics. The visual interface is created based on the intelligence and knowledge repositories that feed its structure using the inherent models that are defined within its structure. This interface is used to help monitor progress towards goals, the effect of actions taken and measurements that support interpretation and judgment in decision making (Senge, Kleiner, Roberts, Ross, Roth, & Smith, 1999). They provide alignment, visibility, and collaboration across the organization by allowing business users to define, monitor, and analyze business performance. Learning interfaces include professional reports, scorecards, and dashboards that provide strategic, operational, and tactical views as follows: •

Strategic reports, scorecards, and dashboards have a scorecard interface that sup-

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Engineering Business Reasoning, Analytics and Intelligence Network (E-BRAIN)

•

•

ports business leaders in tracking performance against strategic objectives. Operational reports, scorecards, and dashboards have an interface that supports senior and supervisory workers to monitor and optimize operational processes. Tactical reports, scorecards, and dashboards provide have an interface that supports business managers in improving their understanding of the processes and activities for which they are accountable.

change. Change practice can be grouped into three categories (Utsahajit 2009): •

Driving Change from what is Learned Organizational inquiry results in thinking and acting that yields a form of change in the design of organizational practices (Argyris & Schön, 1996). Senge, Kleiner, Roberts, Ross, Roth, & Smith (1999) use the word “profound change” to describe organizational change. Senge, Kleiner, Roberts, Ross, Roth, & Smith (1999) view change as the combined inner shifts in people’s values, aspirations, and behaviors with outer shifts in processes, strategies, practices, and systems. Senge, Kleiner, Roberts, Ross, Roth, & Smith (1999) identify that learning is part of profound change as organizations not only do something new, but build a capacity for doing things in a new way. From the body of knowledge, analysis and forecasting, the business has visibility into the best practices and lessons learn that impact its effectiveness and efficiencies. The business begins change initiatives based on the results and findings of the analyses and forecasts. The business uses these results and findings to introduce something new or new ways of doing work into the business environment. However, introducing something new or new ways of doing work into the business environment impacts the work performance of individuals and groups. Change measures the progress towards achieving goals and the measurements of change are not just “hard” measures. It is imperative to measure not only the change itself, but the impact of the

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•

•

Employee Perception: activities are focused on aligning employees’ views toward changes in the business operations. These activities are center on continuous learning through hands-on experience, both mentally and physically. Employee perception activities are directed at creating the readiness for change among employees by promoting the attitude of accepting changes as challenges and pathways to success at the customer, community and corporate levels. In addition, employees are encouraged to believe in the values of commitment, consistency and communication. Team Development: activities are focused on creating a sense of excellence, trust, and collaboration among employees. The organization believes that successful changes when employees embrace excellent quality, communicate truthfully among one another, and are willing to do everything possible to achieve mutual goals. Environmental Improvement: activities are focused on bringing changes into reality. These activities entail improvement both in the physical environment and the work atmosphere.

Petouhoff, Chandler, & Montag-Schultz (2006) describe a trend where improvement projects costs far outweigh their realized benefits. After reviewing, 10 years of independent studies that evaluated the rate of return on projects, it was discovered that a McKinsey study showed the return on investment (ROI) was 143% when an excellent organizational change management program was part of the improvement initiative and 35% when there was a poor organizational change management program or no program. This demonstrates a major value difference from gaining 43 cents on every dollar to losing 65 cents on every dol-

Engineering Business Reasoning, Analytics and Intelligence Network (E-BRAIN)

lar, which could significantly skew the valuation of intangible assets given the impact of change. Although the measures are validated from their quantifiable and qualitative journey from value drivers to knowledge, resulting change initiatives can fail, because their delivery is not aligned with the impact on people. Include the “soft” measures, such as motivation, commitment, ownership and resistance to change, as these are the critical variable for feedback that can help a team reflect, learn and move forward (Senge, Kleiner, Roberts, Ross, Roth, & Smith, 1999).

issues it has a dual role in implementing and updating strategy (Okkonen, Pirttimaki, Lonnqvist, & Hannula, 2002). Performance measurement ((Neely, 1998;Simons, 2000;Kaplan & Norton, 1996)):

Measuring the Performance Contributions of Intangible Assets

•

Performance measurement is a continuous and dynamic process in which measures are first constructed, based on strategically important success factors, then the measures are used to help implement planned strategies, and finally the analysis of measurement results provides feedback for new strategy formulations (Okkonen, Pirttimaki, Lonnqvist, & Hannula, 2002). Performance measurement deals with the implementation of an organization’s strategy (Kaplan & Norton, 1996). A balanced measurement system is used to identify and control critical factors that lead to success. The measures for the performance measurement system are chosen based on the organization’s vision and strategy (Kaplan & Norton, 1996). The aim is to measure success from different perspectives, like customers, employees, processes, and financial, as well as from the perspective of past, current, and future performance, such that these different aspects of performance can be analyzed and managed (Okkonen, Pirttimaki, Lonnqvist, & Hannula, 2002). Differences in the use of performance measurement depend on the time frame monitored. Performance measurement at the short-term or operative level is used for guidance, control, and managing quality, whereas in long-term strategic

• • • •

•

Translates strategy into concrete objectives Communicates the objectives to knowledge workers Guides and focuses knowledge workers’ efforts towards achieving objectives; Controls whether or not the strategic objectives are reached Use double-loop learning to challenge the validity of strategy Visualizes how individual employee’s efforts contribute to the overall business objectives

FUTURE RESEARCH DIRECTION E-BRAIN is a cognitive theoretical valuation model that integrates strategy, culture, and viability. This cognitive performance-based valuation model starts with business value drivers and their capacity, which evolves into intelligence, knowledge, learning, change, and translates into performance measures. These performance measures identify and control critical factors that align with the ability of a business to meet its goal of identifying and controlling the value contributions of intangible capital, assets and property to business performance. Future researches can: •

•

•

Develop models that provide insight into how business systems bring about desired outcomes. Identify, validate and synthesize measurements and indicators for use in enterprise balance scorecard valuation models Identify and validate a composite index as representation of a performance indicator

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for disclosure and market valuation of a business enterprise based on its intangible assets.

CONCLUSION In the words of Leif Edvinsson “The measures by which we all manage only gives us half an understanding of where we are or where we are going. Where do we register the resignation of a key person? If a top developer leaves even a big company like Microsoft it is significant. Where do we register the loss of a key customer or the failure of a key project?” Peter Drucker said that “if you can’t measure it, you can’t manage it.” And, Edward Gurowitz says that intangible assets are “like electrons in a cloud chamber: they cannot be measured directly, but only by the tracks they leave. The elusiveness of intangibles all seems to stem from the complexity of its structure. The way to make a complex system simple is to decompose it to its primitive elements. Its view becomes more holistic and it’s construction more manageable. This chapter is the culmination of the decomposition of a system that names intangible assets. This could be the first step to measuring, valuing and managing this hidden asset that accounts for 85% of the value of an organization. The main objective of intangible asset management is the establishment of tools and indicators to manage “knowledge” and increase earnings within the business enterprise (Sullivan 1998). This chapter expands on an engineering concept– FIVA to E-BRAIN–that decomposes intangible assets to their primitive names to establish the business intelligence (BI) that aligns with operational data. This alignment supports the creation of cross-tab views of data that can be modeled into multi-dimensional views which support the accountability and valuation of intangible assets. It has been stated numerous times that the nature of value within the business enterprise has changed and that new assets cannot be measured

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with old tools. This industry problem of the under valuation of intangible assets in a company’s financial reporting is supported by an abundance of evidence. Leaders of America’s most successful companies recognize that intangible assets rather than physical assets give rise to a new ecology of competition (Rivette & Klein 2000). Industry leaders realize that intangible assets can provide tangible bottom-line results if the sources of value are extracted. A business enterprise creates value through intangibles such as “innovation, employee skill and imagination, customer loyalty, contractual relationships with suppliers and distributors, better internal and external communications, trademarks, know-how, patents, software, brands, research and development, strategic alliances, and product differentiation (Litan & Wallison 2000, 26). With all that is known, why is it so difficult to value intangible assets? Perhaps: “We can’t solve problems by using the same kind of thinking we used when we created them.” … Albert Einstein In practice we are failing at improving business operations within our businesses. In theory, we need to explain why. The application of E-BRAIN in today’s knowledge era business enterprise supports the identification of the principles that explain the phenomena causing the inefficiencies driving today’s businesses.

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Green, A. (2004). Prioritization of Sources of Intangible Assets for Use in Enterprise Balance Scorecard Valuation Models of Information Technology (IT) Firms. Washington, D.C.: School of Engineering and Applied Science, Engineering Management and Systems Engineering Department, George Washington University. Green, A. (2006). The Starting Block: Enterprise (Business) Intelligence - Evolving Towards Knowledge Valuation. Vine, 36(3), 267–277. doi:10.1108/03055720610703560

Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting Methods for Management (3rd ed.). New York, NY: John Wiley. McNair, C., & Vangermeersch, R. (1998). Total Capacity Management: Optimizing at the Operational, Tactical and Strategic Levels. Boca Raton, FL: St. Lucie Press.

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McNurlin, B. C., & Sprague, R. H. (1998). Information Systems Management in Practice (4th ed.). New York, NY: Prentice Hall. Neely, A. (1998). Measuring Business Performance. Why, What and How?London: Profile Books Ltd. Newell, A. (1990). Unified Theory of Cognition. Cambridge, MA: Harvard University Press. Okkonen, J., Pirttimaki, V., Lonnqvist, A., & Hannula, M. (2002). Triangle of Performance Measurement. Business Intelligence and Knowledge Management. Stockholm: Euram. Petouhoff, N. L., Chandler, T., & Montag-Schultz, B. (2006). The Business Impact of Change Management. A Journal of Relevant Business Information and Analyses,9, (3). Rivette, K. G., & Klein, D. (2000). Rembrants in the Attic: Unlocking the Hidden Value of Patents. Boston, MA: Harvard Business School Press. Senge, P. (1990). The Fifth Discipline: The Art & Practice of the Learning Organization. New York, NY: Doubleday. Senge, P., Kleiner, A., Roberts, C., Ross, R., Roth, G., & Smith, B. (1999). The Dance of Change, The Challenges to Sustaining Momentum in Learning Organizations. New York, NY: Doubleday. Shand, D. (1999). Return on Knowledge. Knowledge Management, April, 33-39. Simons, R. (2000). Performance Measurement and Control Systems for Implementing Strategy. New Jersey: Prentice Hall. Stanfill, C., & Waltz, D. (1986). Toward MemoryBased Reasoning. Communications of the ACM, 29(12), 1213–1228. doi:10.1145/7902.7906 Sullivan, P. H. (1998). Profiting from Intellectual Capital, Extracting Value from Innovation. New York, NY: John Wiley & Sons.

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Sullivan, P. H. (2000). Value-Driven Intellectual Capital, How to Convert Intangible Corporate Assets into Market Value. New York, NY: John Wiley & Sons. Sveiby, K. E. (1997). The New Organizational Wealth, Managing & Measuring KnowledgeBased Assets. San Francisco, CA: Berrett-Koehler Publishers, Inc. Sveiby, K. E. (2001). Methods for Measuring Intangible Assets. Retrieved from http://www. sveiby.com.au/IntangibleMethods.htm Turban, E., & Aronson, J. E. (1998). Decision Support and Intelligent Systems (5th ed.). New York, NY: Prentice Hall. Utsahajit, W. (2009). Implementing Change Practice through Learning and Development: A Case Study of Kaeng Khol Cement Plant, Siam Cement Group, Thailand. NIDA Development Journal, 49(2), 110–124. VonKrogh, G., Roos, J., & Kleine, D. (1998). Knowing in Firms: Understanding, Managing and Measuring Knowledge. London: Sage Publication Ltd. Wiig, K. M. (1994). Knowledge Management, The Central Management Focus for IntelligentActing Organizations. Arlington, Texas: Schema Press, LTD.

ADDITIONAL READING Allee, V. (1997). The Knowledge Evolution, Expanding Organizational Intelligence. New York: Butterworth-Heinemann. Allee, V. (2000). Reconfiguring the Value Network. Journal of Business Strategy. 21(4), July-Aug. [Available from http://www.sveiby.com.au/AlleeValueNets.htm.

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Andriessen, D. (2004). Making Sense of Intellectual Capital, Designing a Method for the Valuation of Intangibles. New York: Elesevier Butterworth-Heinemann.

Bosworth, Barry P. and Jack E. (2000). What’s New About The New Economy? IT, Economic Growth and Productivity. [December 2000] Available from http://www.brook.edu/views/papers.

Andriessen, D., & Tissen, R. (2000). Weightless Wealth: Find Your Real Value. In A Future Of Intangible Assets. London: Prentice Hall.

Brooking, A. (1996). Intellectual Capital: Core Asset For The Third Millennium. New York: International Thomson Business Press.

Barney, J. B. (2001). Gaining and Sustaining Competitive Advantage. Upper Saddle River, NJ: Prentice Hall.

Brooking, A. (1999). Corporate Memory: Strategies For Knowledge Management. New York: International Thomson Business Press.

Bart, C. K. (2000). A Model of the Impact of Mission Statements on Firm Performance. DeGroote School of Business, McMaster University.

Calabro, L. (2001). On Balance, Almost 10 years after developing the balanced scorecard, authors Robert Kaplan and David Norton share what they’ve learned. CFO Magazine [cited February 2001]. Available from http://www.cfo.com.

Baruch, L. (2001). Intangibles, Management, Measurement and Reporting. New York: Brookings Institution Press. Blair, M. M., & Wallman, S. M. H. (2000). Unseen Wealth, Report of the Brookings Task Force on Understanding Intangible Sources of Value. [cited October 2000] Available from http://www. brookings.org. Bontis, N. (1998). Intellectual Capital: An Exploratory Study that Develops Measures and Models. Yorkshire, UK: MCB University Press. Bontis, N. (1998). Managing Organizational Knowledge by Diagnosing Intellectual Capital, Framing and Advancing the State of the Field. Journal of the Technology Management, 18, 443–462. Bontis, N. (2002). World Congress on Intellectual Capital Readings, Cutting-edge thinking on Intellectual Capital and Knowledge Management from the World’s Experts. New York: ButterworthHeinemann. Bontis, N. (1999). The Knowledge Toolbox: A Review of the Tools Available to Measure and Manage Intangible Resources. European Management Journal, 17(4), 391–402. doi:10.1016/ S0263-2373(99)00019-5

Chandrasekhar, R., et al. (1999). Case Study: The Case of the (Un) Balanced Scorecard. [cited April]. Available from http://www.zigonperf.com/ resources/pmnews/bsc_case_study.html. Clippinger III, John Henry. (1999). The Biology of Business, Decoding the Natural Laws of Enterprise. Jossey-Bass Publisher. Collingwood, H. (2001). The Earnings Game, Everyone Plays, Nobody Wins. Harvard Business Review, (June): 5–12. Drucker, P. F. (1954). The Practice of Management. New York: HarperCollins Publishers. Drucker, P. F. (1985). Innovation and Entrepreneurship. New York: HarperCollins Publishers. Drucker, P. F. (1994). The Age of Social Transformation. Atlantic Monthly, (November): 53–80. Drucker, P. F. (2001). The Next Society. The Economist Newspaper and The Economist Group. [Available at http://www.economist.com. Eccles Robert, G. (2001). The Value Reporting Revolution. New York: John Wiley & Sons, Inc.

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Gharajedaghi, J. (1999). System Thinking, Managing Chaos and Complexity, A Platform for Designing Business Architecture. New York: Butterworth-Heinemann. Haeckel, S. H. (1999). Adaptive Enterprise, Creating and Leading Sense-And-Respond Organizations. Boston: Harvard Business School Press. Hamel, G., & Prahalad, C. K. (1994). Competing for the Future. Boston:Harvard School Business Press. 16. Hammer Michael and James Champy. (1993). Reengineering the Corporation, A Manifesto for Business Revolution. New York: HarperCollins Publishers. Harvard Management Update. (2001). Getting a Grip on Intangible Assets, What They Are, Why They Matter, and Who Should Be Managing Them in Your Organization. [cited February 2001] Available from http: www.hbsp.harvard. edu/hmu/intangibles. Hodgetts, R. M. (1998). Measures of Quality & High Performance, Simple Tools and Lessons Learned from America’s Most Successful Corporations. Amacom. Hurwitz, J. (2002). The Linkage Between Management Practices, Intangibles Performance and Stock Returns. Journal of Intellectual Capital, 3(1), 51–61. doi:10.1108/14691930210412845 Kaplan, R. S., & Norton, D. P. (1996). Using the Balanced Scorecard as a Strategic Management System. Harvard Business Review, (JanuaryFebruary): 75–85. Kaplan, R. S., & Norton, D. P. (2001). The Strategy-Focused Organization, How Balanced Scorecard Companies Thrive in the New Business Environment. Boston: Harvard Business School Press.

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Kaydos, W. (1999). Operational Performance Measurement, Increasing Total Productivity. St. Lucie Press. Klein, D. A. (1998). The Strategic Management of Intellectual Capital. New York: ButterworthHeinemann. Koulopoulos, T. M. (1997). Smart Companies Smart Tools, Transforming Business Processes into Business Assets. New York: International Thomson Publishing Company. Luthy, D. H. (1998). Intellectual Capital and its Measurements. [Available from: http:www3.bus. Osaka-cu.ac.jp/apira98/archives/htmls/25.htm. Maisel, Lawrence S. and American Institute of Certified Public Accountants, Inc. (2001). Performance Measurement Practices Survey Results. [cited Available from http://www.aicpa.org. Mavrinac, Sarah and Tony Siesfield. (1998). Measuring Intangible Investment, Measures that Matter: An Exploratory Investigation of Investors’ Information Needs and Value Priorities. Ernst & Young Center for Business Innovation: Organisation for Economic Co-Operation and Development. Reilly, R. F., & Schweihs, R. P. (1999). Valuing Intangible Assets. New York: McGraw-Hill. Shand, Dawne. (1999). Return on Knowledge. Knowledge Management. April, 33-39. Stewart, T. A. (1994). Your Company’s Most Valuable Asset. Intellectual Capital. Fortune, (October): 68–72. Stewart, T. A. (1999). Intellectual Capital: The New Wealth of Organizations. Bantam Dell Publishing Group, Inc. Svendsen, A. (1998). The Stakeholder Strategy: Profiting From Collaborative Business Relationships. Berett-Koehlet Publishers, Inc.

Engineering Business Reasoning, Analytics and Intelligence Network (E-BRAIN)

Tapscott, Don. (2000). The Evolving Value Chain. [cited Available from http://www.agilebran.com/ tapscott4.html.

Weick, Karl E. and Kathleen M Sutcliffe. (2001). Managing the Unexpected. Jossey-Bass.

Tissen, R., Andriessen, D., & Lekanne, F. Lopez. (2000).The Knowledge Dividend: Creating HighPerformance Companies Through Value-Based Knowledge Management. Prentice Hall.

Williams, S. Mitchell. (2000). Is a Company’s Intellectual Capital Performance and Intellectual Capital Disclosure Practices Related?: Evidence From Publicly Listed Companies From the FTSE 100. University of Calgary.

Waterhouse, John H. (1999). Measuring Up. CA Magazine. March, 41-44.

Zack, M. H. (1999). Knowledge and Strategy. New York: Butterworth-Heinemann.

Weick, K. E. (1995). Sensemaking in Organizations. Thousand Oaks, CA: Sage Publication, Inc.

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Chapter 12

Measuring and Managing Intellectual Capital for both Development and Protection G. Scott Erickson Ithaca College, USA Helen N. Rothberg Marist College, USA

ABSTRACT This chapter considers the strategic management of intellectual capital, balancing the need to develop knowledge assets with the need to protect them. In making more strategic decisions, metrics on the level of intellectual capital and degree of knowledge management necessary to compete in an industry are required, as are those on the threat from competitive intelligence activity. The authors develop the case for appropriate metrics that accomplish these purposes, noting both potential and limitations. The authors also consider alternatives, additional data that could contribute to the usefulness and understanding of the core metrics, and provide suggestions for further research.

INTRODUCTION The study of intellectual capital (IC) and its close cousin knowledge management (KM) has always carried an implicit assumption that more development of knowledge assets is unambiguously for the better. The case has been built on conceptual and empirical studies often limited to individual firms or a small group of firms. As a result, much of what we know about managing intangible assets is both very specific to a given firm or situation

DOI: 10.4018/978-1-60960-054-9.ch012

and very much dependent on highly specific, often internal data about such assets. While such approaches have been invaluable in terms of defining the field and identifying the nature of the critical knowledge assets comprising an organization’s intellectual capital, there is a definite shortage of more broadly applicable scholarship. There has also been a lack of any question whether the efforts toward developing IC are actually worth the effort. What level of IC development is optimal? Is investment in the biggest information technology (IT) or firm-wide knowledge-sharing systems right for all firms in all circumstances? Should all proprietary knowl-

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Measuring and Managing Intellectual Capital for both Development and Protection

edge be fully shared with collaborators? What conceptual and empirical guidelines do we have to begin to answer such questions? This chapter addresses some of these issues, looking at a broader view of not only measuring intellectual capital but also acting on the results. In order to gauge whether adding to IC through KM initiatives actually makes an impact on competitiveness and financial results, such a broader perspective is necessary and is an important step towards teasing out the actual relationships underlying these questions.

BACKGROUND As this chapter blends concepts from several different but related fields, some definitions are probably in order. For the purposes of this chapter, we employ the following terminology: •

•

Intellectual capital refers to the stock of knowledge assets in the organization. These will be defined more precisely in the discussion, but include the firm’s intangible assets. In addition, we include phenomena such as data and information that typically don’t rise to the level of “knowledge” in some applications. As they are useful, informative, and have potential to become “knowledge”, they are included as knowledge assets or intellectual capital in this chapter. Knowledge management is the practice of administering this intellectual capital. Most KM applications seek to increase the stock of IC. The field of KM includes numerous tools for better management and growth of knowledge assets, from simple apprenticeship programs to massive IT installations for codifying knowledge and/or installing expert identification systems. KM can also grow IC by extending the system to include more participants, particularly by

•

gathering from and sharing with collaborators (usually, again, through IT systems). Competitive intelligence (CI) has to do with organized efforts by firms to uncover competitors’ proprietary knowledge assets and other pertinent information, then subjecting it to analysis, allowing better decision-making. Although CI has aspects of knowledge management in it (gathering and analyzing knowledge assets related to a competitor), we are more concerning with the threat it poses to firms managing their own knowledge. In other words, we are looking at the number and level of CI activities arrayed against an organization.

Intellectual capital is a relatively young discipline but incorporates a long-standing interest in measuring intangible assets into its approach. The basic concept is that softer knowledge-based assets can be identified, measured, and managed in much the same manner as hard knowledge assets such as those formalized in patents, copyrights, and so forth (hence the clear echoes of “intellectual property” in our adoption of the “intellectual capital” terminology). These softer knowledge assets include fuzzier, harder-to-define intangibles such as know-how or social capital. Early work in the field defined and identified these knowledge assets (Stewart, 1997), specifically breaking them down into the now well-accepted categories of human capital (HC), structural capital (SC), and relational (sometimes termed collaborative) capital (RC) (Bontis, 1999; Edvinsson & Malone, 1997). Human capital tends to focus on job-related knowledge, what individuals know about performing their day-to-day tasks, whether on the line, in service delivery, or in management. Structural capital has more to do with enduring knowledge in the organization embodied in a corporate culture, corporate structure (e.g. bureaucratic or networked), and/ or information systems. Relational capital is all about knowledge concerning entities outside the

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organization and how to manage relationships with them, be they customers, suppliers, regulators, or others. Besides these core categories, there have been other pertinent types of intellectual capital suggested in the literature, either in addition to the traditional three categories (e.g. competitive capital, or knowledge about competitors, Rothberg & Erickson, 2002) or as a broader reframing of the relevant categories (Andreou, Green & Stankosky, 2007). But most work continues to stick with the accepted breakdown of human, structural, and relational capital. The important point, however, is the general approach, which succeeds in taking traditional intangible assets such as brand equity and a highly trained workforce and ordering them into clear and potentially measurable groups. The basic framework allowed pioneers such as Skandia to begin to develop metrics (Navigator) and publish intellectual capital reports (Edvinsson & Sullivan, 1996). The Balanced Scorecard also incorporated some of these principles into its structure (Kaplan & Norton, 1992). Once one has an idea of the stock of knowledge assets, then the question of flow and adding to those stocks arises (Dierickx & Cool, 1989). Knowledge management grew from this perspective. KM is closely related to IC in that both are about knowledge assets, but where the latter is focused on identification and metrics, the former is more interested in growing the assets and employing them to their fullest potential. In terms of theory development, the KM literature has been useful in distinguishing between types of knowledge assets, most prominently in terms of tacit and explicit knowledge. While a well-established concept (Polanyi, 1967) before business got hold of it, this distinction as applied to knowledge assets is important (Nonaka & Takeuchi, 1995). Explicit knowledge is relatively easy to express or communicate and can thus often be captured by the firm and stored in information systems. Tacit knowledge is more individualized, hard to explain or communicate, and difficult to capture. As one

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would imagine, the implications for managing the knowledge vary dramatically by the type, with different tools applied to explicit knowledge than to tacit (Boisot, 1995; Choi & Lee, 2003; Schulz & Jobe, 2001). Other such contributions include variables such as knowledge complexity, stickiness/specificity (how specific is the knowledge to a given application or set of circumstances), absorptive capacity of the organization, social capital, and others (Kogut & Zander, 1992; Nahapiet & Ghoshal, 1998; Zander & Kogut, 1995). What these contributions help to establish is that there are clear differences in types of knowledge assets and in the circumstances of organizations looking to manage them. And, as noted above, there have been a variety of tools developed to use in KM, from substantial information systems often installed by the big consulting firms (Matson, Patiath & Shavers, 2003) or expert systems to those designed for more tacit applications such as communities of practice (Wenger, 1998) or storytelling (Brown & Duguid, 2000). What is critical is that all of this research suggests that different types of knowledge assets exist, that they should be managed differently, and, at least implicitly, that not all knowledge in all circumstances can or should be effectively grown. If one follows the scholarship to its natural conclusion, one can not only wonder whether particular tools will work in a given situation, but whether any tools at all will work. Some knowledge assets are invariably going to be harder to develop than others. Are all worth the required investment and effort? Do all firms need to rush to install expensive KM systems and look to spread knowledge assets to all collaborators at all levels? As a result, given the expense of installing and administering many of these KM systems, a natural question arises as to whether the practices actually lead to concrete results. Does more IC lead to better financial performance? There is a case to be made for competitive advantage flowing from knowledge assets. As long ago as Schumpeter (1934),

Measuring and Managing Intellectual Capital for both Development and Protection

knowledge combination leading to innovation was recognized. More recently, well-known scholars noted the potential of organizational knowledge (Winter, 1987) and knowledge workers (Drucker, 1991) to gain competitive advantage. More precisely, as the resource-based view of the firm has gained acceptance, a number of researchers have pointed to the role of knowledge assets as a critical, unique organizational resource leading to superior marketplace performance (DeCarolis & Deeds, 1999; Grant, 1996; Gupta & Govindarajan, 2000a; Zack, 1999a). And what do the data say? We’ll talk shortly about what’s been done in terms of assessing IC development and its impact on financial performance but it’s certainly fair to say that the empirical evidence is slim and limited. There has not been overwhelming feedback that KM installations have been successful, on a wide scale, in contributing to firms accumulating above average returns. Some of the disappointment with KM, however, begs the question of whether massive KM investment and sharing is always the best avenue for every firm. Given what we know about different types of knowledge assets and their potential for development, one would imagine the answer is probably not. In just about every other similar circumstance, we know that firms have different needs and different environments, so a one-sizefits-all KM strategy is unlikely to be optimal. Full-bore R&D, for example, makes sense in certain industries (e.g. pharmaceuticals) but makes little sense in others (retail). Why should KM be any different? Add in the threat from competitive intelligence and an increased inability to protect valuable, proprietary knowledge (Liebeskind, 1996), and you have a recipe for taking more care in developing and distributing knowledge here, there, and everywhere (Rothberg & Erickson, 2005). The rest of this chapter will explore this theme, specifically considering a more strategic conceptualization of managing knowledge assets and what metrics can be used to both make the case and help practitioners looking for a more strategic

approach. For a number of reasons, this approach to identifying and measuring these intangibles has difficulties. We’ll frame the issues for discussion and try to provide some guidance as to where and how we believe practice should move.

THE STRATEGY OF KNOWLEDGE DEVELOPMENT AND PROTECTION SPF Framework The framework for a strategic approach to KM is developed in Rothberg & Erickson (2005). The Strategic Protection Factor (SPF) conceptualization is based on opposing risks: KM Risk and Competitive Intelligence (CI) Risk. KM Risk is found in not developing knowledge assets to the same extent as competitors, thus operating at a competitive disadvantage. As noted in the previous section, there are undoubtedly opportunities for competitive advantage from better developing and sharing knowledge assets. Organizational knowledge can be a unique and sustainable source of competitive advantage. If the potential exists, but a firm does a poorer job developing and sharing knowledge assets than rivals, it will be at a competitive disadvantage. As knowledge is developed and shared more aggressively, this risk goes down. CI Risk comes from developing and sharing knowledge assets to such an extent that they become vulnerable to competitive intelligence efforts by other firms. As knowledge is developed aggressively (more knowledge gathered, more knowledge accessible from a central location, more knowledge shared with collaborators), this risk goes up. Dispersed, digitized knowledge assets are much harder to protect if aggressive CI efforts are organized to acquire them. For any individual firm, the strategic priority is to find the “sweet spot” where knowledge is developed to the appropriate degree without leaving it vulnerable, the optimal point where total risk is minimized.

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In application, the SPF framework provides additional detail about the types of circumstances facing specific firms in specific industries. There are variables that contribute to the level of KM Risk and/or to the level of CI Risk such as types of knowledge used in an industry or the degree of protection provided by the government. And there are prescriptions for managing KM, both in development and in protection, given an assessment of where a firm might find itself within the framework. None of that is really our primary focus here as it’s covered in detail in the book and elsewhere. Some context is helpful, however, as some of this conceptual foundation will undoubtedly come through in the following discussion. Our primary focus, rather, is how to measure these risks. Consider each in turn. On the KM Risk side, the issue is the degree of knowledge asset development and sharing but in a relative manner to what is going on with competitive firms. Quite a lot of the work with metrics related to KM or IC has to do with building up to an overall value by assessing the individual pieces (HC, SC, and RC again) in the manner of the Skandia Navigator. These are essentially bottom-up approaches in individual firms. A number of options are available (Tan, Plowman & Hancock, 2007), including popular choices such as Pulic’s (2004) ValueAdded Intellectual Coefficient (VAIC) which has been applied in a number of recent high-profile studies (Chen, Chang & Hwang, 2005; Firer & Williams, 2003). Such approaches are particularly useful for trying to assess the impact of a specific part of IC (e.g. structural capital in Lev & Radhakrishnan (2003)) or specific IC initiatives (Marr & Schiuma, 2001). They are also typical of approaches to studying IC and KM that look closely at an individual firm, essentially case studies (Davenport, DeLong & Beers, 1998; Gupta & Govindarajan, 2000b; Hansen, Nohria & Tierney, 1999; Zack, 1999b) or at a small group of firms (Mouritsen, Larsen & Bukh, 2002). This focus on the individual firm has been typical of the field (McEvily & Chakravarthy, 2002).

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As our objective is to measure a substantial number of firms at the industry level (and, as it turns out, across industries as well), this sort of approach is not really workable. Instead, we have looked to assess overall IC within the firm with a robust and readily available metric, a variation on Tobin’s q (Tobin & Brainard, 1977). Tobin’s q goes back to intangible asset basics, using the difference between a firm’s worth (market capitalization) and the value of its physical assets (replacement cost) as a measure of those intangibles. As replacement cost is often a difficult number to obtain, a commonly used variation is to instead use book value of the assets, which comes from stockholders’ equity (assets less liabilities). This approach has been used with some success in a number of applications (Villalonga, 2004), from evaluating the relative level of intellectual capital applied in consumer markets as opposed to business markets (Erickson & Rothberg, 2009) to assessing the financial impact of intellectual capital development in a specific industry (Bramhandkar, Erickson & Applebee, 2008). Although there are obvious limitations to the metric, to be discussed shortly, it can be quite useful when one needs a measure with some scope to it. CI Risk is a more difficult nut to crack. Competitive intelligence efforts, whether one is looking at success in procuring competitive information or just spending levels, are usually hidden. This is their nature. But one place where one can look is employment levels, and the membership lists of the Society of Competitive Intelligence Professionals (SCIP) are a useful proxy for CI activity. As with the IC/KM measure, some care is necessary as very small differences can indicate dramatic variations (a SCIP member can be at a fairly senior management level with a sizable non-member staff underneath). On the other hand, a small difference may, in fact, simply be a small difference. Where both of these measures are most valuable is in an application where certain aspects of the analysis can eliminate natural limitations. With the Tobin’s q measure, for example, the structure of the

Measuring and Managing Intellectual Capital for both Development and Protection

physical assets can have an impact on the result, as firms with heavy investment in capital equipment or requiring substantial financial resources will clearly have a much larger denominator than those without such necessities. So limiting the metric to a single industry, where all firms are likely to have similar physical asset structures, makes for more effective comparisons of intangible relative to intangible assets. Extending to more than one industry can be done, but care must be taken, and we have generally only done so when dealing with hundreds of firms at the same time with the hope that most industry-specific difficulties will be minimized by the sheer size and complexity of the data set.

Illustrative Applications To illustrate, we’ve included data on individual firms in four different industries from 1996 (drawn from a larger database we created spanning hundreds of firms from 1993 through 1996). Data were drawn from common sources of financial results (Compustat) and from SCIP. As SCIP data can be rather sensitive, we accepted their offer to provide us with older data which still allows us to establish a benchmark relative to these measures. With this benchmark in place, the ability to do more recent comparisons becomes even more valuable, allowing both context and some sense of changes over time. The older data are also useful from the perspective that we have some idea of what has happened to some of these firm over the past decade, helping to verify whether their standing in 1996 proved propitious for the future or not. In line with the previous discussion, the Tobin’s q is used to measure KM Risk. The underlying concept is that we can gain an industry benchmark of intellectual capital level relative to tangible assets. Those firms within the industry operating at a level higher (or much higher) than the average are at less KM Risk. These firms are apparently developing and sharing knowledge assets to a greater degree and more successfully than direct

competitors. On the other hand, firms lagging the industry standard would have high KM Risk and face the prospect of being left behind by competitors doing a superior job of developing IC. On the other hand, average number of SCIP members is used to measure CI Risk. This is again an industry figure, where the measure shows the degree of CI activity within the industry. Firms facing a high number of SCIP members face high CI Risk as standard practice in the industry is to aggressively conduct CI operations. In the following tables, two versions of the Tobin’s q variation are available as well as the SCIP member metric and a related alternative, the CI months worked that year by SCIP members. We’ve also included the underlying market capitalization, asset, liability, and revenue data for each firm. The industries were chosen to correspond to four scenarios presented in Rothberg & Erickson (2005): •

•

•

•

SPF 45: High KM Risk and high CI Risk. Developed knowledge assets are necessary to compete and are highly valued by competitors as well. SPF 30: High KM Risk and low CI Risk. Developed knowledge assets are necessary to compete but are not easily employed by competitors, who don’t pursue them aggressively. SPF 15: Low KM Risk and high CI Risk. Knowledge assets are difficult to develop but competitors can benefit from identifying and analyzing those that do exist. SPF 5: Low KM Risk and low CI Risk. Knowledge assets are difficult to develop and are also of little value to competitors.

In Table 1 are SPF 45 firms in the photographic equipment industry, including Xerox in this reporting period. The standard KM Risk variable shows a 7.12 average for the industry (the average for the full database runs about 3.5), so this appears to be a relatively high level of KM development,

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Table 1. SIC 386: photographic equipment & supplies 1996 Market Cap (millions) Canon Kodak Polaroid Xerox

17, 686 27,547 1,978 16,346

Assets (millions) 2,461 14,477 2,261 25,969

Liabilities (millions)

Revenues (millions)

1,440 9,356 1,544 20,583

2,165 14,980 2,236 16,611

though, once again, remember that physical asset level also influences this measure and can throw off specific industry-to-industry comparisons. Regardless, one would imagine that in order to keep up with Canon, or even Kodak, some aggressive investment in knowledge asset growth is required. The CI Risk is also quite high (1.0 is about average for the full database and is directly comparable across industries) with 5.5 members per firm. There are a number of aggressive CI operations in this industry as firms clearly value information, data, or knowledge that can be obtained from competitors. Table 2 presents a slightly different scenario, the SPF 30 version with relatively high KM but low CI Risk. The Tobin’s q metric is again high at 5.88 but CI activity is muted with only 1 member across all the firms (and looking at the months measure, the member is less than half-time at that), resulting in an industry average of 0.25. Knowledge asset development is critical but protection is not a great concern. One would speculate that knowledge is highly specific to its applications in the innovating firm and/or is

Tobin’s q (equity)

Tobin’s q (assets)

SCIP members

SCIP months

17.33 5.38 2.76 3.03

7.19 1.90 0.87 0.63

1 7 4 10

12 54 23 85

7.12

2.65

5.5

43.5

strongly protected by intellectual property devices, strong brands, or some other mechanism making the assets hard to copy or use. Table 3 presents an SPF 15 industry, various insurance providers. The Tobin’s q value is now apparently relatively low, at 1.64. Efforts to develop knowledge appear to be less important— often indicative of an industry with relatively little creativity or else very tacit knowledge that is hard to grow with KM techniques. Once again, however, a little care needs to be taken with such conclusions because of the limitations of the measurement across industries (note the high level of assets to revenue in this industry). On the other hand, the SCIP member measure is relatively high, with the majority of firms practicing CI and an overall average of 2.0. This may seem counterintuitive given that knowledge is not as valued by the originating firm, but we’ve tended to see this pattern in industries where a spark of genius or creativity is necessary for innovation (and is thus difficult to duplicate within the innovating firm) but whose results from innovation, once incorporated into a product, can be subject

Table 2. SIC 2835/6: in vitro, in vivo diagnostics 1996

Chiron Idexx Adv Tissue IGI

260

Market Cap (millions)

Assets (millions)

Liabilities (millions)

Revenues (millions)

Tobin’s q (equity)

Tobin’s q (assets)

SCIP members

SCIP months

$3,406 1,382 351 53

$1,490 312 56 32

$817 33 8 23

$1,020 188 1 31

5.07 4.95 7.26 6.25

2.29 4.42 6.21 1.65

0 0 1 0

0 0 5 0

5.88

3.64

0.25

1.25

Measuring and Managing Intellectual Capital for both Development and Protection

Table 3. SIC 633: fire, marine, casualty insurance 1996

Allmerica Allstate AIG Chartwell CNA ITT Hart OH Cas Safeco Selective St Paul USF&G Zurich Re

Market Cap (millions)

Assets (millions)

Liabilities (millions)

Revenues (millions)

Tobin’s q (equity)

Tobin’s q (assets)

$3,830 38,726 88,339 263 6,643 8,012 1,196 5,246 490 4,932 2,349 799

$18,997 74,508 148,431 1,132 59,901 93,855 3,980 18,767 2,113 19,656 14,651 1,923

$16,489 60,306 125,986 980 53,166 89,153 2,869 14,785 1,676 15,719 12,667 1,242

$3,274 24,299 25,862 144 14,699 12,150 1,462 3,722 839 5,409 3,459 616

1.53 2.73 3.94 1.73 0.99 1.70 1.08 1.32 1.12 1.25 1.18 1.17

0.20 0.52 0.60 0.23 0.11 0.09 0.30 0.28 0.23 0.25 0.16 0.42

2 8 2 1 5 2 1 0 2 1 0 0

11 50 9 12 35 24 8 0 13 12 0 0

1.64

0.28

2.0

14.5

to copying if uncovered. Financial services are the most obvious example (a new investing philosophy or new insurance offering may be difficult to create but can be rapidly duplicated). Table 4 presents the final scenario, with low values for both types of risk (SPF 5). Air transportation shows low KM values, with the q measure at 1.68 and also a low CI value at 0.75 (which would be even lower without the somewhat unique industry member Fedex). There is little apparent value to these sorts of firms to aggressively develop knowledge or worry about protecting it from competitors. One could speculate that knowledge in these situations is likely quite tacit or quite specific to its original applications.

Discussion What can we learn from this approach and these metrics? Even with the limitations, which we’ll

SCIP members

SCIP months

discuss shortly, there are some quite useful aspects from this approach. Initially, it seems fairly clear that there really are dramatic differences in the level of knowledge development between industries. The 7.12 and 5.88 values in the first two industries are of a different magnitude than the 1.68 and 1.64 of the others. While it’s important once again to note the limitations of this measure, the dramatic differences are compelling. With further work on the application of using the measure across industries, we should be able to refine Tobin’s q into something that can be used in direct comparisons, giving us a sense of the value of knowledge development and sharing in one industry compared to another. For now, these results do suggest that firms do face very different implications for managing knowledge in these different situations. While looking to better manage knowledge is never a bad thing, there are clearly times when one can overdo it. Within

Table 4. SIC 45: air transportation 1996 Market Cap (millions) AMR Continental KLM Fedex

-$1,562 2,223 5,085

Assets (millions) $17,562 4,821 12,993 6,698

Liabilities (millions) $13,034 4,206 8,791 4,122

Revenues (millions)

Tobin’s Q (equity)

Tobin’s q (assets)

SCIP members

SCIP months

$15,136 5,825 9,536 `10,273

-2.54 0.53 1.97

-0.32 0.17 0.76

1 0 0 2

6 0 0 17

1.68

0.42

0.75

5.75

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Measuring and Managing Intellectual Capital for both Development and Protection

the insurance category, for example, competitors should aspire to emulate AIG or Allstate’s level (3.94 or 2.73) but it would probably be money wasted to invest in knowledge development on the scale of Canon, Kodak, or Advanced Tissue, in the other industries. On the other hand, the level of protection of knowledge assets required in an industry also varies considerably. While employees likely need to take great care in the photographic sector (especially in the case of Canon which seems to have quite an array of SCIP expertise arrayed against it) because of all the CI activity, information and knowledge can be much more freely developed and shared in the diagnostics or air travel sectors. Similarly, if decision-makers are considering the need to do their own CI, this type of information can help with that issue, making clear the level of activity typical of the industry. In both of these cases, these metrics help us to confirm that different conditions do exist pertaining to knowledge development and protection. In selling the idea that knowledge management is therefore strategic, there is fairly strong evidence that at least some KM and CI decisions are situational and should be subject to a more strategic approach. But how much do the metrics help us to understand the differences and the implications for practice? On the surface, there are some difficulties. We already discussed the issues with different asset structures and the need for either intra-industry or large-scale comparisons, something partially violated in our discussion above as we are clearly making direct cross-industry comparisons of relatively small numbers of firms. Some of that can be solved with more data, however, as is the case here where we have included additional data on both revenues and assets. The level of assets necessary to generate a given level of revenue gives some idea of the physical asset needs of the organization and can be used in deeper analysis of these cross-industry comparisons.

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There are also some decisions to be made on exactly what the makeup of the Tobin’s q measure will be. As it is generally thought to be a remainder of market capitalization less book value, there is an implication that subtraction yields the figure. And this approach makes sense when looking for the absolute level of intangible assets. In our case, however, when looking to complete firm-to-firm or industry-to-industry comparisons, however, such an approach has problems. In particular, the metric will virtually always show larger firms in a more favorable light. In the photography industry illustration above, such an approach would show Kodak to be the clear leader in absolute level of intangible assets with Canon and Xerox behind and roughly comparable. But when the measure is treated as a ratio, as in this chapter, Canon is the clear leader with Kodak a substantive second and Polaroid and Xerox trailing well behind. And by that measure, we then know that Canon has done the far superior job of establishing the value of its firm and its intangibles for a given level of tangible assets—what the game is really all about. While this runs the opposite problem of perhaps giving too much weight to smaller firms with few tangible assets but high market valuations based on little but potential, the measure is much closer to the meaning we’re looking for. And some of that problem can be overcome by focusing on larger firms in an analysis. Another open question concerns the structure of the Tobin’s q ratio. In past work and in the discussion so far, we have employed the market capitalization to book value ratio, with the denominator commonly calculated from assets less liabilities. Essentially, it focuses on the value of the firm’s physical assets actually owned by the organization. If they are financed or borrowed, they don’t count. One can make the case that this again misses the main issue we are looking to resolve. Given that we want to identify how firms develop intangible assets given the level of physical assets—does it really matter who owns the physical assets, the firm or its creditors? Perhaps not, and

Measuring and Managing Intellectual Capital for both Development and Protection

so the correct variation on Tobin’s q might be the ratio in the table indicated by “Tobin’s q (assets)” in each of the tables. As shown, it doesn’t really change most of the general inferences that can be drawn from the data but does give us some decidedly different results in the details (relative firm performance, more bunching of the results, etc.). Some additional discussion is probably appropriate for future applications. The SCIP data are generally reliable for the purpose to which we are putting them, and, as noted, we have the alternate measure of SCIP months to serve as a second and validating proxy (though both pieces of data do come from the same source). One could try to add in something related to organizational size (one SCIP employee in a $100 billion firm is very different from one in a $1 billion firm) but that may again be unnecessary if dealing only with firms of a given size. The only real issue with the SCIP data that we have run into is one that also affects the Tobin’s q measure, and that is the tendency of the data to vary over time for reasons that have nothing to do with KM or CI performance. In 2008-2009, for example, with the downturn and the “Great Recession” as it is now being called, market capitalizations collapsed quickly and many have not recovered. That collapse sharply impacted the Tobin’s q figure and our assessment of intangible asset levels within firms. Similarly, the recent market difficulties have caused quite a number of firms to cut back on CI operations as an expense that could be easily cut. This, again, would have little to do with success or failure in obtaining information or with the value of such information in a given industry. As a result, longitudinal comparisons, something that would theoretically be quite useful in the field should be approached with some caution as external factors may make analysis over time difficult. One approach to getting around such difficulties might be the use of indices, allowing relative KM and CI comparisons over time. We have also taken an approach of gathering data over an extensive period of time (1993-1996 here, something similar

in the future) which helps to wash out some of the economic ups and downs. Finally, what we have really found useful with these metrics and this approach is clarifying where and how to do deeper research. We’ll talk momentarily about future research, but by singling out industries and firms of interest with this type of data, we can then begin to do the more in-depth analysis that does yield insights into tangible and intangible asset differences, the nature of knowledge, the difficulties involved in KM approaches, and levels and nature of CI activity in given situations. The broad approach allows the more specific questions that can yield more precise answers. So as long as not too much stock is put in slight differences in these metrics, they can be very useful in clarifying major differences and illustrate an approach that will yield insight into the slight differences and specific recommendations for managerial behavior.

FUTURE RESEARCH DIRECTIONS Future research directions should be relatively clear from the preceding discussion. The Tobin’s q metric has a number of potentially valuable applications in terms of assessing intellectual capital levels across a wide range of industries. Any research project looking to measure IC in a large number of firms would be well-advised to at least consider such an approach as something of a first pass. In doing so, the limitations should be kept in mind (firm size, physical capital levels, market cap fluctuations), but none are unworkable. And the results allow us to ask a number of relevant questions concerning how successful firms appear to be in managing knowledge assets. For those also interested in assessing competitive intelligence activity, similar potential and limitations exist with the SCIP metrics. There are two broad directions to go with these tools. On one hand, wide-ranging future research similar to that discussed here will be useful in

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Measuring and Managing Intellectual Capital for both Development and Protection

clarifying the metrics and the best versions to apply to given circumstances. The questions raised in our discussion such as difference or ratio, book value or physical assets, and useful additional data will only be answered with further research on particular industries and across industries. National-level evaluations are also possible. And, again, the answers on optimal metrics may be situational as we determine the best measures to be applied. On the other hand, as mentioned in the discussion, these metrics can also be quite valuable in clarifying directions for deeper research. If we are able to convincingly identify an industry such as photographic equipment as requiring substantial intellectual capital in order to compete, we can then do more specific and detailed research on that particular industry. What is the nature of the physical assets required? What is the nature of the knowledge assets that are useful in the industry? How tacit or explicit are they? Do they include human, structural, and/or relational capital? In what other ways can we describe them and discern ever better ways to manage them? What are best practices in the industry? Broad-ranging metrics such as these will allow us to offer better and more pertinent suggestions to decision-makers on how to manage knowledge assets to best advantage. These metrics are not necessarily the specific tools that will clarify those matters, but they are a good first step and can point us toward more specific tools that can.

CONCLUSION This chapter has covered two broad measures related to knowledge asset development (Tobin’s q variation) and protection (SCIP membership). By looking at some specific applications of the data in several disparate industries, we’ve shown how the metrics make clear the differences in industry practices and results. There are clear potential uses for these techniques in studying knowledge

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assets at a more macro level than is common in the discipline. If they are to be used, however, there are some limitations and concerns. As a result, we have also looked at some of the issues with the metrics and potential variations that may be more applicable. We have also presented some additional data that can be used in conjunction with the metrics to clarify an analysis, as well as some suggestions for deeper and richer research that can result from their proper application.

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McEvily, S. K., & Chakravarthy, B. (2002). The persistence of knowledge-based advantage: an empirical test for product performance and technological knowledge. Strategic Management Journal, 23, 285–305. doi:10.1002/smj.223 Mouritsen, J., Larsen, H. T., & Bukh, P. N. (2002). Understanding intellectual capital statements: designing and communicating knowledge management strategies. In N. Bontis (Ed.), World congress on intellectual capital readings (pp. 179-202). Woburn, MA: Butterworth-Heinemann. Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational advantage. Academy of Management Review, 23(2), 242–266. doi:10.2307/259373 Nonaka, I., & Takeuchi, H. (1995). The knowledgecreating company: How japanese companies create the dynamics of innovation. New York: Oxford University Press. Polanyi, M. (1967). The tacit dimension. New York: Doubleday. Pulic, A. (2004). Intellectual capital—does it create or destroy value? Measuring Business Excellence, 8(1), 62–68. doi:10.1108/13683040410524757 Rothberg, H. N., & Erickson, G. S. (2002). Competitive capital: a fourth pillar of intellectual capital? In N. Bontis (Ed.), World congress on intellectual capital readings (pp. 94-103). Woburn, MA: Elsevier Butterworth-Heinemann. Rothberg, H. N., & Erickson, G. S. (2005). From knowledge to intelligence: Creating competitive advantage in the next economy. Woburn, MA: Elsevier Butterworth-Heinemann. Schulz, M., & Jobe, L. A. (2001). Codification and tacitness as knowledge management strategies: an empirical exploration. The Journal of High Technology Management Research, 12, 139–165. doi:10.1016/S1047-8310(00)00043-2

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Measuring and Managing Intellectual Capital for both Development and Protection

KEY TERMS AND DEFINITIONS Competitive Intelligence (CI): Organized collection and analysis of data, information, and knowledge concerning a competitor or related target (product, technology, etc.). Intellectual Capital (IC): Tacit and explicit knowledge assets of the firm, comprised of human capital, structural capital, and relational capital. Knowledge Assets: Proprietary data, information, and knowledge of the firm. Knowledge Management (KM): System to identify, develop, and share the knowledge assets or intellectual capital of the firm.

Society of Competitive Intelligence Professionals (SCIP): Professional organization for competitive intelligence practitioners. SPF Framework: Framework to explain and analyze the balance firms should strike between knowledge development and knowledge protection. Tobin’s Q: Measure of the intangible assets of the firm, can be used as a metric for assessing intellectual capital levels.

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Chapter 13

Measuring and Valuing Knowledge-Based Intangible Assets: Real Business Uses Steve Pike ICS Ltd., UK Göran Roos ICS Ltd., UK

ABSTRACT This chapter offers a practical guide to the structure, taxonomy, measurement and use of intellectual capital (IC) in business. It traces the roots of IC and exposes and explains the remarkable lack of consensus that has been allowed to develop over the years and the methods used to try to measure it. In keeping with the practical, yet grounded, approach of the chapter, the chapter focuses on business innovation from an IC perspective. Most importantly, through a case study, the chapter introduces a practical means of measuring IC and modelling businesses predictively connecting soft issues such as human capital and relationship management with hard financial output. Recognising that IC is still an evolving discipline, the chapter offers a number of areas for future research and case study.

INTRODUCTION This chapter will have several facets but the overall mission will be to offer a practical guide to the structure, taxonomy, measurement and use of intellectual capital in business. Although Intellectual Capital (IC) has its roots in the leading-edge economics of the 1930s, the modern implementation of the subject dates back

to 1987 and the Swedish Konrad Group’s attempt to demonstrate the role of intangible assets in business. Since that early work and the enthusiasm shown for the concept through the early and mid1990s there has been a considerable dissipation of effort into subsets and niches and away from the central questions concerning how IC can be used for practical purposes in business such as explaining the process and cost of asset formation, finding the appropriate level of investment

DOI: 10.4018/978-1-60960-054-9.ch013

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Measuring and Valuing Knowledge-Based Intangible Assets

in meta-activities such as knowledge management and supporting the innovation process. Perhaps the most important and damning fact concerning the application of intellectual capital is that there are hardly any companies or government organisations that use it, or if they claim to, it is usually at a most trivial level which is of little practical use to the company or organisation or the outside observer. Recently there have been attempts by supra-national bodies such as the EU to impose intellectual capital systems or at least recommend how to approach the use of intellectual capital. Sadly, these have gained little acceptance although the involvement and interest of the EU is very helpful. At the same time there is renewed interest in measurement and the difference between value measurement and financial measurement. This new interest has led to the involvement and extension of advanced market techniques such as real options theory to the measurement of business value in intellectual capital terms. There have been other attempts using stochastic techniques but it is arguable that these or other recent advances have proved any more useful than earlier attempts to measure and predict business value. On the other hand, there is evidence that the seemingly intractable problem of measuring intellectual capital in a meaningful way can be undertaken and this will be developed from the historical and theoretical arguments. So far in the chapter, three important terms have been used without much concern about their definition or application. These terms are capital, assets and resources. Of these, the first two have precise meanings in accounting and as a result, it is helpful to avoid or abandon their use if confusion with their other meanings is possible. Henceforth in this chapter, “resources” will be used as the preferred term as it has no well defined meaning in other contexts. “Capital” will only be used within the term “intellectual capital” and will use as a collective term for all non-financial and nonphysical resources of a company or other entity. The term “asset” will be avoided as the accounting

meanings and treatments are specific and different from those that emerge in this chapter. This chapter is intended to be practical yet rigorous. It opens with two reviews, the first is a review of intellectual capital and the second is a review of innovation. The first requires little further justification but the second does. The development of intellectual capital including the basics of any usable intellectual capital model is required as it offers a means of showing how IC can explain all company activities in relation to each other. Innovation is the key driver for economic growth on firm level, industry level, national level and global level. OECD estimate that at least 50% of sustainable growth is due to innovation. Only innovation drives above average sustainable financial returns on both the industry and firm level. In firm terms this means that at least half of the future value component of the share price of any firm should come from innovation. The importance of innovation was illustrated by the chairman’s summary of the OECD Council at Ministerial Level in Paris, 15-16 May 2007 which included a section entitled “Innovation: Advancing the OECD Agenda for Growth and Equity” (OECD, 2007a). The section recognised ministers’ agreement that innovation performance is a crucial determinant of competitiveness, productivity and national progress. Ministers agreed that there is a need to improve the framework conditions for innovation through further opening and integrating the product and labour markets. This is an important insight since Ministers support the idea that innovation is more than developed technical ideas but is also concerned with people and business processes. They also underlined the pervasive nature of innovation, noting in particular the importance of education systems to ensure the supply of skills and of researchers and the need to foster greater private investment in innovation. At a more practical level for companies, Ministers welcomed the publication of two reports, Moving up the Value Chain – Staying Competitive in the

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Global Economy (OECD, 2007b) and Globalisation and Innovation in the Business Services Sector (OECD, 2007c).

BACKGROUND TO INTELLECTUAL CAPITAL

approaches to economic understanding but used firm as a collection of deployable resources as starting points. While links back to the 1930s may seem tenuous it should be realised that the economic difficulties of the time led to the search for more radical solutions and there are obvious parallels between then and now.

The Beginnings

Resources

If asked, most people in the IC field would say that the idea first came to the attention of the business world in 1991 with the publication of Stewart’s brief article in Fortune magazine about new ideas in business. That work led to a longer piece, “Brainpower”, properly published in 1992 and “Intellectual Capital: The New Wealth of Organisations,” which appeared in 1994. In 1997, Edvinsson and Malone published “Intellectual Capital: The Proven Way to Establish Your Company’s Real Value by Measuring its Hidden Brainpower”. These books and articles were written in a non-academic style but depended on anecdotal and contemporary business evidences, their accessibility led to a huge explosion of interest in the subject. It is less well known that in Sweden in 1987, consultants, company chief executives and others had convened what became known as the Konrad Group to review the role of resources, both tangible and intangible in value creating or maintaining sustainable competitive advantage in firms. They issued a first publication in January 1988 entitled the “New Annual Report” and issued their final report in 1989 presenting the first method on intangible measurement “The Invisible Balance sheet” (Den Osynliga Balansräkningen). The publication presents key indicators for accounting control and valuation of know-how companies. The Konrad Group’s focus on resources was critical since the resource-based view, the key foundation for IC, is based on the meso-economics of the first third of the 20th century. Chamberlin (1933) and Robinson (1933) published books within months of each other and described new

The early meso-economic views were developed during the second third of the century into the micro-economic (firm-based) views, especially by Penrose (1959). Edith Penrose’s much cited work on the theory of the growth of the firm saw the firm as productive resources at the disposal of managers rather than as a set of administrative units. She suggested that a firm is best gauged by some measure of the productive resources it employs. This led directly to the development of ideas concerning competitive advantage in the last third of 20th century. Penrose’s work provided further guidance for the later development of IC as an approach to business management. For example the clear definition of what a resource can be and how it differs from activities and services is crucial and stems from her work as does the central idea that services are the result of the combinations of particular sets of resources. Of course, most resources can be used in different combinations with other resources to give different services or generate other resources. The development of a firm is therefore dependent on the nature and qualities of the resource it possesses or can utilise. Over the years that followed, others considered the development and deployment of resources and attempted to add rigour and system to this concept. Examples of this are the work of Amit and Schoemaker (1993); Barney (1986), Barney and Zajac (1994), Lei, Hitt and Bettis (1996); Schoemaker (1992). Others focused on the relationship between resources and the scope of the firm (Chatterjee and Wernerfelt 1991; Markides and Williamson 1996; Prahalad and Hamel 1990; Robins and Wiersema

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1995). The issue of achieving and maintaining sustainable competitive advantage by means of combining and using resources naturally leads to the question of how the goodness or suitability of the resources should be described and measured. Barney (1991) proposes four conditions: value, rareness, inimitability and non-substitutability. Grant (1991) argues that levels of durability, transparency, transferability and replicability are important while Collis and Montgomery (1995) suggest five tests: inimitability, durability, appropriability, substitutability and competitive superiority. Amit and Schoemaker (1993) go even further, producing a list of eight criteria including complementarity, scarcity, low tradability, inimitability, limited substitutability, appropriability, durability and overlap with strategic industry factors. In 1984, this work led Wernerfelt (1984) to invent the term “resource-based view of the firm” which he used in a paper which was later awarded the Strategic Management Journal award for best paper. As we have seen, this decade saw the rise of the new economy and the work of the Konrad group to make practical use of it as traditional Porterian structures were found to be inadequate to describe firms and their performance even when in the same industry (Cubbin 1988; Hansen and Wernerfelt 1989). This later observation immediately brought in researchers concerned with strategy and strategic decision making (Amit and Schoemaker 1993; Barney 1986, 1991; Dierickx and Cool 1989; Lippman and Rumelt 1982; Peteraf 1993 and Reed and DeFillippi 1990). Current Position Achieving and maintaining sustainable competitive advantage by means of combining and using resources naturally leads to the question of how resources should be described and measured. Barney (1991) proposes four conditions: value, rareness, inimitability and non-substitutability. Grant (1991) argues that levels of durability, transparency, transferability and replicability are important while Collis and Montgomery (1995) suggest five tests: inimitability, durability, appropriability, substitutability and

competitive superiority. Amit and Schoemaker (1993) go even further, producing a list of eight criteria including complementarity, scarcity, low tradability, inimitability, limited substitutability, appropriability, durability and overlap with strategic industry factors. However, with no generally accepted, complete and working IC model, there is no consensus on the description of resources. The concept of sustainable competitive advantage has transformed in the last decade to the more precise concept of sustained and sustainable innovation and it is reasonable to expect that the appropriate management of the required resources provides the best chance of achieving it. The message of importance here is that possession of the resources is not enough. Within the context of prevailing legislation and trading conditions, sustained and sustainable competitive advantage come about only if the firm possesses or has control over the necessary resources and that they are effectively and efficiently combined and deployed. In 2004 Kaufmann and Schneider (2004) reviewed the IC literature to date. They reviewed over 100 papers published between 1997 and 2003 in six prestigious peer reviewed journals. They selected both books and papers but confined themselves to publications in English and German. Pike and Roos (2007) updated this work with a longitudinal study of papers published in the Journal of Intellectual Capital with 220 papers being reviewed. The journal was selected as it is dedicated to the subject and now has an appreciable history. Other journals such as the International Journal for Learning and Intellectual Capital were excluded as they were relatively new at the time and were yet to fully develop a style. The study found that severe ontological problems with 41% of papers still discuss the nature of IC. From a taxonomic standpoint, 67% follow the resource descriptors devised by Edvinsson or close variants of them. However, there were 15 other significantly different taxonomies in the papers with descriptors based on Sveiby’s work being the next most common at 18%. The remainder

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was idiosyncratic. It is reasonable to assume that the uncertainty and diversity highlighted by this study is a major contributor to the failure of IC to achieve wide-scale acceptability. Measurement At the outset, a distinction must be made between (proper) measurement systems and indicators. Measure: A measurement is a numerical representation of an object in which all the attributes of the object are included in the representation in a manner compliant with measurement theory and all measures and manipulations are also compliant with measurement theory. Indicator: An indicator is a roughly estimated representation of an object which may suffice for local needs but which is prone to errors. The advantages and disadvantages of measures and indicators are compared in Table 1. In the early attempts to measure non-financial features of companies, the object under examination was often described in terms of independent measures or as groups of competencies. The most famous of these and by far the most widely used of all non-financial measurement schemes is the Balanced Scorecard which originated in France with the work of Lauzel and Cibert (1961) as the Tableau de Bord, and was later developed in the US. The European Union IST (Information Society Technologies) and TSER (Targeted Socio-Economic Research) have, in recent years,

sponsored a number of studies on the subject, the most notable of which were the PRISM (2003) and MERITUM (2004) studies. Both studies examined their closely related field in detail reporting on the current state of knowledge. Critically, they both failed to make any significant headway on the question of measurement and indicators. Over the last 10-15 years, a great number of systems have been devised to help managers with business performance and with a special emphasis on non-financial measures. According to Luthy (1998) and Williams (2000), methodologies may be categorized into four groups. These are: A.

B.

C.

Direct Intellectual Capital Methods (DIC). Estimate the $-value of intangible assets by identifying its various components. Once these components are identified, they can be directly evaluated, either individually or as an aggregated coefficient. Market Capitalization Methods (MCM). Calculate the difference between a company’s market capitalization and its stockholders’ equity as the value of its intellectual capital or intangible assets. Return on Assets Methods (ROA). Average pre-tax earnings of a company are divided by the average tangible assets of the company. The result is a company ROA that is then compared with its industry average. The

Table 1. Comparison of proper measurement and indicators (Source: Pike and Roos (2004)) Measurement system

Indicator system

Advantages

Accurate if built properly Produces a complete view of the object Data can be disclosed Results can be benchmarked Can be the basis of derived measures Can be used with other business models Transparent and auditable Takes multiple views of value into account

Quick to build Easy to operate

Disadvantages

Takes care and time to set up Data requirement can be large Data quality requirements are stringent

Purpose specific Cannot be benchmarked Takes a single “average” view of value Cannot be built up to value complex objects Possibility of duplication

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D.

difference is multiplied by the company’s average tangible assets to calculate an average annual earning from the Intangibles. Dividing the above-average earnings by the company’s average cost of capital or an interest rate, one can derive an estimate of the value of its intangible assets or intellectual capital. Scorecard Methods (SC). Various components of intangible assets or intellectual capital are identified and indicators and indices are generated and reported in scorecards or as graphs. SC methods are similar to DIC methods, except that no estimate is made of the $-value of the Intangible assets. A composite index may or may not be produced.

The advantages and disadvantages of these older methodologies are well known and effectively disqualify them from serious consideration. To counter this problem, a fifth was added by Pike and Roos (2004) and, since then, others have emerged: E.

Proper Measurement Systems (MS). Everything of value in or about the company is broken down into attributes which can be measured. These are built into a measurement system, usually a conjoint hierarchy, and real data is used to produce reliable calculations of value. These can be combined with financial data to provide value for money and related outputs.

F. Others. The Intellectual Capital Congress held at INHOLLAND in the Netherlands in May 2007 afforded a showcase for new methodologies. The Congress devoted a stream entirely to measurement and disclosure. The new methodologies of particular interest all attempt to make a positive link between intellectual capital resources, or combinations of resources, and financial value. They fall into three main classes.

a. b.

c.

Financially-based methodologies which proceed from a financial perspective. Forward-looking methodologies that treat the generation of a return from intellectual capital resources as a risk or option-based calculation. Hybrid approaches which are simultaneously founded on a financial approach and a resource-based approach.

The approaches are described briefly in the Table 2. Of particular interest is that the early models assessed by Luthy (1998) and Williams (2000) dealt particularly with issues of classification and visualisation and were, with the exception of IVMTM and CVH weak on measurement. By contrast, the new models exhibited at INHOLLAND were strongly focused on financial issues, and forward-looking valuations. This strong swing into an area which had widely been thought of as difficult and one whose investigation was also undesirable is noteworthy. It may be that the growing desire to value intellectual capital more fully and also to disclose and compare the intellectual capital of companies, cities, regions and even nations is demanding more precise measures. Six criteria were used to judge the systems, these were that they should be mathematically acceptable (complete, distinct, agreeable, acceptable and commensurable), be connected to financial statements, be strategically useful, useful in planning, not cumbersome and as far as possible, objective. Needless to say, the analysis of Pike and Roos (2007) showed that none were faultless. The general observations, set out in the table below, show that the hybrids have the potential to be practical indicator systems.

Disclosure High profile accounting failures, the rise of corporate social responsibility, the needs of the market and other lesser factors have continued to add

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Table 2. (Source: Pike and Roos (2007)) Authors

Type

Type of approach

Basics of the approach

Gavin McCutcheon (2007)

Financial

Defined value streams assessed using DCF modified with a Bayesian risk factor correction.

Definition of value streams where there are risks which may affect the outcome. Use of Bayesian probability to assess the risk and then discount the value stream to account for systemic risk to net present value. Calculation of secondary value release from other streams.

Mart Kivika, Inge Wulf (2007)

Financial

Indicators and DCF-based valuation

Categorisation and description of Intellectual Capital followed by scaled measurement of single influencing factors interdependences between them. Derivation of monetary levels of evaluation based on DCF methods.

Dmitry Volkov, Tatiana Garanina (2007)

Financial

Calculated Intangible Value (CIV) with a residual operating income (REOI) model to calculate the fundamental value of equity.

Compare earnings with assets over a set time period. Find industry average ROA and multiply industry ROA by company’s tangible assets and subtract result from earnings to yield the excess return. Multiply tax rate by excess return and subtract from excess return to give premium attributable to intangible assets. Calculate its NPV and divide by discount rate.

Francesco Baldi, Lenos Trigeorgis (2007)

Option

Real option-based

Assets are classified and valued in themselves and in groups but involving a strong market and customer perspective.

Belén Vallejo-Alonso, Arturo Rodríguez-Castellanos, Gerardo Arregui-Ayastuy (2007)

Option

Real option-based

All assets and competencies are described and the impact on net income is calculated together with the issue of sustainability. The value of competences are calculated as if they were real options.

Stephen Pike (2007)

Hybrid

Activity based costing (ABC) and resource based accounting (RBA)

Complete description of company activities using ABC. Description of activities as qualitative and quantitative resource combinations. Use in a dynamic predictive model.

Jacques van der Meer (2007)

Hybrid

Calculation of value potential by means of a structural functional model.

Functional analysis of the company involving an assessment of the adequacy of means and resources, the degree to which the organization reaches its objectives, the efficiency in which the organization attains its objectives and its procedures and culture,

Table 3. (Source: Pike and Roos (2007)) Grouping

Observation Are strategically most useful and are financially accurate.

Real Option methodologies:

Their problem is that they deal with complex entities (the company or a capability) and so cannot help with more detailed planning and cannot provide information for IC disclosure statements. They require little subjective judgement and are easy to use. They fail if the nature of the option changes through time May sacrifice strategic utility due to less sophisticated prediction

Financially based methodologies:

Are financially accurate Easiest to use – we are all familiar with accounting Rarely subjective but then fail if they try to include IC resources with complex non-linear behaviours. Planning in detail and disclosure are also compromised.

Hybrids:

Try to combine the resource based view of older IC thinking with the competence, activity and financial perspectives but without favouring any. Will always be subjective

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weight to the arguments in favour of disclosing more non-financial information to support and explain annual returns. Nevertheless, two obstacles remain which prevent the widespread adoption of the practice: the lack of a reliable system that all users can trust and the fear that any good disclosure system would reveal too much company information of strategic importance. Despite this, a number of countries have already taken steps to mandate some form of IC disclosure. So far these have been confined to measures which describe the resources in the possession of the company rather than a description of all resources used or the combinations actually used. Four countries have pioneered disclosure. •

•

•

•

Japan: The Japanese guidelines for disclosure (IAbM) consist of 38 indicators in 7 groups. These are: Management stance/ leadership, Selection and concentration, External negotiation power, Knowledge creation and innovation speed, Teamwork/ organisational knowledge, Risk management/governance, Coexistence in society. Denmark: Denmark uses a variable matrix of indicators of case-specific indicators. The matrix columns are: Statements, Management challenges, Initiatives and Indicators are constructed with guidelines. These are followed over time. Australia: Australia uses a variable matrix of indicators similar to Denmark The statement columns are now Relational, Structural and Human capital. The other columns are: Strategic objectives, Managerial efforts (current & planned), Indicators (nature, past/current, target). Austria: Austria has an integrated model connecting corporate vision and goals, the value adding potential of the IC resource groups, the key processes, pointing separately (but not connected) to the key financial and non-financial results.

There are obviously many endogenous and exogenous factors that need to be considered when attempting to augment conventional financial statements. While the ultimate goal of an intellectual capital balance sheet may not be achievable, some meaningful and reliable connection of intellectual capital resources to financial measures assist with the management of companies should be possible since intellectual capital resources are the drivers of value creation in the company. If readers of such data are to be able to trust disclosures and if companies are to make them with safety then they have to be made on a basis which precludes ambiguity. This means that illdefined terms and the use of flexibly calculated indicators will be unacceptable; it will require that the softer approaches are made rigorous or that financial or other mathematically-based approaches are successfully extended.

BACKGROUND TO INNOVATION Innovation is about creating new value and there must be some sustained monetary advantage as a result of innovation. This is best achieved through a carefully designed innovation management system. Three broad, overlapping sub-processes of innovation have been identified (Pavitt, 2003): • • •

The production of knowledge; The transformation of knowledge into products, systems, processes and services; And the continuous matching of the latter to market needs and demands.

It is easy to imagine the full sequence starting with creative idea generation, R&D, productification and then, and most important of all, finding the right business model mix to ensure market success and the sought-after sustainable advantage. A service can be a process or a sequence of operations. It is often time-based and can have a short-term output and a long-term outcome.

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Services have an interactive nature with many services produced and consumed simultaneously and have traditionally been considered difficult to store. Time is an important characteristic of service production / consumption and requires careful management. Service quality can be divided into the technical quality of the service and its functional quality (Levitt, 1981). In addition to the objective attributes of a service, there are subjective and context specific attributes that impact our perception of the service. Great service providers manage these perception drivers well. It is convenient to consider there to be four basic types of innovation (process, product/service, strategy/business model and organisational innovation). There is, however, significant debate on the acceptability of grouping products and services and strategy and business models. There is less debate about the importance of business model innovation. While there is no doubt that new products or services are usually crucial to innovation, based on rate of return calculations, Keeley (2004) has shown that business model innovation is by far the most efficient contributor to the innovation process overall. As the issue business model innovation is so important, it is worth developing the arguments on this issue further. Osterwalder (2004) states that the difference between strategy and business models is unclear with some people using the terms “strategy” and “business model” interchangeably (Magretta 2002). Business model is frequently used as a term to refer to everything which gives a firm a competitive advantage (Stähler 2002). However, the literature shows that the view that business models and strategy are linked but distinct is more common (Magretta 2002; Mansfield and Fourie 2004). Osterwalder seeks a practical distinction and uses Magretta’s description of a business model as “a system that shows how the pieces of a business fit together, while strategy also includes competition”. In contrast, others understand the business model as subordinate to a firm’s strategy and which has the potential to

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apply to many firms [Seddon, Lewis et al. 2004]. In general however, business model literature seems to fit the former definition better, because most of it focuses on describing the elements and interactions that define how a company creates and markets value. Gordijn shows that in the literature the concept of the business model is interpreted in the two ways: 1. 2.

As a taxonomy (such as e-shops, malls, auctions) As a conceptual model of the way we do business.

Taxonomy in this instance describes a finite number of business model types (e.g. Bambury 1998; Timmers 1998; Rappa 2001; Weill and Vitale 2001) since it deals with a finite set of players. Conceptualization of ‘business model’ describes a meta-model or a reference model for a specific industry. This is more open since it opens the way to an infinite number of business models (e.g. Chesbrough and Rosenbloom 2000; Hamel 2000; Linder and Cantrell 2000; Mahadevan 2000; Amit and Zott 2001; Applegate 2001; Petrovic, Kittl et al. 2001; Weill and Vitale 2001; Gordijn 2002; Stähler 2002; Afuah and Tucci 2003; Osterwalder 2004). Each of the three basic types can vary in its level of innovation from sustaining to discontinuous and from incremental to radical. There are also important relations between these types of innovation. For example, a strategy innovation may necessitate product or process innovations. A business model is a representation of how a company buys and sells goods and services and earns money; its business logic. In general, the purpose of creating a model is to help understand, describe, or predict how things work in the real world by exploring a simplified representation of a particular entity or phenomenon. Gordijn and Akkermans et al. (2000) have discussed the difference between business and

Measuring and Valuing Knowledge-Based Intangible Assets

process models but the conceptual business model approach outlined in this chapter is quite different from “business modelling”. There is also debate about the difference between strategy and business models (Stähler 2002; Seddon, Lewis 2003). Combining strategy, business models and process models together allows the problems of earning money in a sustainable way to be considered on different business layers as shown in Figure 1 (Osterwalder, 2004). The development of a business model is a sequential process going from design to implementation. According to Linder and Cantrell (2000) there are three different types or stages of business model development. First, there is the abstract or generic model of elements, components and relationships. Second, there are operating business models that are the implemented. Thirdly, there are the virtual scenario business models that permit firms to trial options, especially for critical sub-disciplines such as innovation, HR, KM and so on. Business model design is founded on the firm’s strategy and aims at a practical plan. Once

alternative models have been designed, financial and implementation models are needed to make the whole process work. The overall scheme is shown in Figure 2 (Osterwalder, 2004). Osterwalder developed a nine element model, shown below in Figure 3. While all the elements are of importance, those in the infrastructure box have the greatest significance since without proper arrangement, the value proposition cannot be delivered effectively and even excellence in the customer interface cannot compensate.

Levels of Innovation Innovation can be incremental or radical. Innovation can be new within a particular limited context or new in terms of the overall marketplace of ideas. Similarly, it can be variation on an old theme or radically new. The impact of innovation can range from a fairly small contribution to the improvement of a product or a process or it can cause a fundamental transformation in products or services and/or the process technology of an entire industry, or it can transform the market

Figure 1. Business layers (Source: Osterwalder (2004))

Figure 2. Business model steps (Source; Osterwalder (2004))

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Figure 3. The nine-element generic business model (Source: Osterwalder (2004))

place and/or the economy as a whole. Christensen (1997) suggested a means for separating the notions of factors of novelty and impact. Since radically new innovations do not always have a significant impact the distinction of sustaining innovation and discontinuous innovation were introduced. Sustaining innovation improves the performance of established products or services while discontinuous innovation involves marketing very different products or services that have the potential to replace established products and services in the particular market sector. Citing steel mini-mills as an example of a discontinuous innovation, Christensen suggests that while the product was not significantly changed, a change in the production process led to a drastic change in prices, firms, and markets. A discontinuous innovation does not always have greater utility; it can result in a product that under-performs established products since the momentum of ongoing sustaining innovations can push product and service functionality beyond what customers want. Christensen explains that the development of innovations to establish products and services eventually overshoots a large segment of their market. He therefore advises companies in all industries to be continually attuned to a potentially discontinuous innovation that could cause their

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demise if they do not quickly adapt and adjust to the fundamentally changing situation. From an IC perspective, the issues of this chapter require a model capable of time-based simulation.

Types of Innovation There are three main types of innovation: process, product/service, and strategy/business. Process innovation became important with the rise of the quality and continuous improvement movements and was reprised with the recent emphasis on change management, organizational learning and knowledge management. Hammer and Champy (1994) note the growing desire for radical process innovation as firms, at least in the developed world, reach the limits of incremental process improvement. They introduced the concept of radical reengineering based on their belief that if firms are to achieve maximum efficiency and effectiveness then this requires a radical process reengineering of the firm and their processes. Since processes tend to lag behind what is possible given technological advancement, they argue that it is not possible to achieve the necessary transformation through incremental reengineering. Carter (1999) supports this, arguing for radical reengineering; many organizations

Measuring and Valuing Knowledge-Based Intangible Assets

undertook large scale reengineering efforts but the results have disappointed with many firms having expended a lot of effort for little return. One possible reason for disappointing returns is that management itself has to be radically reengineered since it is often the barrier to change. Others suggest that firms are often not capable of changing as much and as quickly as radical reengineering requires and hence transition management has not been sufficiently addressed. There appear to have been two problems with reengineering. The first concerns planning change wherein an ambitious plan to reengineer a firm has been initiated without a sufficiently detailed and realistic plan of how to manage current operations during the transition to the new model. The second is a lack of the sustained effort needed to ensure success. In addition, as Carter (1999) notes, downsizing is often called reengineering. Downsizing tends to have short-term and limited benefits since its aims are concerned with scale and note scope. The origins of discontinuities in process innovation can be imported from outside the industry either as a result of a deliberate search or by luck. Thus, in addition to intentional process improvement and reengineering, firms must monitor other industries and have the ability to adapt potential innovations that could affect how they currently operate. As with the levels of innovation, time-based IC simulation is required to demonstrate the importance of management reengineering. This would appear as different management resource deployment in critical resource transformations associated with the particular innovation and differences in the qualities of those resources. Product/service innovation can be radical or incremental. In its incremental form it is designed to improve the features and functionality of existing products and services whereas radical product/ service innovation involves the creation of wholly new products and services. Jonash and Sommerlatte (1999) observe that the reduction in product life-cycles has meant that a firm’s survival depends on new product development and, increasingly,

on the speed of innovation in order to develop and bring new products to market faster than the competition. Although product/service innovation and process innovation are not the same, using different resource combinations, they are often interconnected as the same resources are used.

Practical Interpretations Bender and Leastadius (2005) found the not unsurprising result that the only feature shared by all the firms in the low-tech sector of industry they studied was that none of them based their competitiveness on recent scientific findings with innovation being, to a large extent, the result of reconfiguring general knowledge, an organisational resource, and readily available technologies. They explain also that in principle, the knowledge formation processes in their firms are similar to those in other firms including those thought of as high-tech or medium high-tech. Their concept of “innovation enabling capabilities” was designed to be generic since it is concerned with the general organisational and cognitive preconditions for innovation. Transformative capabilities concern cognition and learning and, according to Cohen & Levinthal (1990), are based to a large extent on absorptive capacity. The nature of the knowledge being formed is not a matter of principle but an empirical question. It is about the ability to learn. This means that the importance and the efficiency of resource transformations which follow the learning process are the same across companies but have different results. The aim of innovation is to make money through the sale of products and services in new ways and in new forms to the customer. If that is accepted then it is important that the customer is involved in the innovation process to the greatest possible extent. Piller, Schaller and Walcher (2004) developed a 3-step framework for an “open innovation process” which involves the customer wherever appropriate. According to Von Hippel (2001) and Reichwald et al (2003), they are be-

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coming co-designers. Thus a relational resource is added to the innovation processes of the firm. In the first step of innovation, Grumer and Homburg (1999) found that the degree of consumer interaction in the different phases of new product development varied. A high degree of customer involvement in the early and late stages increased success. Building on this, a four-stage model of the innovation process following Wheelwright and Clark (1992) was used: Idea, Concept, Prototype and Market. Obtaining a contribution from the customer is the second step and three types of interaction were observed. These, however, are generally at too fine a degree of granularity for all but the most complex IC model and are certainly beyond what is required for a good understanding of innovation in the form. Morris (2003) in his article on business model warfare points out misconception of thinking of innovation as a fixed process. He points out that while innovations in any area within an organization may be important, innovations that pertain broadly and directly to the business model will be life-sustaining. He illustrates this with an analysis of the mortality of major quoted firms showing how innovation is affected by the transparency of the market and its tendency towards positive feedback. Morris believes that while managers may be thinking about change and its impact on their firms, they must be doing so in a way that is ineffective and suggests that thinking about and enacting business model innovation is important for established businesses. Despite this, the temptation to build a business according to a fixed structure that is expected to endure for the long term remains strong with managers focused on stability and continuity, instead of on disruption and change. Morris contends that to survive, all organizations must develop a comprehensive innovation framework, and the perspective offered by the Business Model Warfare framework can help leaders to be more effective.

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USING INTELLECTUAL CAPITAL INNOVATION Apart from the academic difficulties which have resulted in the failure to “push” a very promising management tool into the business arena, there has also been some weakness on the “pull” side with industry and other users contenting themselves with old approaches to new situations. The most pressing in business at the moment is how to survive in a globalised business world which now contains two new and huge players and with more new and large players emerging. In old parlance, sustained competitiveness is the problem although now it is better considered to be sustainable innovation. The challenge for IC is to be relevant to the management of these business imperatives; disclosure is of secondary importance. Studies substantiate that innovativeness, that is, the ability to create novel products and/ or processes, is a critical success factor for the competitiveness. Companies must be innovative if they want to stay survive in the market; to build and permanently reproduce innovation enabling capabilities is a necessity. Bender and Laestadius (2005) argue that, in principle, these general statements apply to all firms. Services tend to be intangible, heterogeneous, simultaneously produced and consumed, and perishable (Levitt, 1981). To be useful, IC must be applicable to all firms no matter how “traditional” they may consider themselves to be.

Identifying Resources The classification system used in this chapter is that of Roos (Roos et al, 1997) which is a member of the Edvinsson-based group of resource definitions and is hierarchical with four levels of resource, level 0 being the highest. In it, a firm’s resources are split into two groups at level 1, the traditional group which are monetary and physical capital and the intellectual capital group which are human, organisational and relational capital.

Measuring and Valuing Knowledge-Based Intangible Assets

Within each level 1 group there is a further level of disaggregation into level 2 and then level 3 resources which describe and define the resources at the company’s disposal. For the purpose of broad calculation, level 2 is the working level. Level 3 used as a means of precisely defining the level 2 resources and may provide detailed metrics by which to measure the business. It is important to note that resources are viewed as containers, for example, human resources are broken into different types based upon their function. It is not necessary or correct to subdivide a human resource into lower levels such as problem solving capability or mental agility since these are inseparable attributes of the resource. It is also common to measure human resources using such measures as age and churn-rate but these do not affect the instantaneous goodness or quality of the human. The important tests of a resource description are that they have the quality of distinctness, that is, no element of meaning in the definition of a resource are duplicated and that they are complete and exclude nothing. It is, of course, mandatory that everything and especially everyone is described, however coarsely. In defining and describing resources, and later in evaluating them, simplicity is crucial but the model must retain detail in areas where it is required. For a discussion of measurement theory, see Scott and Suppes (1958). Penrose (1959) pointed out that it is the combinations and use of resources that generates value through the creation of other saleable resources or services. For example, products to money, creativity to new processes, relationships to reduced search costs, brands to increased revenues and so on. The analysis requirement is to identify and evaluate the firm’s unique transformations structure. There are two problems which are encountered. •

Few of the firm’s resources are additive in the way they are “used”. For example, doubling the number of people does not double the human resource value. This means that

•

the relationship between the amounts of resources involved in a transformation and the amount of the resource(s) produced is complex. Outwardly similar transformations may actually be rather different in detail. For example, parallel and similar production lines may be dependent on resource quality to very different degrees. This makes backward interpretations of aggregated results problematical and small volume transformations may be missed altogether even though they are important.

Despite these potential problems, the activities of the firm must be described and again, must be done in such a way as to be distinct and complete. It must also be done in a way that allows for “post processing” to identify key activities or changes such as innovation (in whatever form it takes) and other meta-activities such as knowledge management. Critically, the support and administrative activities must be included since the IC evaluation process can reveal their true worth.

Measurement The measurement of resources has two parts and their difference is crucial and the failure of most IC measurement systems to appreciate this is at the heart of the failure of IC to become the useful practical system it could be. Resources must be measured in terms of their intrinsic worth and their instrumental worth. • •

Intrinsic worth is their basic transferable worth to any competitor company. Instrumental worth is the calculated worth to the company through their use.

Needless to say, the ratio of intrinsic to instrumental worth is a measure of how efficiently or effectively the company uses its resources and may also be connected to the financial value

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generated by the use of those resources. This may be of considerable importance in setting compensation levels. Measuring intrinsic worth is simple and simply a matter of counting the resources to be measured (at the appropriate level of granularity) and assessing their qualities using Barney’s scheme. A 1-5 continuous scale is perfectly adequate with 1 representing the threshold of uselessness, 3 representing average goodness and 5 representing the best that could reasonably be expected. Intrinsic worth is simply the company’s potential to act, until they are used, they are resources (or assets) that need to be maintained and are just a cost. As suggested above, instrumental value must be calculated as it depends on how the company chooses to use its resources. The final point to note in this simple section on measurement is that the outputs from this simple measurement exercise are not true “measures” in the strict sense of the term, however, they are better than “indicators” and may safely be used in calculations.

Value Value is a much misused term; it is most often confused with price and cost. The value of a resource, or more usually, a group of resources is calculated by assessing the value they create when used. These resources may be creating something saleable, in which case there is a direct connection with the price the market is prepared to pay because the purchaser values the product or service. If there is no employment of the resource then its value is the product of its numerical abundance and quality. As a seller has no complete idea of how highly a purchaser may value the resource, the market price largely reflects the intrinsic value only. The main problem with value calculations is to properly account for all the value creating activities

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of a company and for this, a mapping system is needed. The Intellectual Capital Navigator (ICN) is a numerical and visual representation of how management views the deployment of resources to operate, innovate and create value in a firm. The ICN displays transformations from the set of resources owned or controlled by a firm into other resources, some of which are sold on the market and represent its value proposition. Navigators can be drawn at any level of granularity and with any level of filtering to remove insignificant detail. Several features can be seen in navigators but perhaps the most obvious and interesting is the adherence or otherwise to the recognised forms of firm value creating architecture (Stabell, Fjeldstad 1998,). Stabell and Fjeldstad postulate three basic firm architectures: the (Porterian) value shop typical of production orientated companies, the value shop typical of professional service companies and the value network typical of market facilitating companies. Production orientated companies are typified by important physical to physical transformations supported by human and organisational to physical influences. Professional service companies exhibit a triangular structure involving human, organisational and relational resources, that is, a learning process. Market facilitating companies can either be physically based (such as the physical resources of a telecom firm) or organisationally based (such as the software and processes of an on-line market place). The method of calculation used to generate the navigator is that of Pike (2007). The navigator of the company is derived from a matrix which is the sum of a number of sparse matrices, each describing an activity of the company. In an activity, a, the resource matrix {Sa} transforms to other resources {Pa} according to a transform operator, {Ma}. The order of {Sa}, {Pa} and {Ma} is the same as the number of resources and the elements Saij and Paij are determined by the value equation.

Measuring and Valuing Knowledge-Based Intangible Assets

{Ma}

{

= Cofac (Sa )

T

} {Pa}

Sa The IC Navigator maps and other non-financial analyses are calculated from the master navigator matrix {N} whose elements, nij, are given by: n ij =Σ ma ij a = 1, A In the ICN, a level example of which appears in Figure 4, the size of the resource bubble represents its relative intrinsic importance of the resource and the thickness of the connecting arrows represents the relative instrumental importance of the transformation between them. The ICN in Figure 4 shows the characteristics of a service firm (a value shop) in that there is a triangular interaction structure between relational, human and organisational resources which represent a learning process driven by the customer and a two-pronged delivery process driven by innovative processes and people. Figure 4. IC navigator for an innovative service firm (Source: Pike (2007))

Figure 5 shows the balance between value delivered and value absorbed for the company’s resources at the more detailed level 2. In the figure, the total height of the bar represents the importance of the resource, the dark portion above the x-axis represents the proportion of value creation and the lighter part below the x-axis represents the value absorbed by the resource. Not surprisingly, customers are the big value absorbers since they receive the outcome of the work, that is, the knowledge and skills of the people and the company’s knowledge. Cash appears as an overall value absorber but, uniquely, the lower part of the bar represents the importance of the income from the customer to the company. It is therefore right that this should approximately balance the value delivered to the customer. Finally, Figure 6 shows the balance between the value of the resources in the company’s operations and the net income (income–costs) they generate. In normal company operations this should result in a scatter of point whose trend-line has a positive gradient. This means that the more important resources are “paying their way”. The results for outlying resources such as reputation and sales and marketing team has to be investigated and explained. In the case of this company, the global nature of its business but lack of significant in-country presence other than in the UK made sales and marketing a costly business. As trading conditions change or as new products/services are released or as management takes actions which affect how the company operates, the contributions of the resources will change. Indeed, modelling will show which resources are most affected by changes and which never seem to add much value, or financial value no matter what changes are made. An example of this is taken again from the results of the company featured in the simulation presented above. In Figure 7, a temporary change to trading conditions has been modelled which resulted in management cutting back on innovation activities to concentrate

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Measuring and Valuing Knowledge-Based Intangible Assets

Figure 5. The balance between delivered and absorbed value (Source: Pike (2007))

on the immediate need to generate income with existing products. Figure 7 shows the effects. Simulation models such as that of Pike (2007) can show the forward trajectory of company resource contribution and financial performance into the future. This affords managers with the ability to project how companies are likely to perform but, critically, with an explanation based on the performance of the resources, the real wealth creators. Figures 8 and 9 show the effects

of actions taken by the company through a difficult trading period in 2002. As stated earlier, an analysis of the instrumental to intrinsic value of attributes is instructive. It would be reasonable to expect that high intrinsic value resources should also be high value providers. A 2x2 matrix can be plotted showing the data for the company’s resources and this is shown in Figure 10. It is comforting for the company to note that none of its high intrinsic value resourc-

Figure 6. The financial contribution of the resources (Source: Pike (2007))

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Figure 7. The effects of management actions. (Source: Pike (2007))

es provide little instrumental value. It is also comforting to note that the company has a number of resources which are relatively inexpensive to maintain yet provide high value to the company. The resource-based view is capable of identifying those resources critical for innovation or any other company activity. Adequate financial resources can expand a firm’s capacity to support its innovative activities (Lee et al., 2001; Delcanto

& Gonzalez 1999; Harris & Trainor 1995), whereas the lack of financial funds may limit firm level innovation (Baysinger & Hoskisson, 1989; Teece & Pisano, 1994; Helfat, 1997) and as demonstrated in the example above. According to transaction-costs economics and agency theory, internally generated funds are more conductive to R&D activities and investments than external funds primary because there exist information

Figure 8. The effects on the value generated by resources after management action (Source: Pike (2007))

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Figure 9. The effects on financial performance after management action (Source: Pike (2007))

Figure 10. A comparison of the intrinsic and instrumental value of resources (Source: Pike (2007))

asymmetries between the firm and the external capital market and the potential to lose control or degrade the impact of innovation. Physical resources have also been found to positively affect innovation (Song & Parry 1997; Gatignon & Xuereb 1997; Mitchell & Zmud 1999; Liyanege et al., 1999). Carrying out innovation activities

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in many cases requires a minimum prior investment in appropriate technical equipment, which raises the possibility of producing innovative output of increased value for the firm. Intangible resources have non-linear effects and are often more important from a strategic point of view since they tend to be valuable, rare

Measuring and Valuing Knowledge-Based Intangible Assets

and difficult to imitate or replace by competitors (Barney, 1991; Hitt et al., 2001b). The increasing role of intangible resources has led to the emerging knowledge-based view (KBV) of the firm as an extension of the RBV. Viewing a firm from a knowledge-based perspective places particular emphasis on the firm’s stock of knowledge (tacit or explicit) as a strategic resource and as an important determinant of its competitive success (Kogut & Zander, 1992; Nonaka, 1994; Decarolis & Deeds, 1999). Therefore, according to RBV, not only must firms be able to create knowledge within their boundaries, but they must also expose themselves to a bombardment of new ideas from their external environment in order to prevent rigidity, to encourage innovative behavior, and to check their technological developments against those of competitors (Leonard-Barton, 1995). Entrepreneurship refers to the articulation of a long-term vision for the firm that aims at higher growth through innovation at the expense of shortrun profit maximization and Drucker (1985) has suggested that innovation is the primary activity of entrepreneurship. This has been expanded by Lumpkin and Dess (1996) who argue that a key dimension of an entrepreneurial orientation is an emphasis on innovation. Other studies Lal, (1999), Iansiti and West (1999), Pllai and Meindl, (1998) and Markham (1998) reached similar conclusions examining US and Japanese industrial settings. Teece et al. (1997) proposed a ‘dynamic capabilities’ framework. Dynamic capabilities refer to the firm’s ability to integrate, build, and reconfigure internal and external competences (groups of resources in use) to address rapidly changing environments. In their view, coordination/integration, learning and transformation are the fundamental dynamic capabilities that serve as the mechanisms through which available stocks of resources (e.g. marketing, financial and technological assets) can be combined and transformed to produce new and innovative forms of competitive advantage.

RECOMMENDATIONS AND FUTURE RESEARCH DIRECTIONS The analysis of Pike and Roos (2007) clearly shows that there has been a steady shift in journal paper authorship towards academic research based on case studies with a consequent lessening in commercial authorship. IC is being “done to” companies rather than being “done with” companies. Consultant-based and commercial papers seem concentrated in only 3 nations, the US, the UK and Sweden. With 29 nations now researching and publishing there appears to be a question of robustness in extant groups. If publication is a metric for academics then strength in depth in their centres should be a goal. The impression given by the published work is one of a fragmentation with no accepted ontology, taxonomy and at least 12 separate subordinate areas of research. Less than 5% of published papers addressed more than one subordinate area underlining the lack a central theory to bind areas together. Only when this has been addressed will intellectual capital be equipped to rival the level of acceptance in business enjoyed by the balanced scorecard. The development of a practical central theory will require that scholars build upon the base provided by the early work on resource based theory and avoid the temptation of diversifying into application-based topics before it is completed. Academically, it would seem prudent to take the mid-1980s as a starting point. A most concerning aspect is the enduring confusion concerning the categorisation of IC and the taxonomies utilised and the consequent inability to communicate with business in a coherent manner. Solving this will undoubtedly involve linguistic and philosophical approaches from beyond the present boundaries of intellectual capital. There was a welcome increase in the number of case-based studies although closer inspection revealed that the greater part of this increase featured secondary data already published by

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industry rather than primary data which would have come from the direct involvement of industry. It appears that academia works with companies but the companies do not work with academia by involving their staff to a great extent. There was a concerning rise in the number of papers of a general IC management or review nature. There was also an increase in the number of papers on reporting and disclosure although this can be seen as a direct consequence of the growing demands from regulators for intellectual capital information from companies. No subject area had disappeared but the stagnation elsewhere and especially in the business oriented areas is concerning. Perhaps most concerning of all was the stagnation in measurement since measurement requires the integration of most or all of the subject categories. The recommendations for future areas of work are therefore: 1.

2. 3.

4.

5.

Undertake research to unify the basic resource definitions and the nature of the attributes of resources is urgently required. Researchers should consider the problem of general IC measurement more extensively. Business should be involved more by university and business school research teams to transfer knowledge of IC and curiosity about it. Business should be invited to participate more fully in collaborative case-based research to facilitate IC knowledge transfer. Strength in depth at centres of research is required to protect the research base. Cross border category networks might be considered.

Notwithstanding the need for more work on a unifying concept for intellectual capital, the company example clearly demonstrates what can be achieved even now.

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Chapter 14

Financial Risks and Intangibles David Ceballos University of Barcelona, Spain Ada Ch. Quesada University of Zulia, Venezuela Dídac Ramírez University of Barcelona, Spain

ABSTRACT This chapter briefly analyses the potential impact of financial risks on the valuation of intangibles from a theoretical and heuristic approach. The authors justify the financial risk that has the greatest impact on the value of intangibles for a wide range of intangibles and types of valuation models. Four types of financial risks are considered for the analysis of three principal types of intangibles (resource, capacity and asset). The authors present a study applied to six examples of intangibles and eleven categories of valuation methods. The results are coherent with the literature because the common examples of valuation of intangibles use the recommendable methods according to the lower impact of financial risks.

INTRODUCTION The financial literature on intangibles and risk is not extensive. The valuation of economic and financial assets considers the existence of risks and intangibles. However, the most common financial analysis of intangible assets is a valuation that takes into account risks. Risks may be associated with intangible sources of variability that are either from an unmeasured component of a factor or from an immaterial, rather than a financial and unidentified, fluctuation.

A search for the relation between risk and intangibles reveals a definition of intangible risk as the risk of omitting unmeasured components, sources of capital or sources of value (Hansen, Heaton and Li, 2004). Risk can also be considered a financial intangible, due to the immaterial component of its sources: the uncertainty and the complexity (Ceballos, Sorrosal and Ramírez, 2006). However, in this case, risk only has value in the definition of the potential business to hedge or transfer it. Finally, “intangible risk” can be defined as a type of risk that has 100% probability of occurring, but is ignored by the organisation due to a lack

DOI: 10.4018/978-1-60960-054-9.ch014

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Financial Risks and Intangibles

of ability to identify it, which is generally related to deficient knowledge. This latter type of intangible risk, which is described in a Wikipedia article about Risk Management (Wikipedia, 2010), directly reduces the productivity of knowledge workers and decreases cost effectiveness, profitability, service, quality, reputation, brand value and earnings quality. Hence, intangible risk management can create immediate value by identifying and reducing risks that diminish productivity. However, the purpose of this paper is to briefly analyse the potential impact of financial risk on the valuation of intangibles from a theoretical and heuristic approach, without any quantification of this impact. We can find several definitions and classifications of financial risk, as this is a well documented area in scientific literature. We use the risk classification described in the Bank of International Settlements (BIS) in the Basel II documents. These documents contain a distinction between and some recommendations for three major types of risk: market, credit and operational. Additionally, the last global economic crisis, which involved great financial sources of instability, has reintroduced the concept of systemic risk. This kind of risk has negative effects on the whole system either by contagion or by similarity. These four types of risk are present in intangibles for the following reasons: the valuation of intangibles has a financial basis that involves the market prices of components of intangible assets, the solvency of buyer and seller, fraud and mistakes in operations; intangibles are widely used in international accounting standards (International Financial Reporting Standards, IFRS) in concepts such as goodwill and trademarks, among others. The link or analysis between risks and intangibles is not developed in the literature, as the management and valuation of intangibles are considered an extension of financial management and valuation. Therefore, risk analysis is introduced in the latter fields only.

In the next section of this paper, we present elements of this relationship between intangibles and financial risks. Then, we present a standard classification of intangibles and their valuation methods. Finally, we study the potential impact of each one of the four financial risks (market, credit, operational and systemic) on intangibles and valuation methods.

BACKGROUND This paper is not a comprehensive literature review of the field. Instead, it covers part of the financial literature on intangibles that has not been highly developed theoretically. Sveiby (1997), Ross (1997), Fernández (2004) and other authors analysed the definition and valuation of intangibles. Although they also addressed financial risks, they did not analyse the relationship between these factors. In some previous and unpublished papers, Ceballos, Sorrosal and Ramírez (2006) worked on the financial basis of intangibles, on which this present paper is based. We present a standard analysis (Institute of Risk Management/AIRMIC/ALARM, 2002) of the financial risks considered in Basel II (Bank of International Settlements, 2004) applied to intangibles (Lev, 2001).

INTANGIBLES AND FINANCIAL RISK Intangibles “Intangible” has different connotations according to the field or context in which it is used. In business and finance, its meaning is similar to immaterial: without physical substance. It is applied to economic resources, business and organisational capacities, or accounting assets/liabilities: •

As an economic resource, an intangible is an element that meets an entrepreneurial

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Financial Risks and Intangibles

•

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need, in which the immaterial part of the resource is more important (in value and in effectiveness) than the material support. Therefore, the definition and value of an intangible resource depends on the context, i.e. the situation or the socioeconomic scenario (when the immaterial part has economic value, for whom, why, for how long, and so forth). As an organisational capacity, the concept of intangibles refers to skills and knowledge (intellectual capital, know-how, core competencies and so on). In other words, an intangible capacity is a useful advantage (disadvantage) for the value driver or added value of the firm, business or financial operation. As an intangible asset (liability), it is an accounting entry which is defined by practice and regulations. It is a social institutional fact that is defined by the accounting practice/usages and existing or applied laws. In Spain, the current and future costs and benefits associated with an intangible asset are non-monetary, identifiable, separable and appropriable.

Intangibles have important applications in Finance because they are immaterial and nonmonetary variables which explain the success and market valuation of some financial products, operations or companies. •

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As a resource, examples of intangibles in Finance are the socioeconomic context, including the time, memory, environment, sophistication and so forth (Ceballos, Sorrosal and Ramírez, 2006). These elements are important in Finance because they incorporate the psychological, social, non-rational or cultural biases of financial behaviour and decisions. They represent the context that explains variations in financial valuations and in the state of finan-

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cial variables (interest rate, prospects and so on). As a capacity, intangibles in Finance are related to advantages and value drivers such as innovation, safety or reputation (legal, fiscal or confidence protection). Knowledge and skills are needed to be able to take financial advantage of each situation, always with the aim of an arbitrage operation (benefit without risk) as a result of market inconsistency. This is the first of several advantages related with time and risk. Knowledge and skills are the core competencies of the value drivers (Coof and Laverty, 2002). As an asset, intangibles are mainly related to accounting. However, their fair value is many times the financial valuation of the discounted (monetary and appropriable) costs and benefits associated with an intangible asset. The importance of intangible assets lies in the legal acceptance of the value added by the immaterial part of the business (trademark, know-how, quality of service or product, safety, resilience, social and moral values and so forth). Sveiby (1997) consolidated the analysis and valuation of intangible assets.

However, intangibles also incorporate new risks, above all in financial valuation, because they are less controllable, less predictable, more imprecise and have less substantial value than tangible or monetary resources, capacities or assets. The immateriality of intangible assets makes it difficulty to define and measure them and to use the other quantitative and objective variables required for an accurate and reliable financial valuation.

Financial Risk In general, risk concerns the expected value of one or more results of one or more future events,

Financial Risks and Intangibles

in which there is uncertainty about the occurrence of an event and its result, but not about the determination of the events and its associated results. Although a risky situation can have a positive or negative result, in Economics and Finance its general usage tends to focus only on the potential harm that may arise from a future event, which represents an additional cost or the loss of a benefit. Finance is fundamentally a quantitative science whose development has been based on Statistics and Mathematics in recent decades. In a formal representation, risk is the combination of two components: the likelihood of the occurrence of a hazardous event or exposure; and the severity of injury or ill health that could be caused by the event (Jorion, 2007). The mathematical simplification is: RISK = probability of occurrence (likelihood) · negative impact (severity). In Finance, these two factors include the probability of an unfavourable event (default), the associated loss, the sources and exposure to risk (uncertain variables, prevention measures, guarantees, possibility of assuming the loss) and the term (time horizon, conditions of expiration, and so on). From this point of view, financial risk is often defined as unexpected loss or variability of returns that are worse than the target or reference. The negativity of the unexpected loss or variation must be mitigated or transferred to guarantee the benefits or the value. This policy is called risk management, which consists in the mitigation or transfer of risk through the control of risk components. More formally, risk management is the identification, assessment and prioritisation of risks to minimise, monitor and control the probability or the impact of unfortunate events. Strategies for managing risk include transferring it to another agent (financial operation), avoiding it (immunisation), reducing its negative effects (prevention), accepting some or all of its effects (responsibility) or compensating for it with other unexpected events (diversification).

There are several ways of classifying risk in Finance (financial risk). However, the Basel II Accord recommendations are the most impressive method that respects the treatment of risks in Finance. This document, which was published by the Bank for International Settlements, is a framework for ensuring that any holding company that is the parent entity within a banking group captures the risk of the whole banking group. With this financial approach, the document cites market, credit and operational risks. We also consider a fourth financial risk: systemic risk. •

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Market risk is the possibility of unfavourable development or behaviour of the nominal economy (prices). In the case of intangibles, market risk is related to the context of financial markets and the possible market prices of intangibles (in general, market value over book value). Credit risk is the possibility of loss due to impossibility of payment or default of the operation. In the case of intangibles, credit risk is related to loss of the advantage or utility of the immaterial part. Operational risk is the possibility of loss resulting from inadequate or failed internal processes, people and systems or from external events. This definition includes legal risk, but excludes strategic and reputational risk. Legal risk is the possibility of an unfavourable change in the regulations, fraud or self-regulation. In the case of intangibles, operational risk is related to model errors in the valuation of the immaterial part, a lack or change in protection of the intangible, or an incorrect or negligent use of regulations to define or to value intangibles. Systemic risk is the possibility of global fallout or the possibility of contagion and the long-life of a bad event. In the case of an intangible, systemic risk is related to the scenario in which only the material part has

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Financial Risks and Intangibles

value, a global bad definition of intangible value, or contagion of the negative effects of the immaterial part on the material part of the company or on other companies or intangibles.

INTANGIBLES AND FINANCIAL VALUATION Types of Intangibles There is no standard and exhaustive classification of intangibles (Stolowy and Jeny-Cazavan, 2001), although one can find books and papers with different explanations, including: goodwill, knowledge, skills, competitive advantage, protected rights, privileged position, reputation, organisation and luck, among others. We consider that intangibles should be treated in three ways, as discussed in the first section (as an economic resource, as a capacity, and as an accounting asset). We also address the most frequently cited examples of intangibles: trademarks, patents, reputation, know-how and goodwill. Intangibles can be considered according to their economic category: • •

•

As a resource (ER), they meet a need. This refers to the context of the valuation. As a capacity (CC), they add value through knowledge and skills to products, the productive system or the whole organisation. As an asset (AA) they increase or decrease (intangible liability) the book value of the company.

The immaterial part of intangibles has more economic value than the material part and there are no monetary elements. The same examples can be found in most of the specialised papers (Bueno, 2002; Cañibano, 2003; Fernández, 2004):

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•

•

•

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Human capital (HC) is the economic factor that justifies by knowledge a higher production, quality or efficiency in the combination of the other factors. Know-how (KH): the skills and expertise in the solution of problems or in carrying out some tasks: speed, efficiency, quality and so on. Reputation (RE): the good image or confidence that customers and social agents value in their choices and decisions. Trademark (TM): the higher market value of a company than its book value, or the higher price of the same product of a known company in relation to an unknown company. Patent (PA): the protected right to produce or to sell a useful good or service without competition or in a privileged or dominant situation in relation to other companies. Goodwill (GW): a recognised overvaluation in relation to book value when a company is sold, due to the utility, complementarity, competition, future opportunities and other characteristics for the buyer company.

Classification of Financial Valuation for Intangibles Several methods are used in the valuation of intangibles, as every year a new proposal emerges in the specialised literature. This is because intangibles, like most financial assets, do not have a stable price. Their price cannot be observed in the stock markets, their fair value depends on the utility of intangibles for the buyer and the seller, and their utility depends on the context, the organisation or the accounting regulations. Thus, every valuation method is a different approach to or estimation of a reliable value. Sveiby (2005) distinguishes four different approaches for measuring intangibles:

Financial Risks and Intangibles

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•

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Direct methods (DM). Estimate the monetary value of intangibles by identifying their various components. Once these components have been identified, they can be directly evaluated, either individually or as an aggregated coefficient. Market capitalisation methods (MM). Calculate the difference between a company’s market capitalisation and its stockholders’ equity as the value of its intangibles. Return on assets methods (RM). The average pre-tax earnings of a company for a period of time are divided by the average tangible assets of the company. The result is a company ROA that is then compared with its industry average. The difference is multiplied by the company’s average tangible assets to calculate an average annual earning from the intangibles. If the aboveaverage earnings are divided by the company’s average cost of capital or an interest rate, an estimate of the value of its intangibles can be derived. Scorecard methods (SM). The various components of intangibles are identified and indicators and indices are generated and reported on scorecards or as graphs. SM is similar to DM, except that no estimate is made of the monetary value of the intangibles. A composite index may or may not be produced.

Other classifications of valuation methods of intangibles are based on the nature of the valuation. We can distinguish different additional methods: •

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Non-financial methods (NM), in which time is not a variable and only the present moment or current status of value is considered. These include accounting methods, ratios, multiples and so on. Financial methods (FM), which consider value, risk and time. The interest rate dis-

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•

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counts every monetary quantity in a comparable value. These methods estimate the present value of the future payments that are attributable to the intangible. Market based methods (KM), which involve a comparison with real and recent similar operations. Hence, the valuation is estimated through a comparison with observed prices and transactions. Cost based methods (CM), which are based on the replacement, opportunity or residual cost of the intangible. The value of the intangible is approximated by the value of reproducing it or its economic substitute. Income based methods (IM), which consist in the identification of payments associated with the intangible and an estimation of the net value. In general, IM is similar to FM, except when we do not consider time. Standard rules (SR), which involve the use of expert opinions and commercial usages to estimate the value of an intangible. Some apply a percentage of the total value, others a combination of several measures, and others the intuition of an expert. Statistical methods (ST), which predict or estimate the value through the simulation or the continuation of a series.

RISK ANALYSIS Risk analysis is similar to identifying, measuring and monitoring financial risks to estimate their impact and plan their management. Hence, their impact can be mitigated, controlled or transferred. If we consider the four types of financial risks described in the first section and make a heuristic analysis of the characterisation of the risk, according to the type or example of intangibles, we can obtain the following analyses: •

The value and utility of an intangible economic resource varies according to the

299

Financial Risks and Intangibles

context, as there is a high or urgent demand for the need that intangibles may meet in specific scenarios. Time, human capital or social relations are examples of intangible resources. When we value these types of intangibles in a monetary or financial way, risks appear, as estimations change according to the context. In this situation, financial risks appear when the contextual variables, which affect the demand, have: ◦ either an unstable or a non-public price (market risk); ◦ either an agent who may default or a component of the demand with no guarantee (credit risk); ◦ either the possibility of illegal, fraudulent or bad behaviour or the use of the wrong model in their valuation (operational risk); ◦ either the possibility of contagion among variables or of imitation among agents (systemic risk). An intangible resource solves a necessity in a specific context; therefore, its economic value is totally dependent on the context. If the context is favourable, the solved necessity will have high utility and the solution will be worth. Although market and credit components are common factors of the financial context, the utility of the intangibles are more dependent on relations, opportunities, legal possibilities and so on. Therefore, the operational risk has a big impact because the behaviour and the choice of valuation model are the two elements most important in the valuation of this type of intangible. Consequently, systemic risk has its importance by the contagion and imitation relations. To sum up, an intangible resource has in general a relative low market and credit risks because its commercialisation is not frequent and then neither price nor payment guarantee are important variables. However, operational risk is important because the valuation of the intangible resource is

300

very dependent on the chose method and on a right working system that takes profit of intangibles. Intangible resources are also highly related with other resources and the production system in their value and utility. Then the contagion and imitation are real possibilities and as consequence a high relative systemic risk. •

The value of an intangible capacity varies when there is a complex, competitive or non-efficient process. Know-how, organisation and reputation are examples of capacities. The value of a product, process or organisation is driven by these capacities. In this situation, financial risks appear when the value-drivers of a business have: ◦ either a fluctuating value of components of the value chain or a negotiated price of the final product (market risk); ◦ either the possibility of default of the company or problems of payments on the side of demand (credit risk); ◦ either the possibility of an illegal or fraudulent decision of the company or bad work and relations (operational risk); ◦ either the possibility of contagion in the production chain or contagion among competence (systemic risk).

An intangible capacity is either a competitive advantage or a value driver of a company. Therefore, it is very dependent on market and on prices of its components because either the advantage or the driver depend o the market conditions and, in special, on the prices. If prices are favourable there will be a worth advantage or an added value in the process. That dependence on market conditions makes that operational risk can be important. The chosen model to both represent and value the process of the intangible capacity is also an important success factor.

Financial Risks and Intangibles

To sum up, an intangible capacity as value driver is dependent on prices of the components of the chain of value. Other financial risks are also important overall operational risk because process and organisation are important elements in the identification and measuring of this type of intangibles. Credit and systemic risks are less important because capacities are integrated with the worker or organisation, and then they have the solvency of the worker and the organisation, and they have a low relation to other components and companies. •

The value of an intangible asset varies when it is commercialised, as the utility and advantages are different for each buyer. Moreover, changes in accounting regulations and international and fiscal differences may affect the value of an intangible asset. Trademarks, patents or goodwill are examples of recognised intangible assets. The value of an intangible asset is a book value for the price of purchase or a market value for a theoretical price, according to its utility, future benefits and so on. In this situation, financial risks appear when the asset has: ◦ either a fluctuating price or variability in associated future payments (market risk); ◦ either an agent who may default or a component of the asset with no natural market of negotiation (credit risk); ◦ either the possibility of an illegal or fraudulent valuation of the asset or the use of the wrong model in its valuation (operational risk); ◦ either the possibility of contagion among assets or imitation among companies (systemic risk).

The value of an intangible asset is dependent on an accounting normative and it is a consolidated asset in the balance sheet of the company.

Therefore, there is an important credit risk if the value of intangibles is not very well estimated. Fraud and market conditions can be present in the estimation of this value, and therefore they are also relevant risks to consider. In this sense, an intangible asset is an accounting value and then market and credit risks are very important, overall credit risk because is a value of the balance sheet and the solvency of the company. Fluctuation of prices when the assets are commercialised and the possibility of fraud make that market and operational risks are relevant. Systemic risk appears by the imitation of accounting normative and the contagion of accounting crashes. Then operational risk is the most common risk in the valuation of intangibles, because it is both highly dependent on either the choice or the significance of the valuation model and on a wrong use or work of intangibles. Moreover, fraud is a real possibility in the valuation of intangibles for the complexity of methods, the legal preference for internal methods and the low restrictive accounting normative. If we consider the six examples of intangibles that we are commented in the last section, we can make the same risk analysis of the presence of financial risk. •

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Human capital is the production factor related with knowledge and formation of work factor. As intangible resource the expected financial risks are operational and systemic risks because it is not easy to value this intangible resource (choice and significance of valuation model) and the imitation can be a solution. Market and credit risks are not expected to be important because this intangible resource is neither dependent on market conditions nor a part of the balance sheet of the company. Know-how is the set of skills and organisational culture of a company to produce in an efficient way. As intangible capacity is

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Financial Risks and Intangibles

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302

expected that market risk will be the most important financial risk because the efficient production will be worth if prices are favourable. If there is not demand, if prices of raw materials are unfavourable, if work conditions are problematic, then the utility and value of this intangible capacity is low. In a similar way to the most of intangibles, the complexity to define and to estimate the associated benefits and cost, it makes that there is an operational risk in the significance of the valuation model and the possibility of fraud of monetary numbers. Reputation is the image and good relations of a company. These good image ad relations are a favourable attraction of clients, invertors and so forth. In this scenario, market risk is important because a higher demand, a higher price of production or better financial conditions is justified by a good reputation. For the same reasons, credit and operational risks are also important because part of the solvency of the company (better financial conditions and expectations) and the possibility of fraud are based on the confidence in the good reputation. Trademark is the value of the identification of a company with a name through the associated elements of quality, utility, status and so on. As an intangible asset the most important risk is credit risk of the solvency of its value. A value justified by subjective ad legal elements. However, the other financial risks: market, operational and systemic are also relevant because the trademark is reflected in the price of the company in financial markets, according to the valuation model its value can vary, and the international acceptance of this intangible asset produces risk of contagion by imitation of normative. Patent is the protection of a competitive advantage of a company that it can be com-

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mercialised. Therefore, market risk is relevant to estimate the market value, but the most important risk, as intangible asset, is credit risk of the solvency of the value of asset in the balance sheet. Moreover, patent is an element with low information for the rest of agents, and operational risk by fraud can be important. Goodwill is the higher value in the sell price of a company than its book value. This paid difference can be incorporated as an asset in the balance sheet of the new or buyer company. It is not considered a strictly intangible asset, although it is in the origin of the conceptualisation of intangibles. As asset credit risk is the most important, and the possibility of fraud or change of legal condition for the acceptance of this intangible asset are also important. Market risk of the market value of the company is relevant and systemic risk can also appear by the contagion by the purchase of companies with this type of intangibles.

We compile the commented analysis in tables for a better understanding, where from a theoretical point of view and applying a heuristic approach, the risk analysis of the intangibles is synthesised in the source of variability that produces the financial risk. In a similar way, the description of each financial risk according to the valuation methods allows us to understand the importance of each risk in each valuation. •

Direct methods are dependent on market conditions because they estimate the monetary-value of intangibles by identifying its various components. Therefore, they can reflect an important market risk. For the same reason, credit risk is also important by the solvency of each component of intangibles. The multiplicity of components

Financial Risks and Intangibles

Table 1. Financial risks associated to each class of intangibles Financial Risk Intangibles

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Market

Credit

Operational

Systemic

ER

Contextual variables without stable price

Contextual variables without solvency

Confidence on chosen valuation model (model risk)

Possibility of context change and alteration of values

CC

Competition and relation demand-supply can vary the utility and value

Solvency justified by duration of the skills and advantages

Difficulty of legal protection of skill or advantage

Possibility of scenario where skills are not an advantage

AA

Possibility of market where to commercialise assets

Low intrinsic value of asset

Possibility of change in regulation and fraud (legal risk)

Possibility of contagion by generalised fraud or international change in regulation

HC

Price of other factors

Contextual variables without solvency

Difficulty of model valuation

Possibility of context change and alteration of values

KH

Possibility of higher price due to efficiency

Solvency is justified by effectiveness

Difficulty of model valuation

Possibility of innovation or change in preferences in which know-how is not required

RE

There is no market for reputation

Reputation affects confidence in solvency

Reputation is not considered in operational risk

A loss of reputation can affect a similar business

TM

Price in market

Can affect access to credit

Name is not considered in operational risk

Notorious companies can affect social confidence (contagion risk)

PA

Possibility of market in which to commercialise patents

Low intrinsic value of patent

Necessity of legal protection of value

Global competition leads to new innovations

GW

Possibility of commercialisation of goodwill

Low intrinsic value of goodwill

Possibility of fraud in the generation of goodwill

Global regulations on valuation of goodwill

and their relations introduce as relevant operational and systemic risks. Market capitalization methods reflect evidently a market risk because they are totally dependent on market prices. This dependence on public and financial prices also reflects a systemic risk by contagion of market fluctuations. Operational risk is also present by the possibility of fraud and significance of the valuation model. Return on assets methods are dependent on earning rates, discounting expected earnings and so on. Therefore, market and financial prices are important and consequently market risk. The necessity of com-

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parison of these methods also incorporates operational risk by the choice and possibility of fraud in the comparison. Additionally, credit ad systemic risks are relevant by the solvency of earnings and the contagion relations between earnings of the company and the competence or the industry. Scorecard methods are dependent on neither market conditions nor solvency because there are indexes and relations without necessity of a monetary basis. However, operational risk is very important for the significance of index, and the generalisation of these indexes can produce a systemic risk.

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Financial Risks and Intangibles

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Non financial methods are based on accounting methods and therefore the most important risk is credit risk because the solvency of the components is the utility and the significance of the model. Operational risk can be important by the choice of the model because it standardises some accounting relations and values like value of intangibles. Systemic risk can appear by relations and generalisation of model. Financial methods consider financial variables and, therefore, the most important risk is the marker risk associated to financial variables: interest rates and other prices. The other financial risks are relevant by the use of financial variables and common elements of intangibles: complexity, significance, generalisation and so on. Market based methods compare with recent market operation and, therefore, the market risk is the most important by the fluctuation of prices in market operations. The solvency of this value can be considered, but the other important risk is the systemic by the contagion of information among market operation, for example, in speculative operations. Cost based methods are dependent on prices and book values because the re-

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placement depends on market possibilities and the residual value reflected in balance sheet. Market and credit risks are the most important financial risks. Operational and systemic risks can be relevant for the significance of the model and its possibility of generalisation. Income based methods are very similar to cost based method, but they substitute cost by future payments, and therefore, market risk is even more important. Standards rules have the important risk of significance, and therefore, operational risk, and they are based on their generalisation and, therefore, it is also important systemic risk. Credit risk can be relevant in some cases because there is not an intrinsic value in the estimation of value for these methods. Statistical methods depend on historical data and prices and, therefore, market risk is the most important risk to consider. Their generalisation produces a systemic risk because all these methods have the same theoretical and mathematical basis. Credit and operational risk can be relevant because there is not an intrinsic value in the estimation and the significance of the model is only statistical.

Table 2. Impact of financial risks in intangibles: **big impact, * some impact Financial Risk Intangibles

Market

Credit

ER

Operational **

CC

**

AA

*

*

* **

HC

*

*

**

*

KH

**

RE

**

*

*

TM

*

**

*

PA

*

**

**

GW

*

**

**

304

Systemic

* * *

Financial Risks and Intangibles

We summarise the information about risk analysis of valuation methods in the Tables 3 and 4, where we mark the importance of the financial risks in the selected items.

COMMENTS AND RESULTS An analysis of Tables 2 and 4 reveals that operational risk is important in most of intangibles and their valuation methods. Systemic risk is relevant

above all in intangibles of economic resources and accounting assets, and in market, statistical and expert valuation methods. In contrast, credit risk is the most important in the case of intangible assets and valuation methods based on accounting concepts. Market risk has a major impact on valuation methods, above all in financial and statistical methods. If we compare Tables 2 and 4, we can summarise the information in Table 5.

Table 3. Financial risks associated to each valuation method of intangibles Financial Risk Valuation method

Market

Credit

Operational

Systemic

DM

Valuation of components according to price

Each component has its own solvency affected by its quality

Difficulty in choice of valuation model for each component

Possibility of change in which intangible has no value

MM

Value according to market

Solvency is fixed by book value

Difficulty of choice of valuation model

Possibility of contagion to explain overvalue with respect to book value

RM

Return in comparison to market/industry

Solvency of duration of returns

Estimation or significance of returns (legal and model risks)

Possibility of contagion in low and negative returns

SM

No market value

Low intrinsic value due to valuation through multiples

Importance of significance of chosen ratios

Possibility of contagion in case of generalisation of ratios

NM

Macroeconomic variables

Importance of book value

Variables and models reflect standard definitions. Problem of significance of definition

Possibility of change in which intangible has no value

FM

Basis of market prices

A good estimation of solvency is required

Difficulty in choice of valuation model

Possibility of change in which intangible has no value

KM

Comparison with recent market operation

Low intrinsic value

Low possibility

Possibility of contagion by past errors in recent operation

CM

Value according to current market

Cost justifies current solvency

Difficulty in choice of valuation model

Possibility of change in which intangible has no value

IM

Value according to future market

A good estimation of solvency is needed in the long-term

Difficulty in choice of valuation model

Possibility of change in which intangible has no value

SR

No market value

Low intrinsic value

Low legal confidence in this kind of method

Possibility of contagion because of its low confidence

ST

Based on past and current market

Path-dependence of solvency

Difficulty in choice of valuation model

Possibility of contagion by time series

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Financial Risks and Intangibles

Table 4. Impact of financial risks in valuation methods: ** big impact, * some impact Financial Risk Valuation method

Market

DM

**

MM

**

RM

**

Credit **

Systemic

*

*

*

**

**

*

**

*

**

**

*

*

*

SM NM

Operational

FM

**

*

KM

**

*

CM

**

**

*

*

IM

**

**

*

*

*

**

**

*

*

**

SR ST

**

* **

Table 5. important financial risk (market (M), credit (C), operational (O) and systemic (S) risks) according to the intangibles and the valuation method Intangibles Valuation method

ER

CC

AA

HC

KH

RE

TM

PA

GW

DM

M, C, O, S

M, C, S

M, C, O, S

M, C, O, S

M, C, S

M, C, S

M, C, O, S

M, C, O

M, C, O, S

MM

M, O,S

M, O, S

M, C, O, S

M, O,S

M, O, S

M, O, S

M, C, O, S

M, C, O, S

M, C, O, S

RM

M, O, S

M, O

M, C, O, S

M, O, S

M, O

M, C, O

M, C, O, S

M, C, O

M, C, O, S

SM

C, O, S

M, O

C, O, S

C, O, S

M, O

M, O

C, O, S

C, O,

C, O,, S

NM

M, O, S

M, C, O

C, O, S

M, O, S

M, C, O

M, C, O

C, O, S

C, O,

C, O,, S

FM

M, O, S

M,O

M, C, O, S

M, O, S

M,O

M,, C, O

M, C, O, S

M, C, O,

M, C, O, S,

KM

M, S

M, S

M, C, S

M, S

M, S

M, C, S

M, C, S

M, C, O, S

M, C, O, S

CM

M, C, O, S

M, C, O

M, C, O, S

M, C, O, S

M, C, O

M, C, O

M, C, O, S

M, C, O,

M, C, O,, S

IM

M, C, O, S

M, C, O

M, C, O, S

M, C, O, S

M, C, O

M, C, O

M, C, O, S

M, C, O,

M, C, O,, S

SR

O, S

M, O, S

C, O, S

O, S

M, O, S

M, C, O, S

C, O, S

C, O, S

C, O, S

ST

M. O, S

M, O, S

M, C, O, S

M. O, S

M, O, S

M, C, O, S

M, C, O, S

M, C, O, S

M, C, O, S

We put in red when the financial risks are maximum and in green when is minimum. We can observe that the scorecard methods (SM) are the combination valuation methods-intangibles

306

with less financial risks and the statistical methods (ST) are the most risky. Sveiby (2005) recommend scorecard methods, which are the most numerous and common in intangible valuation.

Financial Risks and Intangibles

In the analysis of Tables 2, 4 and 5, we can also observe that the preferred valuation method for each intangible would be the method that has a low risk impact on the type of financial risk for which the intangible has a major impact, as the absence of risk can positively affect the global risk. In this respect, the ideal valuation methods for: • •

•

intangible resources are direct, financial, income and cost-based methods; intangible capacities are mainly scorecard methods, and, to a lesser extent, non-financial methods and standard rules; intangible assets are market and scorecard methods.

These results are in line with practice and have a theoretical basis (Sveiby, 2005; Lev, 2001), as intangible resources are related to future payments and opportunities, intangible capacities are related to indicators that are easier to standardise than other variables, and intangible assets are book values in comparison with market values.

FUTURE RESEARCH DIRECTIONS The theoretical and heuristic impact of financial risks on intangibles and their valuation methods allows us to develop empirical measures of this impact, which distinguish the type of risk, type of intangible and type of valuation method. The proposed future directions of this research are to estimate the quantitative impact of financial risk using standard measures of volatility for market risk; value at risk (VaR) and weighted assets for credit risk; actuarial and financial models for operational risk; and simulations for systemic risk.

CONCLUSION The analysis of the impact of financial risk on intangibles and their valuation shows the im-

portance of financial risk according to the type of intangibles and valuation methods, as well as the ideal valuation methods for intangibles. The results are in line with practice, as in many cases the ideal model corresponds with the method that is commonly used for the valuation of the intangibles.

REFERENCES Bank of International Settlements. (2004). International Convergence of Capital Measurement and Capital Standards. A Revised Framework. Basel Committee on Banking Supervision. Bueno, E. (Dir.) (2003)Model for the Measurement and Management of Intellectual Capital:Intellectus Model. Intellectus Documents, 5. Cañibano, L., Sánchez, P., García-Ayuso, M., & Chaminade, C. (2002). MERITUM Project. Guidelines for Managing and Reporting on Intangibles (Intellectual Capital Report). Madrid: Vodafone Foundation. Ceballos, D. Mª T. Sorrosal & D. Ramírez. (2006). Tiempo y Memoria Colectiva como recursos intangibles: su valoración en Finanzas [Time amd Collective Memory as intangible resources: their valuation in Finance]. Paper presented at the meeting of 9th Spanish-Italian Conference on Financial and Actuarial Mathematics. Alcalá de Henares, Universidad de Alcalá. Coff, R. W., & Laverty, K. J. (2002). Strategy Process Dilemmas in Exercise Decisions for Options on Core Competencies. Working Paper. Atlanta, GE: Emor University, December. Fernández, P. (2004). Valoración de empresas [Firm valuation]. 3ª ed. Madrid: Ediciones Gestión 2000. Hansen, L., Heaton, J., & Li, N. (2004). Intangible Risk? Working paper. Chicago: University of Chicago.

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Institute of Risk Management/AIRMIC/ALARM. (2002). A Risk Management Standard. London: Institute of Risk Management. International Accounting Standard Board. (2009). Standards (IFRS) and Interpretations (IFRIC). Retrieved from http://www.iasb.org. Jorion, P. (2007). Financial risk manager handbook. New Jersey: John Wiley & Sons. Lev, B. (2001). Intangibles: Management, Measurement, Reporting. New York, NY: Brookings Institution. Roos, J., Roos, G., Edvinsson, L., & Dragonetti, N. (1997). Intellectual capital: Navigating in the new business landscape. New York: New York University Press. Stolowy, H., & Jeny-Cazavan, A. (2001). International Accounting Disharmony: The Case of Intangibles. Accounting, Auditing & Accountability Journal, 14(4), 477–496. doi:10.1108/09513570110403470 Sveiby, K. E. (2005). Methods for Measuring Intangible Assets. Retrieved from http://www. sveiby.com/articles/IntangibleMethods.htm Wikipedia (2010). Risk management. Retrieved, March 3, 2010, from http://en.wikipedia.0rg/wiki/ Risk_management.

ADDITIONAL READING Amit, R., & Schoemaker, P. (1993). Strategic assets and organizational rent. Strategic Management Journal, 14, 33–46. doi:10.1002/smj.4250140105

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Ceballos, D. (2007). Una propuesta de indicador de riesgo legal. Valoración a través de la Teoría de Seguros. [A proposal of index for legal risk. Valuation through Insurance Theory]. Paper presented at the meeting of 2ª Reunión de Investigación en Seguros y Gestión de riesgos. (Riesgo 2007), Castro Urdiales. Edvisson, L., & Malone, M. (1997). Intellectual Capital. Realizing your company’s true value by finding its hidden brainpower. New York: Harper Collins Publishers. García, M. (2004). Intangibles: Activos y Pasivos. Management&Empresa, 37, 32–42. Sveiby, K. E. (1997). The Intangible Assets Monitor. Journal of Human Resource Costing and Accounting, 2(1), 73–97. doi:10.1108/eb029036

KEY TERMS AND DEFINITIONS Financial Risk: negative impact in the monetary value for the fluctuation of a financial variable or context. Intangibles: component with higher value of its immaterial part than the material one. Impact: quantitative or qualitative effect of a cause, phenomenon or case of study. Valuation Method: algorithm or objective process to estimate the value of a concept or variable Resource: component to solve a necessity. Capacity: useful ability or competitive advantage to do something. Asset: account value of a right or a property.

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Chapter 15

Motives for the Financial Valuation of Intangibles: Reasons and Results José Domingo García-Merino University of the Basque Country, Spain Gerardo Arregui-Ayastuy University of the Basque Country, Spain Arturo Rodríguez-Castellanos University of the Basque Country, Spain Belén Vallejo-Alonso University of the Basque Country, Spain

ABSTRACT This chapter aims to analyse the Basque Country companies’ view about the financial valuation of intangibles relevance and its influence on business performance. To achieve this objective, a field study has been done with 440 telephone calls to Basque Country companies’ financial managers. Then, their responses and theirs firm’s performance are analysed. The results show that the companies that are interested in the financial valuation of the intangibles, especially for internal motivation, perform better; however, this improvement is not statistically significant. Otherwise, the companies that are more interested in the valuation of their intangibles for external reasons need to provide information to stakeholders about their ability to generate income.

INTRODUCTION1 Wealth and growth in today’s economy are driven primarily by intangible resources (Lev & Zambon, 2003). The importance of intangible assets as strategic resources is nothing new. Marshall (1890)

already recognised the importance of knowledge as a significant resource and a powerful production factor. However, a growing interest in their management and in developing valuation and measurement models did not emerge until halfway through the 1990s (Edvinsson, 1997; Edvinsson & Malone, 1997).

DOI: 10.4018/978-1-60960-054-9.ch015

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Motives for the Financial Valuation of Intangibles

According to Lev (2001), two characteristics of today’s economy are behind that change: a more intense competitive business environment and the advent of information technologies. The economies of scale which underpinned traditional production activities, and were intensive in tangible assets, have been exhausted. Investments in financial and tangible assets result in sustainable competitive advantage and intellectual capital is now the source of competitive advantages (Prahalad, 1983). There are numerous studies that find evidence of the positive relationship between investment in intangibles and the value creation of the company (Tan et al., 2007; Firer & Williams, 2003; Engström et al., 2003; RiahiBelkaoui, 2003; Sáenz, 2005; Iñiguez & López, 2005; Prieto & Revilla, 2006). The interest in intangibles is not limited to the academic field. A growing preoccupation has also been detected in the business world. Studies such as Hall (1992), Gray et al. (2004), Gallego & Rodríguez (2005), Ochoa et al. (2007), Lonnqvist et al. (2008) or García Merino et al. (2008) find proof of that. These papers consider the conviction of the business community regarding the key role that intangibles play in the development of competitive advantages. Rodríguez & Ordóñez (2003) point out that the majority of companies are focusing on intangibles and on improving their management. The relevance of intangible assets to generate competitive advantages and value creation in the companies (Hall, 1992, 1993; Teece, 1998), and the obvious constraints of the information provided by the markets and the accounting systems have fostered a current of research, which emerged in the 1990s, to identify and assess the intangible resources of the companies. Greater knowledge regarding intangibles and their valuation, among other benefits, allows an efficient allocation of the resources (Cañibano et al., 1999), reduces the risk of opportunist behaviour by managers (Abbody & Lev, 2000) and reduces capital costs (Botosan, 1997; Lev 2001).

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There are many papers published on the measurement and valuation of intangibles and their relationship with business performance (Bontis et al., 2000; Bontis & Fitz-enz, 2002; Riahi-Belkaoui, 2003; Chen et al., 2004; Sáenz, 2005; Chen, 2005; Chen et al., 2005; Bollen et al., 2005; Tan et al., 2007). However, there are very few that analyse whether there is a relationship between the driving forces that may lead to a financial valuation process of the intangibles being deployed and the business performance obtained. Brennan & Connell (2000) conclude that a durable successful behaviour is present in those companies seeking to improve the management of their intangibles. In his study of Finish SMEs, Salojärvi (2004) found that companies that implement active practices to manage their intangibles obtain better results in innovation and developing new products. Chaminade (2001) stresses that companies are not interested in valuing all intangibles, but rather only those that they can manage. That is to say, only the internal use of the information regarding the intangibles is considered. Lonnqvist (2008) likewise finds that companies pay greater attention, when measuring their intangibles, to internal motives than to external motives. Due to this lack of empirical studies, we have analysed the relationship between the motives driving a valuation process and the results obtained by the companies. The starting point was a survey to business managers, where they were asked about the importance of intangibles valuation and the reasons why they believe such a process is useful. Using this information and the data on the economic and financial results of the companies, we have sought to verify the existence of the aforementioned relationship. The study shows that the companies that consider the financial valuation of their intangibles to be important for internal reasons get better results, without these differences being statistically significant. On the other hand, companies that believe that the financial valuation of their intangibles is

Motives for the Financial Valuation of Intangibles

important to facilitate information for external stakeholders because they are pressurised to do so, as they have higher levels of leverage and the weight of the intangibles resources out of the total resources is greater. The chapter is structured as follows. First, the role that intangibles play in determining business competitiveness is justified, using the ResourceBased Theory. Subsequently, the advantages and difficulties generated by the financial valuation of the intangibles are then analysed, together with the different methodologies developed so far. The third section considers the hypothesis of this chapter, namely, if the different motives driving the companies to perform a financial valuation of their intangibles are reflected in the economic results and are conditioned by financial structure. In the following section, the methodology followed to prove the hypothesis is described. This methodology is based on a survey conducted with a representative sample of the directors of companies in the Autonomous Community of the Basque Country and on the analysis of their responses regarding the economic-financial structure and the performance of its companies. The results are then explained and the chapter ends with the main obtained conclusions.

INTANGIBLES AS STRATEGIC RESOURCES, THEIR FINANCIAL VALUATION AND THE REASONS FOR IT Intangible Resources as a Source of Competitive Advantage Two complementary perspectives coexist in strategic management to understand value creation: the activities or sector based approach and the one based on resources. During the last two decades, there has been a fundamental change in the field of strategic management when it comes to explaining the differences in inter-business results,

as the focus has switched from the predominance of the first approach (Porter, 1980 and 1996) to the development and consolidation of the second (Hoopes et al., 2003). Bettis & Hitt (1995) state that the traditional limits of the sectors become blurred, the markets are intermingled and overlap in highly volatile environments. It is therefore more difficult and less evident to determine what constitutes a sector. Therefore, the strategy must be defined in terms of what the company is capable of doing, instead of using the customers and their needs as the benchmark (Quinn, 1992). The strategy considers the adjustment of the resources and capacities of a company to respond the opportunities that emerge in its environment (Grant, 2002). Yet not all the resources are equally important for business success. Barney (1991) justifies that those resources must be valuable, rare, inimitable and non-substituible. With rare exceptions, the resources that comply with these criteria −Value, Rareness, Inimitable, Non-substituible (VRIN)− are usually intangible by nature (Itami & Roehl, 1987; Hall, 1992; Barney, 1991; Grant, 1991, 1996; Galbreath, 2004; Díaz et al., 2006; Kristandl & Bontis, 2007). A single advantage is sustainable if it is based on imperfectly mobile and uniform assets (Amit & Schoemaker, 1993; Grant, 1991; Barney, 1991; Rumelt, 1984; Lippman & Rumelt, 1982). Barney (1991) adds another requirement: their duration. This does not refer to a period of existence but rather the time when competitive duplication is impossible. The source of value and economic wealth does not lie in the production of tangible assets, but rather in creating and managing intangible resources. The management of the intangible assets of a company is the key factor to develop and consolidate a competitive advantage (Boisot, 1998; Cañibano et al., 1999; Teece, 2000). Their specific characteristics provide them with great differentiating potential with respect to the competitors (Salas, 1996; Bueno, 1998; Villalonga, 2004) and they are very difficult

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Motives for the Financial Valuation of Intangibles

to imitate by the competitors (Kaplan & Norton, 2004; Rodríguez & Ordóñez, 2003; Vicente, 2000). However, intangible resources are not productive in themselves. Their value is obtained in conjunction with other resources and within a work group and with a specific objective. Even though the resources may be considered as inputs of the production process and configure the basic analysis unit (Grant, 1991), it is the combination of resources, associated to a specific competitive context, which determines the capacities of a company. From this approach, a distinction must be made between owning a resource and the capacity to use it effectively and efficiently (Ventura, 1996). A third level would include the competencies that coordinate and integrates the capacities, by presenting an interfunctional character (Grant, 1991). Therefore, even though all resources are likely to create value, recent literature has focused on competencies. Competencies are the means by which the companies deploy their resources in a characteristic way. Within the competencies, Prahalad & Hamel (1990) define “core competencies” as those that they are fundamental for the results and strategy of the company. “Core competencies”, according to those authors, are those that generate a significant value or benefit for the customer or determine the efficiency with which that value is generated and lay down the base for entering new markets. The sustainable competitive advantages of a company will be essentially based on the intangible assets that it holds and specifically on its capacities and competencies (Fernández, et al., 1997; Camisón, 1997a, 1997b, 1999; Sonnenberg, 1994; Grant, 1991). The capacity of a resource to create a sustainable competitive advantage is linked to its combination with capacities and competencies that inject it with social complexity and subsequently obscure the casual relation (Camisón, 2002, p. 139). The Resource-Based Theory has evolved in recent years and mainly focuses on three directions: knowledge-based approach, relational ap-

312

proach and the intellectual capital approach. The intellectual capital approach (Reed et al., 2006), based on the Resource-Based Theory, involves primacy of the intangible resources and capacities to achieve better business results that are sustainable in time. This pragmatic-theory approach represents a focalisation or specialisation of the Resource-Based Theory on those intangible resources or factors that may lead to business success (Martin de Castro et al., 2009). This current that emerged from professional practices (Brooking, 1996; Edvinsson & Malone, 1997; Bueno, 2003) distinguishes different categories of intangibles, around which there is a certain consensus: i) human capital, or knowledge, skills, experiences and attitudes held by the members of an organisation (Bueno, 2003; Subramaniam & Yound, 2005); ii) structural capital, which includes the knowledge responsible for providing coherence and a common thread to the whole organisation (Edvinsson & Malone, 1997); and iii) relational capital, which emerges from the relation processes that the organisation has with its external stakeholders (Bueno, 2003; Reed et al., 2006). Specifically, Reed et al. (2006) point out that the different types of intangibles are complementary resources, so that the allocation in each increases the allocation in the others, resulting in a new undividable resource that directly affects the performance of the organisation. This characteristic precisely increases the difficulty of valuing the intangibles, as it is not simple to determine the performances that a specific intangible is capable of generating. Complexity may be considered to be an essential feature when evaluating the strategic potential of the capacities, as it significantly affects their possibility of being imitated and/or substituted by the competition (Lippman & Rumelt, 1982; Reedy DeFillipi, 1990; Black & Boal, 1994; Miller & Shamsie, 1996; Vicente, 2001; Wilcox-King & Zeithaml, 2001; González Álvarez & Nieto Antolín, 2005; Díaz, et al., 2006; Nieto Antolín & Pérez Cano, 2006).

Motives for the Financial Valuation of Intangibles

Financial Valuation of Intangibles The importance of intangibles, in general, and core competencies, in particular, as determining factors of competitive position come up against the difficulty of identifying and quantifying them (Grant, 1991). Since the 1990s, the interest in the measurement and the financial valuation of intangible assets has increased enormously. With respect to the measurement, great progress was made in 1995, with the publication by Skandia of the first report on intellectual capital (Skandia, 1995). Other pioneering studies in this respect were those by Brooking (1996), Kaplan & Norton (1996), Edvinsson & Malone (1997), Sveiby (1997a, 1997b) and more recently Bueno (2003). The measuring basically consists of two tasks: on the one hand, it tries to identify and order intangibles in a structured way, that is, to discover which types of intangibles exist in the company, which are the generators of core competencies, what the existing relations between them are, etc. On the other hand, it searches for indicators that enable the intangibles to be measured, that is, to monitor their development, along with comparing, where applicable, the situation of the company with other benchmark firms. These indicators may be financial or non-financial and, according to the level of specificity, they may be: specific to the company, specific to an industry or general. These indicators must comply with the following properties, put forward by Cañibano et al. (2002): useful, relevant, comparable, relevant and feasible. These indicators are mainly ratios and therefore the measurement of the intangibles has been, basically, approached from a non-monetary stance2. Other studies have sought to measure in monetary terms the contribution of each of the elements that configure the intangible resources, which have been called “financial valuation”. The main methods developed along this line can be grouped, according to the methodology used, into3: •

those that are based on the efficiency of the stock markets, which include the work

•

•

of Caballer & Moya (1997) and Rodov & Leliaert (2002) those based on flow discounts, such as Khoury (1998), Andriessen & Tissen (2000), Lev (2001), Gu & Lev (2001), Andriessen (2004), Rodríguez-Castellanos et al. (2006, 2007), McCutcheon (2007) and Olivé Tomás (2008); those that are underpinned by the options theory, such as those by Pakes (1986), Newton & Pearson (1994), Mayor et al. (1997), Kossovsky (2002), Bose & Oh (2003) and Rodríguez-Castellanos et al. (2007).

They all have pros and cons4, and therefore it is no easy task to look for straightforward and accurate methods and models for the financial valuation of the intangibles. According to Olivé Tomás (2008), there are no standardised procedures for the majority of intangibles, but rather that the intangible in question has to be analysed in-depth. This makes it difficult to apply the generally accepted models. Measurement, but above all the financial valuation of the intangibles, help to recognise critical intangibles and allow the learning processes to be accelerated, identify the best practices and disseminate them through the company, and increase innovation and partnership activities (Kannan & Aulbur, 2004). As Johanson et al. (2001) point out, one of the main problems when it comes to managing intangibles is the lack of reliable financial information about them. Ross & Ross (1997) and Liebowitz & Suen (2000) state that to be able to better manage a resource, it is necessary to be able to measure it. Therefore, the materialisation of a valuation process for the intangible assets of a company will improve their knowledge and management. The lack of information about the scope of the intangibles and their role in value generation reduces the incentives to improve their management (Cañibano, 1999). Likewise, the lack of an explicit valuation of the intangibles resources 313

Motives for the Financial Valuation of Intangibles

generates the existence of information asymmetries and lack of allocation of resources. Greater knowledge of these resources, among other effects, will facilitate access to capital market under better cost conditions. Yet improved understanding of the intangibles goes further, as, and this is perhaps the most decisive contribution, they enable the managers to rethink their business (Lev & Zambon, 2003). Thus, Nevado and López (2007, p. 54) claim that it is not so important to establish an exact value, as to know what its evolution is in order to be able to extend it, where applicable; in other words, to manage the intangible. The rules and management principles applicable to the financial and tangible resources are not fully applicable to intangible resources. Lev (2001) highlights some of the differences: non-rivalry and scalability (few opportunity costs), importance of network economies, difficult to transmit the intangibles, diffused ownership and greater inherent risks. The managers must consider whether the decisions being adopted are increasing the value of the intangible risks and their use. Therefore, it is necessary to establish what its value is and how a specific decision can affect them. Having an explicit value of the intangibles can allow us to question some decisions given their impact on the value of the intangibles.

Motives for the Financial Valuation of Intangibles: External and Internal The motives behind a company beginning a valuation process of its intangibles can be determining factors, both to establish the valuation methodology to be applied and the expected results of this process. Thus, Marr & Gray (2002) argue that the motives behind a valuation process can be subdivided into external and internal. Specifically, Marr et al. (2003) put forward the following: 1.

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Helping to formulate the strategy. In order to formulate the strategy, it is fundamental to establish the resources available, the exist-

2.

3.

4.

ing relations between the intangibles and the other resources, and the connection of the intangibles and the obtained return (Grant, 1991). Assessing the implementation of the strategy (Meyer & Gupta, 1994; Neely et al., 1996; Kaplan & Norton, 1996; Bassi & Van Buren, 1999). The intangibles are part of the inputs that a company has to execute a specific business strategy, but they are also outputs when the strategy is developed. Using the value of the intangibles as a benchmark to establish incentive and remuneration systems. The majority of companies have realised that trusting only in financial measures may encourage operations to be seen from an excessively short-term perspective (Johnson & Kaplan, 1987; Kaplan & Norton, 1992), particularly if the incentive systems are linked to them (Bushman et al., 1995). Furthermore, traditional financial indicators are a measure of the immediately prior performance of the company, and are basically aimed at quantifying the results obtained for the shareholders or owners, while overlooking the targets of other stakeholders. The incentive systems need to be established according to how the company manages to increase its capacity to generate value in a future, which is going to greatly depend on the development of its intangibles. Helping with the expansion and diversification decision taking (Teece, 1980; Montgomery & Wernerfelt, 1988). Changes in the economic environment over recent decades have led to numerous merger operations. The economic and financial information arising from those processes cannot be left on the sidelines (Apellániz Gómez, 2004). Many companies wish to better exploit their resources and plan to diversify, merge or enter into partnership agreements with other companies. Lev (2001) suggests that, in this respect, the

Motives for the Financial Valuation of Intangibles

5.

network economies and synergies associated to R&D investments and other intangibles are fundamental. Morck & Yeung (2003) found that diversification generates value in the presence of R&D or advertising intangibles, but destroys value in other cases. It is therefore essential to establish the intangible resources of a company and the possibility of their being combined with the resources of other companies to generate synergies. Communicating the value of the company’s resources to stakeholders. Even though it is not yet mandatory to publish the information on intangibles, some companies do so (DATI, 2000; Williams, 2001; Cañibano et al., 2002). The lack of information on the intangibles has a negative effect due to: (i) possible opportunist behaviour by the managers, if they internally exploit that information on the intangibles unknown by other parties (Aboody & Lev, 2000); (ii) excessive volatility and incorrect valuing of the securities and (iii) increase in the cost of capital (Leadbeater, 2000; Gu & Lev, 2001). The majority of analysts believe that an open communication strategy would lead to a higher and more stable share price (Rylander et al., 2000). Other empirical studies have shown that companies capable of revealing their long-term perspectives achieve more satisfactory market prices (Narayanan et al., 2000; Gu & Lev, 2001). In general, the dissemination of information about intangibles has a positive impact on the image of the company (Cañibano et al., 2002).

External motives are considered to be those relating to the communication of information on the intangibles to external stakeholders: shareholders, creditors, suppliers, possible partners in a future merger and the general public. The fifth and also the fourth of the above motives, insofar as they refer to possible mergers, come under this category.

Motives related to generating information for internal stakeholders, mainly managers, are internal ones. Lev (2001) considers that managers show a significant lack of information about the intangible resources that they have, which is particularly serious, as they are precisely those resources, in the majority of cases, which enable firms to achieve sustainable competitive advantages. Therefore, the valuation of intangibles for internal motives is associated to managing the company. This interest includes the first three motives set out above, and also the fourth with regard to internal growth strategies.

METHODOLOGY Hypothesis Prior Considerations When analysing the background and consequences of the motives driving a company to value their intangibles, we must consider two aspects: the “whys” and the “wherefores”. Thus, beginning with the external motives, possible responses to the “why” leads us to consider the type of companies that may be so motivated. They will be companies that are seeking to reach an agreement with external stakeholders, whether they are partnership agreements with possible partners, mergers or cash injections. In the last case, which is perhaps the most common, they will be companies that particularly depend on external financial resources and which have important intangible resources to be reported. Well, if we consider the “wherefores”, one of the objectives of a strategy to disclose information about the intangibles to external stakeholders is to improve the results, by means of reducing the cost of financial resources. The results obtained by a company may be of different types, depending on the approach used, and it will therefore be necessary to define what

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we understand by results in this context. Firer & Williams (2003) believe, in the same way as many other authors (Cuervo, 2001; Edvinsson, 1997; Stewart, 1997; Bontis, 1998, 1999, 2001, 2002, 2003; Pulic, 1998, 2000; Sveiby, 2000, 2001), that traditional performance measures, based on accounting principles, are not adequate in the new economy, where competitive advantages are based on intangibles. However, given that traditional measures continue to be prevailing5, it is necessary to establish to what degree disclosing information about intangible resources improves their value. The most common of the traditional measures are ROE, ROA, increased profits and increased turnover. In the case in question, if the aim is to reduce the cost of financial resources - particularly debt -, ROE (financial variable) should improve, without any changes occurring to the purely economic variables (ROA, increased operating profit and turnover). If we now turn to the internal motives, with respect to the “whys”, the answer seems to be same as for the “wherefores”. Given that intangible resources are the basic resources of the companies, greater attention to the valuation of the intangibles should result in better understanding of the value generating processes in which they intervene, and in their most efficient use. Therefore, companies with a greater knowledge of their intangibles should achieve a better performance. Knowledge of the intangibles is necessary to adopt efficient decisions. It is therefore essential to establish the effect of the intangibles on the performance of the companies (Cañibano et al. 1999, pp. 58-59). In this respect, it is difficult to identify a priori business characteristics that foster the valuing of intangibles for internal reasons to a great extent, as the importance of the intangible resources is generalised for any type of company. With regard to the expected results from better management of these resources, they will be fundamentally economic, affecting the aforementioned four variables (ROE, ROA, increased operating profit and increased turnover).

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Valuing Intangibles for External Motives As has already been indicated, external motives are those relating to generating information for external stakeholders. In the majority of cases, the fundamental sources of information that such stakeholders have is financial information. Well, many authors stress the shortcoming in how the information on intangible assets is processed on financial statements, which is the reason for the differences between the book value and the market value in the case of companies listed on the stock exchange (Lev, 1989; Lev & Zarowin, 1998; Martínez Ochoa, 1999). For a specific resource to be entered in the accounting system, the different accounting principles require, among other criteria, the existence of clearly defined ownership rights regarding the return that an asset is capable of generating. However, the majority of intangibles, or at least those considered to be more important when it comes to generating competitive advantages, such as capacities and competencies, do not comply with that requirement. In this context, companies that wish to reduce the existing information asymmetry shall have to opt to publish voluntary information on their intangibles. Yet, as Rylander et al. (2000) have pointed out, there are curbs on publishing this type of information: (i) managers are afraid of providing the competitors with sensitive information and (ii) the additional costs that have to be incurred, costs associated with gathering and disclosing the information. Therefore, companies that voluntarily disclose information about there intangibles must have specific incentives to do so. Therefore, the following hypothesis is advanced: H1: Companies that consider external motives as driving forces for a financial valuation of their intangibles have incentives to disclose the value of the intangibles to external stakeholders. Well, what characteristics of the companies can generate those incentives?

Motives for the Financial Valuation of Intangibles

A characteristic that is widely covered by the literature is the level of leverage. Thus, according to the perspective of the Agency Theory, the higher the leverage of the companies, the more likely conflicts between internal and external stakeholders -creditors and external owners- are, which would imply greater agency costs, as has been found by numerous studies (Kim & Sorensen, 1986; Brennan, 1995; Giner, 1997; Leland, 1998). Therefore, the greater the debt, the greater the propensity of management to disclose information on intangible assets as they aim to partly reduce agency costs. Vincente Lorente (2001) found that highly specific and opaque resources (specifically, they analyse internal investments in R&D and investment in highly specific human capital) limit the leverage capacity of the company. The signalling theory also justifies leverage as a factor to explain the need to reveal information on business intangibles, as by reducing the information asymmetries, the confidence of the external stakeholders in the indebted company is increased. Therefore, greater leverage should increase the interest of the company to disclose information about its intangibles as a means to justify the need for funds (Macagnan, 2005). Even though this relationship has been widely researched, the results in this respect are contradictory. This, it is confirmed in studies such as those of Apellániz & Zardoya (1995), Mitchell et al. (1995), Cooke (1996), Richardson & Welker (2001), Vicente(2001), Camfferman & Cooke (2002), Watson et al. (2002), Guimón (2005) and Prencipe (2004). However Chow & Wong-Goren (1987), Craswell & Taylor (1992), McKinnon & Dalimunthe (1993), Christopher & Hassan (1995), Meek et al. (1995) and Gómez Jiménez (2007) cannot validate it. Therefore, new information should be used to check the hypothesis. Therefore, we put forward the following secondary hypothesis: H1a: Companies that consider external motives as driving forces to carry out a financial valuation of their intangibles have a higher level of leverage.

For an intangible to be accepted as a guarantee, it must be easily identified and retain its value when it is removed from the company, conditions that very few intangibles meet (Guimón, 2005). Bezant and Punt (1997) conclude that intangibles are accepted as guarantees on very few occasions. Moreover, analysts tend to remove intangibles from the balance sheet when they calculate financial ratios (FASB, 2002) to offset the different accounting criteria that the companies may present. Therefore, those companies with the greatest volume of intangible resources have, in comparative terms, fewer resources to present as guarantees to lenders. They feel more sharply the aforementioned negative impact more sharply arising from the lack of information regarding intangibles, particularly, the greater cost of financial resources, and they will therefore have incentives to disclose the value of their intangibles. Thus, for example, Shi (1999) found that for listed companies, increase in R&D expenses -in reality, investment in a significant intangible- are associated with increases in the cost of the debt. Lev et al. (2000) examined over 1,500 companies intensive in R&D and found that the companies with a high rate of growth of investments in R&D, but with relatively low results in the growth rate (typical of young companies that are intensive in intangibles) are systematically undervalued. In fact, the share portfolios of companies with the highest levels of investments in R&D, in proportion to their market values, show systematically positive and high returns. This undervaluing of the shares implies an excessive capital cost for companies. Well, how can we tell which are the companies with the highest volume of intangibles? Even though it is difficult to answer that question, one possibility may be to use the Market Value/Book Value ratio as proxy. Yet this is only possible with listed companies, when, as we will see, the great majority of the companies are not listed on the Stock Exchange. As Spanish accounting legislation allows some intangibles to be activated, such as R&D expenditure6, patents, licences, brands,

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etc., we will use the book value of the intangibles as the proxy (Intangible Assets). We thus put forward that companies with a higher proportion of intangibles entered in the accounts are more interested in disclosing information about them, for the intangibles to be considered as resources held by the company. Therefore, we have formulated the following secondary hypothesis: H1b: Companies that consider external motives as driving forces to carry out a financial valuation of their intangibles present a higher level of book intangibles. As has already been indicated, the foreseeable consequences of a strategy to disclose information on the value of the intangibles will consist of a reduction of the cost of financial resources that should lead to increased ROE. Therefore, the following hypothesis is advanced:

put forward considering all the aforementioned measures. H3: Companies that consider internal motives as driving forces to carry out a financial valuation of their intangibles will obtain better results: H3a: Companies that consider internal motives as driving forces to carry out a financial valuation of their intangibles will obtain a better ROE. H3b: Companies that consider internal motives as driving forces to carry out a financial valuation of their intangibles will obtain a better ROA. H3c: Companies that consider internal motives as driving forces to carry out a financial valuation of their intangibles will obtain better growth of profits.

H2: Companies that consider external motives as driving forces to carry out a financial valuation of their intangibles have a greater ROE.

H3d: Companies that consider internal motives as driving forces to carry out a financial valuation of their intangibles will obtain better growth of turnover.

Valuing of Intangibles for Internal Motives

Figure 1 graphically represents all the hypotheses put forward.

With regard to the internal motives for valuing the intangibles, the companies prioritise improving their management where these motives prevail. Given the information shortcomings noted by Lev (2001), and the need for this type of information, the development of a financial valuing process of the business intangibles should generate profits for the companies. Efficient strategic management must be backed by quantitative and qualitative information regarding the intangibles (Vitale et al., 1994; López, 1996; Sánchez, 1996). This greater efficiency must lead to better results. And in that respect, it does not seem that one way of measuring results is more appropriate than another, and the following hypothesis and sub-hypothesis are

Process to Obtain the Data

318

Presentation of the Process In order to obtain the necessary data to check the hypotheses, a telephone survey was first conducted among company executives about aspects relating to business intangibles and their assessment, the degree of knowledge that they have about them and their motives for valuing them. A questionnaire had to be prepared, a population selected, a sample obtained and the field work carried out. Subsequently, information was collected about the necessary economic and financial variables to check the proposed hypotheses using the SABI

Motives for the Financial Valuation of Intangibles

Figure 1. Study hypotheses

database. The phases of this process are described in more detail below.

Preparing the Questionnaire With respect to the questionnaire used, the research team prepared a preliminary proposal. Subsequently, in order to be able to improve it and check its validity, a pre-test was carried out in conjunction with the members of the Basque Country Finances and Management Forum, consisting of conducting semi-structured interviews with the financial directors of eleven companies. The opinion of the directors about the questionnaire was thus gleaned, their suggestions for improvement collected and the problems existing in the interpretation of the items identified. The questionnaires were also simplified, as the simpler the questions are, more similarities are found in the interpretations by the people surveyed (Baruch, 1996).

Selection of the Population The study focused on the companies of the Autonomous Community of the Basque Country. The choice of this geographical area was for several reasons, relating to its differentiating characteristics: •

•

•

The Basque Country, located in the North of Spain, is an autonomous community with legislative capacity in certain areas and its own government. Its population is just over two million inhabitants, which accounts for around 5% of the total Spanish population. Another notable aspect is that its three provinces have fiscal autonomy, as they collect all the taxes and have certain capacity to establish their characteristics. The Basque Country, despite lacking natural resources for some time, and as it does

319

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not enjoy other advantages such as “proximity economies” to the capital of Spain, has been one of the areas of Spain with a strong business and industrial tradition. Given precisely this lack of natural resources and of proximity economies, we estimate that Basque companies will give special value to their intangible resources. Information on the companies was likewise obtained from the SABI database. Out of the initial population, consisting of 44,637 companies, micro-companies, that is, those with less than ten employees or invoicing less than two million euros a year were excluded due to their reduced size. The population in question was reduced to 3,477 companies. Starting from the population, a random sample of 517 companies was obtained, which provided a level of confidence of 95% and a maximum error level of +4%.

Field Work The field work was carried out between 20 November 2007 and 14 January 2008. The average number of contacts with the responding companies has been 2.6. Prior to the survey being carried out, an introductory letter was sent to 1,500 companies along with the questionnaire, addressed to the financial director or, failing that to the person carrying out those duties in the company.

Collecting Information about the Importance of Internal and External Motivations In order to determine the type of motives that can drive the executives of a company to financially value its intangibles, whether they consider the financial valuation of their intangibles to be important first needed to be analysed. If that was not the case, the driving motive was taken not to be relevant.

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Different motivations were put to the executives in the questionnaire, in order to determine whether the financial valuation of the intangibles was important from an internal or external point of view. Specifically, two related to internal motives (“Improving the management of the company” and “Correctly assessing the financial and economic situation of the company”) and two related with external motives (“Providing better guarantees to obtain financial resources” and “Determining the price of the company in the case of business transactions - purchase of companies, partners leaving or joining, mergers, inheritances…”). The quantification of the importance granted to the motives by the executives was carried out by calculating the average valuation obtained for the external and internal motivations, with it being understood that the executives considered the internal (or external) motives to be important if the average value was at least a value of 4 (the importance is measured on a likert scale of 1 to 5)

Collecting Information about Business Results The information about the economic and financial results of the companies was obtained from the financial statements in the SABI database. The period for which economic and financial information was collected was the four financial years prior to the survey, that is, 2004-2007. The companies where data were not available for the full period were excluded from the analysis. Very extreme cases, where there were more than 4 deviations from the mean, were likewise eliminated. The total number of companies to be analysed was reduced to 440, which provided a maximum error level of ±4.4%, for a confidence level of 95%. The basic characteristics of the process are summarised in Table 1. In order to check the H1a hypothesis, the level of leverage variable was measured using the liabilities between net equity ratio. On the other hand, to check the H1b hypothesis, the level of

Motives for the Financial Valuation of Intangibles

Table 1. Technical details of study Population

3477 companies domiciled in the Basque Country

Sample

517 valid questionnaires to CFOs

Random error

For the entire sample, random error of ±4%, with confidence level of 95%, p=q=0.5

Interview data collection technique

Telephone interviews with CFOs

Calendar

20 November 2007 to 14 January 2008

Financial performance data collection technique

SABI database

Calendar

October 2009

Final sample

440 firms

Final random error

Random error of ±4.4%, with confidence level of 95%, p=q=0.5

book intangibles variable was defined as the quotient between intangible fixed assets and the total assets. In the case of increased profits and higher turnover, the growth recorded in 2004-2005, 2005-2006 and 2006-2007 was considered. The operating profit was considered to calculate the ROA and the increased profits. In all the cases, the mean value of the variables in the analysis period was taken in order to carry out the contrasts.

Statistical Analysis First of all, a descriptive analysis was carried out to establish to what extent the data met the relations indicated by the hypotheses. Secondly, the statistical contrast of the hypothesis was performed. When checking H1a, it was seen that for the “logarithm of the leverage” variable, the normality hypothesis at 5% was not rejected, according to the Kolmogorov-Smirnov test. In order to check the uniformity of variances with the Levene statistics, an ANOVA test was performed of a factor to analyse the mean difference. However, with respect to H2 and H3, given that, once the Kolmogorov-Smirnov test had been performed, the variables obtained were not in line with a normal distribution and that the standard transformations in order to ensure normality were not successful, non-parametric checks were carried out. The same occurred in the case of book

intangible variables, when checking the H1b hypothesis. Specifically, and given that in these hypotheses the behaviour of two sub-samples is being compared, we resorted to the Mann-Whitney U test (Mann & Whitney, 1947).

RESULTS In relation to the two first hypotheses, Table 2 shows that companies consider the external motives to be important as driving forces of a financial valuation process if they have greater incentives to start it. That is to say, their level of leverage and the weight of the intangible fixed assets of the total assets are greater than in the case of the companies that do not consider the external motives important as driving forces for a financial valuation of a process. The result of a company’s desire to carry out a financial valuation of its intangibles leads to a small improvement of the financial performance. When statistically checking the existence of significant differences in the results, it can be seen in Table 3 that the difference in the book intangibles level is significant at 5%, and, therefore, the H1b hypothesis can be accepted. Once the uniformity of the variances (Levenne test) is guaranteed, checking the differences in the logarithm of the leverage level between the two col-

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Motives for the Financial Valuation of Intangibles

Table 2. External motives for valuing intangibles and business performance. Descriptive statistics.

Consider financial valuation of intangibles important for external motives. Not consider financial valuation of intangibles important for external motives.

N

Mean

Standard deviation

Mean Leverage (%)

189

234.02

185.61

Mean Intangible Assets / Total Assets (%)

204

3.58

5.35

Mean ROE (%)

197

11.07

13.54

Mean Leverage (%)

218

210.70

189.21

Mean Intangible Assets / Total Assets (%)

228

2.36

3.99

Mean ROE (%)

218

11.06

13.02

lectives is significant at 10% (Table 4), and therefore the H1a hypothesis is also accepted. However, this propensity regarding the dissemination of information about the company’s intangibles is not transferred to the results. Even though the financial return is greater, the differences are not significant (Table 3) and we can therefore not accept the H2 hypothesis. The business results obtained by the companies that consider it important to perform a financial valuation process of the intangibles driven by internal motives are greater (Table 5), except in the case of increased sales. Given that the variables are not in line to a norm and that the standard transformation to achieve normality were not successful, we performed a non-parametric check. The results showed that the ROE, the ROA and the increased operating profit were greater in the case of companies that consider the valuation of their intangibles for an internal motive to be important. However, the differences are not statistically significant, according to the Mann-Whitnes U test (Table 6). Therefore, we cannot accept the compli-

ances of H3 hypothesis, nor of the secondary H3a, H3b, H3c, H3d ones. It should be pointed out that the results are contrary to the forecast in the case of increased sales, even though they are not statistically significant. One possible explanation for this discrepancy in the results in hypothesis 3 is that they are measures that can strengthen the intangibles of a company, by consolidating its position on the market, but which are detrimental to its more immediate results. Many of the investments in intangibles are considered as expenditure in the financial year. Staff training policies, advertising costs, etc, even though they suppose an increase in the value of the resources of the company and its capacity to generate profits, negatively affect Table 4. Intangible valuation driven by external motives and logarithm of leverage Degrees of freedom

F

Asymptotic Sig. (2-tailed)

Inter-group

1

3.377

0.067

Intra-group

405

Table 3. Intangible valuation driven by external motives related to intangible assets and business performance Mann-Whitney U test

Wilcoxon W test

Z

Asymptotic Sig. (2-tailed)

Mean Intangible Assets / Total Assets (H1b)

20237

46343

−2.332

0.020

Mean ROE (H2)

20908

44779

−0.463

0.643

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Motives for the Financial Valuation of Intangibles

Table 5. Intangible valuation driven by internal motives and business performance

Consider financial valuation of intangibles important for internal motives.

Not consider financial valuation of intangibles important for internal motives

N

Mean

Standard deviation

Mean ROE (%)

219

11.38

14.00

MeanROA (%)

236

5.31

6.94

Mean growth in operating profit (%)

219

-0.44

144.68

Mean growth in turnover (%)

229

5.91

11.19

Mean ROE (%)

196

10.71

12.40

Mean ROA (%)

204

5.29

6.32

Mean growth in operating profit (%)

184

-6.48

157.35

Mean growth in turnover (%)

196

6.10

11.23

the result for the immediate year. The beneficial impact on the results is therefore tempered over the initial years. The prudent intention of the accounting methods to measure profits tends to particularly undervalue the return on investments in intangibles (Vicente, 2000). Another further explanation, as argued by Rodríguez Domínguez (2004) through a similar study, is that even though the valuation of intangibles is considered to be fundamental for companies, this option is not accompanied by active intangible management policies. The preoccupation with valuing intangibles signifies a change in the mentality of the companies, but if it is not applied to specific practices, it will be difficult to obtain notably better results. A third justification of the discrepancy in the results is that carrying out a financial valuation process of the intangibles presents theoretically internal and external advantages. However, the

deployment of a valuation process can incur a series of costs. Apart from the costs of identifying and collecting information, there are other related to the disclosure of that information. As far as the intangible assets are strategic resources for the companies, the dissemination of information may lead to a loss of competitive advantage (Macagnan, 2005). This danger arises both from the risk of their being imitated by the competitors and the fact that it is often the very ambiguity about the cause that is the strength that converts the intangibles into competitive advantage. As Gray et al. (2004) pointed out, companies only collect information on their intangibles and therefore incur costs, when they are forced to. Managers may not find sufficient incentives to improve knowledge of their intangibles through a financial valuation process. However, the publication of external information does generate immediate effects and reduces the capital costs, as established by Botosan (1997),

Table 6. Intangible valuation driven by internal motives and business performance. Test statistics for H3. Mann-Whitney U test

Wilcoxon W test

Z

Asymptotic Sig. (2-tailed)

Mean ROE (H3a)

20346

39652

−0.915

0.360

Mean ROA (H3b)

24003

51969

−0.052

0.959

Mean growth in operating profit (H3c)

19696

36716

−0.388

0.698

Mean growth in turnover (H3d)

22289

41595

−0.121

0.904

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and in this case the managers are not inclined to apply measures to provide information about their intangibles. Likewise, a study performed by Díez et al. (2007) shows that companies that prepare some type of comprehensive report on their intangibles, and disseminate information about them, do so for the influence that that could have on the image of the company and on gaining and keeping customers. To conclude, Figure 2 summaries the results achieved.

CONCLUSION Intangibles have become the basic resources for generating competitive advantages, according to the Resource-Based Theory. The management of intangibles is one of the main challenges within Figure 2. Study results

324

the field of business management. Management thus faces numerous difficulties, mainly due to lack of information, which is precisely the result of their intangible nature. Accounting information is increasingly less important and financial valuation of the intangibles can help to reduce, at least in part, the existing lack of information. Under this approach, any measure aimed at improving the management of the intangibles should help to obtain better results. This chapter is an attempt to measure the existing relationship between the concern of companies to financially value its intangible resources, the reasons behind that valuation and the results that are obtained. The results of the study show that interest in providing information is greater as the weight of the intangible resources in terms of the other resources of the company increases and as there is a greater need for leverage. The presentation of voluntary reports on the intangibles of the com-

Motives for the Financial Valuation of Intangibles

panies creates advantages for the companies that do so. This type of report will reduce the information asymmetries and the agency costs, which should result in an improved company image, in general (Díez et al., 2007), and, in particular, a better image by the creditors. Managers believe that providing information about intangibles to external stakeholders may make it easier to obtain financial resources. Nonetheless, preparing reports on the intangibles of the company should be a widespread practice. The improvements to the image of the companies are not only limited to those companies with high levels of leverage and which have significant intangible resources, but rather to any type of company. The lack of information on intangibles applies both to the managers and to external stakeholders. Therefore, it should be thought that those companies that consider it important to value their intangibles and that, therefore, consider the possibility of increasing their knowledge of said assets, should be able to manage them better and obtain better results. Nonetheless, the analysis of the data in this study shows that the improvements obtained in the company results are not significant. A possible explanation would be that the managers do not have sufficient incentives to do so. When companies have a greater level of leverage and/or have a greater proportion of intangible resources and are required to show their profit-generating capacity to third parties, the companies do find the necessary motivation to start a financial valuation process that enable them to reduce their capital costs. However, when the motivation is internal and there is no specific pressure, the results obtained are better (higher ROA, ROE and better profits), but not significantly. Changes need to be made to the remuneration systems of the managers, which force managers to improve the management of the key competitiveness elements such as intangibles. The traditional incentive systems are based on accounting measures that may act as a disincentive to invest in intangibles, as they reduce the immediate profits.

Implementing a valuation process may incur a series of costs, which reduce the potential positive impact of better knowledge of the intangibles. These costs depend on how difficult it is to identify the intangibles of a company and the complexity of the valuation method applied. The scientific community therefore needs to focus on developing valuation models that are applicable to companies, and viable from the point of view of cost. Salojärvi (2004) and García Merino et al. (2008) find that the large companies show the greatest interest in valuing their intangibles, possibly because they have more resources to identify and manage them. It should be stressed that many of existing studies on business intangibles and the results obtained are applied to companies listed on the stock exchange. One of the contributions of our study has been to analyse the relationship between the intangibles, the motivations that may trigger a financial valuation process and the results of any type of companies and the conclusions are not restricted to listed companies. One of the restrictions of our study is that it only considers the effect that a greater interest in the financial valuation of the intangibles has on the immediate results and that the data was only collected using traditional methods to measure results. It should be considered if this greater interest in valuing intangibles, driven by external and internal motives, will improve the future results of the company. In order to check this effect, the analysis could be repeated several years later. This relationship could also be analysed by performing another study where subjective performance measurements are collected, that is, questions should be included about the expected results from the implementation of a valuation process, motivated by external or internal aspects. Any difficulties arising from the accounting measures used could be solved. A future study should compare the relations considered not at a time of economic prosperity, but rather in a period of economic crisis, in order to establish to what extent the results of the

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companies with greater interest in its intangibles have been able to better weather the crisis than those that have not been so preoccupied with their management. In addition, the analysis in this chapter should be expanded to other geographical areas. Despite the arguments put forward regarding the suitability of the Basque Country as a territorial area for the analysis performed, we have to acknowledge that it is a limited area and it is therefore obvious that similar studies should be carried out in other geographical areas.

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ENDNOTES

Wilcox-King, A., & Zeithaml, C. P. (2001). Competences and firm performance: Examining the causal ambiguity paradox. Strategic Management Journal, 22, 75–99. doi:10.1002/10970266(200101)22:13.0.CO;2I

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Williams, M. (2001). Are intellectual capital performance and disclosure practices related? Journal of Intellectual Capital, 2(3), 192–203. doi:10.1108/14691930110399932 3

KEY TERMS AND DEFINITIONS Intangibles: Those assets of a company that do not have a physical basis. Intellectual Capital (IC): The aggregate intangible assets that contribute to the value and competitiveness of an organization, including, for example, human capital, customer capital and structural capital. Assessment: In this context the term assessment is used to include measurement and valuation of intangibles. Financial Valuation of Intangibles: The measurement in monetary terms of the contribution of the intangibles resources to firm value.

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This chapter is part of the activities done by the Research Group in Financial Valuation of Intangibles (VALINTE) funded by the University of the Basque Country. Nevado Peña & López Ruiz (2007) group these ratios into two categories: (i) absolute indicators: measurements in monetary units and not related with another magnitude; (ii) efficiency indicators: percentage indexes that fluctuate between 0 and 1, with 0 indicating the most unfavourable and 1 the most favourable situation. For further discussion on the subject, see Rodríguez & Araujo (2005). Rodríguez-Castellanos et al. (2007) contains a review of the different methods for the financial valuation of intangibles. Vicente Lorente (2000) proposes the uses of quotient between market and book value of the equity. However, even though this measurement may be the most appropriate, it would not be applicable in our study given that many of the companies in our sample are not listed on stock markets and their market value would therefore not be available. Accounting legislation in force until 2007 allowed the majority of R&D costs incurred to be considered assets.

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Chapter 16

Model of a Knowledge Management Support system for Choosing Intellectual Capital Assessment Methods Agnieta B. Pretorius Tshwane University of Technology, South Africa F.P. (Petrie) Coetzee Tshwane University of Technology, South Africa

ABSTRACT Existing literature propagates a variety of methods for assessment of intellectual capital (IC). This research argues that, due to complexities involved in selecting and customizing an appropriate method or combination of methods for assessing intellectual capital, mechanisms are needed for managing and applying the evolving body of knowledge concerning such assessment. The assumption of complexity is supported by the results obtained from a survey (employing a self-administered questionnaire as instrument for data collection). This research proceeds to develop a model, referred to as a conceptual design, for a system to (i) provide management support to the process of selecting and customizing an appropriate method (or combination of methods) for assessment of intellectual capital, (ii) utilize past knowledge and expertise to accelerate and improve decision-making, (iii) promote synergism through integration of methods, and (iv) manage the evolving body of knowledge concerning the assessment of IC.

INTRODUCTION The shift from the industrial to the knowledge economy has impacted significantly on the way business operates and on the relative value of its value components (Green, 2005; Lev, Cañibano & Marr 2005). Intellectual capital (IC) – also

referred to as intangible assets, knowledge assets, core competencies or goodwill – is increasingly acknowledged as a dominant strategic asset, and a major source of competitive advantage for organizations (Harrison & Sullivan, 2000; Holsapple, 2003; Housel & Bell, 2001; Kalafut & Low, 2001; Kannan & Aulbur, 2004; Koulopoulos &

DOI: 10.4018/978-1-60960-054-9.ch016

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Model of a Knowledge Management Support system for Choosing Intellectual Capital Assessment Methods

Frappaolo, 1999; Mouritson, Bukh & Marr, 2004; Park, 2005; Sánchez, Chaminade & Olea, 2000; Teece, 2003). Despite a rich and evolving body of literature on methods, model systems and frameworks for assessment of IC (Andriessen, 2004b; Bontis, 2001; Chen, Zhu & Xie, 2004; Green, 2005; Smith & McKeen, 2003) and an increased awareness of the need for such assessment, relatively few organizations are actively and comprehensively assessing their IC (Bontis, 2001; Green, 2005; Marr, 2006; Smith & McKeen, 2003). IC has surfaced as a major value contributor (Hope & Frazer, 1997) – estimated to account for up to 70% of the value of organizations (Sullivan, as cited in Green, 2005) – but still is not adequately reflected in current accounting practices (Green, 2005; Lev, Cañibano & Marr, 2005; Mouritson, Bukh & Marr, 2004). This research argues that, due to the complexities involved in choosing (selecting and customizing) an appropriate method or combination of methods for assessment of IC, and the cognitive limits of human problem solving, there is a need for knowledge management support systems (KMSS) – management support systems (MSS) with knowledge components – to manage (organize, store and retrieve) the evolving knowledge concerning such assessment (see also Pretorius & Coetzee, 2005). Note that the sensibility and usefulness of a KMSS for choosing IC assessment methods is dependent on judgment concerning the complexity of the process of choosing IC assessment methods. In this research, the assumption is made that the complexity of the decisions to be made in choosing an appropriate method (or combination of methods) for assessment of IC (given any particular context) warrants the development of a KMSS, as defined earlier. This assumption needs to be explored and tested as part of this research. It is argued that:

•

•

It is not necessary to obtain an absolute and all-inclusive answer to the question of whether the complexities involved in choosing IC assessment methods warrant a KMSS to justify that the development of such a system makes sense. For such a system to be potentially useful, it needs to be useful to some relevant decision makers (individuals making decisions related to the choosing of IC assessment methods) and not necessarily to all relevant decision makers.

Subsequently it is proposed that, if in the context of this research, a substantial portion of a suitable group of individuals knowledgeable on IC or aspects thereof perceive the decisions involved in choosing IC assessment methods as at least moderately complex (moderately or very complex) it is likely that: • •

There is a need for such a system; and It makes sense to develop such a system – at least in a scientific sense – but even also in a business sense.

Accordingly, the fundamental question addressed in this research is phrased as: If a suitable group of individuals perceive the decisions involved in choosing IC assessment methods as complex, what should the conceptual design be of a KMSS for choosing IC assessment methods? In dealing with the research question, the following five subsidiary research questions are attended to: •

•

SRQ1: What methods are available for assessment of IC, how can they be classified and what factors determine appropriateness of methods? SRQ2: What are perceptions on the levels and types of complexities involved in choosing IC assessment methods?

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•

•

•

SRQ3: What are the contextual determinants of a KMSS for choosing IC assessment methods? SRQ4: What should the conceptual design of an appropriate KMSS for choosing IC assessment methods be? SRQ5: How can aspects of a KMSS for choosing IC assessment methods be demonstrated?

The aim of this research is to explore the concept of and develop a model, referred to as a conceptual design, of a system for supporting the decision making process about the choice of IC assessment methods. The type of support system proposed in this research, is referred to as a KMSS (as introduced earlier). The requirements of the proposed KMSS are to: •

• • •

Provide management support to the process of selecting and customizing an appropriate method (or combination of methods) for assessment of IC; Utilize past knowledge and expertise to accelerate and improve decision-making; Promote synergism through integration of methods; and Manage the evolving body of knowledge concerning the assessment of IC.

More specifically, this research is intended to address the research question and subsidiary research questions introduced earlier in this section. The scope of each of these subsidiary questions is such that a separate in-depth study (with a narrow focus) for each could easily be justified. Due to the interrelatedness of these subsidiary questions, however, and an insufficient existing base of literature from which such interrelations and implications thereof could be deduced, it is not considered advantageous (at this point) to consider them independently. Rather it is deemed necessary to consider these subsidiary research questions in such a manner that insight gained

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from exploring a particular subsidiary research question could complement the efforts directed toward the other subsidiary research questions. Therefore, an exploratory approach (with a wide focus) – as opposed to a formal approach (with a narrower focus) – is followed in addressing these research questions. The resultant objectives of this exploratory study (corresponding to subsidiary research questions SRQ1 to SRQ5 respectively) are: •

•

•

•

•

O1: Review and report on methods available for assessment of intellectual capital, their classifications and factors determining appropriateness of methods. O2: Gather perceptions of a suitable selection of individuals knowledgeable on IC or aspects thereof regarding the levels and types of complexities involved in choosing (selecting and customizing) IC assessment methods. O3: Establish contextual determinants of a KMSS for choosing (selecting and customizing) IC assessment methods. O4: Produce a conceptual design of an appropriate KMSS for choosing (selecting and customizing) IC assessment methods. O5: Develop and apply a partial prototype to demonstrate selected aspects of a KMSS for choosing (selecting and customizing) IC assessment methods.

BACKGROUND This section provides background to this research by discussing relevant terminology and concepts.

Intellectual Capital The notion of “intellectual capital” (IC) is variously conceptualized by different authors. Our intention is not to provide a definite or generally accepted definition. Rather, we accept the variety

Model of a Knowledge Management Support system for Choosing Intellectual Capital Assessment Methods

of definitions – and corresponding categorizations – and need to accommodate the various definitions and categorizations, given our aim. For example (as illustrated in Table 1): •

•

•

•

•

According to Brooking (1999) IC refers to the collective intangible assets that enable an organization to function, including human centered assets, market assets, infrastructure assets and intellectual property assets. Similar to the components of IC identified by Brooking – but not explicitly including intellectual property assets – Sveiby refers to these components as individual competence, external structure and internal structure respectively (as cited in Bontis, 2001). Stewart observes that academics typically group IC into at least three categories, namely human capital, customer capital and structural capital (as cited in Smith & McKeen, 2003). Customer capital is also referred to as relational capital, including not only relationships with customers, but also relationships with other stakeholders (e.g. De Pablos, 2004). Edvinsson and Malone subdivide structural capital into organizational capital, process capital and innovation capital (as cited in Kannan & Aulbur, 2004).

Assessment Although the terms “measurement”, “(e)valuation” and “assessment” are often used interchangeably, authors such as Andriessen (2004a), reflecting on the work of Rescher and of Swanborn, note a distinctive difference between measurement and (e)valuation: Rescher (1969) portrays valuation (employing the term evaluation) as “a comparative assessment or measurement of something with respect to its embodiment of a certain value” (p. 61). Swanborn, on the other hand, describes measurement as “the process of assigning scaled numbers to items in such a way that the relationships that exist in reality between the possible states of a variable are reflected in the relationships between the numbers on the scale” (as cited in Andriessen, 2004a, p. 238). In this research the term assessment includes measurement, (e)valuation and all other such notions for determining value.

Choosing, Selection, Customization, Implementation and Application The term “choosing” (IC assessment methods) is employed in this research to include both the “selection” and the “customization” of an appropriate method (or combination of methods) for assessment of IC, given any particular context. It should be noted that the selection process (of IC assessment methods) includes consideration

Table 1. Components of IC (Adapted from Pretorius & Coetzee, 2006a) Intellectual Capital (IC)

Brooking

Sveiby

Stewart

Edvinsson and Malone

Human Centered Assets

Individual Competence

Human Capital

Market Assets

External Structure

Customer Capital (Relational Capital) Organizational Capital

Infrastructure Assets

Internal Structure

Structural Capital

Process Capital Innovation Capital

Intellectual Property Assets

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of the customizability of the selected method to suite a particular context. In this research the term “customization” (of an IC assessment method) is used to refer to the adaptation of a method (or methods) to suit a particular context, i.e. the detailed design of the manner in which a particular selected method (or methods) will be implemented or applied in a particular context. The term “implementation” is employed to refer to the putting into operation of a method and the term “application” to refer to the customization and implementation of a method.

Model and Conceptual Design A “model”, as described by Olivier (2004), “captures the essential aspects of a system or process, while it ignores the nonessential aspects”. He explains that it is neither possible, nor required, to provide “a single comprehensive model for any given system or process” (p. 45). Any model embeds assumptions about its context of application, sometimes not explicitly recognized and defined, yet important for its plausible application. Models can be employed in different contexts, on different levels and with different areas of focus. Terms related to model, with overlapping meanings, include “conceptual design”, “architecture” and “framework”. In this research: •

•

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The Zachman Framework for Enterprise Architecture (Hay, 2000; Sharp, 1999; Zachman, 1997) is employed to structure the process of modeling the proposed KMSS. Such modeling spans across various levels of detail (represented by the rows of the Zachman Framework) and various areas of focus per level (represented by the columns of the Zachman Framework). The term model is employed in the contexts of business modeling and information systems development, on the higher levels (first, second and upper level part of

•

the third row) of the Zachman Framework, spanning all six of the columns of the Zachman Framework (data, function, network, people, time and motivation). Particularly note the following: ◦ The first (that is, uppermost) row of the Zachman Framework is referred to as objectives/scope, ballpark view or contextual level (i.e. the level where the context of the enterprise and its systems is viewed/described); ◦ The second row is labeled the enterprise, business or owner’s view or conceptual level (i.e. the level where the enterprise is viewed/described at the conceptual level); and ◦ The third row as called the system model or logical level (i.e. the level where a system serving or intended to serve the enterprise is viewed at the logical level). The term conceptual design (of a KMSS for choosing IC assessment methods) is used to refer to a model (of a KMSS for choosing IC assessment methods), in an information systems context, on the upper level part of the third row (system model/ logical level) of the Zachman Framework, considering all six columns of the Zachman framework but explicitly focusing on the first two columns (data and function). The other four columns (network, people, time and motivation) should be considered in more detail in lower-level (more detailed) analysis and design.

Knowledge Management Support System As illustrated in Figure 1, this research refers to a management support system that includes a mechanism for the management of knowledge, as a knowledge management support system (KMSS).

Model of a Knowledge Management Support system for Choosing Intellectual Capital Assessment Methods

The term “management support system” (MSS), as defined by Turban, Aronson and Liang (2005), “refers to the application of any technology, either as an independent tool or in combination with other information technologies, to support management tasks in general and decisionmaking in particular” (pp. 11-12). Such technologies include management information systems (MIS), executive information systems (EIS), group support systems (GSS), decision support systems (DSS), expert systems (ES), knowledge management systems (KMS), neural networks, online analytical processing (OLAP) and data warehousing.

CHOOSING IC ASSESSMENT METHODS This section presents issues relating to choosing (selection and customizing) IC assessment methods as well as solutions and recommendations.

Issues in Selecting and Customizing Methods for Assessment of IC The following sub-sections elaborate on challenges and existing supportive initiatives concerning the choosing of IC assessment methods.

Challenges

zations have “only a vague understanding of how much they invest in their IC, let alone what they receive from those investments” (p. 72). Almost a decade later Sullivan and McLean (2007) refer to assessment of this kind as the “confusing task of measuring intangible value” (p. 36). Experiences on choosing (selection and customization) of methods are not systematically organized and stored within organizations and channeled into academic literature. In particular there are limited case studies on the challenges of the customization process. Consequently: •

There are no clear supportive guidelines on which methods are suitable in which contexts: Selecting an appropriate method or combination of methods for assessment of IC in a particular context appears to be a complex process entailing complex choices. Smith and McKeen (2003) note that “no single metric would work in all circumstances” (p. 361), Sveiby (2007) that “no one method can fulfill all purposes” (p. 2) and Andriessen (2004a) that the “array of problems that is being addressed by many of the methods is so broad that is

Figure 1. Schematic representation of a KMSS (Adapted from Pretorius & Coetzee, 2006a)

The growing interest in the assessment of IC is not matched by the practical application of IC assessment methods (Bontis, 2001; Green, 2005; Marr, Smith & McKeen, 2003). Both practitioners and academics have conveyed “frustration and dissatisfaction” with the capability of current methods to assess intangibles such as IC (Smith & McKeen, 2003, p. 354). According to Klein (1998) the IC of professionals (constituting the building blocks of the IC of organizations) is typically measured by “rough indicators such as education and years on the job” (p. 6). Van Buren (1999) notes that organi-

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•

[sic] seems questionable whether they can all be solved using one method” (p. 239). There are insufficient guidelines for customization. Instructions – e.g. for determining IC indicators: “look for good measures to monitor the levels of … intangible resources” (Sánchez, Chaminade & Olea, 2000, p. 319) or “identify the indicators that best reflect those key success factors” (Andriessen, 2004b, p. 313) – may sound straightforward, but do not provide a clear indication of how such customization should take place. Customization decisions include, e.g.: ◦ which indicators and measures to use (Bontis, 2001; Castellanos, Rodriquez & Rangelov, 2004; Chen, Zhu & Xie, 2004; Klein, 1998; Sánchez, Chaminade & Olea, 2000); ◦ what weights to allocate (Bontis, 2001; Chen, Zhu & Xie, 2004); and ◦ how to interpret, present and disclose results (Housel & Bell, 2001; Klein, 1998; Mouritson, Bukh & Marr, 2004; Sánchez, Chaminade & Olea, 2000).

Furthermore, the human mind has cognitive limits in processing and storing information, limiting individuals’ problem solving capabilities when a wide range of knowledge and diverse information is required (Newell & Simon, 1972; Simon & Associates, 1986). Knowledge about methods, models, systems and frameworks for assessing IC is scattered across existing literature, the operations and processes of organizations, various databases and the minds of academics and practitioners. In decision-making situations, rather than attempting to reinvent the wheel each time, there is a need to draw upon past knowledge and expertise to solve reoccurring problems and problems similar to those solved before (Baria, 2005; Falk, 2005; Turban, Aronson & Liang, 2005).

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Supporting Initiatives Existing initiatives that, to some extent, support the process of choosing IC assessment methods include the sharing of experiences through publication of case studies, the compilation of guidelines, and electronic support in the form of websites, tools and toolkits. A number of case studies are available (from e.g. journal articles and conference proceedings) on the customization (application/implementation) of methods for assessment of IC, occasionally explaining how the method was selected and/or whether or to what extent it was “successful”. Some general guidelines are starting to evolve on the spheres of relevance and/or strengths and weaknesses of methods, or categories of methods, e.g.: •

•

•

The “why” by “how” matrix of Andriessen (2004a) categorizes methods according to motive (“why”) and approach (“how”); Sveiby’s (2007) four categories with advantages and disadvantages for categories, provide an indication of application areas of methods belonging to these categories; and The November 2006 report of the Value Measurement and Reporting Collaborative proposes a set of nine selection criteria “to relate the needs of the organization for additional measurement information with the characteristics of various approaches” (VMRC, 2006, p. 41).

Electronic support for selection and customization of appropriate methods for assessment of IC (initiatives whose requirements/objectives appear to overlap with - but not fulfill all of - the ones of the KMSS proposed in this research) include: •

Websites of researchers, practitioners and consultants from the IC community; e.g. the Intellectual Capital Startpage of

Model of a Knowledge Management Support system for Choosing Intellectual Capital Assessment Methods

•

•

Christiaan Stam, providing links (related to IC and IC assessment) to e.g. articles, journals, books, research, conferences, toolkits, personal profiles of other researchers, practitioners, consultants, IC methods, services, solutions and simulations; An experimental version of An online Measurement Approach Selection Tool (VMRC, 2006) “to enable a company executive or professional advisor to select, from the variety available, the measurement approaches that best meet their needs”; and Toolkits, e.g. the Sveiby Toolkit of Karl Eric Sveiby, that assists with the customization of his “most popular tools” e.g. the Intangible Asset Monitor and Invisible Balance sheet (Sveiby, 1999).

Other initiatives that could possibly be added to the list include electronic libraries (e.g. Emeraldinsight.com), internet search engines (e.g. GOOGLE), communities of practice and discussion forums.

subsequent research questions. Relevant sources were analyzed and synthesized to uncover existing methods available for assessment of IC, classification schemes for such methods, factors determining appropriateness of methods and potential for synergism. The main findings and insights originating from the literature review of IC assessment methods are: •

•

Solutions and Recommendations for Choosing IC Assessment Methods An iterative, non-linear approach was followed in dealing with the challenges outlined earlier in this section. Solutions and recommendations, ordered according to the five subsidiary research questions identified earlier, are presented in the sub-sections to follow.

SRQ1: Existing Methods for Assessment of IC In dealing with SRQ1, a literature review was performed, utilizing secondary sources such as journal articles, textbooks and dictionaries as well as tertiary sources such as online catalogues and Internet search engines. This literature review serves as theoretical base for the answering of

•

Existing literature proposes over a hundred methods for assessment of IC, including Market-to-Book ratio, Tobin’s q, Return on Assets, Technology Broker’s IC Audit, BSC, HRA, Intangible Asset Monitor, ICIndex, Skandia Navigator, Chen, Zhu and Xie’s model, as well as Citation-Weighted Patents (Andriessen, 2004b; Bontis, 2001; Chen, Zhu & Xie, 2004; Sveiby, 2007). Classification schemes for IC assessment methods include those by Andriessen (2004a), Housel and Bell (2001), Kannan and Aulbur (2004), Luthy (1998), Smith and McKeen (2003) and Sveiby (2007). Such classification schemes could serve as a starting point for determining which method(s) to utilize in a particular context and how to customize such method(s) to suit a particular context. Contextual factors (also referred to as dimensions) that could potentially be used to select an appropriate IC assessment method to be used in a particular assessment scenario include: ◦ Audience (Sveiby 2007); ◦ Business sector (Malhotra 2003); ◦ Goals and objectives of organization (Harrison & Sullivan, 2000; Smith & McKeen 2003); ◦ Industry and line of business (Van Buren 1999); ◦ Level of assessment (Sánchez, Chaminade & Olea 2000; Smith & McKeen 2003);

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◦

•

•

Purpose of or motivation for assessment (Andriessen 2004a; Housel & Bell 2001; Sveiby 2007); ◦ Level of resources the organization is willing to commit towards assessment of IC (Harrison & Sullivan 2000); and ◦ Size of organization (O’Sullivan 2005). Selection criteria derived by an initiative of the VMRC for a similar purpose, extend the above list of contextual factors. Synergism could potentially be achieved by integrating steps from different methods for assessment of IC.

SRQ2: Perceptions Concerning Complexity Sensibility and usefulness of a KMSS for choosing (selecting and customizing) methods for assessment of IC is dependent on judgment concerning the complexity of this process. It is proposed that, due to the complexities involved in selecting and customizing an appropriate method for assessing IC in a particular context, a KMSS is needed for managing the evolving body of knowledge concerning such assessment. The assumption is made that the complexity of decisions involved in selecting and customizing IC assessment methods warrants a KMSS. To explore and test this assumption, perceptions of consultants, practitioners and researchers on the complexity of the decisions involved in selecting and customizing methods for assessment of IC were gathered and scrutinized. More specifically, perceptions were investigated (given any particular context) on: • •

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Complexity levels of decisions to be made in selecting a method for assessment of IC; Complexity levels of decisions to be made in customizing a method for assessment of IC; and

•

Factors influencing appropriateness of methods for assessment of IC.

Primary data was collected, since the accessible secondary data sources materialized as inadequate for answering SRQ2. The principle design type or strategy selected for dealing with SRQ2 is a survey. (Please note that this survey could also be viewed as a case study with a multiple-case design). Utilizing the descriptors of research design (appropriate for collection of primary data) proposed by Cooper and Schindler (2006), the design type can furthermore be characterized as exploratory, communication study, ex post facto, descriptive, cross-sectional, case (versus statistical study), field setting and modified routine. The instrument employed was a self-administered questionnaire (distributed via e-mail) since it allows for contact with respondents who would be inaccessible through other means, enables extensive graphical coverage at low cost and allows respondents time to think about questions. This questionnaire contains quantitative components, allowing for quantitative analysis of results, as well as qualitative components, providing respondents the opportunity to include narrative explanations. The data collected on respondents themselves includes years of experience as consultants, practitioners and/or researchers, the number of methods they have consulted on, used in practice and/or tested empirically and the number of methods they have studied. Such data collected on these respondents appear to confirm their expert status regarding IC and aspects thereof. Consequently they are considered a suitable group of individuals for answering questions concerning the levels and types of complexities of the decisions involved in choosing IC assessment methods. Findings were provided in the form of a descriptive summary. The main findings of the survey are (Pretorius & Coetzee, 2009a; Pretorius & Coetzee, 2009b):

Model of a Knowledge Management Support system for Choosing Intellectual Capital Assessment Methods

•

•

•

•

•

•

65% of respondents indicated that the decisions involved in selecting an appropriate method for assessment of IC, given any particular context, is often or always very complex. 55% of respondents indicated that the decisions involved in customizing an appropriate method are often or always very complex. Decisions involved in selection are perceived as marginally more complex than decisions involved in customization. Respondents provided valuable insights and rich examples of scenarios on the higher and lower regions of the complexity scale, for the decisions involved in the selection, as well as, for the decisions involved in the customization of IC assessment methods. The majority of respondents considered goals and objectives of organization, purpose of assessment, level of assessment, and audience as very important factors in selecting an appropriate method for assessment of IC, given any particular context. Level of resources, business sector, industry and line of business and size of organization were considered at least moderately important. Additional factors (suggested by correspondents to be considered for incorporation into the list of contextual factor) pertain to the selector of IC assessments methods (e.g. his or her experience, knowledge and understanding), the organization for which IC is assessed (including management, assessors, other key-individuals, history and culture), level of assessment, method employed for assessment of IC, audience, factors derived from VMRC’s work and others (e.g. certification schemes, availability of data and whether an established model exists).

•

As the list of proposed factors grows it would become increasingly important to have a means of grouping factors together in order to keep the size of the list manageable and to ensure that the same factor by different names does not prevent matching of solutions to old problems with new problems. Identifying combinations of factors that correlate could be useful in reducing the number of factors, since factors that correlate (on a level deemed sufficiently significant) could probably be grouped together.

The results of the survey indicate that the decisions involved in choosing (selecting and customizing) an appropriate IC assessment method to be used in a particular context, are indeed perceived as complex by respondents to the selfadministered questionnaire, with the majority of respondents perceiving such decisions as always or often very complex. With a substantial portion of a suitable group of individuals knowledgeable on IC or aspects thereof perceiving the decisions involved in choosing IC assessment methods as always or often very complex, it is deemed likely that there is a need for such a system and that it makes sense to develop such a system.

SRQ3: Contextual Determinants of KMSS The design of a system (e.g. for choosing IC assessment methods) is dependent on the context in which such system is expected to operate. In dealing with SRQ3, contextual determinants of the proposed KMSS were uncovered by examining the wider knowledge management industry as well as the individual generic consultancy firm expected to develop, maintain and use the proposed KMSS for choosing IC assessment methods. The primary design type or strategy employed in establishing contextual determinants is model-building, supplemented by literature review of suitable

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frameworks and models and their application in related contexts. Building upon the work by Pretorius and Coetzee (2006b), we employed Porter’s “five forces” framework for industry analysis (Ghemawat, 1999; Nickols, 2000a; Nickols, 2000b; Thompson, Strickland & Gamble, 2005), extended to include “complementors” (Brandenburg & Nalebuff, as cited in Ghemawat, 1999) and “regulators” (Carr, 2005; Rugman & Verbeke, 2000), to examine influences from the wider management consultancy industry on the individual consultancy firm. Drawing upon the insights and conclusions derived from analyzing the wider management consultancy industry, the first row of The Zachman Framework for Enterprise Architecture was used to systematically (cell-by-cell) consider characteristics of the individual consultancy firm as it impacts on the business of assessing IC, directing attention towards data, function, network, people, time and function. These contextual determinants provide a context for the conceptual modeling process incurred in addressing SRQ4. Contextual determinants with a direct bearing on the conceptual design of the proposed KMSS (adapted from Pretorius & Coetzee, 2006b) include: • • •

• • • •

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Access to knowledge on methods for assessment of IC; Access to knowledge on appropriateness of existing methods for assessment of IC; Knowledge concerning client organizations’ strategic goals and motivations for assessment of IC; Effective management and application of knowledge; Infrastructure for obtaining access to knowledge; Mechanisms controlling quality of knowledge; Appropriately structured networks enabling effective knowledge sharing/ brokerage;

• •

Infrastructure for real-time response to requests; and Consideration for legislatory requirements as well as cultural and language differences.

Contextual determinants (considered to be outside the boundaries of the proposed KMSS) that can be viewed as critical success factors for the successful implementation and use of the proposed KMSS (adapted from Pretorius & Coetzee, 2006b) include: •

• • • • • • • • •

Competencies and resources to provide mechanisms for on-going assessment of IC; Recruitment, retention, and development of suitable employees; Adherence to ethical principles; Building and maintenance of reputation; Incentives and management processes for effective sharing of knowledge; Mutual-beneficial coopetition through effective knowledge sharing; Effective diffusion of knowledge within relevant client industries; Existence of sound expansion strategy concerning client base and physical locations; Development and nurturing of customer relationships; and Sound medium to long-term strategy concerning development of competencies, competitive positioning, safeguarding of reputation, knowledge sharing and extraction of optimal economic value.

SRQ4: Conceptual Design of KMSS The main design type or strategy selected for dealing with SRQ4 is model-building, supported by elements of literature review, deductive reasoning and inductive generalization. A conceptual design of a KMSS was produced by iterating through incremental cycles of modeling and evaluation.

Model of a Knowledge Management Support system for Choosing Intellectual Capital Assessment Methods

Modeling was performed by scanning and analyzing existing literature for potentially suitable structures, matching possible structures and combinations of structures with the requirements of the proposed KMSS. Evaluation was performed through personal reflection and sound-boarding against knowledgeable academics and practicing experts in the form of conference feedback and formal and informal discussions. Building upon the characteristics of the individual consultancy uncovered by iterating through the cells of the Zachman Framework’s top-most row, the characteristics of the business of assessing IC and of the proposed KMSS respectively, were considered according to the second and third rows of the Zachman Framework respectively. Within the context of these characteristics, a conceptual design was constructed for a system to (i) provide support to the process of selecting and implementing an appropriate method, or a combination of methods, for assessment of IC, (ii) utilize past knowledge and expertise to accelerate and improve decision-making, (iii) promote synergism through integration of methods, and (iv) manage and apply the evolving body of knowledge concerning the assessment of IC. The proposed KMSS, mapped to the upper level part of the third row of the Zachman Framework, builds upon existing knowledge on KMS, DSS, ES, and hybrid support systems, and in particular upon the components of DSS’s proposed by Turban, Aronson, Liang and Sharda (2007). The resultant model of a KMSS incorporates decision-making capabilities, knowledge management capabilities and intelligence, and facilitates interaction with consumers and providers of knowledge. It consists of (Pretorius & Coetzee, 2006a): •

•

A data management subsystem maintaining internal data sources and facilitating access to internal and external data sources; A model management subsystem maintaining internal models and facilitating access to internal and external models (i) for

•

•

analyzing the problem space, and (ii) for assessing IC; A user interface subsystem facilitating input, output and language processing, including a user and developer/builder interface, a user interface management system (UIMS) and an explanation facility; and A knowledge-based management subsystem (KbMS) providing knowledge management capabilities and intelligence (i) for the selection and customization of appropriate models, (ii) for knowledge acquisition from a knowledge repository for keeping track of current and best practice scenarios, and (iii) for the explanation facility. The KbMS acquires knowledge from various internal and external sources through a combination of automatic, semiautomatic and manual knowledge acquisition methods.

Data Management Subsystem The data-management subsystem (refer to the bottom-left quadrant of Figure 2) maintains internal data sources and facilitates access to internal and external data sources. It consists of: •

•

•

A decision support database containing data extracted from internal (to the KMSS) and external (to the KMSS) data sources (Mallach, 2000; Oz, 2002; Sprague, 1980; Stair, 1996; Turban et al., 2007), as well as from users’ personal and unofficial data (Mallach, 2000; Sprague, 1980; Turban et al., 2007); A database management system (DBMS) managing (creating, accessing and updating) the decision support database (Sprague, 1980; Stair, 1996; Turban et al., 2007); A data directory (also referred to as data dictionary) serving as a catalogue of all data in the decision support database and by answering questions concerning the

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•

availability of data items, their sources and their meanings (Stair, 1996; Turban et al., 2007) as well as on responsibility for maintenance (Mallach, 2000); and A query facility accessing, manipulating and querying the data of the decision support database (Mallach, 2000; Stair, 1996; Turban et al., 2007).

The DBMS interacts with (and therefore contains direct links to) the management systems of the other subsystems – namely the model base management system (MBMS), UIMS and knowledge base management system (KBMS) – of the proposed KMSS.

•

•

Model Management Subsystem The model management subsystem (refer to the top-left quadrant of Figure 2) maintains internal (to the KMSS) models and facilitates access to internal and external models. It is composed of: •

•

348

A model base containing internal models (Mallach, 2000; Oz, 2002; Stair, 1996; Turban et al., 2007) and a facility providing access to external models (similar to the database management subsystem facilitating access to external data). The model base of the proposed KMSS includes/ provides access to models supporting all phases of the decision-making process, including models for analyzing the problem space (e.g. organizational goals and objectives) as well as for assessment of IC; A model base management system (MBMS) performing model creation, model updating, model integration, manipulation of model data and generation of new routines and reports (Mallach, 2000; Sprague, 1980; Stair, 1996; Turban et al., 2007). It employs model building blocks (Sprague, 1980), model building routines, modeling languages and modeling tools (Turban et al., 2007);

A model directory providing a catalogue (Sprague, 1980) with definitions for models internal to the KMSS as well as for some models external to the KMSS. The model directory furthermore provides information concerning the availability and capability of models (Turban et al., 2007), as well as information on the sources of data, e.g. responsibility for maintenance and access limits (Mallach, 2000). Through providing information on the capability of models, the model directory assists with determining the sphere of relevance of models; and A model execution, integration, and command processor controlling the execution of models, combining the operations of more than one model when required, and accepting, interpreting and routing modeling instructions from the user interface subsystem (Turban et al., 2007).

The model base of the proposed KMSS includes/provides access to models supporting all four phases of the decision-making process, including models for analyzing the problem space (e.g. organizational goals and objectives) as well as for assessment of IC. The MBMS interacts with (and therefore contains direct links to) the management systems of the other subsystems (DBMS, UIMS and KBMS) of the proposed KMSS. User Interface Subsystem The user interface subsystem (refer to the top-right quadrant of Figure 2) facilitates input, output and language processing. It is composed of: •

A user interface (including a natural language processor, stand-alone graphical user interface and web browser) enabling the user to interact with the other subsystems via input action languages and output display languages (Mallach, 2000; Oz,

Model of a Knowledge Management Support system for Choosing Intellectual Capital Assessment Methods

Figure 2. Conceptual design of a KMSS for choosing IC assessment methods (Adapted from Pretorius and Coetzee, 2006a)

•

2002; Sprague, 1980; Stair, 1996; Turban et al., 2007); A user interface management system (UIMS) facilitating interaction with the user in a variety of formats (Lauden & Lauden, 1996; Oz, 2002), with multiple, different dialog styles and with a variety of input- and output devices and provid-

•

ing training by example, help capabilities, prompting, diagnostic, and suggestion routines (Turban, Aronson and Liang, 2005; Turban et al., 2007); and An explanation facility explaining the reasoning of the ES (Coppin, 2004; Giarratano & Riley, 2005; Jackson, 2000; Negnevitsky, 2002; Turban, Aronson &

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Liang, 2005; Turban et al., 2007) so that reasoning can be verified (Coppin, 2004; Giarratano & Riley, 2005). Negnevitsky (2002) adds that an ES should be able to “justify its advice, analysis or conclusion” (p. 32). The explanation facility enables the KMSS to explain to the user why certain causes of action are recommended, allow the user to experiment with different causes of action and encourage reflection and learning by the user. The UIMS interacts with (and therefore contains direct links to) the management systems of the other subsystems (DBMS, MBMS and KBMS) of the proposed KMSS. Knowledge-Based Management Subsystem The knowledge-based management subsystem (KbMS) of the proposed KMSS (refer to the bottom-right quadrant of Figure 2) supplies intelligence/expertise (as provided by an intelligent system such as an ES) and knowledge (as provided by a KMS) to the other subsystems of the proposed KMSS. The KbMS consists of: •

•

•

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A knowledge base management system (KBMS) responsible for the execution of the intelligent system and for the integration of the intelligent system with other subsystems requiring intelligence (Turban et al., 2007); An intelligent system such as an ES (Coppin, 2004; Giarratano & Riley, 2005; Mallach, 2000; Negnevitsky, 2002; Oz, 2002; Stair, 1996; Turban et al., 2007) for (i) the selection and customization of appropriate models (e.g. for assessment of IC) and (ii) knowledge acquisition; A knowledge base (Coppin, 2004; Stair, 1996; Turban et al., 2007) containing facts about problem situations and rules for applying knowledge to solve problems;

•

•

A knowledge repository (Turban, Aronson & Liang, 2005) storing knowledge on current and best practice scenarios (for assessment of IC) to be re-used in the solving of new problems; and A KMS (Kumar, 2005; Liebowitz, 1998; Turban et al., 2007) maintaining the knowledge repository.

Acquired knowledge (e.g. on current and best practice scenarios) that is not structured as required for inclusion in the knowledge base is stored in the knowledge repository. Knowledge residing in the knowledge repository may (at a later stage) be converted, by the knowledge engineer or some automated process, to a format suitable for incorporation into the knowledge base and/or the rules/inference engine of the intelligent system extended to cater for this new type of knowledge. Alternatively, acquired knowledge could be routed directly to the knowledge base (with or without the help of a knowledge engineer), or, where appropriate (in different formats), to both the knowledge repository and knowledge base. The (text-based) knowledge stored in the knowledge repository of the proposed KMSS (Figure 2) is sourced from actual implementations of methods and strategies for assessment of IC, and from external sources such as experts, literature, publications, current and best practices (in any format and from any origin), the Internet, intranets and extranets (Pretorius & Coetzee, 2006b). Refer to Table 2 for sources of knowledge acquisitions as suggested by other authors. The KBMS interacts with (and therefore contains direct links to) the management systems of the other subsystems (DBMS, MBMS and UIMS) of the proposed KMSS.

SRQ5: Demonstrating Aspects of Suitable KMSS In dealing with SRQ5, a partial prototype was developed and utilized to demonstrate selected

Model of a Knowledge Management Support system for Choosing Intellectual Capital Assessment Methods

Table 2. Sources of knowledge acquisition for intelligent systems (Derived from Coppin, 2004; Giarratano & Riley, 2005; Jackson, 1990; Turban, Aronson & Liang, 2005) Jackson

Coppin

Giarratano and Riley

Turban, Aronson and Liang

Experts Examples Texts

Domain experts Examples of problems and their solutions (“cases”)

Human experts The Web Books Other documents

Human Experts Books Films Databases (public and private) Pictures Maps Flow diagrams Sensors Songs Observed behavior The web Multimedia documents

aspects of a suitable KMSS. The prototype contributes by serving as a tool for content analysis to explore case studies and academic literature as sources of knowledge acquisition, casting light on the types of knowledge that can be acquired from secondary sources. The prototype further serves by illustrating the kind of functionality intended for systems of this kind and by exposing facets of actual IC assessment scenarios. By providing a concrete medium for visualizing and exploring issues involved in choosing IC assessment methods, the prototype contributes towards the efforts involved in addressing the other subsidiary research questions, e.g. iterative construction of conceptual design. The prototype employs case-based reasoning to learn from previous solutions of problems, in order to solve new problems. The appropriateness of IC assessment methods – and of specific customizations of such methods – depends on factors such as the eight contextual factors identified while addressing SRQ1. These factors, referred to as dimensions in the context of the prototype, are used to mark cases as similar in order to match appropriate solutions to old problems with new problem scenarios. After initial matching, the resultant set of possibly suitable IC assessment methods can be further explored by drilling into more detailed knowledge about these. Once a

particular method (or combination of methods) is selected, more matching can take place in order to assist with customization, e.g. with the selection of suitable indicators for scorecard methods such as the Skandia Navigator. Two types of secondary sources were explored, namely case study type journal articles and literature review type journal articles and papers. Selected case studies (as first secondary source of knowledge acquisition) were sourced from the Emerald database and literature reviews (as second secondary source of knowledge acquisition) from the Emerald database as well as from the Internet. The 13 case studies selected from the Emerald database provide a wealth of knowledge. Employing the prototype as tool for content analysis, it was found that, on average, the selected cases provide details for 73% of the contextual factors introduced earlier and for 58% of the other data types experimented with. Knowledge acquired from literature reviews includes descriptions of (IC assessment) methods, classifications of such methods into categories according to different classification schemes, suggestions on the spheres of relevance of methods or categories of methods, customization details, and advantages and disadvantages of categories of methods.

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FUTURE RESEARCH DIRECTIONS This research reports on the development of a model of a system – referred to as a KMSS – for supporting the decision making process concerning the choice of IC assessment methods (on the upper level part of the third row of the Zachman Framework). It is recommended that, building on the efforts of this research, more detailed design (on lower levels in terms of the Zachman Framework) and development of the proposed KMSS is performed. Thus, the sub-sections that follow, elaborate on future research possibilities, supporting such development of the proposed KMSS. These future research possibilities originate from insights gained during the research process. Furthermore, quite a number of these suggestions could be suitable as independent research projects.

SRQ2: Perceptions Concerning Complexity Through dealing with SRQ2, the following future research possibilities were identified: •

•

SRQ1: Existing Methods for Assessment of IC Through dealing with SRQ1, the following future research possibilities were identified: •

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A comprehensive set of IC assessment methods could be categorized according to various classification schemes. The results from such categorizations could feed into the database/knowledge base of the proposed KMSS. A systematic literature review, geared towards uncovering potential for synergism, could be performed to obtain a more inclusive list of possible ways of achieving synergism, and of possible combinations of methods that could yield such synergisms. The results of such a literature review could feed into the database, model base and/or knowledge base of the proposed KMSS.

The questionnaire could be placed on a website, inviting more candidates to respond. Furthermore, such a website could cater for ongoing responses to questionnaires, retrieving of results and commenting on results, and could, potentially, become a source of knowledge acquisition for the KMSS proposed in this research. The list of eight contextual factors provided in the questionnaire, could be extended to include more factors, e.g., as proposed by respondents to the questionnaire discussed in this research, as proposed by respondents to a questionnaire placed on a website, and/or by selection criteria proposed by the VMRC.

SRQ3: Contextual Determinants of KMSS Through dealing with SRQ3, the following future research possibilities were identified: •

•

Contextual determinants of alternative contexts for the proposed KMSS, e.g. academics using it as an analytical framework to investigate the problems, challenges, influencing factors and steps pertaining to the selection and customization of methods, could be explored, and the resulting contextual determinants could assist with tailoring/extending/generalizing the proposed KMSS to cater for such additional contexts. During lower-level design (in terms of the Zachman Framework) of the proposed

Model of a Knowledge Management Support system for Choosing Intellectual Capital Assessment Methods

KMSS, locations (physical or virtual) of potential knowledge brokers, suppliers and consumers, could be investigated in more detail.

SRQ4: Conceptual Design of KMSS Through dealing with SRQ4, the following future research possibilities were identified: •

•

•

Since proper functioning of the proposed KMSS depends on efficient and effective knowledge acquisition, a detailed investigation of the implications for conceptual design of cooperation, competition and coopetition – in respect of acquisition, diffusion and use of knowledge – is required. Possible incentives and mechanisms to enable and encourage sharing of knowledge, possibly between competitors, and the application thereof in the context of the proposed KMSS, could be researched. The model of a KMSS proposed in this research could be extended to explicitly contain elements of (or links to) a knowledge brokering system.

SRQ5: Demonstrating Aspects of Suitable KMSS

•

•

•

•

•

Through dealing with SRQ5, the following future research possibilities were identified: •

•

Additional sources of knowledge acquisition could be explored, e.g. case studies from conference proceedings, actual implementations of methods and strategies for assessment of IC, current and best practices, experts, other literature types, other Internet sources, intranets, extranets and databases. More electronic databases could be scanned for case studies reporting on aspects of the utilization of a specific method for assess-

•

ment of IC (for one or more individual organizations or for a set of organizations), to learn more about the types of knowledge that can be sourced from case studies and/ or to serve as source of knowledge acquisition for the proposed KMSS. Case studies could be scanned for additional factors or dimensions, e.g. as identified by the VMRC’s initiative. Occurrences of dimension-data captured by case studies could be analyzed to determine suitability for matching between existing IC assessment solutions and new problems. Occurrences and instances of more data types captured (from case studies) by the prototype KMSS, could be analyzed. The availability of indicators of success of existing IC assessment solutions from various knowledge sources (including case studies) could be analyzed and the objectivity and reliability of such indicators assessed. In order to be able to do this, prior consideration of how the success or failure of an IC assessment solution applied in a particular context, could/should be assessed, may be necessary. A larger selection of literature reviews could be explored as potential sources of knowledge acquisition (e.g. through using the prototype KMSS as tool for content analysis) in order to learn more about the types of knowledge that can be sourced from literature reviews and/or to serve as source of knowledge acquisition for the proposed KMSS. A more advanced version of the prototype with the following characteristics could be developed: ◦ Exhibiting extended functionality; ◦ Capable of handling multiple simultaneous users; ◦ Suitable for deployment on an opensource platform;

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◦

◦

Suitable for deployment over the Web (e.g. incorporating a browser interface); and/or Employing intelligent system (e.g. ES) technology.

CONCLUSION IC has no universal value. There is no single scale or method for assessing IC to be applied regardless of circumstance. Any worthwhile assessment of IC has to be exercised in relation to a specific known context. Therefore, to effectively choose an IC assessment method, suitable to a particular context, the chooser has to be knowledgeable – or have access to knowledge – on IC assessment methods and on the context in which the assessment solution is to be deployed. Existing literature proposes a multitude of methods for assessment of IC. This research argues that, due to complexities involved in choosing an appropriate method or combination of methods for assessment of IC, systems or mechanisms – referred to as KMSS’s – are needed for managing and applying the evolving body of knowledge concerning such assessment. The embedded assumption of complexity is supported by the results obtained from a survey employing a self-administered questionnaire as instrument for data collection. Having explored the levels and types of complexities involved in choosing IC assessment methods, contextual determinants are derived, a conceptual design produced through iterative cycles of modeling and evaluation, and a partial prototype developed and applied to demonstrate selected aspects of the proposed KMSS. The outcomes of this research are intended to guide more detailed design and development of a KMSS for choosing IC assessment methods.

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ADDITIONAL READING SECTION Dagmar Reclies. (2001a). The management consultancy industry – An analysis: Part 1 – Current state. Retrieved November 28, 2009, from http:// www.themanager.org/Resources /Consulting%20 Industry.htm Dagmar Reclies. (2001b). The management consultancy industry – An analysis: Part 2 – Future prospects. Retrieved November 28, 2009, from http://www.themanager.org/Resources/ Consulting%20Industry%20II.htm Dalkir, K. (2005). Knowledge management in theory and practice. Burlington, MA: Elsevier. Holsapple, C. W. (Ed.). (2003). Handbook on knowledge management 1: Knowledge matters. Berlin: Springer. Holsapple, C. W. (Ed.). (2003). Handbook on knowledge management 2: Knowledge directions. Berlin: Springer.

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Model of a Knowledge Management Support system for Choosing Intellectual Capital Assessment Methods

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KEY TERMS AND DEFINITIONS Intellectual Capital (IC): The aggregate intangible assets that contribute to the value and competitiveness of an organization, including, for example, human capital, customer capital and structural capital.

Assessment: In this chapter the term assessment is used as umbrella term to include measurement, (e)valuation and all other methods for determining value. Context: Assessment of IC can be required in various contexts. Various methods can be used for the assessment of IC. In this chapter context (of an IC assessment method) is interpreted as a vector comprised of factors determining appropriateness of IC assessment methods. Data: A collection of (raw) facts, measurements and/or statistics, about e.g. things, events, activities and transactions not organized to convey any particular meaning. Information: Data that is timely and organized in such a manner that it is more valuable that the (raw) facts. Knowledge: Information (or data) that is contextual, relevant and actionable, e.g. rules, guidelines and procedures for the manipulation of data or information. Model: Portrays the essential aspects of a system or process, ignoring unnecessary detail, and embedding assumptions about its context of application. Models can be employed in different contexts, on different levels and with different areas of focus. Knowledge Management Support System (KMSS): Management support system (MIS) that includes a mechanism for the management of knowledge.

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About the Contributors

Belén Vallejo-Alonso As a Lecturer in Financial Economics at The University of the Basque Country, her research activities are oriented towards the fields of financial management, portfolio management, firm valuation and the financial valuation of intangibles. She belongs to the Research Group in Financial Valuation of Intangibles at the University of the Basque Country and is Author or co-author of numerous articles in scientific magazines. She is Lecturer of the Master in Finance of The University of the Basque Country and is member of the European Academy of Management and Business Economics. She is the person in charge of the Master on Financial Advisor and Fund Management of The University of the Basque Country and the editor of the Journal Cuadernos de Gestión. Her doctoral research received the award for the Best dissertation in The University of the Basque Country, and is Member of the Evaluation committee of the European Academy of Management and Business Economics congress and Member of the Editorial Board and Evaluation Board of some scientific magazines. Arturo Rodriguez-Castellanos As a plain professor in Financial Economics at The University of the Basque Country, belongs to the Research Group in Financial Valuation of Intangibles at this University and is author or co-author of several books and numerous articles in scientific magazines. His research activities are oriented towards the fields of financial management and especially international financial management, R&D and knowledge management and its relation with finance, and the financial valuation of intangibles. He is Member of the Council of Innobasque, the Basque Innovation Agency, and Member of the Scientific Councils and the Evaluation Committees of numerous Spanish and International congresses. He is the person in charge of the Master on Business Management for Innovation and Internationalisation of The University of the Basque Country. Also he is actually Dean of the Faculty of Economics and Business of the University of the Basque Country. As Scientific editor is Member of the Editorial Board of various scientific magazines. He is also Member of the Spanish Royal Academy of Economics and Finance, vice-President of the European Academy of Management and Business Economics, Member of the Spanish Association of Accounting and Management and Member of the International Association for Fuzzy-set Management and Economy. Gerardo Arregui-Ayastuy As a Lecturer in Financial Economics at The University of the Basque Country, his research activities are oriented towards the fields of financial management, option valuation and the financial valuation of intangibles. He belongs to the Research Group in Financial Valuation of Intangibles at the University of the Basque Country and is Author or co-author of numerous articles in scientific magazines. He is Lecturer of the Master in Finance of The University of the Basque Country and is member of the European Academy of Management and Business Economics. He is Vicedean

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About the Contributors

of Business Relations in The University of the Basque Country, is the person in charge of the MBA Executive of The University of the Basque Country and is Member of the Evaluation committee of the European Academy of Management and Business Economics congress. *** Nekane Aramburu is PhD in Economics and Business Administration and member of the Strategic Management Department of the Faculty of Economics and Business Administration of the University of Deusto (San Sebastián, Spain). She is specialized in the fields of Organizational Learning, Change Management, and Business Organization. Her research focus is currently on Organizational Learning, Knowledge Management, and Innovation. Her research work has been published in several international journals, such as: The Learning Organization, Journal of Knowledge Management, Journal of Intellectual Capital, Management Research News. She has also published several book chapters in books edited by international publishers. Ettore Bolisani (Laurea “Electronic Engineering” – Padua University, Ph.D. “Innovation Studies”– Padua University), after being an E.U. “Marie Curie” research fellow at PREST (University of Manchester) and a researcher at the Universities of Trieste and Padua, is now Associate Professor at the Faculty of Engineering of the University of Padua. His research centres on ICT management and Knowledge Management. He has participated in various research projects funded by the E.U., Italian Institutions, and private organisations. He was Chair of the European Conference on Knowledge Management (University of Padua – 3-4 Sept. 2009) and Editor of “Building the Knowledge Society on the Internet. Sharing and Exchanging Knowledge in Networked Environment”–IGI Global, Hershey PA (2008). David Ceballos holds a Ph.D. in Economics and assistant lecturer at the Department of Economic, Financial and Actuarial Mathematics (University de Barcelona - Spain), he principally develops his scientific research in the field of Financial Valuation, applied to investments, environment and intangible assets. He has published several articles and he has participated in a big variety of specialised financial and economic congresses, seminars and research projects. He has made different research stages and he has contact with the Universidad de Cantabria (Spain), Universidad del Zulia (Venezuela), Universidad del Pacífico (Peru), Universidad Autónoma de Nuevo León (Mexico) and Cardiff University (United Kingdom). Enrique Claver-Cortés (PhD, University of Valencia, Spain) is a Professor and Head of the Department of Management at the University of Alicante, Spain. His primary research interests are strategic management, knowledge management, intellectual capital and international management. He has published research papers in international journals including Journal of Business Research, Journal of Knowledge Management, Journal of Intellectual Capital, Information & Management, Total Quality Management & Business Excellence, International Business Review, Asia Pacific Journal of Management, Emerging Markets Finance and Trade, Journal of General Management, Journal of Asia Business Studies, Revue Française de Gestion, Cross Cultural Management: An International Journal, International Journal of Contemporary Hospitality Management, Journal of Small Business and Enterprise Development, and International Journal of Knowledge Management Studies.

402

About the Contributors

Petrie Coetzee is professor extraordinaire at the Tshwane University of Technology. His working life covers from having performed and managed operations research and systems analysis in industry for some twenty years, and teaching computer science and business informatics at tertiary level for a further twenty years. He holds an honours degree in applied mathematics, an honours degree in business administration, a masters’ degree in theoretical computer science, a PhD in management of technology, and a postgraduate diploma in education. His research interests focus on knowledge systems - particularly on the interplay between innovation and standardization in socio-technical systems development. He has delivered and published many papers / articles. Kimiz Dalkir is currently an Associate Professor in the McGill School of Information Studies, where she developed and now coordinates the Knowledge Management stream. Her book, Knowledge Management in Theory and Practice, has been widely adopted by both the academic and practitioner communities. In 2006, she received the Faculty of Education Excellence in Teaching Prize. Her most recent research grant is to investigate how knowledge management can be applied to universities. Prior to joining McGill University, Dr. Dalkir was Director of Global KM Services at DMR Consulting where she was actively involved in knowledge transfer to clients around the world. Susanne Durst is a research assistant and lecturer at the Chair in International Management, Institute of Entrepreneurship at University of Liechtenstein. Before joining the University of Liechtenstein she worked in different positions with private enterprises of different industries. Her current research interests include the meaning of intangible assets in the SME succession process and other SME related topics such as strategic management and internationalisation. Susana Elena is a Scientific Fellow in the Knowledge for Growth Unit at the Institute for Prospective and Technological Studies (IPTS), a Joint Research Centre of the European Commission. She has been an Associate Professor, in the Department of Business Management (2003-2007), at the Pablo de Olavide University in Seville (Spain). She was a member of the PRIME Network of Excellence and has been involved in various competitive European and national research projects. She holds a PhD (European award) in Economics and Management of Innovation and Technology Policy from the Autonomous University of Madrid (focus on how to improve universities' internal management and governance using intellectual capital approaches) and was a visiting PhD student at SPRU (Science and Technology Policy Unit), Sussex University (UK). Her main research interests are intellectual capital, higher education institutions, management and governance of public organisations and science and technology policy. Jose Domingo García-Merino As a Lecturer in Financial Economics at The University of the Basque Country, he researches towards international financial management, the financial valuation of intangibles and e-learning and is author of numerous articles in scientific magazines. He is member of the Research Group in Financial Valuation of Intangibles at the University of the Basque Country. He participates in e-learning practices developed by the University of the Basque Country. He is member of the Evaluation Committee of several journals. G. Scott Erickson is Associate Professor, Department of Marketing/Law, in the School of Business at Ithaca College, Ithaca, NY. He holds a Ph.D. from Lehigh University, an MIM from Thunderbird and an MBA from SMU. He has published widely on intellectual capital, intellectual property, and competi-

403

About the Contributors

tive intelligence. His book with Helen Rothberg, From Knowledge to Intelligence: Creating Competitive Advantage in the Next Economy was published by Elsevier in 2005. Eduardo S. Fiol. Computer Programmer (1983), Information Systems Engineer (1994), MSc in Information Engineering (2001) and Postitle in Industrial Management (2001), both at Technical University Federico Santa Maria, Valparaiso. More than 25 years as University Professor, Manager, Consultant and Applications Developer. Broad experience in Programming, Databases, Strategic Planning and Knowledge Management. Sintesys Corporation associate researcher since 2006 and Project Management Institute member since 2007. Annie Green has a Doctor of Science from the School of Engineering and Applied Sciences (SEAS) at George Washington University and a Masters in Information Systems from the George Mason University. She is a part time faculty at George Washington University. Dr. Green has over 20 years of experience in Information Technology (IT), Knowledge Management (KM) and Intangible Asset Valuation in the private and public sector. She currently leads a major Knowledge Management/Business Intelligence effort for Keane Federal Systems for Pension Benefits Guaranty Corporation. Dr. Green has delivered presentations at professional seminars and international academic conferences on subjects such as systems engineering, requirements engineering, intangible asset valuation and knowledge management. She is the author of “Framework of Intangible Asset Valuation Areas: The Sources of Intangibles within an Organization” (2008), has published in journals such as the Journal of Intellectual Capital and Journal of Information and Knowledge Management Systems (VINE), as well as authored a chapter in the “Creating the Discipline of Knowledge Management” (2005) book. Dr. Green was the Knowledge Valuation Portfolio Editor for Vine Journal and is an Associate Fellow of the Institute of Knowledge and Innovation (IKI). Her research interests include systems engineering, knowledge management, and intangible asset valuation. Matti Koivuaho is a M.Sc. (Eng.) He currently works as Head of Portfolio Management in Tampere Power Utility. His research interests concern organizational knowledge flows and business information management. Harri Laihonen, Ph.D., works as a senior researcher in the Department of Business Information Management and Logistics, Tampere University of Technology, Finland. Recently his research has focused on the knowledge flows in health system management. Wider research interests lie in service productivity and the role of knowledge flows in knowledge-intensive service organizations. Leonardo P. Lavanderos. PhD in Biological Sciences, University of Chile (2000), Professor at the Catholic University of Chile (1996). Research Director of the Space Studies Center (Ex NASA Chile, 1998). Since 1995, Research Director of the Center for Studies in Relational Theory and Knowledge Systems (Sintesys), Chile. Currently using the relational theory framework for modeling decision processes. Research Interests: Integrated modeling of relational systems, theory of ecotomo, Decision Models in Complex Systems, Developing techniques for understanding, modeling, and predicting change in ecotomo systems.

404

About the Contributors

Susan McIntyre is the Knowledge Manager for Defence R&D Canada – Centre for Security Science where she works with whole-of-government initiatives in science and technology (S&T). Ms. McIntyre has a Master’s degree in Library Science and has worked in scientific information services, communications, management and policy before becoming immersed as a practitioner in knowledge management in 2000. Her current areas of interest are in meta-organizational learning, lessons learned processes for public security S&T and building communities from disparate sectors. Antonella Padova (Laurea “Computer Science” – Milan University), after being an IT consultant for ten years in Accenture on national and international projects and having covered the Chief Knowledge Officer role in several consulting organizations, is now part of global EY Knowledge, coordinating the knowledge team in Italy, Spain and Portugal and acting as the knowledge advisor for the Ernst & Young consulting community at the EMEIA level (Europe, Middle East, India and Africa). In this role Antonella is primarily responsible for the definition and implementation of KM strategies, processes and tools. She has participated as a speaker and testimonials in several KM events, taught several innovation master classrooms and co-edited KM books and co-led several KM researches and surveys in collaboration with relevant Italian and international universities. Steve Pike is Research Director at Intellectual Capital Services where he splits his time between researching innovation, intellectual capital measurement and assessment methodologies, value measurement methodologies and direct client interaction.He has worked, lectured and taught widely in the US, Europe and the Far East and has served on European Industry Management Associations and EU panels in a variety of capacities. Dr Pike has published numerous book chapters and is a regular author of academic and cased-based articles for peer-reviewed journals and is a regular contributor at international conferences. He won the 2005 Literati Club Awards for Excellence outstanding paper award for a paper on measuring intangibles in the Journal of Intellectual Capital. Agnieta Pretorius joined the Tshwane University of Technology, South Africa, as Junior Lecturer and is currently Academic Manager of the Information and Communication Technology Section at the eMalahleni campus. Prior to this she was a software developer. She holds a DTech degree from the Tshwane University of Technology and MBL, BSc (Hons) and BCom degrees from the University of South Africa. Her current domains of research and teaching include software engineering, decision support systems, knowledge management and assessment of intellectual capital. She delivered papers at international conferences in Ireland, Hungary, Chile, the Netherlands, Portugal, Canada and South Africa. Didac Ramirez holds a Ph.D. in Philosophy and in Economics from the Universitat de Barcelona (Spain). He is professor at the Department of Economic, Financial and Actuarial Mathematics of the University of Barcelona with a wide curriculum in articles and projects. He is the responsible of the research group IAFI. His principal research line is the analysis of the uncertainty, especially in financial operation. Helen N. Rothberg is Professor of Strategy in the School of Management at Marist College, Poughkeepsie, NY. She holds a Ph.D. and M. Phil. from CUNY Graduate Center and an MBA from Baruch College, CUNY. She has published in numerous academic and practitioner journals on competitive intel-

405

About the Contributors

ligence, knowledge management, and shadow teams. Her book with Scott Erickson, From Knowledge to Intelligence: Creating Competitive Advantage in the Next Economy was published by Elsevier in 2005. Göran Roos is Honorary Professor at Warwick Business School in the UK, Visiting Professor of Innovation Management and Business Model Innovation at VTT Technical Research Centre of Finland, Visiting Professor of Intangible Asset Management and Performance Measurement at the Centre for Business Performance at Cranfield University and Visiting Faculty at Helsinki School of Economics Executive Education an both Helsinki and Singapore. Göran is one of the founders of modern intellectual capital science and a recognised world expert in this field and a major contributor to the thinking and practice in the areas of strategy and innovation management. Göran is the author and co-author of over one hundred books, book chapters, papers and articles on Intellectual Capital, Innovation Management and Strategy many of which have been recognised with awards. Christiaan D. Stam is Associate Professor at the Centre for Research in Intellectual Capital at INHolland University of Applied Sciences. Central themes in his work are knowledge management, intellectual capital measurement and knowledge productivity. The latter topic was the subject of his Ph.D. thesis (2007), which was a joint initiative of INHOLLAND University and de Baak - Management Centre of the Dutch Federation of Industries. Before becoming a scholar, he was a consultant in the fields of knowledge management and intellectual capital measurement. He wrote several books and many articles. Josune Sáenz is PhD in Economics and Business Administration and member of the Finance and Accounting Department of the Faculty of Economics and Business Administration of the University of Deusto (San Sebastián, Spain). She is also head of the Innovation chair sponsored by BBVA at Deusto Business School. She specializes in Management Accounting and Strategic Management Control. Her research focus is currently on Innovation, Intellectual Capital, and Knowledge Management. Her research work has been published in several international journals, such as: Journal of Intellectual Capital, the Journal of Knowledge Management, The Learning Organization, and the International Journal of Learning and Intellectual Capital. Enrico Scarso is Associate Professor of Engineering Management at the Department of Management and Engineering, University of Padua (Italy). He received his Ph.D. degree in Industrial Innovation from the University of Padua. His current research interests are in the area of technology and knowledge management, with a particular focus on the role of knowledge-intensive business services in local innovation systems. He has published in Technovation, Int. Journal of Technology Management, Int. Journal of Electronic Commerce, Journal of Knowledge Management, Management Decision, Int. Journal of Operations & Production Management and has presented various papers at international conferences. He is member of IAMOT (International Association for Management of Technology) and IEEE. Campbell Warden is an accountant, translator and international research administrator. He did a Master in Conference Interpretation (La Laguna University - 1992) and an MBA (UK’s Open University Business School, 1999-2002). He served as the President of the European Association of Research Managers and Administrators (2000-2002). He worked as a Detached National Expert (on behalf of Spain) in the European Commission (at DG RTD) between 1998 and 2001. He currently serves as an

406

About the Contributors

advisor on Research Infrastructure policy for the Government of one of the new EU Member States and as an external expert, evaluator and trainer to UNESCO, UNIDO, DG-RTD & DG-ENTR. He has organised and participated in management training courses for over 15 years, especially in the fields of International Scientific Collaboration, Gender Equality, Intellectual Capital and Technology Foresight. He was a member of the High Level Expert Groups that produced the reports on “Women in Industrial Research” and “Reporting Intellectual Capital to Augment Research, Development and Innovation in SMEs” for the European Commission. He has been employed by the Instituto de Astrofisica de Canarias (Tenerife, Spain) since 1983 and is currently the Executive Secretary. Patrocinio Zaragoza-Sáez (PhD, University of Alicante, Spain) is an Associate Professor at the Department of Management at the University of Alicante, Spain. Her primary research interests include several topics of knowledge management, intellectual capital and international management, such as knowledge creation and transfer in multinationals and the knowledge management process. She has published research papers in international journals including Journal of Business Research, Business Strategy and the Environment, Journal of Knowledge Management, Journal of Intellectual Capital and International Journal of Knowledge Management Studies.

407

408

Index

A Activity based costing (ABC) 274 asset (AA) 298, 303, 304, 306 Austrian Institute of Technology (AIT) 182, 184, 185, 186 Austrian Research Center (ARC) 185, 186, 200, 202, 206

B balanced scorecard (BSC) 95, 96, 97 Bank of International Settlements (BIS) 295 business intelligence (BI) 248 Business Reasoning, Analytics and Intelligence Network (BRAIN) 232, 233, 234, 235, 236, 238, 239, 241, 242, 243, 244, 245, 247, 248 buy-in initiatives 72, 73, 80, 84 buy-ins 64, 65, 68, 69, 70, 72, 73, 74, 75, 76, 77, 79, 80, 83, 84 buy-out initiatives 64, 69, 70, 71, 73, 75, 76, 77, 78, 79, 80, 85 buy-outs 69

C Calculated Intangible Value (CIV) 274 call-center 57 Capability 111 capacity (CC) 298, 303, 304, 306 case study 45, 47, 49, 59, 60, 61, 63 Center for Business Knowledge (CBK) 216, 217, 223 Chemical, Biological, Radiological, Nuclear (CBRN) 157, 163, 164, 166, 167, 169, 170

Chemical Research and Technology Initiative (CRTI) 156, 157, 158, 159, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 176 Chief Knowledge Officer (CKO) 49 Cognitive Map 121, 123, 124 Cognitive Sciences 115, 132, Cognitive Type Affinity (NCA) 122 Coherence 126, 129, 130, 132 Communities of Practices (CoPs) 53 company succession 64, 65, 66, 67, 68, 69, 72, 75, 76, 77, 78, 80, 84 Competitive Advantage 90, 111, 311 Competitive Intelligence (CI) 255, 257, 258, 259, 260, 261, 262, 263, 267 Comprehensive Intellectual Capital Model (CICM) 161 computer integrated manufacturing (CIM) 10 Congruity 132, Connective Type (CT) 122 contextual determinants 338, 345, 346, 352, 354 Core Competencies 335 Corporate Curriculum 135, 137, 138, 139, 140, 143, 144, 145, 147, 149, 151, 153, 154 Cost based methods (CM) 299, 305, 306 creative economy 133

D Dashboard 218, 230 database management system (DBMS) 347, 348, 350 decisional modeling 112, 120, 126 decision support systems (DSS) 341, 347 Derivative Corporate Foundation 85

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Index

destructive chaos 140 Direct Intellectual Capital Methods (DIC) 272, 273 Direct methods (DM) 299, 305, 306 dynamic capabilities 9, 10, 21, 45, 46, 87, 88, 89, 90, 91, 98, 111 dynamic characteristic 2 dynamic model 17, 18, 21

G

E

H

economic-value-added analysis (EVA) 239 empirical illustration 22 empirical research 90 Engineering-Business Reasoning, Analytics and Intelligence Network (E-BRAIN). 232, 235, 236, 238, 239, 241, 242, 244, 245, 247, 248 ERAWATCH 180, 201 European Association of Research Managers and Administrators (EARMA) 187, 188 European Center for the Strategic Management of Universities (ESMU) 187 European Higher Educational (EHEA) 180 European Research Area (ERA) 180 executive information systems (EIS) 341 expert systems (ES) 341, 347, 349, 350, 354 external structure 4, 5, 6 external succession 64, 68, 85 external successor 64, 65, 67, 68, 80

health services 24 HERO 188 Higher Education Institutions (HEIs) 178, 179, 181, 183, 185, 187, 193, 194, 195 human capital (HC) 2, 5, 7, 8, 12, 15, 16, 17, 18, 21, 24, 25, 28, 32, 33, 35, 37, 43, 50, 62, 66, 67, 68, 92, 95, 97, 98, 100, 101, 105, 106, 109, 111, 255, 258, 298, 303, 304, 306 human resources 24, 92, 96

F family succession 64, 68, 80 Financial methods (FM) 299, 305, 306 Financial Risk 296, 303, 304, 305, 308 Financial Valuation of Intangibles 309, 313, 314, 335 five forces framework 90 Framework of Intangible Valuation Areas (FIVA) 232, 233, 248 Full Time Equivalent (FTE) 220 fuzzy concept 1, 12, 15 fuzzy front end 94, 95

German Aerospace Research Center and Space Agency (DRL) 187 globalization 65 Global Knowledge Survey (GKS) 222 Goodwill (GW) 298, 303, 304, 306 group support systems (GSS) 341

I imperfectly imitable 90 Income based methods (IM) 299, 305, 306 Industrial Research Institute (IRI) 93, 94, 95 information and communication technologies 47, 59 information economy 133, 152 information society 133 Information Society Technologies (IST) 272 information technology 65 information technology (IT) 219, 222, 223, 227, 254, 255 innovation 87, 89, 93, 103, 104, 105, 106, 107, 110, 111, 133, 136, 137, 140, 141, 142, 143, 144, 145, 147, 149, 151, 153, 154, 155 Innovation and Knowledge Management Institute (INGENIO) 185, 187, 201 innovation capability 88, 93, 94, 95, 98, 102, 103, 105, 106 innovation imperative 87 Innovation Management 175

409

Index

innovation measurement 88, 93, 94, 95, 96, 97, 105 innovation performance 87, 88, 98, 104, 105, 106, 108 innovation projects 88, 98, 101, 102 innovation system 88 innovation-value path 88, 98, 99, 105, 106 Institutional Autonomy 205 intangible assets (IA) 24, 64, 65, 66, 67, 68, 70, 79, 80, 82, 84 intangible economy 133 intangible resources 88, 90, 91, 92, 93, 96, 97, 98, 100, 101, 110, 111 Intangible Resources 311 intangibles 65, 66, 67, 68, 69, 70, 79, 80, 82, 84, 132, , 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 313, 314, 316, 318, 324, 326, 328, 330, 331, 333, 335 integrators 1, 2, 3, 8, 9, 10, 11, 12, 14, 16, 17, 18, 21, Intellectual capital dynamics 21 intellectual capital (IC) 1-13, 15-25, 27, 28, 30, 31, 33, 35-41, 43, 44, 45, 46, 47, 48, 49, 52, 55-63, 88, 91, 92, 93, 96, 97, 98, 101, 102, 106-112, 115-119, 130-134, 155, 160, 161, 162, 165, 171, 175, 177-205, 207, 211, 227, 254-259, 263, 267-275, 278-283, 287, 288, 290-293, 329, 331, 333, 335-354, 359 Intellectual Capital Report Act (ICRA) 7, 20 Intellectual Capital Statement (ICR) 178, 180, 181, 182, 183, 184, 185, 186, 187, 196, 197, 198, 199, 206 Intellectual Property (IP) 188, 203 Intellectual Property Rights (IPR) 188 intelligence data base (IDB) 241, 242 interdependence 2, 8, 9, 10, 12, 18, 21, internal structure 4, 5, 6 International Financial Reporting Standards (IFRS) 295, 308 investor market 67 IQ (intelligence quotient) 14, 15

K Key Players 132

410

knoware tree 6, 21 Know-how (KH) 298, 303, 304, 306 knowhow society 133 Knowledge 49, 50, 52, 55, 56, 62, 63, , 118, 128, 129, 132, knowledge acquisition 44, 45, 46, 52, 54, 57, 58, 59, 63, knowledge assets 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 42 knowledge-based management subsystem (KbMS) 347, 350 knowledge-based performance 136, 142 knowledge-based view (KBV) 287 knowledge capital 91 knowledge creation 133, 135, 136, 137, 140, 141, 143, 144, 146, 148, 149, 151, 154 knowledge economy 133, 134, 135, 139, 142, 148, 149, 150, 151, 153, 154 knowledge flow audit 22, 23, 24, 27, 28, 29, 30, 31, 34, 35, 36, 37, 39, 42 knowledge flows 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42 knowledge flow survey 22, 30, 31, 32, 33, 35, 36, 37, 42 knowledge-intensive firms 43, 44, 57 knowledge management (KM) 43-46, 48, 52, 55-60, 63, 135-154, 156-168, 170, 172176, 208-230, 254-263, 265, 267 knowledge management performance index (KMPI) 213, 226, 229 knowledge management support systems (KMSS) 337, 338, 340, 341, 342, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354 knowledge management systems (KMS) 341, 347, 350 knowledge productivity (KP) 133, 134, 135, 136, 137, 140, 142, 143, 144, 145, 146, 147, 148, 149, 151, 152, 154 knowledge sharing 25, 26, 28, 30, 31, 33, 34, 35, 38 knowledge society 133, 134 knowledge transfer 23, 25, 26, 32, 33, 34, 35, 36, 37, 39, 40, 41, 58, 61, 63, knowledge utilization 28

Index

Knowledge Web (KWeb) 216, 217 knowledge-worker productivity 135 KP-enhancer 144, 145, 146, 149

L learning economy 133 learning functions 137, 138, 139, 140, 142, 143, 144, 145, 147, 149, 151 learning society 133

M management information systems (MIS) 341, 359 management support systems (MSS) 337, 341 Market based methods (KM) 299 Market capitalisation methods (MM) 299, 305, 306 Market Capitalization Methods (MCM) 272 measurement system 87, 88, 94, 99, 100, 104, 105, 106 Measurement Systems (MS) 273 MERITUM 272, 291 model base management system (MBMS) 348, 350 Most Admired Knowledge Enterprise (MAKE) 216

N Netout 112, 129, 130 Network Cognitive Affinity (NCA) 120, 122, 123, 125 Network Interactivity Affinity (NIA) 120, 124, 125, 126, 127 Network Semiotic Affinity (NSA) 120, 123, 124, 125 network society 133, 150 neural networks 341 NEUS method 115, 120, 123, 126 non-family succession 64, 68 non-family successors 65, 67 Non-financial methods (NM) 299, 305, 306

O

online analytical processing (OLAP) 341 operational capabilities 9 organizational capability 91 organizational capital 92, 93, 95, 97 organizational structure 2, 10 original corporate foundation 67, 85

P Pablo de Olavide University (UPO) 194 Patent (PA) 298, 303, 304, 306 peace and stability 139, 140, 147 PRISM 272, 289 Production Cognitive Capital (PCC) 112, 115, 117, 119, 120, 122, 123, 125, 126, 129, 130, 131, 132 Productive Cognitive Capital 130

R R&D 158, 160, 162, 163, 173, 176, 187, 188, 189, 202, 257, 275, 285, 288, 289, 290, 315, 317, 326, 330, 332, 334, 335 Recursion Degree (RD) 122, 128 Relational Approach 132, relational capital (RC) 2, 5, 6, 7, 8, 15, 16, 17, 18, 21, 44, 45, 46, 48, 50, 51, 57, 58, 59, 60, 63, , 66, 68, 93, 95, 97, 98, 102, 105, 111, 255, 256, 258, 264, 267 Relational Organization Approach (ROA) 114 Reputation (RE) 298, 303, 304, 306 residual operating income (REOI) 274 Resource 106, 111 resource based accounting (RBA) 274 Resource-Based Theory 311, 312, 324 Resource-Based View 111 Results-based Management Accountability Framework (RMAF) 157, 164, 165, 166, 167, 168, 169, 170, 171, 172 Return on assets methods (RM) 299, 305, 306 Return on Assets Methods (ROA) 272, 274, 299, 316, 318, 321, 322, 323, 325 return on investment (ROI) 212, 223, 246 R&I (Research and Innovation) 115, 126 ROE 316, 318, 322, 323, 325

Observatory of the European University (OEU) 189, 191, 193, 194, 202, 206

411

Index

S

T

science and technology (S&T) 156, 157, 158, 159, 161, 162, 167, 169, 170 Scorecard Methods (SC) 273 Scorecard methods (SM) 299, 305, 306 SECI model 26 Service Level Agreement (SLA) 217 small and medium-sized enterprises (SMEs) 65, 66, 67, 68, 69, 72, 75, 77, 80, 81, 82, 83, 84 social capital 46, 61, 92 Society of Competitive Intelligence Professionals (SCIP) 258, 259, 260, 261, 262, 263, 264, 267 Spanish Research Council (CSIC) 187 stakeholder knoware 6 stakeholders 43, 44, 46, 48, 55, 56, 57, 58, 59 Standard rules (SR) 299, 305, 306 static models 21, Statistical methods (ST) 299, 305, 306 strategic ambiguity 140 strategic confusion 140 strategic disorder 140 strategic distance 140 strategic imbalance 140 Strategic Protection Factor (SPF) 257, 258, 259, 260, 261, 267 structural capital (SC) 2, 5, 6, 7, 8, 15, 16, 17, 18, 21, 24, 25, 28, 31, 33, 34, 35, 43, 46, 50, 66, 68, 92, 93, 98, 101, 102, 105, 111, 255, 258 structural knoware 6 Sustainability 112, 132, synergy 2, 3, 6, 7, 8, 9, 10, 11, 12, 18, 21, systemic risk 295, 297, 300, 301, 302, 303, 304, 305, 307

tangible assets 1, 2, 6 tangible economy 113, 115 Targeted Socio-Economic Research (TSER) 272, 291 tautology 134 technology value pyramid 94, 95 time variable 1, 2 Trademark (TM) 298, 303, 304, 306 triangulation technique 48

412

U Uncertainty 132 UNIKNOW 189 University of Madrid (UAM) 194 user interface management system (UIMS) 347, 348, 349, 350

V Valuation Method 308 Value 87, 90, 110, 111 Value-Added Intellectual Coefficient (VAIC) 258 value creation 43, 44, 45, 47, 48, 57, 58, 59 value creation map 97 Value, Rareness, Inimitable, Non-substituible (VRIN) 311 VIMaK 188 vision 9, 10, 11, 12, 21,

Z Zachman Framework 340, 346, 347, 352, 355, 357 zero-profit condition 89