Innovative Knowledge Management: Concepts for Organizational Creativity and Collaborative Design (Premier Reference Source)

Innovative Knowledge Management:

Concepts for Organizational Creativity and Collaborative Design Alan Eardley Staffordshire University, UK Lorna Uden Staffordshire University, UK

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Published in the United States of America by Information 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 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 Innovative knowledge management : concepts for organizational creativity and collaborative design / Alan Eardley and Lorna Uden, editors. p. cm. Includes bibliographical references and index. Summary: "This book provides a valuable resource for promoting current academic discourse on innovation in knowledge-intensive organizations and contexts"--Provided by publisher. ISBN 978-1-60566-701-0 (hardcover) -- ISBN 978-1-60566-702-7 (ebook) 1. Knowledge management. 2. Creative ability in business. 3. Technological innovations. I. Eardley, Alan. II. Uden, Lorna, 1946HD30.2.I5365 2011 658.4'038--dc22 2010041635

British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher.

Table of Contents

Foreword . ..........................................................................................................................................xvii Preface . ................................................................................................................................................ xx Acknowledgment............................................................................................................................. xxxvi Section 1 Knowledge Management and Innovation Chapter 1 Universities as Knowledge-Intensive Learning Organizations................................................................ 1 Constantin Bratianu, Academy of Economic Studies, Romania Chapter 2 Key Characteristics Relevant for Selecting Knowledge Management Software Tools......................... 18 Hanlie Smuts, University of South Africa and Mobile Telephone Networks (Pty) Ltd, South Africa Alta van der Merwe, University of South Africa and Meraka Institute, CSIR, South Africa Marianne Loock, University of South Africa, South Africa Chapter 3 Knowledge-Based Diffusion in Practice: A Case Study Experience..................................................... 40 Hilary Berger, University of Wales Institute Cardiff, UK Paul Beynon-Davies, Cardiff University, UK Chapter 4 Deploying Knowledge Management in R&D Workspaces................................................................... 56 Won-Chen Chang, National Cheng Kung University, Taiwan Sheng-Tung Li, National Cheng Kung University, Taiwan Chapter 5 Innovation in New Technology and Knowledge Management: Comparative Case Studies of its Evolution during a Quarter Century of Change..................................................................................... 77 Sean Tung-Xiung Wu, Shih Hsin University, Taiwan

Section 2 Applications of Knowledge Management Chapter 6 A Survey of Epistemology and its Implications for an Organizational Information and Knowledge Management Model............................................................................................................................... 95 Ah-Lian Kor, Leeds Metropolitan University, UK Graham Orange, Leeds Metropolitan University, UKa Chapter 7 An Ontology-Based Expert System for Financial Statements Analysis.............................................. 125 Li-Yen Shue, National Kaoshiung First University of Science and Technology, Taiwan Ching-Wen Chen, National Kaoshiung First University of Science and Technology, Taiwan Chao-Hen Hsueh, National Kaoshiung First University of Science and Technology, Taiwan Chapter 8 Knowledge Democracy as the New Mantra in Product Innovation: A Framework of Processes and Competencies........................................................................................................... 141 Angelo Corallo, University of Salento, Italy Marco De Maggio, University of Salento, Italy Alessandro Margherita, University of Salento, Italy Chapter 9 Knowledge Management under Institutional Pressures: The Case of the Smartcard in France.......... 157 Rémy Magnier-Watanabe, University of Tsukuba, Japan Dai Senoo, Tokyo Institute of Technology, Japan Chapter 10 Does Knowledge Management Really Work? A Case Study in the Breast Cancer Screening Domain................................................................................................................................ 177 V. Baskaran, Ryerson University, Canada R.N.G. Naguib, Coventry University, UK A. Guergachi, Ryerson University, Canada R.K. Bali, Coventry University, UK H. Arochen, Coventry University, UK Chapter 11 Knowledge Management: The Key to Delivering Superior Healthcare Solutions.............................. 190 Nilmini Wickramasinghe, RMIT University, Australia Chapter 12 The Use of ‘Web 2.0’ and Social Software in Support of Professional Learning Communities......... 204 Alan Eardley, Staffordshire University, UK Lorna Uden, Staffordshire University, UK

Chapter 13 Knowledge Sharing in the Learning Process: Experience with Problem-Based Learning.................. 215 Lorna Uden, Staffordshire University, UK Alan Eardley, Staffordshire University, UK Chapter 14 Culturally-Bound Innovation in Romanian Teaching and Research Hospitals................................... 230 Mihaela Cornelia Dan, Academy of Economic Studies, Romania Simona Vasilache, Academy of Economic Studies, Romania Alina Mihaela Dima, Academy of Economic Studies, Romania Section 3 Creativity and Collaboration in Organizations Chapter 15 Exploiting KM in Support of Innovation and Change......................................................................... 242 Peter A.C. Smith, The Leadership Alliance Inc., UK Elayne Coakes, University of Westminster, UK Chapter 16 Knowledge Management Profile: An Innovative Approach to Map Knowledge Management Practice........................................................................................................................... 253 Zoltán Gaál, University of Pannonia, Hungary Lajos Szabó, University of Pannonia, Hungary Nóra Obermayer-Kovács, University of Pannonia, Hungary Zoltán Kovács, University of Pannonia, Hungary Anikó Csepregi, University of Pannonia, Hungary Chapter 17 Recognizing Innovation through Social Network Analysis: The Case of the Virtual eBMS Project....................................................................................................................................... 264 Grippa Francesca, University of Salento, Italy Elia Gianluca, University of Salento, Italy Chapter 18 Complexity and Clarity: The Knowledge Strategy Dilemma – Some Help from MaKE................... 286 Peter Sharp, Regents Business School London, UK Alan Eardley, Staffordshire University, UK Hanifa Shah, Staffordshire University, UK

Chapter 19 Knowledge Management and Innovation............................................................................................ 300 Lorna Uden, Staffordshire University, UK Marja Naaranoja, Vaasa University of Applied Sciences, Finland Chapter 20 Holonic Management: Innovation and Creative Entrepreneurship..................................................... 319 Akira Kamoshida, Tokyo Institute of Technology & Nagoya University of Commerce and Business, Japan Compilation of References ............................................................................................................... 334 About the Contributors .................................................................................................................... 375 Index.................................................................................................................................................... 384

Detailed Table of Contents

Foreword . ..........................................................................................................................................xvii Preface . ................................................................................................................................................ xx Acknowledgment............................................................................................................................. xxxvi Section 1 Knowledge Management and Innovation Chapter 1 Universities as Knowledge-Intensive Learning Organizations................................................................ 1 Constantin Bratianu, Academy of Economic Studies, Romania The purpose of this chapter is to critically analyze the universities as knowledge intensive learning organizations. It is axiomatic that universities are knowledge organizations since by their own nature universities create, acquire, and transfer knowledge in complex ways. They are knowledge intensive organizations since the density of knowledge field and the dynamics of knowledge processing are much greater than many other organizations. Since learning is one of the major processes within any university, people may consider universities as being by definition learning organizations. This idea induced by a semantic halo effect may lead to a major error. Although a university is an organization based on learning processes, it is not necessary a learning organization. This paper performs a functional analysis of the specific knowledge processes in order to identify the necessary conditions for a generic university to become a learning organization. Chapter 2 Key Characteristics Relevant for Selecting Knowledge Management Software Tools......................... 18 Hanlie Smuts, University of South Africa and Mobile Telephone Networks (Pty) Ltd, South Africa Alta van der Merwe, University of South Africa and Meraka Institute, CSIR, South Africa Marianne Loock, University of South Africa, South Africa The shift to innovation and knowledge as the primary source of value results in the new economy being led by those who manage knowledge effectively. Today’s organizations are creating and leveraging

knowledge, data, and information at an unprecedented pace—a phenomenon that makes the use of technology not an option, but a necessity. Software tools in knowledge management (KM) are a collection of technologies and are not necessarily acquired as a single software solution. Furthermore, these KM software tools have the advantage of using the organization’s existing information technology infrastructure. Organizations and business decision makers spend a great deal of resources and make significant investments in the latest technology, systems, and infrastructure to support KM. It is imperative that these investments are validated properly, made wisely, and that the most appropriate technologies and software tools are selected or combined to facilitate KM, knowledge creation, and continuous innovation. In this chapter, a set of characteristics are proposed that should support decision makers in the selection of software tools for knowledge creation. These characteristics were derived from both in-depth interviews and existing theory in publications. Chapter 3 Knowledge-Based Diffusion in Practice: A Case Study Experience..................................................... 40 Hilary Berger, University of Wales Institute Cardiff, UK Paul Beynon-Davies, Cardiff University, UK This chapter uses a case study to consider how development methods shape information systems practice and how organizations adapt, deploy, and use such knowledge in situ. The authors explore how an information system development method (ISDM) acting as a de-contextualized “knowledge bundle” is diffused and infused within an organization through the process of contextualization. The case study looks at a regional government project responsible for the distribution of European Community (EC) monies through agricultural grants and subsidies. A new IT/IS system was designed and developed to improve the administration and management of the EC’s agricultural policy across the region. A longitudinal research project was conducted over three years and was situated within the project environment. It involved a sustained period of fieldwork (nine months of intensive observations), and data was collected through 126 semi-structured interviews, shadowing of key participants, and informal discussions and conversations. Secondary data involved an in-depth and systematic analysis of published literature, project documentation, and artifacts. The authors consider how the structure and culture of organizations affect implementation and processes of diffusion and infusion. Chapter 4 Deploying Knowledge Management in R&D Workspaces................................................................... 56 Won-Chen Chang, National Cheng Kung University, Taiwan Sheng-Tung Li, National Cheng Kung University, Taiwan The active and effective management of valuable knowledge is widely believed to be a core competency for solidifying the competitive advantage of an organization. Whether knowledge management (KM) is a new idea or just a recycled concept per se both managerial and academic campuses have sought a vast array of KM strategies, solutions, frameworks, processes, barriers and enablers, IT tools and measurements over the past decade. Although there are many KM studies for both public and private sectors, most of them focus on the practice of international companies and western experiences, relatively few cases are reported on KM deployment and implementation in the Chinese community, especially for knowledge intensive research and development (R&D) institutes whose missions are to

serve traditional industries. To reveal some of the accomplishments gained in the Asia-Pacific region, this chapter presents and discusses the lessons learned from a particular case study in fostering the KM initiative and system in a research-oriented institute serving the metal industry. Chapter 5 Innovation in New Technology and Knowledge Management: Comparative Case Studies of its Evolution during a Quarter Century of Change..................................................................................... 77 Sean Tung-Xiung Wu, Shih Hsin University, Taiwan The research on which this chapter is based monitors the evolution of IT innovations and their effect on human emotions, including longitudinal influential factors, and examines some of the resulting syndromes, which are termed Computer Fear Syndrome (CFS) and User Alienation Syndrome (UAS). The research involves an analysis of the empirical data derived from several case studies and concludes with a funnel model that explains appropriate management action and puts forward new ideas for developing knowledge management systems in a variety of organizations that may alleviate or prevent such syndromes in the work place. Section 2 Applications of Knowledge Management Chapter 6 A Survey of Epistemology and its Implications for an Organizational Information and Knowledge Management Model............................................................................................................................... 95 Ah-Lian Kor, Leeds Metropolitan University, UK Graham Orange, Leeds Metropolitan University, UKa This is a theoretical chapter which aims to integrate various epistemologies from the philosophical, knowledge management, cognitive science, and educational perspectives. From a survey of knowledge-related literature, this chapter collates diverse views of knowledge. This is followed by categorising as well as ascribing attributes (effability, codifiability, perceptual/conceptual, social/personal) to the different types of knowledge. The authors develop a novel Organisational Information and Knowledge Management Model which seeks to clarify the distinctions between information and knowledge by introducing novel information and knowledge conversions (information-nothing, information-information, information-knowledge, knowledge-information, knowledge-knowledge) and providing mechanisms for individual knowledge creation and information sharing (between individual-individual, individual-group, group-group) as well as Communities of Practice within an organisation. Chapter 7 An Ontology-Based Expert System for Financial Statements Analysis.............................................. 125 Li-Yen Shue, National Kaoshiung First University of Science and Technology, Taiwan Ching-Wen Chen, National Kaoshiung First University of Science and Technology, Taiwan Chao-Hen Hsueh, National Kaoshiung First University of Science and Technology, Taiwan

Financial statements provide the main source of information for all parties who are interested in the performance of a company, including its managers, creditors, and equity investors. Although each of these parties may have different perspectives when viewing financial statements, all parties are concerned with the financial quality of an enterprise, which requires carefully analyzing financial statements to estimate and predict future conditions and performance. When analyzing financial statements, due to the complexity of the task, even professional analysts may be subject to constraints of subjective views, physical and mental fatigue, or possible environmental factors, and are not able to provide consistent appraisals. As a result, researchers and practitioners have resorted to expert systems to imitate the decision processes and inferencing logics of financial experts. Chapter 8 Knowledge Democracy as the New Mantra in Product Innovation: A Framework of Processes and Competencies........................................................................................................... 141 Angelo Corallo, University of Salento, Italy Marco De Maggio, University of Salento, Italy Alessandro Margherita, University of Salento, Italy In this chapter we carry out a critical analysis of “knowledge democracy” as a new mantra or buzz-word in product innovation leadership. A new paradigm has revolutionized the traditional process of invention, which was previously associated with a hierarchical dissemination of new ideas and competitive hoarding of knowledge assets. This chapter contends that at this environment has been replaced by a collaboration economy (based on so-called “wikinomics”) in which democracy governs the process of knowledge creation and its strategic application. Leadership in product innovation does not rely on the innate internal qualities of organizations, but on the collaborative contribution of stakeholders in many of the activities that make up the NPD lifecycle. The authors suggest a new approach to mitigate factors that can otherwise reduce the value of the NPD process. The chapter then examines how to promote such open collaboration through the development of a new managerial mindset, the acquisition of new distinctive competences, the development of new organizational models, and the management of new collaborative technologies. The authors’ proposed framework of processes and competencies offers the potential for organizations to meet these needs. Chapter 9 Knowledge Management under Institutional Pressures: The Case of the Smartcard in France.......... 157 Rémy Magnier-Watanabe, University of Tsukuba, Japan Dai Senoo, Tokyo Institute of Technology, Japan This chapter explores how knowledge management, an enabler of change due to its knowledge creation capability, is subject to several forces that shape its processes and outcomes. A qualitative analysis based on data from a case study of the first major rollout of smartcard technology in France shows how institutional isomorphic pressures affect not only knowledge management processes but also resulting innovations. Government impetus, legal authorities, and cultural expectations in French society produced coercive isomorphic pressures on the credit card industry, while existing credit card solutions, systems, and standards played the role of mimetic pressures, and professional networks and network externalities acted as normative pressures. The study suggests that a systems perspective which ac-

knowledges these institutional isomorphic pressures can lead to greater strategic alignment and can provide a basis for meaningful differentiation and competitive advantage. Chapter 10 Does Knowledge Management Really Work? A Case Study in the Breast Cancer Screening Domain................................................................................................................................ 177 V. Baskaran, Ryerson University, Canada R.N.G. Naguib, Coventry University, UK A. Guergachi, Ryerson University, Canada R.K. Bali, Coventry University, UK H. Arochen, Coventry University, UK Contemporary organizations, including those involved with healthcare, are constantly under pressure to produce and implement new strategies for delivering better products and/or services. Knowledge Management (KM) has been one of the paradigms successfully applied in such business environs. However, a lack of proper application of KM principles and its components have reduced the confidence of new adopters of this paradigm. KM-based healthcare projects are moving forward, and innovation is the driving force behind such initiatives. This chapter sets the scene by outlining the KM’s core elements, facets and how they can be appropriately applied within an innovative, real-time healthcare project. It further enumerates a case study which targets the screening attendance issue for the NHS’ breast screening program. The case study not only discusses the need of a balanced approach to address both the technological and humanistic aspects of KM, but also answers the question “Does knowledge management really work?” A questionnaire-based study was conducted with the General Physicians (GPs) on the KM’s aspects and its relationship to the interventions proposed in the study. The study provided ample proof that a balanced approach will definitely increase the efficacy of such initiatives. Such studies can increase the confidence of future KM adopters in healthcare domain. This chapter provides credibility for such balanced KM-based initiatives and highlights the importance of a focused approach on the various facets of KM to maximize benefits. Chapter 11 Knowledge Management: The Key to Delivering Superior Healthcare Solutions.............................. 190 Nilmini Wickramasinghe, RMIT University, Australia The proliferation of ICT (information communication technologies) throughout the business environment has lead to exponentially increasing amounts of data and information generation. Although these technologies were implemented to enhance and facilitate superior decision making, the result is information chaos and information overload; the productivity paradox (O’Brien, 2005; Laudon & Laudon, 2004; Jessup & Valacich, 2005; Haag et al. 2004). Knowledge management (KM) is a modern management technique designed to make sense of this information chaos by applying strategies, structures and techniques to apparently unrelated and seemingly irrelevant data elements and information in order to extract germane knowledge to aid superior decision making. Critical to knowledge management is the application of ICT. However it is the configuration of these technologies that is important to support the techniques of knowledge management. This chapter discusses how the process oriented knowledge

generation framework of Boyd and the use of sophisticated ICT can enable the design of a networkcentric healthcare perspective that enables effective and efficient healthcare operations. Chapter 12 The Use of ‘Web 2.0’ and Social Software in Support of Professional Learning Communities......... 204 Alan Eardley, Staffordshire University, UK Lorna Uden, Staffordshire University, UK This chapter examines the ‘happy convergence’ of two emerging social and technological trends. The first is the evolution of educational processes and methods from a traditional didactic approach towards a paradigm that seeks to empower the learner and enable a more involving learning experience to take place. This paradigm includes such approaches as student-centred learning, collaborative learning and problem-based learning. The second is the development of IT-based systems that enable the democratic involvement of end-users in their development and use and that encourage computer-mediated collaboration between individuals and groups having a common interest in a domain. Initially, at least, the main purpose of such software was for social networking and leisure purposes, but the chapter identifies a number of instances of its use in practice for professional education purposes. The chapter then highlights some examples of professional learning communities in practice in UK educational institutions. It concludes by speculating on and discussing some possible future trends in the use of social software for professional learning and by summarising the phenomenon and identifying the factors that distinguish it from other approaches to learning. Chapter 13 Knowledge Sharing in the Learning Process: Experience with Problem-Based Learning.................. 215 Lorna Uden, Staffordshire University, UK Alan Eardley, Staffordshire University, UK Knowledge is the most important resource of an organisation. The exchange of knowledge and knowledge management enhance organisational learning that in turn leads to innovation. Central to knowledge management is the concept of knowledge sharing. The future of knowledge sharing is not technical, but social. Knowledge sharing is fundamental to learning among students. This paper begins with a brief review of knowledge sharing, followed by the importance of knowledge sharing for learning, especially in problem-based learning. The authors then describe how successful knowledge sharing can be achieved for students to share knowledge in problem-based learning. The paper concludes with implications for effective knowledge sharing for student learning. Chapter 14 Culturally-Bound Innovation in Romanian Teaching and Research Hospitals................................... 230 Mihaela Cornelia Dan, Academy of Economic Studies, Romania Simona Vasilache, Academy of Economic Studies, Romania Alina Mihaela Dima, Academy of Economic Studies, Romania This chapter discusses innovation in the Romanian healthcare sector, from the point of view of organizational learning, which is influenced by the components of organizational culture. Starting from the

premise that hospital organizational culture differs from other types of organizations, we investigated the perceptions of a mixed sample of doctors and nurses from an internal medicine clinic of a large teaching and research hospital. The Dimensions of the Learning Organization Questionnaire and items selected from a questionnaire developed by the authors were used in order to study how the two groups perceived organizational culture and, subsequently, innovation, as both a component and a result of it. The results of the study show differences in perception between physicians and nurses, consistent with the ones presented in literature, and account for which facets of hospital organizational culture affect learning easiness versus which factors are negatively correlated with it. Section 3 Creativity and Collaboration in Organizations Chapter 15 Exploiting KM in Support of Innovation and Change......................................................................... 242 Peter A.C. Smith, The Leadership Alliance Inc., UK Elayne Coakes, University of Westminster, UK This chapter emphasizes the importance of formally promoting close social interaction and open knowledge sharing to achieve superior innovation capability. It does so by discussing the advantages of developing Communities of Innovation and citing a case study that exemplifies these concepts. This chapter addresses the challenges and opportunities faced by businesses in today’s complex and often unpredictable business environments. For success, an organization must be able to combine and recombine their resources in novel ways, eliminating or reconfiguring resources that are no longer relevant, and acquiring new resources. An organization’s capability to change by manipulating resources continuously and rapidly—to innovate—is a competitive advantage that is not readily imitated by competitors. Innovation is critical to an organization’s viability since it enables the development and introduction of new products and services and thus enables an organization to maintain, or improve, its current business position. The chapter reviews the numerous theories of change and change management in the literature based on practice and precept. However, research shows that competitive advantage is increasingly located by authorities in an organization’s intellectual resources including the skill base, business systems and intellectual property of its employees: its Human Capital. Organizational innovation depends on the individual and collective know-how of employees, and innovation is characterised by an iterative process of people working together, sharing insights, and building on the creative ideas of one another. The chapter emphasizes that an organization’s intellectual resources have significant potential to realize innovation and change capabilities, but that the impact of these capabilities largely depends on the means of an organization to foster close community social interaction and open knowledge sharing, and to leverage its informal leadership as a precursor to and part of any related Knowledge Management (KM) initiative.

Chapter 16 Knowledge Management Profile – An Innovative Approach to Map Knowledge Management Practice........................................................................................................................... 253 Zoltán Gaál, University of Pannonia, Hungary Lajos Szabó, University of Pannonia, Hungary Nóra Obermayer-Kovács, University of Pannonia, Hungary Zoltán Kovács, University of Pannonia, Hungary Anikó Csepregi, University of Pannonia, Hungary For knowledge-intensive organizations, it is important to carry out an objective assessment of their current position in the area of knowledge management activities and processes. Uncertainty presents a barrier to the introduction of suitable activities for improving knowledge management. We believe that the results of the research will be significant to practice and will provide substantial support for leaders and managers. Moreover the right knowledge management activities can help push thinking beyond the everyday in a way that spurs innovative creativity. To ensure success and long-term existence of any organizations effective application of organizational knowledge and knowledge management practice is of critical importance. Besides simply assessing the benefits inherent in knowledge management, the organizations must learn to recognize and manage the different areas of their knowledge management practice. Our innovative solution, the “Knowledge Management Profile” is devoted to the formulation of a new knowledge management maturity model, which is believed to be of vital importance in the quest of the successful knowledge management practice. Chapter 17 Recognizing Innovation through Social Network Analysis: The Case of the Virtual eBMS Project....................................................................................................................................... 264 Grippa Francesca, University of Salento, Italy Elia Gianluca, University of Salento, Italy Advances in communication technologies have enabled organizations to develop and operate decentralized organizational structures by supporting coordination among workers in different locations. Such developments have lessened formality in control structures and replaced formal channels of communication with less formal social networks. The chapter describes the development and application of a ‘Social Network Scorecard’ (SNS) managerial tool to monitor social interchanges and relationships within and across organizations in order to assess the effectiveness of knowledge networks. In this chapter, a project team made up of individuals from academia and industry collaboratively implemented an integrated technological platform for KM, e-Learning, e-Business, and project management disciplines in a higher education environment. This VeBMS platform, consisting of a collaborative working environment within the University of Salento, Italy, was used as a ‘test bed’ to evaluate the validity of the scorecard in practice. The chapter describes how the SNS tool can help in monitoring the evolution of an organizational community, recognizing creative roles and initiatives, and tracing the connections between such initiatives and innovative outcomes. Looking at trends at individual, team, inter-organizational, and organizational levels, researchers identified the most innovative phases within the team’s life cycle using network indicators like density and degree centrality. The SNS provided

feedback on the effectiveness of the team and helped discover the phases in which the team acted in a manner conducive to innovation. The Virtual eBMS project team followed the typical structure of an innovative knowledge network where learning networks and innovation networks co-exist with a more sparse interest network. Chapter 18 Complexity and Clarity: The Knowledge Strategy Dilemma – Some Help from MaKE................... 286 Peter Sharp, Regents Business School London, UK Alan Eardley, Staffordshire University, UK Hanifa Shah, Staffordshire University, UK Organisations face a great problem. How can they create a knowledge management (KM) strategy that takes account of the complexity of knowledge issues in their organisation and be able to clearly communicate it? This issue, called here the Knowledge Strategy Dilemma, is the main theme of this chapter and is vital for KM success in practice. The authors argue that literature reveals that the Dilemma is one that can be tackled. They also argue that whilst the literature reveals approaches that help address different parts of the Dilemma, the best approach to address it in a coherent way is a KM method called MaKE. MaKE is presented and two of its principles—Traceability and Transparency— are explained. Also visual tools that help implement these principles in practice are critically discussed along with feedback from industry. The principles, when applied, are helpful in tackling the Dilemma with some success. Also, the authors argue that different forms of communication (including face-to-face meetings with visual aids) should be used to address the Dilemma. The question that remains is: are organisations willing to devote the time to do these things in practice?. Chapter 19 Knowledge Management and Innovation............................................................................................ 300 Lorna Uden, Staffordshire University, UK Marja Naaranoja, Vaasa University of Applied Sciences, Finland In today’s society, innovation and knowledge management are no longer luxury items. Instead, they are necessities and a means of economic development and competitiveness. Knowledge and innovation are inseparable. Knowledge management competencies and capacities are essential to any organisation that aspires to be innovative. Innovation and knowledge management are closely related. This paper discusses the importance of knowledge management in innovation for organisations. It describes how innovations can be achieved through the role of knowledge management using a case study involving the renovation and building of a school project in Finland. The case study shows how knowledge creation and sharing were used to help innovation using vision building. Chapter 20 Holonic Management: Innovation and Creative Entrepreneurship..................................................... 319 Akira Kamoshida, Tokyo Institute of Technology & Nagoya University of Commerce and Business, Japan

The aim of an innovative management is to intentionally create a “chaos edge” and to foster and organize the ideas which are born. Chaos edge is a term usually used in complexity studies, but it is also highly applicable to management. In this paper, the management concept used to create innovation is referred to as “Holonic management.” Holonic management requires the following three elements: 1) cultivating the soil from which innovation shoots can grow, 2) introducing an appropriate competition principle, and 3) preparing a strict evaluation and proper support system. Constructing the field of chaos edge in holonic management can activate an internal environment to create ideas, which result in the internal cooperative work possible to generate innovation. The “Heretic management” finds the innovation shoot created by a minor group within a corporation and allows it to grow without fear of failure. This is not just the most effective tool. It is also the method for the realization of knowledge management. Compilation of References ............................................................................................................... 334 About the Contributors .................................................................................................................... 375 Index.................................................................................................................................................... 384

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Foreword

Knowledge Management as a discipline, despite being 20 years old or so, is still in its infancy. And although there have been many pronouncements of its death, I would argue that it is still very much alive and thriving. What you may find surprising though, is that, if you spent some time in the online KM discussion forums, you would discover extensive, heated debate on the nature of KM. First, people still cannot agree what constitutes knowledge and I don’t believe they ever will. For many, knowledge can only exist in the human mind - anything in written form is information but others argue that knowledge can exist in two forms: implicit knowledge, that exists only in the human mind and explicit knowledge that is recorded, such as in a book or a digital format. These two mindsets are the root cause of much confusion and argument. And even when this difference is recognised, there is still great debate as to “What is knowledge?” In addition, the relationship of data to information and knowledge and even wisdom is hotly argued and the validity of the so called data-information-knowledge-wisdom hierarchical model (D-I-K-W) that is much hallowed in some circles has been called into question. Even the SECI model of Nonaka and Takeuchi—long a staple of KM academics and practitioners—is dismissed by many and has been held responsible by some for the failure of a large number of KM projects. Yet another hot debate is the role of incentives and rewards in motivating people to share their knowledge. Most people still believe that you need rewards and incentives, whilst the works of Alfie Kohn and Dan Pink show that research demonstrates that tangible rewards in the main do not work and, worse, do great harm. As for a precise definition of KM itself, there is even less agreement. You can find hundreds of definitions on the web. They have a lot in common but they are also very different. And the definitions you are drawn to vary depending on whether you are an academic, a KM practitioner, an HR manager, a technologist, or a hard nosed business manager. Definitions are also coloured by the industry you are in. Someone in the oil industry may have a very different view of KM than a software developer at Google. The subject is rich. The subject is broad. The subject is diverse. There is wide disagreement as to the nature of knowledge, what knowledge management is and how you best go about it. But personally, I don’t think it matters too much. I am quite capable of working with several definitions of knowledge inside my head, and I would recommend that anyone working in the KM field define what KM means to them in terms of their specific business and business objectives. This lack of a clear definition and at times ambiguity is what I think makes the field an exciting and fulfilling one to work in, though I doubt every one would agree. What I think is interesting is that KM as a discipline has emerged and is evolving and developing in the age of the World Wide Web. In the past, the ownership, forming, and shaping of a new discipline was

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restricted to a relatively small number academics and high profile early practitioners and evangelists. This is not true today. With the web, regardless of knowledge or experience, anyone can contribute to the debate and the evolution of the discipline. The shaping of KM is a more open, democratic process, and there is much to be learnt about the evolution of knowledge and KM itself in observing the conversations, dialogue, and debate that are taking place world wide. But despite all this argument and debate and a dip in enthusiasm for KM in the mid 90s, I believe that KM today is reviving and thriving. There are many KM societies and networks world-wide. There are a growing number of conferences and an ever increasing number of on-line forums and KM educational courses. And the number of people with "knowledge" in their title and a responsibility for managing knowledge in some way grows daily. It is still a hard fact however that most KM projects have failed or have not lived up to their expectations. I don’t believe that this is inherent in KM tools or methodologies but due more to the fact that KM projects are often poorly conceived and implemented. I think that for KM to be successful, it needs to do three things. 1.

2.

3.

It needs to focus intensely on the critical business issues that need to be addressed within an organisation and not on visionary concepts such as creating a knowledge sharing culture or a knowledge driven organization. Such concepts deflect us from the real issue of solving business problems, mitigating business risks and identifying and exploiting new business opportunities and are too often a one way street to frustration and ultimate failure. It should place more emphasis on working with and obtaining buy-in from senior managers in the organization, not only by developing a business case but recognizing that managers are human and can be swayed by other motivations other than a traditional ROI analysis. It needs to obtain the buy-in from people in the organization by working with them, engaging and involving them much earlier in the project life cycle then most traditionally managed projects. Unlike other systems, people cannot be coerced into using a "KM system” —they need to have ownership.

I am often asked “How do you do KM?” My response is that “You don't do KM! We should respond to business problems and develop business opportunities using KM tools.” I also don’t believe there should such things as KM initiatives. Again, we should not “do KM”. There is no such thing as a KM strategy. There are only business problems, business challenges and opportunities, business strategies, and business projects. To my mind, the problem with KM initiatives and strategies is that they conceptualize problems and make it far too easy for us to take our eye off the business, and this is one of the key reasons why so many KM projects fail. It is also rare that a business issue is purely a KM one. We usually need more than just KM tools and techniques to fully address a business problem or opportunity. We should use KM tools and methodologies to help respond to business problems and opportunities. If we must have a KM strategy it should be in response to clear business goals and tie in to the top level business objectives of the organization or our organizational unit. The business purpose and outcomes should come first! I also believe that there are no benefits to KM as such. As KM is about improved communication, learning and knowledge sharing it can be applied to any human endeavor. So asking what are the benefits

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of KM is a meaningless question as the answer is “what ever we want them to be!” We need to start by asking “What do we want to achieve in terms of business outcomes and how can KM thinking, KM tools and KM techniques help?” Not everyone will agree with my views. And that’s fine. That’s the nature of KM. What I like about this book is that it includes contributions from academics, researchers, managers and practitioners in a wide variety of areas relating to KM and innovation. Much of what they have to say is in disagreement with each other or represents alternative view points. This is good. This is how we take the discipline forward. So let me finish by saying something I say again and again about KM. There are no recipes for KM. There are no prescriptions for KM. There is no substitute for thinking for yourself about KM. Read the book, reflect, think hard and join the conversation—both the dialogue and the debate! Help shape KM for the 21st century. David Gurteen Gurteen Knowledge, UK David Gurteen has over 30 years’ experience working in high technology industries. He was a professional software development manager and in the late 80s worked for Lotus Development, ensuring that Lotus products were designed for the global marketplace. Today he works as an independent knowledge management advisor, facilitator and speaker, helping people to innovate and to work together more effectively. He is the founder of the Gurteen Knowledge Community—a global learning network of over 17,000 people in 160 countries. He publishes a monthly Knowledge Letter, now in its 11th year, and the Gurteen Knowledge Website—a resource website that contains book reviews, articles, people profiles, event calendars, inspirational quotations, an integral weblog and more on subjects that include knowledge management, learning, creativity and innovation. He is known for his Gurteen Knowledge Cafés and the Knowledge Café Masterclasses that he runs regularly in London and in other cities around the world. In June 2010, He won the Ark Group’s lifetime achievement award for services to KM.

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Preface

INTRODUCTION The purpose of this book is to collect and to present the results of theoretical and empirical research into various aspects of Knowledge Management (KM), innovation and, especially, the conjunction of KM and innovation. To this end, chapters are included from both academics and practitioners, and the contributions represent diverse views and versions of KM and innovation. It is this richness and diversity that makes the topics so fascinating and, it can be contended, so important to the future work of academics and practitioners in a variety of fields alike. It is possible to conclude that both topics—KM and innovation—are different aspects of the same phenomenon, which is the use of “human capital” to generate new ideas and to promote creativity. It is axiomatic that advances and innovations in science, technology, industry, education, and the arts all begin at one source: the knowledge of the people on whom businesses and organizations of all sorts rely for their new products, services, technologies, and systems. The potential audience for this book is therefore very broad. The term “Knowledge Management” (KM) is most frequently used to describe the range of practices and activities that are used in a variety of organizations to identify, create, represent, store, disseminate, and encourage the adoption of relevant human insights and experiences. Such insights and experiences (i.e. the knowledge) may be embodied in individuals’ minds or embedded in a group’s or an organization’s artifacts, processes, and practices. KM research has for some time embraced the fields of business administration and management, information systems and technology, learning and psychology, and library and information sciences (Alavi & Leidner, 1999). More recently, other contributions to KM research have included applications of KM in public health and public policy. Most large commercial companies and many non-profit organizations have dedicated considerable efforts and resources to KM, often as a part of their business strategies, IT strategies, or even human resource management (HRM) strategies (Addicott et al. 2006). Consulting companies have grown up to provide advice and expertise on KM to these organizations at strategic and operational levels. KM efforts to date have typically focused on organizational objectives such as improved performance, competitive advantage, innovation, and quality improvement. KM initiatives often overlap with existing directions with organizations such as organizational learning (OL), Total Quality Management (TQM), and even Business Process Re-engineering (BPR). KM may be distinguished from these disciplines (and others) by a greater focus on managing knowledge as a strategic asset and on enabling the sharing of knowledge. KM efforts can therefore help individual workers and groups to share valuable organizational learnings, to reduce redundant work (e.g. avoiding reinventing the wheel), to reduce training time for new employees, to retain intellectual capital when employees leave and to adapt continually to changing environments (Thompson & Walsham, 2004).

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There are currently a number of schools of thought or movements that have had an influence on Knowledge Management and its relationship to innovation, the following being among the more important: •

•

•

•

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The information theory movement, usually associated with the frequently-cited work (Chun, 2003) by Davenport and Prusak (1998), Nonaka (1991), and later with Takeuchi (1995). This work has been an important influence on KM and includes later developments by Probst (1998), Von Krogh (1998), and Malhotra (2000) which cite the process of KM as an important enabler of innovation; The collaboration school of KM that focuses on CoPs and collaborative ICTs, originating from research at Lotus (and later IBM) by Wenger, McDermott, and Snyder (2002), that views knowledge as a commodity that relates directly to interpersonal contact and the communication of new ideas; The intellectual capital movement with Edvinsson and Malone (1997), Sveiby (1997), and later developed by Bontis (2002), which views knowledge as valuable corporate resources and assets that can provide competitive advantage through innovation; The social network analysis school, typified by Krebs (2008) and influenced by the work of Borgatti with Cross (2003) and with Carboni (2007), that concerns the theoretical mapping and measuring of the relationships and flows of information between people, groups, and organizations, which can be an important source of invention and innovation; Narrative approaches associated with Snowden (2000a, 2000b), Boisot and Canals (2004), Spender (2007), and others. Variations of this approach are described by Snowden (2004), Boje (2001), and others as a form of KM.

INNOVATION AND INVENTION Innovation is often equated with creativity and is generally understood to be the successful introduction of a new artefact, method, or process. The ideas behind the development can come from a deliberate process of deductive development from a knowledge base or from an intuitive and even accidental ‘bright idea’ in which it may be difficult to identify the role of knowledge. However, there generally appears to be a fundamental link between knowledge and innovation. Luecke and Katz (2003) believe that “... innovation is the embodiment, combination, or synthesis of knowledge in original, relevant, valued new products, processes, or services”. Amabile, Conti, Coon, Lazenby, and Herron (1996) suggest that innovation is related to creativity (as all innovation begins with creative ideas) but is not necessarily identical to it. Action is required to develop the creative ideas to make some tangible and lasting contribution, and it is this contribution that is defined as innovation. We define innovation as the successful implementation of creative ideas within an organization (Amabile et al., 1996). In this view, creativity (whether by individuals and teams) is a suitable starting point for innovation, but creativity may not always lead to successful innovation. For innovation to occur, then, something more than the creative generation of an idea or insight is required, as the insight must be enacted to make a real difference. Innovation is also described as a part of a management process that may need to draw on other organizational resources that creativity does not. Davila, Epstein, and Shelton (2006) believe that “Innovation, like many business functions, is a management process that requires specific tools, rules, and discipline”. This appears to place emphasis on the general organizational processes and procedures for generating, evaluating, developing, and acting upon creative insights to produce significant organizational improvements. Creativity may therefore be seen as the basis for innovation and innovation as the successful implementation of creative ideas (Amabile et al., 1996). Davila et al. (2006) appear to confirm that it is

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this ‘bringing ideas to life’ in an organizational context that makes innovation the distinct undertaking it is. Many of the contributions to this book contend that this undertaking or organizational process requires knowledge as an input to the innovation process, uses knowledge to make the process efficient and also produces knowledge as an outcome of the process in the form of experience and organizational learning. The various contributions to this book tend to confirm this view in a variety of organizational contexts. Fagerberg (2004) notes that “An important distinction is normally made between invention and innovation. Invention is the first occurrence of an idea for a new product or process, while innovation is the first attempt to carry it out into practice”. Innovation, in contrast, occurs when an invention or an idea is used to change how society functions, how organizations are structured, or how people live their lives. Innovation is also distinct from improvement in that it permeates society and can cause reorganization, and from problem-solving in that it should have more lasting and far-reaching effects. The term ‘innovation’ therefore refers to a new way of doing something or achieving new ends, perhaps by radical and revolutionary or by more gradual and incremental means. This may be through changes in modes of thinking, through new products and/or processes, or the way in which organizations are structured and managed. A distinction is sometimes made between inventions, which are ideas put into practice, and innovations, which are inventions applied successfully over time (McKeown, 2008). On these terms an invention may be completely new (e.g. based on a “step-change” in thinking or technology) and yet not be an innovation if it does not have lasting influence (e.g. start a trend) or contribute substantially to a body of knowledge. In many fields, an invention must be substantially different from its predecessors to be classed as innovative and must have a measureable and valuable effect. In business the innovation may have to increase the value of a product or service in the eyes of the consumer or producer. The goal of invention is therefore to effect a positive change—to make something better—and innovation leading to increased productivity is the fundamental source of increasing economic wealth. Although innovation is considered to be the output of the process of invention, in a KM/innovation context it is usual to focus on the whole process, from the origination of an idea, through its transformation into something useful, to its implementation and subsequent effect on the environment. While innovations typically add value or produce benefits, they can have disruptive or destructive effects as innovations replace outdated or obsolete products, organizational forms, and social practices. It is therefore possible that organizations that fail to innovate effectively may be superseded by those that do. Conversely, as invention and innovation may harm the organization that bears the cost and efforts of the process, innovation projects may involve a degree of risk. A key challenge in innovation projects is to achieving a balance between process and product innovations—where process innovations tend to involve a business model which may develop shareholder satisfaction through improved efficiencies, while product innovations develop customer support however at the risk of costly research and development (R&D) that can erode profitability and therefore shareholder return. Many of the contributions to this book, by definition, describe current applications of KM and innovation, but which are founded on established KM models, processes and technologies that have developed over time. It may therefore be useful to describe the generations through which the subject of KM has progressed in reaching its present state.

‘GENERATIONS’ OF KM AND INNOVATION The original generation of thought on KM and innovation focused on the mental process of understanding, problem-solving, and generating new ideas. This may therefore be termed the ‘Psychological’ generation

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of KM. Early work on creative mental processes by Michael Polanyi challenged the previous “scientific” view that innovation (at least in science and technology) was a completely logical and deductive process—a cold, logical progression from one stage of the development of a new idea to another. In his seminal work “Personal Knowledge: Towards a Post Critical Philosophy” Polayni (1967) neatly summarizes the thesis of his later life as a philosopher (he was originally a chemist) that creative or innovative acts often arise from “unscientific” stimuli such as guesses, hunches, and emotional commitments. This view challenged the commonly accepted position that science is value-free, and it is possible to see creative tension between Polanyi’s approach to knowledge and enquiry and that of Thomas Kuhn, who later held that typical scientists (and, by extension, technologists) are not independent and creative thinkers. On the contrary, they accept and react to what they have been taught (especially in the form of accepted theories) and apply their knowledge to solving the problems that such theories suggest. In this view scientific people are problem-solvers who aim to prove (or disprove) what they already know in advance: “The man who is striving to solve a problem defined by existing knowledge and technique is not just looking around. He knows what he wants to achieve, and he designs his instruments and directs his thoughts accordingly” (Kuhn, 1970). It is perhaps characteristic of the richness of the phenomenon—the conjunction of KM and innovation—that these original points are not as mutually exclusive as they may at first seem. Kuhn’s view recognises the inevitability of scientific revolution—“the tradition-shattering complements to the tradition-bound activity of normal science” (Kuhn, 1970) —through “paradigm shifts”, which occur when the developed solutions to existing problems show the theories to be incorrect or inadequate to explain the facts. Polanyi saw creativity as a more individual or internal activity, but recognised that the tacit knowledge of many individuals can be collected and combined to form a new model or theory (Polanyi, 1967). Some authorities therefore call this the “first generation” of KM. A significant development to the discipline of KM occurred in the early 1990s (e.g. Nonaka, 1991) which developed some aspects of the work of Polanyi into a conversion process or cycle that, it was claimed, could be used to change tacit knowledge (i.e., peoples’ creative ideas and thoughts) into explicit knowledge, based on more readily accessible and usable organizational artefacts such as repositories and knowledge bases. This approach is typified by work by Probst which, building on systems theory, follows a process that starts with knowledge identification, followed by knowledge acquisition, knowledge development, knowledge distribution, usage of knowledge and knowledge retention in codified form (Probst et al. 1997). Such models were well accepted by software developers, who were quick to develop products such as knowledge bases and document management systems to exploit the growing interest. This has been termed second generation KM (Schütt, 2003). In discussing such models based on system thinking, Stacey (2001) comments, “This reflects an underlying way of thinking in which knowledge is reified, treated like a ‘thing’ that can be possessed, that corporations can own”. In his view, “knowledge itself cannot be stored, nor can intellectual capital be measured and certainly neither of them can be managed” (Stacey, 2001). His comments appear to apply to knowledge when it is regarded as a single commodity, rather like information. Subsequent approaches to KM seek to identify and explore the components that make up the phenomenon of knowledge and examine their role in putting knowledge into action in organizations. The second generation of KM developed from the first through a movement that stressed the uses of knowledge in organizations, going beyond the mental processes of the individual knowledge worker and recognising the complexity of modern, knowledge-creating organizations. This may therefore be termed the organizational generation of KM. Nonaka and Takeuchi (1995) held that companies are more like living organisms than machines, and most of them viewed knowledge as a static input to the

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corporate machine. Later work by Nonaka and Takeuchi (1995) appears to integrate Nonaka’s cyclic approach (known as SECI) with systems theory to provide a more systematised, a view that implied that knowledge could be moved from one form to another in a process rather like information systems design and development. Nonaka and Takeuchi therefore view knowledge as a renewable and changing resource and hold that knowledge workers are the agents for effecting that change. Knowledge-creating companies, they believe, should focus their KM activities primarily on the task of innovation rather than knowledge hoarding. Schütt observes that “Knowledge, in this sense, is not so much a thing or a higher (quality) level of information, but more a kind of capability to put data into context” (Schütt, 2003). Snowden separates knowledge into five components that are capable of being analysed and assessed, the first letters of which form the mnemonic ASHEN (Snowden 2000), which is intended for use in optimising knowledge management. The framework is based on artefacts, or useful information stored in the form of things that are documented (i.e., explicit knowledge assets in Nonaka’s view); skills or the ability of individuals and groups to acquire, manipulate and put to use high-level information in knowledge contexts; heuristics or the rules of thumb used by experts to manipulate information to make decisions in complex situations; experience or the cumulative ability of individuals and groups to apply skills and employ heuristics and to identify and develop artefacts to achieve beneficial results based on the use of knowledge in practice; natural talent or the innate ability of certain individuals (the most expert knowledge workers) to manipulate knowledge and to achieve desired results from its use. This raises the issue of knowing not just what knowledge is codified or documented in an organization, but what knowledge should and could be codified or documented. It may be contended that second generation KM practices promoted the collection of knowledge resources without a clear need, the later approach implies that the organization needs to have a clear judgement of what knowledge is likely to be needed. An analogy can be drawn with production supply chain practices; to be cost effective, some knowledge provision has to follow just–in-time rules (acquisition upon need), and other provision has to be just-in-case (acquisition in advance of need). In this view explicit (i.e., existing, documented) knowledge resources follow usual organizational information management practices and might end up in a document or content management system on the organization’s intranet. The innovation in KM that is a part of this approach is the way the things are handled that cannot be codified. These tacit resources are linked to single experts or sometimes groups of experts, who interact with the KM system(s) in a number of ways. Communities of Practice (CoP) combine groups and individuals who have an interest to share knowledge about a relevant area of expertise (usually on a voluntary basis). Usually a CoP maintains a best practice database or a log of lessons learned. Physical conferences or virtual meetings and the use of web forums and blogs help in harvesting innovative ideas and sharing knowledge. Such technologies are not, of course, ephemeral, as they may continue to exist and find use as knowledge artefacts. Debriefing sessions or after action reviews (originating in a military context) identify and preserve the learnings from significant experiences (e.g., projects and innovations) and help to spread knowledge-based skills and develop heuristics and rules of thumb for later use by others. Teaching sessions by key knowledge owners can have a similar effect. Storytelling or narratives are used as an alternative (and perhaps more natural) way to identify and discuss critical knowledge and to disseminate it. These techniques (and others) can be used to prepare and promote interventions leading to cultural change (Snowden, 2001). This marks a significant departure from many of the precepts of the previous generation of KM and leads to an explicit connection between KM and organizational learning (OL). Senge (1990) originally identified OL as a sustainable source of competitive advantage in business, discussing holistic systems thinking as a fifth discipline of business. This can enable an organization to develop a superior capacity to learn, to retain, and to benefit from that learning through new ideas and new products and services that

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competitors find difficult to imitate. By definition, this process is sustainable and can provide a renewable source of competitive advantage, placing the links between KM, OL, innovation, and competitive advantage at the very heart this generation of KM. Second generation KM may therefore be defined as a management discipline that focuses on organizational learning to encourage business innovation as a renewable source competitive advantage. Third generation KM followed on naturally from this set of precepts and seeks to appreciate and understand what the effects of the process of managing knowledge can have on an organization and its workers. “In a complex domain we manage to recognise, disrupt, reinforce and seed the emergence of patterns; we allow the interaction of identities to create coherence and meaning” (Snowden, 2002). Third generation KM frameworks such as Snowden’s Cynefin model tend to recognise the conjoint complexity of organizations and knowledge, seeking to make sense of the relationship between them in complicated and complex work processes and organizational structures. In Snowden’s view, the third generation of KM will require the “clear separation of context, narrative and content management” and seeks to challenge the orthodoxy of scientific management on which the second generation of KM was based. Therefore, in the third generation of KM, complex adaptive systems theory is used to create a model that seeks to utilise the self-organising capabilities of informal communities and identifies a natural flow model of knowledge creation, disruption, and utilisation. This implies a new and important role for the knowledge worker using new generation KM technologies and informal networking communities to acquire knowledge and promote innovation. To some extent, this marks an evolution of second generation KM rather than a revolution (some authors therefore do not recognise the existence of the later generation). Others term the third generation of KM the ecological’ generation, as it involves the interaction of knowledge workers, knowledge assets, and organizational and environmental factors interacting together as a complex adaptive system.

KM, INNOVATION AND TECHNOLOGY Tapscott (2006) sees a clear link between the role of the knowledge worker and innovation in interacting with peers and with organizational KM resources, but believes that the nature of the interaction has become more advanced. He describes social media tools on the World Wide Web that can initiate and enable more powerful forms of collaboration than were possible in the second generation of KM. He points out that knowledge workers routinely engage in peer-to-peer knowledge sharing across organizational boundaries, forming networks of expertise in a more complex way than would be possible with traditional CoPs. They do this by using a variety of Web 2.0 social networking technology applied in an organizational context, calling them Enterprise 2.0 technologies. The key technologies in Enterprise 2.0 are wikis, social networking systems, blogs, search engines, mashups, portals, Web/videoconferences, bulletin boards, and discussion forums. Using this technology, enterprise social networking enables corporate knowledge workers to use a network that allows exchanges that can lead to new knowledge creation. Enterprise 2.0 therefore appears to provide a collaborative platform that enables organizational and trans-organizational knowledge to be exchanged and consolidated. The connectivity of wikis, portals and forums makes the exchange of knowledge possible, and the use of search engines and content management systems makes the acquisition of knowledge (from inside and outside of the organization) more easily possible. Enterprise 2.0 therefore has the power to change the way an organization collects new knowledge and how it stores it, changing the way organizations approach Knowledge Management in

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the third generation of KM. When compared to previous models of industrial competition (e.g. Porter 1985) that assume that organizations will seek to gain advantage from knowledge that competitors do not have and cannot access (except, of course, for strategic partnerships that are negotiated and conducted at a high level in the organization), there are concerns over copyright and intellectual property law being challenged in practice, inhibiting organizations’ KM practices. In fact, the approach may not be contrary to conventional models of industry competition, as Porter maintains that companies will change the basis of competition, seeking to gain whatever advantage they can (temporarily or permanently) from their knowledge relationships. In this view, businesses must collaborate in order to survive. There are instances of alliances of public (i.e. government) and private (i.e. commercial) concerns collaborating to develop innovations with no discernable competitive commercial outcome for any party, with the open-source Linux operating system and the Human Genome Project as oft-cited examples. These may be seen as examples of innovation for the long term public good although, needless to say, there are always likely to be opportunities for the commercial exploitation of developments arising from such fundamental innovations in technology and science. Denning (2000) envisions two basic different approaches or mindsets relating to KM and innovation. The first of these he calls the Napoleonic or engineering approach (Dyson 1998), which refers to the application of scientific discovery to practical invention. It assumes the existence of a controllable path—a process based on a series of linked tasks—from the generation of the idea to its exploitation. In Denning’s terms, the approach represents an effort to reduce all knowledge to a set of mechanistic propositions which he attributes to “…a continuing itch for reductionist simplicity” (Denning 2000), which he feels can lead to attempts to micro-manage, to over-control, and to rely on unwarranted hierarchical organizations, procedures, and rules that may ultimately stifle the processes of creativity and innovation in a rapidly-changing environment. The opposite of this is what Denning sees as the Tolstoyan or ecological approach (Dyson, 1998), which is based on the creative chaos and freedom on which creativity apparently thrives and which seeks to exploit the connections between things and people that are features of the collaboration and social networking schools of KM. Both Denning (2000) and Dyson (1998) maintain that humans naturally grasp the natural connectedness of things and are able to exploit these connections in new and innovative ways without formal rules or controls. This process is said to be more rapid and more reliable than rigid mechanistic processes of management, which tend to rely on analytical (rather then creative) thinking. As organizational initiatives such as business process re-engineering and redesign appear to conform to this paradigm, it may be difficult for organizations to design their way out of this problem without a fundamental paradigm shift.

2009 - EUROPEAN YEAR OF CREATIVITY AND INNOVATION The European Commission decided that Europe needed to boost its capacity for creativity and innovation for both social and economic reasons, so it declared 2009 as the European Year of Creativity and Innovation (EYCI 2009). In doing so, it recognised the need for better use of knowledge and more rapid innovation, and acknowledged that a new emphasis was needed to broaden the creative skills of whole populations. The initiative instils a need for managers and national leaders to embrace this change of paradigm as an opportunity and to prepare the way for a more culturally diverse, innovative society based on the creative use of knowledge. The initiative therefore aims to raise public awareness of the importance of creativity and innovation for generating economic development, in order to contribute to economic prosperity as well as to improve social and individual well-being. The activities of EYCI

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2009 are aimed at a range of different social groups including educators, public and private sector policy makers and the general public in a process that is called the knowledge triangle.

ORGANIZATION OF THE BOOK The contributors to this book include academics, researchers, managers and practitioners in a variety of areas relating to KM and innovation. They come from the USA and a number of countries in Europe and Asia, giving a global coverage and interest to the book. Their contributions represent their individual views on a number of different topics and, in some cases, different views of the same topic. This variety of interest and experience produces a diverse and culturally rich view of the phenomena that make the book a relevant and timely publication in the areas of KM and innovation. This very diversity, however, makes it important to organize the book in a way that makes sense of the diversity and provides a logical link between the various contributions. Section 1 of the book therefore focuses on the phenomenon of the learning organization and the role of KM in the OL process and in the first chapter, Bratianu examines, in a Romanian context, the role of universities as knowledge-intensive learning organizations, observing that such bodies are knowledgeintensive, since the density of knowledge and the dynamics of knowledge processing are more important than other types of organizations. He notes the common assumption that since learning is one of the major processes within universities, they are by definition learning organizations. Bratianu’s point is that this assumption may be an error, and although a particular university may meet the accepted definition of knowledge intensive based on learning processes, it is not necessarily a learning organization. The chapter performs a functional analysis of the specific knowledge processes before identifying the necessary conditions for a generic university to become a learning organization. This is a useful prerequisite to successful innovation in universities, academic institutes, or indeed any knowledge-intensive organizations. From South Africa, Smut, van der Merwe and Loock examine KM through an innovative learning solution, linking the growth and earnings of knowledge-intense companies to their increasing efforts to extract wealth from individual and organizational know-how as a replacement for plant, machinery or other conventional factors of production, which the authors (following Becker) no longer see as providing strong differentiation. In this view, the shift to a knowledge-oriented economy has favoured organizations that can turn knowledge into new products and services faster than their competitors can (Kanter, 1997). Also, an organization’s (and, by extension, a nation’s) knowledge is seen as a primary source the source of its wealth. In this chapter the authors quote Porter, who regards the application of knowledge to those tasks, products, and services that exist as productivity and to those that do not yet exist as innovation. They observe that of all commodities, only knowledge can achieve these two apparently conflicting objectives. This chapter provides an overview of the application of KM in a mobile telecommunications company operating in 21 countries in Africa and the Middle East. The concept of a corporate university is described, with specific reference to an innovative technology solution for one of the learning components of this enterprise. Berger and Beynon-Davies examine knowledge-based diffusion in practice through a case study. The authors encourage the reader to consider how the use of particular development methods shapes information systems (IS) practice and how organizations adapt and use knowledge in support of such projects. They conceptualize an IS development method as a de-contextualized knowledge bundle, in

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which relevant knowledge (e.g. experience of similar projects, or of the use of certain IS development methods) is diffused between organizations and infused within organizations through the processes of contextualization. The authors particularly consider the way in which the structure and culture of organizations affects the processes of diffusion and infusion. Their discussion is grounded in case-study material collected from an ethnographic study of a large-scale IS development project. In this project, an agile IS development method promoted by external vendors experienced initial problems in organizational deploymentdue to its poor acceptance in that particular context. Gradually, benefiting from developing experience and therefore increasing knowledge, the IS development method was adapted to its context and was finally used successfully by the project team. In this chapter the authors identify and consider the factors that allowed this transformation to occur, focussing on the role of knowledge in the process of IS innovation. Chang and Li examine the factors that are important in fostering the deployment of KM in research and development (R&D) workspaces in Taiwan. Their discussion begins with a discussion of whether knowledge management (KM) is a new idea or just a recycled concept, and they comment on the number of claimed solutions to a variety of problems in the form of KM strategies, frameworks, processes, barriers and enablers, IT tools and measurements. The chapter points out that, although many KM studies exist in both public and private sectors, most of these studies focus on western experiences and relatively few cases are reported on KM deployment and implementation in the Asia-Pacific region. They especially make a case for studying knowledge-intensive R&D institutes whose missions are to serve traditional industries. To discuss some of the successes of KM deployment in this region, this chapter presents and discusses the lessons learned from a case study in fostering a KM initiative and system development in an R&D institute serving the metal industry, and it recommends a five-stage approach to KM deployment that may find applications in similar environments in other parts of the world. Tung-Xiung indicates that there may be problems with continued innovation and the use of new technology through a series of comparative longitudinal case studies recorded over more then two decades in China. The research on which the chapter is based has monitored the evolution of technological innovation, its effect on human factors, and discusses some of the resulting syndromes, such as Computer Fear Syndrome (CFS) and User Alienation Syndrome (UAS). The research involves an interesting analysis of the empirical data derived from these case studies and concludes with a proposed Funnel Model that suggests appropriate courses of management action and puts forward new ideas for developing KM systems in a variety of organizations that may prevent such syndromes from occurring or alleviate their effects in typical organizations. Orange and Ah-lian Kor present theoretical work that seeks to integrate various epistemologies from the philosophical, KM, cognitive science, and educational perspectives. Surveying the knowledge-related literature, the authors collate a number of apparently diverse views of knowledge and then categorise and ascribe attributes to the different types of knowledge in order to make them more useful in practice. They develop a new Organizational Information and Knowledge Management Model which seeks to clarify the distinctions between information and knowledge by introducing a novel conversion framework accompanied by a proposal for mechanisms that will improve individual knowledge creation and information sharing that will benefit Communities of Practice and organizations. Finally, Shue addresses the issue of extracting knowledge from financial statements, which could become important sources for investigating achievements on the primary business activities of planning, financing, investing, and operating in a variety of corporate types. He maintains that better knowledge of planning activities could assist managers in focusing their efforts and identifying business opportunities and obstacles to strategy. Accurate knowledge of financial activities may assist a company in acquiring

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and managing its financial resources. Knowledge of investment opportunities including land, buildings, equipment, legal rights, inventories, and all components necessary for the competitive operation of a company, while operational knowledge represents the execution of the business plan requires knowledge from a variety of sources. As a result, according to Shue, financial statements provide the main source of information for all parties who are interested in the performances of a company, including its managers, creditors, equity investors, and others. Although each of these parties may have different perspective in viewing financial statements, one major concern to all parties is the financial quality of an enterprise, which requires knowledge that the author says can be derived from a careful analysis of financial statements. Section 2 of the book moves on to examine the role of KM in the process of envisioning, inventing, and developing new products and explores the role of innovation in that process. From the same institution in Italy, De Maggio and Margherita carry out a critical analysis of “knowledge democracy” as the new mantra or buzz-word in product innovation leadership. The chapter begins by discussing this phenomenon, which identifies participation in KM, co-operation, and the co-creation of innovative value as the new paradigm of business. The authors show how this new paradigm has revolutionized the traditional process of invention, which was previously associated with a hierarchical dissemination of new ideas and competitive hoarding of knowledge assets. They contend that this competitive environment has been replaced by a collaboration economy (a.k.a. wikinomics) in which democracy governs the process of knowledge creation and its strategic application. The chapter introduces terms such as “peering” (i.e., eliminating management hierarchies in favor of meritocracies based on the quality of ideas and inventions), “openness” (i.e., participation and involvement of any stakeholder who has something to contribute), “free sharing” (e.g., sharing of intellectual property to facilitate participation and cross-fertilization), and “total action” (i.e., the increasing use of virtual and distributed resources). The authors show that leadership in product innovation does not rely on the innate internal qualities of organizations, but on the collaborative contribution of stakeholders, including customers, partners and co-developers, to many of the activities that make up the NPD lifecycle. This new approach can enable advantageous product customization, improving a number of the factors that can reduce the value of the NPD process (e.g., time to market, cost reduction, investment risk). The chapter examines the factors that are needed to promote such open collaboration, from the development of a new managerial mindset, the acquisition of new distinctive competences, the development of new organizational models, and the management of new collaborative technologies. Within this setting, the adoption of knowledge democracy often implies the introduction of new interactive NPD processes, probably in a period of turbulent change, which can present organizations with significant change management problems. This creates a need to identify new models of process integration and to develop new organizational forms that are able to exploit the potential of knowledge democracy in NPD. The authors’ proposed framework of processes and competencies offers the potential for organizations to meet these needs. Magnier-Watanabe and Senoo explore how KM, as an enabler of change, using its capability to create knowledge is subject to internal and external forces that shape the KM processes and the ways that knowledge is used. The chapter is a qualitative analysis based on a case study of the first major roll-out of Smartcard technology in France. The resulting analysis shows how institutional pressures affected not only the KM process but also the resulting innovation. The external factors that can impinge on the process include government initiatives, legal authorities, and cultural expectations. While this example is from French society, but it may be implied that such coercive pressures on the credit card industry are global in nature. In the case study of the introduction of a credit card (viewed as the development of an

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innovative financial product), existing credit card systems and solutions acted as what the authors call “mimetic pressures” (i.e., they promoted copying from existing successes) and professional networks and network externalities acted as normative pressures (i.e., they tended to encourage the formation or adoption of norms or standards). The authors suggest that by acknowledging these institutional pressures and viewing these forces from a systems perspective, organizations can achieve improved strategic alignment and can provide a sounder basis for differentiation in their market places and sustaining competitive advantage. Section 3 of the book focuses on common application areas of KM and considerable arenas for innovation—healthcare and education. Contributions to his section of the book are less numerous than some of the other application areas (e.g. NPD or OL), but are nevertheless important due to the innovative nature of the sector and the public service that can be offered by innovations in this area. Baskaran, Naguib, Guergachi, Bali, and Arochena, in a joint Canadian/UK study, observe that contemporary healthcare organizations, like organizations in business sectors, are constantly under pressure to develop new strategies for delivering better services and that KM has been successfully applied in a business environment. However, they argue that failure to apply proper KM principles has reduced the confidence of new adopters of KM. This chapter suggests how KM can be appropriately applied within an innovative healthcare project and offers a case study which describes attempts to improve attendance at breast screening clinics. The case study discusses the need for a balance between the technological and human aspects of KM, and assesses the success of the use of KM in this application. A survey was conducted of doctors and clinical staff that appears to provide proof that a balanced approach will definitely increase the success of such initiatives. The outcomes of this project can increase the confidence of future KM adopters in healthcare generally, provides useful guidelines for conducting balanced KM initiatives and highlights the importance of taking a focused approach KM development, allowing innovative uses of KM in healthcare. Wickramasinghe also pursues the theme of KM innovations in healthcare, but from an Australian perspective. He identifies the contribution of ICT systems to the increasing amounts of data and information that many organizations have to manage. He points out that although these systems and technologies were implemented to enable superior decision-making, the result has often been a state of information overload, referred to as the productivity paradox. He goes on to discuss KM as a way of making sense of this chaos by applying strategies and techniques to apparently unrelated (and perhaps irrelevant) data and information in order to extract the necessary knowledge to aid decision making. He contends that it is the configuration of these technologies that is important to support the techniques of KM, discusses the process-oriented knowledge generation framework of Boyd, and recommends ways in which the role of technology can enable the design of a network-centric healthcare operation that is an innovative and important contribution to healthcare applications of KM. Eardley and Uden from the UK examine the innovative convergence of two emerging social and technological trends: the evolution of educational processes and methods from a traditional didactic approach towards a new learner-based paradigm and the development of software systems that enable the democratic involvement of learners and computer-mediated collaboration between individuals and groups with a common interest in learning. Originally the main purpose of such Web 2.0 software was social networking and leisure, but the chapter identifies a number of instances of its use in practice for professional education purposes. The chapter then highlights some examples of professional learning communities in practice in UK educational institutions and concludes by discussing possible future trends in the use of social software for supporting professional learning communities.

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Again, in the next chapter, Uden and Eardley examine a different aspect of education: Problembased Learning (PBL). This chapter begins with a brief review of knowledge sharing, followed by the importance of knowledge sharing for learning, especially in problem-based learning. The authors draw on material demonstrating that knowledge is often the most important resource and asset of many organizations. They show that the exchange of knowledge and KM often enhances OL, which in turn leads to the potential for innovation in those organizations. The authors contend that knowledge sharing is central to the concept of knowledge management and that the future of knowledge sharing is not technical, but social. They then demonstrate how successful knowledge sharing can be achieved for students in a PBL environment. The chapter concludes by identifying the most important implications for effective knowledge sharing in PBL. Vasilache, Dan, and Dima discuss the issue of innovation in the Romanian healthcare sector from the point of view of organizational learning (OL). They contend that the extent to which OL can promote innovation is influenced by the dominant organizational culture. Adopting the premise that hospital organizational culture in hospitals exhibits singular features when compared to other organizations, the authors of this chapter surveyed medical and nursing staff from a clinic within a large teaching and research hospital. A questionnaire developed by the authors was used to study the perceptions of the two sub-cultures (physicians and nurses) regarding the relationship between organizational culture and innovation. The results of their study confirm the differences in perception between physicians and nurses previously found in the literature and identify the factors which promote OL and innovation in a typical clinical environment, comparing these to the factors that correlate with them negatively. The recognition of cultural factors in this context and the resulting recommendations may be transferable to hospitals in other cultures. Section 4 of the book includes some interesting submissions on the role of KM in the process of innovation and some examinations of how invention and innovation may be achieved by the use of knowledge. A similar theme to that addressed by Vasilache et al., but from a resource-based perspective, is pursued by Smith and Coakes, who discuss the instability of the business environment and observe that organizations must constantly strive to match or exceed the rate of this change in order to maintain or improve their competitive position. This change, they observe, must be managed effectively and consistently and factors such as organizational competence must be in place to allow the maintenance and development of innovative processes and products that meet business needs. To address the opportunities and threats presented by such complex and unpredictable environments, the authors show that organizations must combine and recombine their resources in novel and innovative ways, reconfiguring or eliminating obsolete resources and acquiring appropriate new resources. They show that innovation accompanied by repeated and rapid resource manipulation can achieve competitive advantages that are not easily imitated by rivals. This capability may be critical to an organization’s business performance, as it enables the development of new products and services that enable it to improve its competitive position. The authors explore the numerous theories of change in the literature, but observe that competitive advantage is increasingly located by authorities in an organization’s intellectual resources and its human capital. This includes the capability to innovate by mixing human skills with knowledge. In their view innovation is “characterized by an iterative process of people working together, sharing insights, and building on the creative ideas of one another”. This chapter emphasizes the role that an organization’s intellectual resources have in enabling superior innovation and change capabilities. Whereas Chang and Li recommend a process, Smith and Coakes conclude that an organization’s ability to use resources to foster close social interaction and open knowledge sharing in the workplace and to leverage its informal

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leadership is a vital part of a related KM initiative. It is possible that both views are valid and that their conclusions and recommendations can complement one another. Gaál, Szabó, Obermayer-Kovács, Kovács, and Csepregi offer an innovative approach to KM by suggesting a framework, based on KM profiling, for mapping KM practice in organizations. This approach involves an objective assessment process that can be used by knowledge-intensive organizations for gauging their current position with regard to KM activities and processes. The chapter shows that uncertainty can present a barrier to the introduction of suitable activities for improving KM processes in most organizations. The authors believe that the research can be offer significant practical advantages and will provide substantial support for leaders and managers in establishing KM. Moreover, the right KM activities can help to stimulate creative thinking and provide a spur to inventions and innovations. The authors show that to ensure success and the long-term benefits from effective applications of OL and KM practice is of critical importance to many organizations. Besides simply evaluating the benefits that are inherent in KM, the work indicates that organizations must learn to recognize and manage the different areas of their KM practices. The authors’ innovative solution, their Knowledge Management Profile, involves the formulation of a new KM maturity model that, it is suggested, may be of vital importance in improving KM practice. The issue of recognizing innovation through social network analysis is addressed in Grippa and Elia through their case study of their Virtual eBMS (VeBMS) project at an Italian university. The authors contend that advances in communication technologies have enabled organizations to develop and operate decentralized organizational structures by supporting coordination among workers in different locations. Such developments have led to a lessening of the degree of formality in control structures and a replacement, to a certain extent, of formal channels of communication with less formal social networks. The authors offer the observation that managers need to match such changes with new processes and tools to continuously monitor the less-obvious social interchanges and relationships within and across their organizations to manage and assess the effectiveness of this innovative type of knowledge network. They describe the development and application of a ‘Social Network Scorecard’ (SNS) tool that can be used to monitor how an interdisciplinary and inter-organizational project team (made up of individuals from academia and industry) was able to collaborate on the implementation of a technological platform that allowed the integration of KM, e-Learning, e-Business, and project management disciplines in a higher education environment. The VeBMS platform consists of a collaborative working environment that supports a range of knowledge-sharing and learning processes within the University of Salento, Italy and was used as a ‘test bed’ to evaluate the validity of the scorecard in practice. The chapter describes how the SNS tool can help monitor the evolution of an organizational community, recognizing creative roles and initiatives and tracing the connections between such initiatives and innovative outcomes. Looking at trends at individual, team and (inter-)organizational levels, we identified the most innovative phases within the team’s life cycle, using network indicators like density and degree centrality. The SNS provided feedback on the effectiveness of the team working to create the integrated platform and helped discover the phases in which the team acted in a manner conducive to innovation. They recognized in the Virtual eBMS project team the typical structure of an innovative knowledge network where learning networks and innovation networks co-exist with a more sparse interest network. Sharp, Eardley, and Shah observe that organizations face the problem of creating a KM strategy that takes account of the complexity of their knowledge issues while being able to communicate them clearly. This issue, called here the Knowledge Strategy Dilemma, is the main theme of this chapter, and the authors maintain that a solution to the dilemma is vital for KM to succeed in practice. They argue,

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however, that the literature shows that the dilemma can be tackled, although the literature includes approaches that help address different parts of the dilemma, there is a lack of an overall or integrated solution. The authors suggest that the best approach to address it in a coherent way is by using a KM method called MaKE, which they have developed for this purpose. The MaKE method is presented and two of its most important principles (traceability and transparency) are explained. Also a set of visual tools that help implement these principles in practice are critically discussed along with some indications of feedback from industry. The authors conclude that these principles, when applied, are capable of some success, and that different forms of communication (including face-to-face meetings with visual aids) are vital in addressing the dilemma. Uden and Naaranoja demonstrate that innovation and knowledge management can no longer be considered to be luxury items. Rather, they should be regarded as strategic necessities and an important source of competitiveness and economic development. As resources, say the authors, knowledge and innovation cannot be separated and knowledge management is essential to innovative organisations. This chapter relates innovation closely to knowledge management and discusses the importance of knowledge management in innovation for organisations. It describes how innovation can be achieved through knowledge management through the use of a case study involving a school renovation and building project in Finland. The case study shows how the process of knowledge creation and sharing was used in practice to help innovation through ‘vision building’. Kamoshida shows that the aim of innovative management is to intentionally create a chaos edge and to foster the innovations that are created as a result. The term “chaos edge” is usually used in complexity studies, but Kamoshida observes that it is also highly applicable to management, where innovation results from creative competition. In this chapter, the management concept used to create such innovation is called “holonic management”. Holonic management requires the elements of cultivation, competition, evaluation, and support. According to Kamoshida, constructing a chaos edge in a holonic management system can foster an internal environment in which ideas are created within a framework of cooperative work. The author’s idea of “heretical management” takes the innovations that are created by a minor group within a corporation and allows them to grow without fear of failure. Kamoshida suggests that this is not just the most effective tool but is a sound method of knowledge management. It is our belief that the twenty chapters in this book, from a wide range of authors, makes a significant contribution to the body of literature on the important topics of KM and innovation and their conjunction. The concept of knowledge management and the capture and use of knowledge in a variety of organizations are topics that have been explored in a number of recent publications. Much of this recent work is theoretical or generic in its approach and applies to the process of KM, rather than to its application. This book explores a specific aspect of knowledge management: the exploitation of knowledge in support of innovation and change. It is hoped that this book will help to create and inform useful thought and debate in this important area of knowledge. Alan Eardley Staffordshire University, UK Lorna Uden Staffordshire University, UK

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REFERENCES Amabile, T. M., Conti, R., Coon, H., Lazenby, J., & Herron, M. (1996). Assessing the work environment for creativity. Academy of Management Journal, 39(5), 1154-1184. Boisot, M., & Canals, A. (2004). Data, information and knowledge: Have we got it right? Journal of Evolutionary Economics, 14, 43- 67. Boje, D.M. (2001). Narrative methods for organizational and communication research. Thousand Oaks, CA: Sage. Bontis, N. (2002). World congress of intellectual capital readings. Boston: Elsevier. Borgatti, S.P., & Cross, R. (2003). A relational view of information seeking and learning in social networks. Management Science, 49(4), 432-445. Borgatti, S.P, & Carboni, I. (2007). Measuring individual knowledge in organizations. Organizational Research Methods, 10(3), 449-462. Byrd, J. (2003). The innovation equation: Building creativity & risk taking in your organization. San Fransisco: Jossey-Bass. Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology. Boston: Harvard Business School Press. Chun, C. W. (2003). Perspectives on managing knowledge in organizations. Cataloging and Classification Monthly, 37(1-2). Davenport, T. H., & Prusak, L. (1998). Working knowledge: How organizations manage what they know. Cambridge, MA: Harvard Business School Press. Davila, T., Epstein, M. J., & Shelton, R. (2006). Making innovation work: How to manage it, measure it, and profit from it. Upper Saddle River, NJ: Wharton School Publishing. Denning, S. (2000). The springboard: How storytelling ignites action in knowledge-era organizations. London: Butterworth. Denning, S. (2005) The Leader's Guide to Storytelling. Jossey-Bass, San Francisco. Dyson, F. (1998). Imagined Worlds. Cambridge, MA: Harvard University Press. Edvinsson, L., & Malone, M. S. (1997). Intellectual capital: Realizing your company's true value by finding its hidden brainpower. New York:Harper Collins. Fagerberg, J. (2004). Innovation: A guide to the literature. In Fagerberg, J., Mowery, D. C., & Nelson, R. R. (Eds.), The Oxford Handbook of Innovations. Oxford: Oxford University Press. Kuhn, T. S. (1970). The structure of scientific revolutions (2nd Ed.). Chicago: University of Chicago Press. Krebs, V. (2008). Social capital: The key to success in the 21st century organization. [Special edition on social networks]. IHRIM Journal, 12(5), 38.

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Luecke, R., & Katz, R. (2003). Managing creativity and innovation. Boston: Harvard Business School Press. Malhotra, Y. (2000). Knowledge assets in the global economy: Assessment of national intellectual capital. Journal of Global Information Management, 8(3), 5-15. McKeown, M. (2008). The truth about innovation. London: Pearson/Financial Times. Nonaka, I. (1991). The knowledge creating company. Harvard Business Review, 69, 96-104. Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. New York: Oxford University Press. Polanyi, M. (1997). Personal knowledge: Towards a post-critical philosophy. Chicago: University Of Chicago Press. Probst, G. (1998). Practical knowledge management: A model that works. Prism, 2, 17-29. Snowden, D. (2004). Narrative patterns: The perils and possibilities of using story in organizations. In E. Lesser & L. Prusak (Eds.), Creating value with knowledge. Oxford: Oxford University Press. Spender, J. C. (2008). Organizational learning and knowledge management: Whence and whither? Management Learning, 39(2), 159-176. Sveiby, K. E. (1997). The new organizational wealth: Managing and measuring knowledge-based assets. San Francisco: Berrett-Koehler. Thompson, M. P. A., & Walsham, G. (2004). Placing knowledge management in context. Journal of Management Studies, 41(5), 725-747. Von Krogh, G. (1998). Care in knowledge creation. California Management Review, 40(3), 133-153. Wenger, E., McDermott, R., & Snyder, R. (2002). Cultivating communities of practice: A guide to managing knowledge. Boston: Harvard Business School Press.

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Acknowledgment

The authors would like to acknowledge the contribution of the many people without whose help and patience this book would not have been possible. It is not possible to name them all, so the absence of a particular mention should not imply a lack of appreciation. First we would like to thank the authors from many countries and academic and business areas who have contributed their time and expertise to create a series of chapters that will, it is hoped, provide a varied and informative range of views on the conjoint topics of knowledge management and innovation. Without their work, there would not be a book to add to the body of knowledge in this interesting and important area. Our special thanks go to the editorial team at IGI Global, especially Christine Bufton and Hannah Abelbeck, both of whom have shown great patience and support throughout the lengthy process of editing of the book. Thanks to their care and diligence, any faults in the book are purely the work of the editors. On the personal side, Lorna would like to thank her family for supporting her. She would also like to give thanks to God for His Grace throughout the period. Alan Eardley Staffordshire University, UK Lorna Uden Staffordshire University, UK

Section 1

Knowledge Management and Innovation

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Chapter 1

Universities as KnowledgeIntensive Learning Organizations Constantin Bratianu Academy of Economic Studies, Romania

ABSTRACT The purpose of this chapter is to critically analyze the universities as knowledge intensive learning organizations. It is axiomatic that universities are knowledge organizations since by their own nature universities create, acquire, and transfer knowledge in complex ways. They are knowledge intensive organizations since the density of knowledge field and the dynamics of knowledge processing are much greater than many other organizations. Since learning is one of the major processes within any university, people may consider universities as being by definition learning organizations. This idea induced by a semantic halo effect may lead to a major error. Although a university is an organization based on learning processes, it is not necessary a learning organization. This paper performs a functional analysis of the specific knowledge processes in order to identify the necessary conditions for a generic university to become a learning organization.

INTRODUCTION Universities are among the oldest institutions in Europe, solving creatively the paradox of continuity for many centuries. The paradox is generated by the mission of the university which integrates conflicting tasks ranging from knowledge preservation to knowledge creation: ‘Their survival, often in the same locations, even in the DOI: 10.4018/978-1-60566-701-0.ch001

same buildings, with many of the same activities, may on one level be proof of their conservatism. I believe that on another level it is also proof of the ability of the university to anticipate, to generate or incorporate new knowledge and new ways of thinking – sometimes hesitantly, sometimes slowly, but always with its essential intellectual values and mission intact’(Mayor, 1997, p. 143). Based on a minimum set of functional characteristics, experts in the history of higher education consider the first European universities those cre-

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Universities as Knowledge-Intensive Learning Organizations

ated in Bologna, Paris and Montpellier, followed by those developed at Oxford and Salamanca (Rüegg, 2004). The venerable Bologna University dates from 1088, and the famous Oxford University dates from 1187. However, the mission and the functional matrix of those initial institutions of higher education differ considerably from the present day universities. Main activities associated with these universities were collecting knowledge, preserving it and transfer it to the new generations of students. Knowledge generation was not a part of their mission. A professor was mostly a scholar and not a researcher. Knowledge was considered as a complete set of concepts and ideas about the world, and it was quite static in time. Thus, the purpose of professors was only to transfer this knowledge body to the students. We may say that these first institutions have been designed to acquire and process knowledge, and to deliver value for society in terms of mental representations. The second generation of universities have been established mostly by religious and political powers aiming at developing professional elites to serve their social institutions (Harayama, 1997; Jongbloed et al., 1999). Their main functional structures were designed for professional oriented knowledge processing. In 1810, the University of Berlin was founded on a new paradigm developed by Wilhelm von Humboldt. In this new perspective, a university should approach knowledge scientifically (Gibbons, 1997; Marga, 2005; Mehallis, 1997). It should produce knowledge, not re-produce it. ‘According to Humboldt’s conception, research progress contributes to the elaboration of a system of values that has an influence beyond the walls of academic institutions.’(Harayama, 1997, p. 9). The new Humboldtian paradigm is founded on the unity and the complementary role of teaching and research functions: ‘The subjects to be taught are composed not only of already consolidated knowledge, but also of those elements that remain to be discovered. Therefore, the teaching

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and learning process through the activities of research.’(Harayama, 1997, p. 13). Knowledge generation proved to be a natural constituent of the modern university, its contribution being taken into account in any evaluation metric and any system of ranking universities (Aguillo, Ortega & Fernandez, 2008; Cheng & Liu, 2008). Thus, we may say that universities are entities dedicated to create, preserve and transfer knowledge. Some authors discuss now about a third mission of the university which is that of creating services for society. This is a rather debatable issue since: ‘There is a part of the academic community that is already processing the first academic revolution, i.e. the evolution from teaching to research. Similar resistances found in the first revolution appeared over the last thirty years during the process of applying the second revolution: from teaching and research to services.’(Montesinos et al., 2008, p. 259). The mission of the university, as resulted from its historical evolution, is to create, preserve and transfer knowledge to students and to society. Since all of these mission components involve knowledge creation and knowledge transformation processes, the university is a knowledge intensive organization. Also, universities are by their nature learning based organizations. They deliver knowledge to the students through teaching processes. Students acquire knowledge through learning processes, from their professors and from other different knowledge resources. Since learning is a fundamental process within any university, people may consider universities as being learning organizations. This would be a major mistake, since the transition from individual to collective learning and from collective to organizational learning requires some critical functional conditions that are not fulfilled by most of the universities. The purpose of this chapter is to critically asses and analyse the functional processes within a generic university, and then to

Universities as Knowledge-Intensive Learning Organizations

develop a theoretical investigation concerning the necessary conditions for such a university to become a learning organization. The chapter will be structured as follows: (1) conceptual background; (2) functional analysis of the knowledge processes within a university; (3) university management and leadership; (4) future research directions; (5) conclusion.

BACKGROUND It is an axiom that a university is a knowledge organization. Knowledge is the basic resource used by professors and the main outcome used by students. However, it is extremely difficult to show and to measure knowledge as an outcome since it is intangible and it can be found in the mind of students. Knowledge is the result of processing information and other knowledge forms. Since it is a concept with a complex semantic, it is difficult to be defined. Some authors prefer to work with operational definitions, which are good enough to be used, but remain fuzzy and incomplete. One of the mostly used one is the following: ‘Knowledge is a fluid mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information. It originates and is applied in the mind of knower. In organization, it often becomes embedded not only in documents or repositories, but also in organizational routines, processes, practices, and norms.’ (Davenport & Prusak, 2000, p. 5). Nonaka and Takeuchi (1995) prefer to discuss about explicit knowledge and tacit knowledge. Explicit knowledge ‘can be articulated in formal language including grammatical statements, mathematical expressions, specifications, manuals and so forth’ (Nonaka & Takeuchi, 1995, p. VIII). Tacit knowledge ‘is personal knowledge embedded in individual experience and involves intangible

factors such as personal belief, perspective, and the value system’ (Nonaka & Takeuchi, 1995, p. VIII). Japanese companies have a different view of knowledge by comparison with the western companies. For the Japanese companies explicit knowledge plays a minor role in the organizational life being only the visible part of an iceberg. The major role is played by the tacit knowledge which is highly personal and hard to formalise: ‘Subjective insights, intuitions, and hunches fall into this category of knowledge. Furthermore, tacit knowledge is deeply rooted in an individual’s action and experiences, as well as in the ideals, values, or emotions he or she embraces.’(Nonaka & Takeuchi, 1995, p. 8). It is interesting to remark the fact that knowledge in the western perspective is mostly rational knowledge, while in the eastern perspective knowledge means both rational and emotional knowledge (Baumard, 1999; Debowski, 2006; Eucker, 2007; McElroy, 2003; Styhre, 2004). Some other authors prefer to work with metaphors. They use metaphors in order to give meaning to knowledge (Andriessen, 2008; 2006; Bratianu, 2008a). Metaphors are mental models (Senge, 1990) people use in order to better understand the real world. They are cognitive approximations developed throughout our education in family, schools, community, church and university (Bratianu, 2007a). A metaphor is not just a semantic similarity between two concepts, but an instrument to develop a new cognitive approximation using a well known concept. It helps in providing a perspective for the new concept, emphasizing certain key characteristics and ignoring others. The known concept is considered the semantic source domain, and the knowledge concept is considered the semantic target domain. It is of interest to mention the fact that in the West dominant metaphors of knowledge are based on the idea of knowledge as stuff. In the East dominant metaphors of knowledge are based on the idea of knowledge as feelings. Organizational knowledge is like a field of forces, highly nonlinear and strongly dynamic

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Universities as Knowledge-Intensive Learning Organizations

(Bratianu & Andriessen, 2008). Any variation of this field generates fluxes or flows, and these fluxes generate processes. When such a process yields a new knowledge configuration leading to a better understanding and decision making, we consider it a learning process. At the individual level, learning produces mental events (Naeve, Sicilia & Lytras, 2008) and relatively permanent changes in the behavioural potential (Maier, Prange & Rosenstiel, 2003) in a given context. Lytras and Pouloudi (2006) frame their research in terms of a learning flow, which ‘corresponds to the archetype of human behaviour that through action and feedback promotes the understanding and adoption to the environment. The contextual character of learning is of critical importance’(p. 67). Individual learning incorporates analysis, decision making, knowledge structuring and storing, action and reflection. It is a highly nonlinear and dynamic process (Baron, 2000; Bratianu & Murakawa, 2004; Senge, 1990; Sternberg, 2005; Vance et al, 2007). Organizational learning has been conceived as a collective process done by integrating all individual constituents and changing the individual pattern of behaviour to an organizational one. Integration is a highly nonlinear process which aggregates individual contributions based on their restructuring flexibility, and synergy generation. Argyris and Schon (1978; 1991; 1996) developed a multi-dimensional analysis of organizational learning known as single and double loop models. They consider that organizations learn through the agency of individual members, by aggregating nonlinearly their contributions. In the single loop model of learning, outcomes are continuously checked against some reference parameters, and errors will be corrected through the feedback action. This is a continuous improvement process which greatly contributed to the TQM success (Hedberg & Wolf, 2003; Oakland, 2003). In the double loop model of learning, the governing vari-

4

ables can be changed, which means a deeper level of transformation. According to Argyris (1999), ‘Governing variables are the preferred states that individuals strive to satisfy when they are acting. These governing variables are not the underlying beliefs or values people espouse. They are variable that can be inferred, by observing the actions of individuals acting as agents for the organization, to drive and guide their actions.’ (p. 68). From organizational learning researchers directed their investigation efforts and imagination toward learning organization. These two concepts should not be used interchangeably since they represent different semantic entities. Organizational learning means activities and processes of learning in the organization, while learning organization refers to a certain type of organizations (Ortenblad, 2001). According to Senge (1990), the basic meaning of a learning organization is: ‘an organization that is continually expanding its capacity to create its future. For such an organization, it is not enough merely to survive’ (p. 14). Senge distinguishes between adaptive and generative learning. Adaptive learning is for survival and quality improvement. It is a reactive process stimulated by the external environment dynamics. Generative learning is stimulated by the internal environment dynamics, and it is concerned mainly with developing new perspectives, options, and exploring new possibilities for future structuring. Reinterpreting the learning organization, Stewart (2001) considers that Senge’s theory received a large attention from the business environment since it embraces ‘many of the vital qualities for today’s organisations, i.e. teamwork, empowerment, participation, flexibility and responsiveness’ (p. 143). Organizational learning can be greatly enhanced if the university develop a significant absorptive capacity, defined by Cohen and Levinthal (1990) as being: ‘the ability of a firm to recognize

Universities as Knowledge-Intensive Learning Organizations

the value of new, external information, assimilate it, and apply it to commercial ends’ (p. 128). For a generic university, these commercial ends can be substituted with its mission objectives. The absorptive capacity is the ability to exploit external knowledge, as an open system with respect to the knowledge field and well defined mechanisms for integrating internally and externally knowledge generation. Thus, prior related knowledge constitutes a functional prerequisite to recognize the value of new knowledge, assimilate it, and use it in the framework established by the university vision and mission. This concept of absorptive capacity can be best understood and developed through an examination of the functional structure of the knowledge processes, which will be discussed in the next section. Also, this concept is closely related to other two concepts: dynamic capabilities (Teece et al., 1997; Eisenhardt & Martin, 2000), and corporate universities (Meisner, 1998; Stumpf, 1998) which will be addressed in the next sections of the present chapter. The semantic halo effect of the learning processes within a university makes people think that universities are learning organizations. This is not the case with many of them due to some organizational learning barriers. Actually, a paradox might be formulated from this perspective: ‘Although a university is an organization based on learning processes, it is not necessary a learning organization’ (Bratianu, 2007b). A given university can become a learning organization if and only if there is at least a strong integrator to assure the transition from individual learning to team and organizational learning. Also, it would be important to advance from adaptive to generative learning. Most universities are far away from being learning organizations due to some mental and functional barriers. Identifying and evaluating these barriers would help in designing adequate solutions to transform these universities in successful learning organizations, able to compete on the new global market of higher education.

FUNCTIONAL ANALYSIS OF KNOWLEDGE PROCESSES The core competences for a generic university are: teaching and learning, performing research, and performing service to society. The degree to which each of these core competences is developed differs from one university to another, according to its mission, its structure, its management and its capacity to compete. Some universities concentrate their energies on developing only teaching programs, others develop teaching and research programs, and the world class universities focus on research through their doctoral programs and research grants (Shattock, 2003). The functional structure of a generic university is illustrated in Figure 1. Teaching and learning activities for students are structured into universities programs at the level of Bachelor and Master. Doctoral programs integrate both learning and research activities. These universities programs are organized under the authority of schools or faculties, according to the existing legislation. In Europe, the Bologna process developed in the last ten years contributed essentially to a new structure of the higher education process in many countries: Bachelor program, Master program and Doctoral program. Although there are some differences in the periods of time needed to successfully accomplished each of this program in different countries, this new functional structure became the main characteristic of the continental European universities. Bachelor and Master programs are mainly based on knowledge transfer processes, while the doctoral program is based mainly on knowledge generation through research. In Table 1 there is a matrix presentation of all types of knowledge processes associated to university programs, and the university management. They are not clear cut entities and their names might have some overlapping meanings. However, they represent the main manifestations of the organizational knowledge field, and we have to understand them in order to analyse the learn-

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Universities as Knowledge-Intensive Learning Organizations

Figure 1. Functional structure of knowledge processes

ing competencies of the university. It is also important to remark the fact that many authors ignore the management contribution to the knowledge dynamics within a university, although this management plays the fundamental role in developing the university as an intensive knowledge learning organization.

journals, research reports, video conferences etc. It is a conscious and oriented managerial process to buy or to get through exchange new knowledge from the external knowledge environment. In this category enter also new links to virtual libraries of other universities and research centres, as well as open access to internet knowledge portals. All major publishing houses and professional associations editing scientific journals have created huge data bases from which universities may perform any particular forms of acquisitions. In learning organizations this type of knowledge process is conceived at the organizational level, and run by

Knowledge Acquisition This is a generic process aiming at developing the organizational knowledge field through embedded knowledge in books, software programs, scientific Table 1. Knowledge processes at organizational level Knowledge processes Acquisition

Bachelor program

Master program

x

x

Socialization

x

x

Externalization

x

Combination Internalization

Doctoral program

Research program

Management

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

Dissemination

x

x

x

x

x

Storage

x

x

x

x

x

Retrieval

x

x

x

x

x

Generation

6

x

Universities as Knowledge-Intensive Learning Organizations

professional knowledge managers. In non-learning organizations this knowledge process is still conceived and run at individual level, with low efficiency and decreased motivation. Acquisition is ultimately an intelligent investment with long term benefits. It is strongly related to the absorptive capacity of the university which refers not only to the acquisition or assimilation of information by an organization but also to the organization’s ability to exploit it. This organizational absorptive capacity depends on the absorptive capacities of its individual members, but it is not, however, simply the sum of them (Cohen & Levinthal, 1990). Being a highly nonlinear process, many managers just cannot understand how to equate short term costs with long term benefits, and how to stimulate students, professors and researchers to navigate into these new learning environments of exceptional values. As a consequence, they are not able to find the necessary financial resources in order to increase the acquisition role in developing the learning university.

Knowledge Generation There are several categories of knowledge generation processes, ranging from restructuring knowledge to creating new knowledge. Since Nonaka (1991; 1994), Nonaka and Takeuchi (1995), and Nonaka and Konno (1998) developed a series of theories concerning knowledge creation based on reciprocal transformations of tacit knowledge into explicit knowledge, and their transfer in a social environment, this process of knowledge generation refers mostly to research outcomes. Scientific discoveries and innovations in all fields of activities resulted from an organizational structure of the university, as a specific outcome of the research projects constitute important contributions to the knowledge universe. World class universities are those universities able to bring important contributions to the progress of science and technology through major results obtained in research activities, published in the main stream of scientific journals. World class universities are

intensive knowledge learning organizations since they generate knowledge more than any other kind of organizations. Their knowledge management played the major role in developing such a perspective and in making continuous and significant investments in strengthening knowledge generation as a core competence able to produce a sustainable competitive advantage. Knowledge generation and its embedding into published papers in the most prestigious scientific journals constitute the main criteria for universities rankings. For instance, the famous ‘Shanghai ranking’ of the world universities performed by the Institute of Higher Education of Shanghai Jiao Tong University is based on the following indicators: (1) Alumni of an institution winning Nobel Prizes and Fields Medals (10%); (2) Staff of an institution winning Nobel Prizes and Fields Medals (20%); (3) Highly cited researchers in 21 broad subject categories (20%); (4) Articles published in Nature and Science (20%); (5) Articles in Science Citation Index-expanded, Social Science Citation Index (20%); (6) Academic performance with respect to the size of an institution (10%), (http://www.arwu.org/rank/2007/ARWU2007_ Top100.htm). These indicators may discourage at first glance any attempt of relating knowledge generation to learning organization and the significance of world class universities. However, if we consider the simple fact that winning a Nobel Prize by any professor means years of research at organizational level, best equipped laboratories, tradition and a special organizational culture able to stimulate young researchers and doctoral students for performance, then we conclude that such indicators measure the knowledge generation competence of the university. Research universities have developed strong doctoral programs and their knowledge management demonstrated a successful leadership. Just to have an idea of what means to be a world class university we present in Table 2 the first ten universities ranked by the Institute of Higher Education of Shanghai Jiao Tong University for 2007.

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Universities as Knowledge-Intensive Learning Organizations

Socialization According to Nonaka, Toyama and Byosiere (2003) ‘Socialization is the process of bringing together tacit knowledge through shared experiences’(p. 495). Since tacit knowledge is context dependent and very difficult to express, the key to socialization is to share the same experience through joint activities. Socialization is strongly linked with the cooperation and team working, values which are specific for Japanese education (Ohmae, 1982). In Western cultures is encouraged individual work and competition between individuals, which means a reduced level of socialization. Considering the type of programs shown in Table 1, is easy to understand that Bachelor and Master Programs have little socialization in their structure, with the exception of those fields of knowledge where internship programs are developed. For instance, in the medical education internship programs in hospitals are contributing to sharing tacit knowledge. By contrast to them, individual homework developed in American universities reduces drastically the socialization process. Learning universities, like learning companies, must find new ways of developing socialization as a part of university education. Socialization is

difficult to manage because it is the conversion of tacit knowledge. It is necessary to cultivate the cultural values of trust, friendship, care and love in order to overcome more easily the individualism barriers. Then, the knowledge management must design learning activities for groups of students, and for internship modules. Only transcending individual boundaries socialization can be effective in tacit knowledge transfer.

Externalization Externalization means to extract valuable parts of the tacit knowledge and to express them into a rational form, easy to be transferred and understood by other individuals. Externalization is a pure individual process and it is the key element for knowledge creation. ‘When tacit knowledge is made explicit, knowledge becomes crystallized, at which point it can be shared by others and can be made the basis for new knowledge’(Nonaka, Toyama & Byosiere, 2003, p. 495). The success of externalization comes from an efficient use of metaphors, analogies and models. Metaphors help understanding new concepts intuitively by using semantic domains of known concepts. They are able to connect concepts which apparently are

Table 2. The top ten world universities for 2007 (http://www.arwu.org/rank/2007/ARWU2007_Top100. htm, retrieved on 29 October 2008) Institution

World rank

8

Country

National rank

1

Harvard University

USA

1

2

Stanford University

USA

2

3

University of California at Berkeley

USA

3

4

Cambridge University

UK

1

5

Massachusetts Institute of Technology (MIT)

USA

4

6

California Institute of Technology

USA

5

7

Columbia University

USA

6

8

Princeton University

USA

7

9

University of Chicago

USA

8

10

Oxford University

UK

2

Universities as Knowledge-Intensive Learning Organizations

uncorrelated, and to highlight some common features which can then be further clarified by using analogies. These analogies help one understand the unknown through the known, and yield a link between an image and a rational model. Modelling and simulation are very useful teaching instruments in the learning environment. In universities, externalization processes are extremely important for researchers to formulate their results and to interpret them in new perspectives. Thus, externalization plays an important role especially in doctoral programs, where research is essential and individual experience of students is not so large. Also, in teaching activities associated to Bachelor and Master programs externalization is an important process for those professors who have a rich experience in the discipline field, and who can use it for teaching. Unlike undergraduate students, those attending MBA or EMBA programs have already a great deal of professional experience. For them, the structure of the program must contain more activities adequate to promoting externalization activities. However, in order to develop this process as an organizational capability, and to make use of all the tacit knowledge in the university it is necessary to stimulate continuously people to make the effort of self-analysis and self-interrogation. The university becomes a learning organization if its knowledge management understands the importance and the benefits of encouraging externalization in all academic areas and research activities.

Combination Combination is a process by which discrete pieces of knowledge are aggregated into a set of rational knowledge, and then transfer to some other people. The aggregated product may be considered as a new organizational knowledge. For example, let us consider the annual report of the university. It is the practical result of integrating knowledge

coming from each department and school, and then interpreted at the organizational level. This report is written, distributed to all stakeholders and discussed. The knowledge contained into this report has been obtained through the combination of different components, and it brings new knowledge about the university successes in the past year. Creative use of computerized communication networks and large scale databases can improve this combination process. From learning perspective, combination is a pivotal process since all the knowledge transfer from professors toward students is realized through it. In the Nonaka knowledge cycle combination is the only rational and explicit process having a social dimension. Socialization, on the other hand is a non-rational and implicit process having a social dimension. Learning universities develop combination by replacing the classical professor – students transfer process with creating learning environments where students are active knowledge agents. Learning universities break down the virtual walls between classical disciplines and invite professors, researchers and students to an inter- and multidisciplinary approach. It is known that knowledge variety is stimulating creation of new knowledge, and that combination is used in all intensive creativity activities like focus groups and brainstorming. Knowledge management in such a learning university will find always ways to stimulate combination through networking, e-learning and virtual campuses. Combination increases the knowledge entropy and stability of the system through dissemination, and stimulates organizational innovation at all levels. Combination integrates formal and informal communicating networks, and contributes to improving the decision making process. Combination can be used a vehicle process in problem-based learning, to increase students contribution to the learning process and to develop their critical thinking (Eardley & Uden, 2008).

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Universities as Knowledge-Intensive Learning Organizations

Internalization This is the final process in the Nonaka knowledge cycle. Internalization means transforming explicit knowledge into tacit knowledge at the individual level. According to Nonaka, Toyama & Byosiere (2003) ‘Internalization is the process of embodying explicit knowledge as tacit knowledge. It is closely related to learning-by-doing’ (p. 497). From a practical point of view, internalization contains two aspects. First, explicit knowledge is embodied in action and actualizes concepts about strategy, innovation, or improvement (i.e. training programs for employees). Second, explicit knowledge can be embodied through simulations or experiments in order to stimulate learning-by-doing mechanisms. In classical teaching of the non-learning universities internalization is assumed by professors, such that there is no organizational effort of stimulating and improving it. Knowledge management in the learning universities uses motivation as a leverage mechanism, and de-construct all bottlenecks by implementing high speed knowledge fluxes and user friendly systems. It is important to stress the fact that internalization is a highly nonlinear and personal process. It can be improved only by creating an adequate motivational field and stimulating individual innovation. Considering the overall perspective of the Nonaka cycle, ‘Knowledge is created through a continuous and dynamic interaction between tacit and explicit knowledge. This interaction is shaped through the SECI process, that is, through the shifts from one mode of knowledge conversion to the next: socialization, externalization, combination, and internalization’(Nonaka, Toyama & Byosiere, 2003, p. 497). This ‘Nonaka cycle’ is not confined to a certain organizational level. It is produced from individual to group and organizational levels, and from a very simple configuration to a very large complexity of the social environment. Learning universities must analyse their knowledge cycles

10

and develop strategies to increase the speed and densities of all knowledge fluxes.

Dissemination Dissemination might be considered identical with knowledge transfer. However, it can be distinguished from it since it is an asynchronous and unidirectional process. Dissemination is done using different media systems, from virtual books to web pages. Knowledge is posted for potential consumers, but there is no certain end user. Thus, we speak about dissemination as a specific knowledge transfer from a higher level to a lower level of understanding, when the end users are not synchronous receivers with the knowledge disseminator. This type of processes is frequently used in e-learning, online learning and virtual campuses. Learning universities are aware of these new opportunities and use dissemination for all categories of learners from inside and outside of the university. Dissemination became an important knowledge process in the third mission of the university, i.e. performing service to the community. Dissemination assumes a high level of transparency, new mechanisms for validating the knowledge sources, and improved semantic webs.

Storage and Retrieval Storage is an organizational process by which knowledge can be deposited in a structured way, such that retrieval can be done efficiently. In a classical campus, storage could have been done by organizing library spaces for books and scientific journals. In the new university campuses storage means using intelligently computer facilities and advanced software programs for knowledge retrieval and data mining. These processes become more important for the virtual universities, which should incorporate the features of the learning organizations.

Universities as Knowledge-Intensive Learning Organizations

LEARNING ORGANIZATIONS Beyond the debate between individual learning and organizational learning (Argyris, 1999; Dierkes et al., 2001; Senge, 1990), empirical and theoretical research demonstrated the existence of learning organizations. They are ‘organizations where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspirations is set free, and where people are continually learning how to learn together’ (Senge, 1990, p.3). Learning occurs when the organization is able to construct solutions for new problems, or within new environments. Learning organizations have complex and dynamic functional structures able to display two learning loops: a first loop characterized by feedback, and a second loop characterized by feedforward. The first loop is important for connecting outputs to inputs, in order to eliminate any mismatch between the actual product and the target product. Feedback is necessary for production control and its adjustment. However, single loop learning is effective especially in static environments. For dynamic environments and turbulent changes, single loop learning is not enough anymore. A second loop is necessary for changing the system settings and to create conditions for organizational adaptation to the new environment requirements. For an university, the theory of single loop learning emphasizes the production process, which means the levels of individual and group learning incorporated in the Bachelor, Master and Doctoral programs. Improvements can be obtained through individual efforts and an efficient quality management at the organizational levels. However, single loop learning is based only on feedback effects and it aims to improve knowledge processes by corrections with respect to some standard values or norms. Thus, the results of single-loop learning are limited by the level of these standards. Also, single loop learning entails the development of the organization dynamic capabilities (Eisen-

hardt & Martin, 2000; Teece et al., 1997). These dynamic capabilities consist of specific strategic and operational processes that create value for organizations within dynamic external business environment by manipulating efficiently tangible and intangible resources. Dynamic capabilities can be defined as: ‘The firm’s processes that use resources – specifically the processes to integrate, reconfigure, gain and release resources – to match and even create market change. Dynamic capabilities thus are the organizational and strategic routines by which firms achieve new resource configuration as markets emerge, collide, split, evolve, and die.’(Eisenhardt & Martin, 2000, p. 1107). Actually, these dynamic capabilities enable the organization to achieve a dynamic equilibrium between the internal field of forces and the external field of forces. They emphasize the key role of strategic management in ‘appropriately adapting, integrating, and reconfiguring internal and external organizational skills, resources, and functional competences to match the requirement of a changing environment’ (Teece et al., 1997, p. 515). The theory of double loop learning emphasizes the importance of the management process, and of the organizational culture. Values and standards are formulated by the top management, and they can be changed only through a conscious process of knowledge management. This is a powerful integrator, especially in a university where the production process is mainly a learning process (Bratianu, 2008a). According to Bratianu, Jianu & Vasilache (2007), ‘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 posses the capacity of interacting in a controlled way’. The interdependence property is necessary for combining all elements into a system. The

11

Universities as Knowledge-Intensive Learning Organizations

synergy property makes it possible to generate an extra energy or power from the working system. It makes the difference between a linear and a nonlinear system. A learning university must have its management as a powerful integrator of all professors and students, able to learn itself and to contribute to the synergy of the whole system. Unfortunately, in many European universities where the top management members are elected professors, based on democratic procedures, there are very few chances to have real managers. They might be excellent researchers and professors, yet not having any managerial experience and talent. It is only the halo effect that a good professor should be in the same time a good manager. Actually, this halo effect has been used consciously in the former socialist countries, where the political regime had no interests in choosing those professors with managerial abilities (Bratianu, 2008b). These professors acting like deans and rectors were passive managers since all the decisions have been taken in a strongly centralized way at the level of Ministry of Education. For these managers conforming to the ministerial decisions has always been the law, while learning could trigger penalties. Conforming processes are generated by the inertial organizational forces and the influence of the external forces. These external forces represent cultural values, legislation, social an economical developments, education and technology, as well as a given political ideology. The most explicit forces are represented by the specific legislation in education. This legislation establishes how much autonomy a university has in performing its mission to society. For instance, in the former socialist countries the legislation gave no autonomy to universities. Their management had only to apply at the organizational level decisions coming from the Ministry of Education. After the political regime change, universities received in a progressive way some degree of liberty and academic autonomy. That means that the university management can decide upon the curriculum structure and content, specific

12

admission and graduation procedures, selecting and promoting faculty staff, as well as electing by democratic votes their representatives for different managerial positions. Although we have to acknowledge a lot of improvements by comparison with the former political regime, the real decision making process is bounded by financial means which are almost completely in the power of the governmental forces. From this point of view, conforming is much safer then learning and many rectors operate in the domain of strictly applying the imposed regulations without any effort oriented toward changing them (see Figure 2). These universities cannot be considered learning organizations, even if there are some innovations done at the level of individuals or groups of students and professors. These innovations will fit the single loop learning model. In order that a university to become a learning organization is necessary that the innovative forces which are the driving forces of change to be larger than the inertial forces, i.e. the learning component to be greater than the conforming component of the management vector. European universities are in a powerful field of external inertial forces, especially due to their traditions and to their integration into national higher education systems. For instance, the Humboldtian university linked the university to the State, since its mission was to develop professionals for the German State needs (Gare, 2005). That is why these universities will not have full autonomy while receiving financial support from the states, and thus their management learning component will not overcome easily the conformity component. ‘Universities are so linked to their countries that the examination of their governance structures cannot leave aside the governance structures of national higher education systems. Apart from particular characteristics, in fact, throughout Europe, universities are steered and coordinated by central states, directly or through the national

Universities as Knowledge-Intensive Learning Organizations

Figure 2. An illustration of the competing learning and conforming processes

university system. That is why we can say that university governaqnce refers both to the single institution and to the national higher education system.’(Lazzaretti & Tavoletti, 2006). Exceptions come from the traditional English universities which subscribed to the principle of ownership of individual institutions rather than to the incorporation of universities into their national systems. Their management vector enters the learning zone, and there is no surprise why these universities developed so earlier the strategic management by comparison with the French and German universities. The learning university must change its governance structure and reduce its governmental conformity component of the management. This change can be done by a radical reform of corporatization of national universities, and develop a governance based on leadership and competitiveness. This reforming happened in Japan starting on April 1st 2004 (Bratianu, 2004). The main idea of this reform is to replace the democratic election system of managerial functions by a corporate selection procedure based on personal competence and visionary leadership, and to substitute the former academic decision making with a corporate like decision making. Thus, universities get more autonomy in their governance in exchange for more competitive management and leadership. Thus, the conforming component

of the management vector is drastically reduced, and conditions set up for innovation. Universities can become now learning universities. There is a new class of universities that make sustainable efforts to become learning organizations based on their market driven vision. They are the corporate universities, those that invest in flexible learning and innovation to manage knowledge (Meister, 1998; Stumpf, 1998). A corporate university can be defined as ‘a strategic umbrella for developing and educating employees, customers, and suppliers to meet an organization’s strategy’ (Meister, 1998, p. 267). In a synthesis, the most important characteristics of a learning university are the following: • • • • • • •

Visionary leadership; Value driven management; Double loop learning; Intensive communication network; Dynamic organizational culture; Developed absorptive capacity; Decentralized decision making process at the levels.

Vision is a virtual state of the organization in a possible future. Leaders can develop such visions and then develop convergent strategies to achieve such targets. Managers are caught mostly in operational activities and tasks, being under the

13

Universities as Knowledge-Intensive Learning Organizations

pressure of efficiency and conformity. ‘To be sure, it is sometimes difficult to act for the future when the demands of the present can be so powerful and the traditions of the past so difficult to challenge. Yet, perhaps this is the most important role of the university president’ (Duderstadt, 2000, p. 258). Value driven management, which is a direct result of the rising creative class must substitute the controlling management coming from the industrial era. The second loop of learning cannot be functional in the rigid structure of the controlling management, and the controlling management cannot be efficient in a dynamic environment. A substantial change has to be done in the traditional university management and this change has to be supported by a new dynamic organizational culture. Learning means absorptive capacity both at the individual and organizational levels. And all of these characteristics of the learning university cannot exist in a centralized decision making process, as it happens in most former socialist countries. It is imperative to decentralize the decision making down to the levels of the schools and departments, and to develop dynamic capabilities able to sustain double loops of learning.

FUTURE RESEARCH DIRECTIONS The topic of intensive knowledge universities as learning organizations is almost in its incipient phase, due to its complexity and the new perspectives of research. However, it is a crucial research field due to the changing role of the university in society and to the new mission formulations. It is almost an axiom that universities are intensive knowledge organizations due to their knowledge creation and knowledge dynamics. However, this intrinsic characteristic cannot be fully developed due to some barriers in their governance coming from tradition and their links to the nation states. Future directions of research could be the following: (1) university as an intelligent organization; (2) university as an entrepreneurial organization

14

through innovation and knowledge creation; (3) university knowledge dynamics and competitiveness; (4) governance and leadership for successful 21st century universities.

CONCLUSION Globalization and business turbulence changed the economic paradigms and the future predictability of our times. In a complex and strongly nonlinear world, these changes triggered many other changes at lower levels and smaller scales which are context dependent. In this category, we may include a series of changes produced in the academic world that imposed new perspectives of understanding and analysis. Although universities are next to Church the oldest social institutions, they must adapt to the new political, technological, social and economical environments. They are knowledge intensive organizations and their missions integrate learning, research and service to community objectives. Due to a semantic halo effect of their learning processes, universities are considered by many people as learning organizations. The purpose of this chapter is to analyse the evolution of university mission, the functional knowledge processes, and to see the necessary conditions for a university to become a learning organization. Focus is on the European universities due to their traditions and to recent changes triggered by the Bologna process. Since continental universities have democratic election systems for their managerial functions, and they are strongly linked to their governmental field of forces, directly or indirectly through the national university systems, their behaviour is still in the conformity zone. In order to become learning organizations they must change the governance system and to increase innovation in the management process.

Universities as Knowledge-Intensive Learning Organizations

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Lytras, D. M., & Pouloudi, A. (2006). Towards the development of a novel taxonomy of knowledge management systems from a learning perspective: An integrated approach to learning and knowledge infrastructures. Journal of Knowledge Management, 10(6), 64–80. doi:10.1108/13673270610709224 Maier, G. W., Prange, C., & Rosenstiel, L. (2003). Psychological perspectives of organizational learning. In Dierkes, M., Antal, A. B., Child, J., & Nonaka, I. (Eds.), Handbook of organizational learning and knowledge (pp. 14–35). Oxford: Oxford University Press. Marga, A. (2005). University reform today (4th ed.). Cluj-Napoca, Romania: Cluj University Press. Mayor, F. (1997). The universal university: The university – the crucible of Europe. CRE-CEPES UNESCO Papers on Higher Education, 111, 143–151. McElroy, M. W. (2003). The new knowledge management: Complexity, learning, and sustainable innovation. Amsterdam: Butterworth Heinemann. Mehallis, M. V. (1997). Teaching versus research: An outmoded debate in the knowledge society. Higher Education in Europe, 22(1), 31–43. doi:10.1080/0379772970220104 Meister, J. (1998). Corporate universities: Lessons in building a world-class work force. New York: McGraw-Hill Trade.

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Chapter 2

Key Characteristics Relevant for Selecting Knowledge Management Software Tools Hanlie Smuts University of South Africa & Mobile Telephone Networks (Pty) Ltd, South Africa Alta van der Merwe University of South Africa & Meraka Institute, CSIR, South Africa Marianne Loock University of South Africa, South Africa

ABSTRACT The shift to innovation and knowledge as the primary source of value results in the new economy being led by those who manage knowledge effectively. Today’s organizations are creating and leveraging knowledge, data, and information at an unprecedented pace—a phenomenon that makes the use of technology not an option, but a necessity. Software tools in knowledge management (KM) are a collection of technologies and are not necessarily acquired as a single software solution. Furthermore, these KM software tools have the advantage of using the organization’s existing information technology infrastructure. Organizations and business decision makers spend a great deal of resources and make significant investments in the latest technology, systems, and infrastructure to support KM. It is imperative that these investments are validated properly, made wisely, and that the most appropriate technologies and software tools are selected or combined to facilitate KM, knowledge creation, and continuous innovation. In this chapter, a set of characteristics are proposed that should support decision makers in the selection of software tools for knowledge creation. These characteristics were derived from both in-depth interviews and existing theory in publications. DOI: 10.4018/978-1-60566-701-0.ch002

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

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Introduction Imagine that, in the same way that a disc failure on your personal computer or laptop erases all information in the file folders, all intellectual capital within your organization is erased from the employees’ minds and the organization’s storage media. There is no doubt that the market value of such an organization will be affected severely as decisions in an organization are made based on sufficient, relevant and accurate knowledge. Stewart (1997) supports this notion that the management of knowledge turned out to be the most important economic responsibility of individuals, businesses and nations, as it forms a key component of what is acquired, produced and sold. Knowledge assets are of much greater value than any tangible asset, which includes natural resources, large factories, equipment and land – all of which provided organizations with a competitive edge in the past (Alavi & Leidner, 2001; Davenport & Prusak, 1998). This knowledge asset provides the basis for creating sustainable competitive advantage in the knowledge age (Nonaka, Toyama, & Byosiere, 2001; Vandaie, 2007). Furthermore, as new technologies, innovation, organizational flexibility and new and better forms of leadership propel the growth and earnings of knowledge-intensive companies, so the need to extract wealth from brainpower and knowledge (individual and organizational) becomes increasingly pressing. This importance of knowledge is confirmed by Becker et al (2001) who conclude that machinery and equipment are not the distinguishing aspects any more, but rather the capability to use it resourcefully. An organization that kept its workforce skills and expertise could operate quickly even though it lost all of its equipment. An organization that lost its workforce, while keeping its equipment, would never recover. This shift to knowledge as the primary source of value results in the new economy being led by those who manage knowledge effectively - orga-

nizations that create, find and combine knowledge into new products and services faster than their competitors (Moss-Kanter, 1997). Drucker (Hibbard, 1997, p. 46) states that “We now know that the source of wealth is something specifically human: knowledge. If we apply knowledge to tasks we already know how to do, we call it productivity. If we apply knowledge to tasks that are new and different, we call it innovation. Only knowledge allows us to achieve those two goals.” Today’s organizations are creating and leveraging knowledge, data and information at an unprecedented pace and the extraordinary growth in on-line information makes the use of technology not an option, but a necessity (Folkens & Spiliopoulou, 2004; Lindvall, Rus, Jammalamadaka, & Thakker, 2001). This influence of technology on the maintenance of KM actions is widely accepted, as technology adds value by reducing time, effort and cost in enabling people to share knowledge and information (Chua, 2004). It is especially relevant when it is closely aligned with organizational requirements - the way people work and are supported by and integrated with relevant processes (Hoffmann, Loser, Walter, & Herrman, 1999; Wind & Main, 1998). In addition to the growth in information technology (IT), organizations embark on employee information access projects, like the creation of knowledge bases, intranets, chat rooms, fulltext indexing tools and document management tools as necessitated by KM (Lindvall, Rus, Jammalamadaka, & Thakker, 2001). KM agility and optimal support of technology motivate the need for research in which the focus is on an understanding of the key characteristics of a KM solution by exploring and describing the nature of knowledge. Therefore, this chapter focuses on providing guidelines in the selection of a KM system solution and provides an example where the selection criteria have been applied as a cost saving solution.

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Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Background While some epistemologists spent their lives trying to understand what it means to know something (Clarke & Rollo, 2001; Davenport & Prusak, 1998). Plato first introduced the concept of knowledge as justified, true belief in 400 B.C. (Meno, Phaedo and Theaetus as quoted by Nonaka & Takeuchi, 1995). Advances in knowledge described the achievements of the ancient Greek, Roman, Egyptian and Chinese civilisations and the transforming impact of the industrial revolution was characterised by the application of new knowledge in technology (Clarke & Rollo, 2001; Moteleb & Woodman, 2007). For the purpose of this chapter, a more pragmatic approach has been followed and the following working description of knowledge has been explored (Davenport & Prusak, 1998, p. 5): “Knowledge is a fluid mix of framed experiences, values, contextual information and expert insight that provides a framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of knowers. In organizations, it often becomes embedded not only in documents or repositories but also in organizational routines, processes, practices and norms.” Knowledge can either be categorized as being explicit or implicit. Explicit knowledge can be defined as knowledge that has been articulated in the form of text, diagrams, product specifications and so on (Clarke & Rollo, 2001). Nonaka (1995) refers to explicit knowledge as formal and systematic, like a computer program. In organizations today, explicit knowledge resides in best practices documents, formalised standards by which goods and services are procured and even within performance agreements that have been documented in line with company and divisional goals and objectives. Implicit knowledge is far less tangible than explicit knowledge and refers to knowledge deeply embedded into an organization’s operat-

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ing practices (Kothuri, 2002). Tacit knowledge, as a dimension of implicit knowledge, includes relationships, norms and values. It is knowledge that cannot be articulated and it is much harder to detail, copy or distribute, to the contrary, the knowing is in the doing in this instance (Clarke & Rollo, 2001). Tacit knowledge can provide competitive advantage to organizations as it is protected from competitors (Hahn & Subramani, 2000; Wessels, Grobbelaar, & McGee, 2003) unless key individuals are lured away, of course (Lindvall, Rus, Jammalamadaka, & Thakker, 2001). The management of explicit and implicit knowledge is a multifaceted subject based on the dimensions of knowledge and therefore there are various and varied definitions for it (Newman & Conrad, 1999). McCullough (2005) concludes that, based on the vast majority of academic research into KM, there is a general difficulty for organizations to explain what they mean when they use the term KM. Sveiby (1997, p. 37) defines the management of knowledge as “the art of creating value by leveraging intangible assets”. Meyer and Botha (2000, p. 278) define it as “the management of corporate processes designed to create, disseminate and protect knowledge in support of sound decisions leading to profit”. Godbout (1999) defines KM by suggesting that it is not knowledge that gives the competitive edge, but the capacity to transform knowledge into competencies and replicate know-how. According to Drucker (Edersheim, 2007), the most direct use of knowledge within an organization is to build its own capabilities, and that the application of knowledge to knowledge is the critical factor in productivity moving forward. Lindvall, Rus et al (2001, p. 3) define KM as “the practice of transforming the intellectual assets of an organization into business value”. For the purpose of this chapter the following definition of KM as suggested by Choo (2000) will be used: “a framework for designing an organization’s goals, structures and processes so that the

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

organization can use what it knows to learn and to create value for its customers and community”. Technology is a key enabler of KM and KM processes as it extends the reach and enhances the speed of knowledge transfer (Chua, 2004; Wilson & Snyder, 1999; Yu, Kim, & Kim, 2004). Technology permits the knowledge of an individual or group to be structured and codified and allows distribution of knowledge across the world (Davenport & Prusak, 1998; Wessels, Grobbelaar, & McGee, 2003). KM technology is a broad concept and organizations apply a wide variety of technologies to the objectives of KM (Davenport & Prusak, 1998; Lindvall, Rus, Jammalamadaka, & Thakker, 2001). Explicit knowledge is found in reports, documents and manuals and can easily be gathered and stored as a knowledge base (Dix, Wilkinson, & Ramduny, 1998; O’Leary, 1998). Organizations use groupware applications to collect, store and share their explicit knowledge, and once this has reached a sufficient level of efficiency, collaborative technologies such as intranet, the internet, extranet, e-mail, video-conferencing and tele-conferencing are used to assist in the growth of tacit knowledge transfer. In order to enable organizations to retrieve captured knowledge, knowledge route maps and directories are developed to create an understanding of the location of knowledge (Alavi & Leidner, 2001; Clarke & Rollo, 2001). Knowledge networks are created using virtual business environments such as chat rooms, team web sites and learning communities with the development of specific applications of technology such as databases, workflow systems, personal productivity applications and enterprise information portals (O’Leary, 1998; Wilson & Snyder, 1999). According to Tsai and Chen (2007, p.258) are “knowledge management systems more than just information systems or IT-enabled tools in support of knowledge management activities. Instead, a knowledge management system must be a socio-technical system as a whole which comprises the knowledge itself (the intellectual

capital of the organization), organizational attributes (intangibles such as trusting culture), policies and procedures, as well as some form of electronic storage and retrieval systems.” Different ways of classifying KM technologies are utilised in the literature and Antonova, et al (2006) categorised technological solutions according to the following KM processes: (1) generation of knowledge, (2) storing, codification and representation of knowledge, (3) knowledge transformation and knowledge use and (4) transfer, sharing, retrieval, access and searching of knowledge. These specific implications of knowledge and KM on KM solutions are important as these different views lead to different perceptions and definitions of KM systems (Asgarkhani, 2004). As such a KM solution enables knowledge creation, it provides the basis for continuous innovation as one innovation leads to another (Nonaka & Takeuchi, 1995).

Key considerations impacting knowledge management Organizations today face the challenge of creating an infrastructure that facilitates knowledge transfer – both explicit and implicit. Explicit knowledge is easy to identify based on the definitions above, but implicit – and specifically tacit – knowledge transfer, remains an area of focus. Organizations have to manage this process and key issues in order to enable the organization to transform tacit knowledge into explicit knowledge and make it available and accessible company-wide (Clarke & Rollo, 2001; Gordon, 1999).

Information Technology Nonaka, Reinmoller and Toyama (2001, p. 829) identify several problems with the current use of software tools as the challenge for IT is to aid a dynamic process of knowledge creation, not a stagnant process of information management

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Key Characteristics Relevant for Selecting Knowledge Management Software Tools

and often emphasises the efficiency of processing existing information rather than creating new knowledge. Furthermore, current IT-based KM mainly focuses on knowledge that has been articulated in some tangible form and fails to deal with implied knowledge such as hunches and gut feelings. Less or no emphasis is placed on new visions and innovation as these KM software tools extract profits through knowledge economies of scale by re-using existing knowledge only (Marwick, 2001). KM that relies only on such packaged tools, cannot gain sustainable competitive advantage due to the rapid dissemination of best practice in IT (Davenport & Prusak, 1998). A long-term view of fostering the knowledgebase competence of an organization is required when selecting KM software tools and IT is needed that aids an effective and efficient knowledgeconversion process while increasing the swiftness and ease of switching from one such process to another (Yu, Kim, & Kim, 2004).

Knowledge Work KM is defined in this chapter as a framework for designing an organization’s goals, structures and processes so that the organization can use what it knows to learn and to create value for its customers and community (Choo, February 2000). In addition to this framework, organizations must take key strategic steps to define and quantify the source and nature of the bodies of knowledge that need to be included in the KM framework (Wilson & Frappaolo, 1999). The organization must protect itself from knowledge leaving the organization in order to optimally use what it knows across all perspectives – vision and strategy, roles and skills, policies and procedures and tools and platforms (Holloway, 2000; Lindvall, Rus, Jammalamadaka, & Thakker, 2001). An understanding of knowledge in organizations, the modes and context of conversion of knowledge and the technologies

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used in this conversion are tactical approaches to knowledge creation. A strategic knowledge creation solution encompasses all of these steps in one seamless and complete procedure for knowledge work (Hoffmann, Loser, Walter, & Herrman, 1999; Kothuri, May 2002; Marwick, 2001; Vequist & Teachout, 2006). According to Naisbitt (1982, as quoted by Nickols, 2000), white-collar workers first outnumbered blue-collar workers in the USA in 1956 where the ratio of manual workers to knowledge workers was 2: 1 in 1920 and 1: 2 by 1980. The number of knowledge workers in the computer industry in the USA was estimated at 72% based on a testimony before a senate sub-committee (Nickols, 2000). This new type of worker requires a different type of management (Edersheim, 2007; Frappaolo, 2006; Garvin, 1998; Westhuizen, 2002) and although knowledge is not new, the recognition of knowledge as a corporate asset, is new (Davenport & Prusak, 1998; Hoffmann, Loser, Walter, & Herrman, 1999; Stewart, 1997). Davenport (1998) concludes that there is currently a greater need than in the past to optimize organizational knowledge and to obtain as much value as possible from it. Table 1 summarises the definition of manual work and knowledge work (Nickols, 2000). A major difference between knowledge work and manual work is that knowledge work is information-based and manual work is materialsbased. A manual work process, regardless of how much skill and knowledge is required of the worker, consists of converting materials from one form to another with tangible results. Knowledge work, on the other hand, consists of converting information from one form to another with frequently intangible results (Nickols, 2000; Stewart, 1997). This difference in work output informs how these workers will be performance managed and how they will be measured (Edersheim, 2007; Krogh, Ichijo, & Nonaka, 2000).

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

The Learning Organization Senge (1990) presented tools and ideas for the learning organization during the early 1990s. He claimed that learning organizations can be built “where people continually expand their capacity to create results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free and where people are continually learning how to learn together” (Senge, 1990, p. 3). He identified five key dimensions for building organizations that can truly learn namely systems thinking, personal mastery, mental models, building shared vision and team learning. The learning organization creates an environment where the behaviours and practices involved in continuous learning, are actively encouraged and facilitated. This process of continuous learning includes the exchange of both explicit and implicit knowledge (Asgarkhani, 2004; Garvin, 1998; Kotelnikov, 2001; Marwick, 2001; Salisbury, 2003; Senge, 1990; Vequist & Teachout, 2006). Compared to a learning organization, a coaching organization goes beyond this exchange and also

focuses on how to unlock the inner power of people in the organization in order to make them innovators and self-leaders (Hoffmann, Loser, Walter, & Herrman, 1999; Kotelnikov, 2001). The next stage entails moving from a teaching organization where both learning and teaching take place to a coaching organization where coaching is added to the learning and teaching dimensions. Kotelnikov (2001) defines unique advantages of such a coaching organization. The first advantage entails ensuring enhanced development of individuals and collective tacit knowledge through cross-coaching conversations. The second advantage is creating enhanced teamwork through facilitating a better understanding among team members and fostering a deeper integration of their activities (Agostini, Albolino, Boselli, Michelis, Paoli, & Dondi, 2003). The third advantage is ensuring improved development of people and the utilisation of their talents by building their personal capabilities (Yu, Kim, & Kim, 2004) and the last advantage is creating better employee empowerment by developing them as self-leaders (Edersheim, 2007).

Table 1. Manual work vs. knowledge work (Nickols, 2000) Manual work

Knowledge work

Materials-based

Information-based

Manual work process consists of converting materials from one form to another

Knowledge work process consists of converting information from one form to another

Tangible results

Often intangible

Works with knowledge, information

Works with and on knowledge, information

Working behaviours are public

Working behaviours are private

Visibility of working is high

Visibility of working is low

Results almost always immediate

Results are not so apparent and rarely immediate

Relatively simple matter to observe linkage between manual worker, tools or equipment being used and materials being processed

Linkage between behaviour and results not apparent

Locus of control over work with manager

Locus of control over work shifted to worker

Political and positional balance of power

Political and professional balance of power

Worker is focus of control

Work is focus of control

Compliance is measure of performance

Contribution is measure of performance

Efficiency, the ability to get things done is key measure

Effectiveness, the ability to get the right thing done is key measure

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Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Knowledge Management Barriers Organizations in the new economy deal with two major management tasks given the dynamics of hyper-competition and globalization: the resulting re-invention of businesses and pressure for innovation, as well as the related re-alignment of corporate activity (Barclay & Murray, 1997; Kothuri, 2002). Further changes in this landscape that organizations need to deal with include global integration (Kotelnikov, 2001; Kothuri, 2002) and geographic distribution associated with the globalisation of markets and growth in organizational scope – organizations have to do more with less and at an accelerating pace of change (Barclay & Murray, 1997; Gordon, 1999). Obstacles for KM reveals three main groups of factors when staff attrition due to downsizing and reengineering, growing knowledge-intensity of products and services and the revolution in IT are considered (Barclay & Murray, 1997; McCullough, 2005). The first factor refers to flaws in the organizational KM process (Murray, 2004), the second factor points to misconceptions of the role of technology in the process (Moteleb & Woodman, 2007) and the third factor is a disregard of

the importance of the human factor in realising a successful knowledge managing and knowledgesharing culture (McCullough, 2005). Van der Westhuizen (2002) describes some of the opponents of successful KM as follows: •

•

The empowered middle manager. A middle manager that forms part of a cross-functional value chain running an autonomous operation as if his/her small section is the whole business, creates internal competition rather than focusing on the external competitors; The knowledge management software vendor. The software vendor becomes an enemy of knowledge management if software products are sold as if it is a solution.

In addition to obstacles of knowledge management that organizations deal with, there are also barriers to sharing knowledge as summarised in Table 2. These barriers are organization specific and include organizational hierarchy, geographical barriers, human nature and personality. Motivating users of a KM system to contribute their knowledge to the system is critical for the success of the overall KM initiative (Muller,

Table 2. Summary of knowledge management barriers Knowledge management barriers Hierarchy (Andrew & Westhuizen, 1999; Kotelnikov, 2001)

• Implicit assumption that wisdom accrues to those with the most impressive organizational titles • Inequality in status among the participants in a knowledge sharing session is a strong inhibitor for tacit knowledge sharing, especially when aggravated by different frameworks of reference

People and Human Nature (Frappaolo, 2006; Godbout, 1999; Krogh, Ichijo, & Nonaka, 2000)

• Knowledge transfer is often a case of who you know versus what you know • Sharing one’s best thinking, data, understanding and opinion with others diminishes one’s personal competitive advantage • Improving by generating new ideas continuously while getting rid of old conventional ideas is difficult due to resistance to change • Use of other people’s knowledge often presents a problem as the notion of ”it-was-not-invented-here” is difficult to break down

Geographical barriers (Kotelnikov, 2001; Marwick, 2001)

• Distance – both physical and time – is a strong inhibitor for tacit knowledge sharing.

Personality (Marwick, 2001; McCullough, 2005; Muller, Spiliopoulou, & Lenz, 2005)

• Strong preference for analysis over intuition discourages employees to offer ideas without hard facts to back it up • Penalties for failure discourage experimentation

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Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Spiliopoulou, & Lenz, 2005). In view of the barriers to sharing knowledge, the motivation of people to share their knowledge remains a challenge (Frappaolo, 2006; Muller, Spiliopoulou, & Lenz, 2005). Any KM initiative in an organization must address and alleviate these barriers to optimize knowledge sharing as it forms the basis of value creation and leveraging of the intangible assets of the organization.

Knowledge management and innovation Organizations today realize that leveraging the already-accumulated corporate intellectual property is by far the lowest-cost way available to increase their competitive standing (Frappaolo, 2006; Koenig, 1998; Stewart, 1997; Tsai & Chen, 2007; Wind & Main, 1998) and to harness innovation (Krogh, Ichijo, & Nonaka, 2000; Leonard & Straus, 1997; Nonaka & Takeuchi, 1995). KM practices make bottom line differences to all types of organizations (Frappaolo, 2006) and promote the methods and technologies that facilitate the efficient creation and exchange of knowledge at an organization wide level (Krogh, Ichijo, & Nonaka, 2000; Lee, Kim, & Yu, 2001; Tsai & Chen, 2007). In such a knowledge-based economy with knowledge creation and innovation as the outcome, the infrastructure supporting KM must not be forgotten, as the components of intellectual capital, namely know-how and experience, must be channeled and made available (Frappaolo, 2006). Knowledge that is accumulated externally is shared widely internally, stored as part of the organization’s knowledge base and utilised again to develop new technologies, products and services stimulating continuous innovation that in turn leads to competitive advantage (Nonaka & Takeuchi, 1995).

Knowledge management solution selection An interpretive case study was concluded at a large telecommunication corporate within the South African context and the issues described in the previous sections of this chapter were considered. Emphasis was placed on an understanding of the key characteristics of a KM solution by exploring and describing the nature of KM. Potential research participants were selected based on their area of expertise and the knowledge work that they perform, by utilising both theoretical and convenient sampling (Whitman & Woszcynski, 2004). These criteria were then applied across different management (job grade) levels and leadership styles in the organization to obtain different perspectives from a global, as well as local context. By applying the criteria as defined in Table 3, research participants (referred to as RP) with different profiles were selected, as depicted in Table 4. In order to ensure that all research participant profile criteria were addressed, as well as different combinations of criteria, eight participants were identified. This selection ensured that different perspectives on the research questions were obtained in order to contribute to the richness of interview data. Based on the findings of this study, a list of key characteristics that a KM solution must comply with was collated. The criteria and rationale used to identify the research participants are summarized in Table 3 and both these components informed the typical profile of the research participants. The main criteria that informed the participant profile were environments where knowledge and knowledge sharing are key priorities, behaviors regarding knowledge sharing and some knowledge on human resource aspects in order to obtain input on the human-computer interface and related issues. Furthermore, research participants with a technical background, who understand systems with broad business process knowledge, as well as a

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Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Table 3. Identification of research participants Criteria

Rationale

Typical participant profile

1

Technical / technology / systems background

Utilise their understanding of systems and systems architecture

Information Systems and Network Group (engineering) participants

2

Human resources / behavioural background

Obtain input in the human computer interface and any issues regarding this interface; capturing of implicit knowledge

Organizational Development (Human Resources) participants

3

Environments where knowledge and knowledge sharing are key for success; environments where these key assets leave the organization’s premises every day

Determine issues regarding knowledge sharing within the whole company and regarding key specialist skills and knowledge

System specialists, business architecture and system architecture specialists

4

Job grade

Obtain input from different levels of work and different operational levels; obtain input from different management and leadership styles

Different levels of participants with regards to job grades e.g. executives, general managers, senior managers, etc.

5

Broad business, people, process and system knowledge

Obtain input on “big picture” issues / requirements regarding business, people and knowledge management

Generalists, participants required to integrate all management aspects in order to deal with their respective departments

systems and business architecture background, informed the profile.

Knowledge Management Solution Classification In a previous section of this chapter, a classification method for KM technologies was referenced consisting of generation of knowledge, storing, codification and representation of knowledge, knowledge transformation and knowledge use and lastly, transfer, sharing, retrieval, access and searching of knowledge (Antonova, Gourova, & Nikolov, 2006). This classification was utilised to

group characteristics identified from the literature and to collate it with the characteristics obtained from the research participant interviews. Some characteristics are relevant to more than one classification dimension and in such instances a primary grouping (■) and a secondary allocation (□) have been defined.

Classification 1: Generation of Knowledge The first classification dimension is generation of knowledge which comprises of activities for

Table 4. Research participant profile Criteria (referTable 5-1(a)) 1

Technical / technology / systems background

2

Human resources / behavioural background

3

Environments where knowledge and knowledge sharing are key for success

4

Job grade (5 = executive; 4 = general manager; 3 = senior manager, 2 = team leader) in global group (G) and in local operation (O)

5

Broad business, people, process and system knowledge

26

RP1

RP2

√

√

RP3

RP4

RP5

√

√

√

RP6

RP7

√

√

RP8

√

√

√

√

√

√

√

√

√

G5

O3

G4

O3

O4

O2

O4

O5

√

√

√

√

√

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

knowledge creation, acquisition and capturing as shown in Table 5. With regard to knowledge content generation, authoring, knowledge creation, knowledge objects and content validation are important. Authoring encompasses sources of explicit knowledge line documents, manuals, proposals, e-mail messages, etc., as well as implicit knowledge. Knowledge creation refers to the generation of new knowledge through thinking or reasoning and knowledge objects encompass an object of structured information, un-structured information, insight, facts, practical and theoretical experience, as well as best practice to be stored and manipulated. Content validation points to the validation and auditing of knowledge objects when they are captured and resolves data and information conflicts. Knowledge discovery allows the generation of knowledge through knowledge harvesting, content evolution and ensuring that this is made easily accessible and available via various distribution bearers. Knowledge harvesting is the process of pro-actively facilitating the harvesting and capturing of ideas. Knowledge, expertise and content

evolution refer to the creation of knowledge by combining new sources of knowledge, optimising feedback loops and by re-applying and re-creating knowledge. Data capturing tools enable the capture of knowledge and consists of characteristics such as externalization, maintenance and update, storing and content capture. This toolset ensures that knowledge in the repository is maintained by providing mechanisms to refresh data and information. Externalization refers to the connection of information source to information source and to creating interrelationships while maintenance and update ensure that knowledge objects in the KM system stays valid and recent. It includes a formal change process for captured knowledge and also provides versioning of content. Storing supports knowledge creation through exploitation, exploration and codification and content capture facilitates the capture of knowledge through mechanisms such as a keyboard, optical character recognition, bar code identification and real-time location sensors.

Table 5. Characteristics for the generation of knowledge Generation of knowledge Dimension

Knowledge content generation

Characteristic

Source Literature

Research participant interview ■

Authoring

■

Knowledge creation

■

Knowledge objects

■

Content validation Knowledge discovery

□

Knowledge harvesting

■

■

Content evolution

■

■

Various distribution bearers Externalisation

□ ■

■

□

□

Maintenance and update Data capturing tools

Storing

■

Content capture

■

Refresh data and information

■

■ primary grouping □ secondary grouping

27

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Classification 2: Storing, Codification and Representation of Knowledge The second classification dimension is storing, codification and representation of knowledge, which comprises of activities contributing to effective storage, human-readable knowledge and the organization of knowledge, as depicted in Table 6. The storing, codification and representation of knowledge classification dimension focuses on

KM processes and the quantity, quality, accessibility and representation of the acquired knowledge. Several technologies for storage consisting of several relevant characteristics have been identified in the literature and obtained from the research participant interviews. Archiving refers to archiving ability based on certain criteria and business rules specified by knowledge base administrators, while capability is the characteristics indicating the potential to influence action, pro-

Table 6. Characteristics for storing, codification and representation of knowledge Storing, codification and representation of knowledge

Source Literature

Research participant interview

Archiving

■

■

Capability

■

Dimension

Characteristic

Customization Flexibility Technologies for storage

□ □

Distributed architecture Security

■ ■

■

■

■

Hardware platform independent Storing

□

Back-up and housekeeping

■ ■ □

Content upload

□

Content validation

■ ■

■

Customization

■

Date and time stamp

■

Externalization

□

□

Flexibility

■

■

Indexing

■

■

Internalization

□

□

Application scalability

■

Knowledge gap identification

■

Appropriateness

□

Content upload

■

Taxonomy

■

■ primary grouping □ secondary grouping

28

■

Content capture

Classification

Knowledge organization

■

Application scalability Heuristic Human-readable knowledge

□

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

cessing, decision-making and application. Customization points to the configuration and set up of the system reflecting the specific organization or user context (‘personalization’). Flexibility refers to the characteristic regarding the handling of various media. Security is an important characteristic that addresses physical and logical security, since knowledge is such a valuable asset, while storing in this context refers to the commitment of knowledge to the data warehouse, knowledge warehouse, lessons learnt knowledge base or the data mart. Some characteristics like application scalability, back-up and housekeeping, hardware platform independence and distributed architecture ensure that the KM application can be adapted to the size, application and hardware configuration of an organization while ensuring accessibility and proper housekeeping of the physical infrastructure. Human-readable knowledge consists of the characteristic set including heuristic and content capture, upload and validation. Heuristic means that the solution should constantly learn about its users and the knowledge it possesses as it is used. Its ability to provide a knowledge seeker with relevant knowledge should therefore improve over time. Content capture, upload and validation refer to the characteristics that ensure that knowledge is committed to the knowledge repository based on certain rules. Knowledge organization includes classification, customization, externalization, flexibility, indexing, internalization, appropriateness, taxonomy and content upload. Classification handles content management according to the context of the organization, while customization refers to the configuration and set-up of the system reflecting the specific organization or user context. Externalization refers to the connection of information source to information source and creating interrelationships, as well as the integration of organizational interdependencies. Flexibility ensures that knowledge objects of any form as well as different subjects, structures, taxonomies and media can be

included, while indexing means content management according to the context of organization. Corporate taxonomy refers to the definition of how the knowledge is stored, where internalization involves the extraction of knowledge from the external repository and subsequent filtering ensuring greater relevance and appropriateness to the knowledge seeker. Knowledge gap identification is a feature that allows a knowledge user to identify areas of the knowledge repository that is utilized significantly vs. underutilization, as well as to identify areas where more content can be uploaded and populated in the knowledge repository. Two features, namely date and time stamp and application scalability, refer to the tagging of knowledge to track ‘recency’ and the mechanism to add more knowledge areas respectively.

Classification 3: Knowledge Transformation and Knowledge Use Classification dimension three is depicted in Table 7, being knowledge transformation and knowledge use. This refers to the fact that once knowledge has been acquired it cannot be used in its raw form and must be transformed in order to become a valuable knowledge asset. Knowledge transformation ensures that the knowledge conforms to the format of the target repository and consists of two secondary allocated characteristics namely search and retrieval and access to information, encompassing the transformation of end-user collected data and information before it is committed to the knowledge repository. Knowledge reconstruction ensures that knowledge is presented in the particular reasoning method that is used by the KM system, e.g. editing into case formats to support case-based reasoning or a business intelligence dashboard. Knowledge use and retrieval encompasses expert systems, decision support systems, visualisation tools and knowledge simulation. This classification dimension consists of processes of

29

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

applying expertise to knowledge, the ease of learning and teaching how to utilize the KM system through a user-friendly user interface, which is a secondary characteristic allocation in this dimension. Application includes the timely availability of organizational and individual memory and just in time learning, as well as inter-group knowledge access. Cognition refers to the connection of knowledge to process and suggestive, another secondary allocation in this dimension, proposes knowledge associations that the user is not able to make through the user interface.

Classification 4: Transfer, Sharing, Retrieval, Access & Searching of Knowledge The fourth classification dimension is transfer, sharing, retrieval, access and searching of knowledge, which comprises of knowledge access, searching, collaboration and sharing characteristics, as shown in Table 8. With regard to knowledge access and transfer, only primary allocation of characteristics and features consisting of content delivery, access to information, multi-language support, user-friendly user interface and various distribution bearers, were concluded. Access to information is facilitated via a user-friendly user

interface and the delivery of content consisting of the gathering of user-information and delivering appropriate content to meet specific user needs. Collaboration includes person to person as well as team collaboration features encompassing the support of the knowledge sharing process through a social network analysis and collaborative tools, as well as collective insights across operations and different geographical locations. Workflow enablement connects people in different ways supporting increased work performance and productivity. Knowledge sharing includes intermediation - the connection of people to people, i.e. bring together those who are looking for a certain piece of knowledge and those who are able to provide this piece of knowledge – and internalization, the connection of explicit knowledge to people or knowledge seekers. For the search and find dimension accessibility, appropriateness, context-sensitivity, heuristic, suggestive, relevance, search and retrieval, timeliness and responsiveness are important. A multilanguage user interface feature supports search and find. Accessibility provides an effective search and retrieval mechanism for locating relevant information, while appropriateness indicates the

Table 7. Characteristics for knowledge transformation and knowledge use Knowledge transformation and knowledge use Dimension Knowledge transformation Knowledge reconstruction

Knowledge use and retrieval

Characteristic Search and retrieval

Literature

Research participant interview

□

□

Access to information

□

User sensitive

■

Application

■

Cognition

■

■

Suggestive

□

□

■

Expertise applying process

■

■

System learning agility

■

User-friendly user interface

□

■ primary grouping □ secondary grouping

30

Source

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

appropriateness level based on the filtering of multiple inputs for the same knowledge object. Context-sensitivity refers to the feature that the solution should be able to understand the context of the knowledge requirement and tailor responses accordingly. Heuristic indicates that as the solution responds to many requests on a particular subject, it should learn how to assist multiple users in more depth on that subject, while suggestive deduces what the knowledge seeker’s knowledge needs are. Relevance indicates the significance of knowledge objects retrieved, and search and retrieval are primarily concerned with enhancing the interface

between the user and information, knowledge sources, user-friendliness and learning agility. Timeliness and responsiveness refer to the feature that knowledge must be available whenever it is needed with almost immediate retrieval and presentation cycles.

Knowledge Management System Characteristics Application A list of KM system characteristics were extracted from the literature, obtained from research participant interviews and collated and discussed in the previous sections. The characteristics were

Table 8. Characteristics for transfer, sharing, retrieval, access and searching of knowledge Transfer, sharing, retrieval, access and searching of knowledge Dimension

Characteristic Content delivery

Knowledge access and transfer

Source Literature

Research participant interview

■

■

Access to information

■

Multi-language support

■

User-friendly user interface

■

Various distribution bearers

Person to person and team collaboration

Knowledge sharing

■

Collaboration

■

■

User sensitive

□

□

Expertise applying process

□

□

Refresh data and information

□

Workflow enabled

■

Intermediation

■

■

Internalisation

■

■

Accessibility

■

Appropriateness

Search and find

■

Context sensitivity

■

■

Heuristic

□

□

Multi-language support Suggestive

□ ■

■

Search and retrieval

■

■

Timeliness

■

■

Relevance

Responsiveness

■

■

■ primary grouping □ secondary grouping

31

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

grouped using a classification mechanism for technological solutions (Antonova, Gourova, & Nikolov, 2006) according to the KM processes, and primary and secondary groupings were identified. This list of grouped and defined characteristics can be applied in two ways. The first is as a specification of the requirement of a KM system before technology is acquired. The second way is to evaluate existing technologies for compliance to KM solutions, to identify gaps in existing technologies and to assess suitability before purchasing new technology. The set of characteristics compiled based on the nature of knowledge and KM, can be used to evaluate technologies in order to establish whether it will be suitable as KM applications. Such a typical checklist is depicted in Table 9, where one dimension, namely person to person and team collaboration, with the characteristics collaboration, user-sensitivity, expertise applying process, refreshing of data and information and workflow enablement, was used as a requirement of a KM solution. Three technology solutions, namely eGain Knowledge, SharePoint and videoconferencing, were evaluated against these characteristics to establish whether it complies with requirements for a KM solution. From the result of the evaluation reflected in Table 9, a combination of eGain Knowledge and video-conferencing will comply with all the requirements listed for person to person and team collaboration, and a

combination of these two technologies can then facilitate KM according to this example. Merriam-Webster’s on-line dictionary (2007) defines a characteristic as “a distinguishing trait, quality or property” and this broad definition guided the collation of the set of characteristics shown in Table 10 as defined in previous sections of this chapter. Each characteristic is listed showing the distinguishing feature of a KM system, a description of the distinguishing characteristic and an example clarifying the characteristic where appropriate. According to Offsey (1997), KM systems share many basic features although a specific KM system would be informed by the specific organization. The list of characteristics depicted in Table 10 is such a list of common, basic features that knowledge management solutions share. These characteristics may inform the description of a typical knowledge management system architecture from a knowledge management point of view. This architecture description uses multiple, concurrent views as the initial description of a KM architecture, and such an initial architectural prototype can be evolved to become a real system through several iterations.

Future research possibilities An issue accentuated by this research is the evaluation of technologies suitable for knowledge man-

Table 9. Knowledge management system characteristics checklist (illustration only) KMS characteristic checklist Dimension

Person to person and team collaboration

32

Technology eGain Knowledge

Share-point

Video-conferencing

Collaboration

√

√

√

User sensitive

√

Characteristic

Expertise applying process

√

Refresh data and information

√

√

Workflow enabled

√

√

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Table 10. Knowledge management system characteristics KM Solution Characteristics Characteristi

Description

Example

Accessibility

Knowledge is a condition of access to information via different mechanisms (e.g. web based) and locations.

Role of IT is to provide effective search and retrieval mechanisms for locating relevant information.

Application

Timeous availability of organizational and individual memory, just in time learning. Inter-group knowledge access

Expert systems, rapid application of new knowledge through workflow systems

Appropriateness

Indicates the appropriateness level based on the filtering of multiple inputs for the same knowledge object

Prioritised search results

Archiving

Refers to archiving ability based on certain criteria specified by knowledge base administrators

Archiving of project specific information

Authoring

Encompasses knowledge objects i.e. sources of explicit (e.g. documents, manuals, proposals, email messages) or implicit knowledge (e.g. people)

Supported by standard authoring tools like word processors and database management systems (DBMS)

Capability

Knowledge is the potential to influence action, processing, decision-making, application.

Role of IT is to enhance intellectual capital by supporting development of individual and organizational competencies.

Classification

Handles content management according to context of organization

Corporate taxonomy as knowledge map supported by classifying and indexing tools

Cognition

Refers to connection of knowledge to process

Functions of systems to make decisions based on available knowledge

Collaboration

Support the knowledge sharing process through a social network analysis and collaborative tools; collective insights across operations and different geographical locations; multidimensional collaboration

Facilitate communication between users, collaboration among users and workflow management

Content capture

Enable direct capture of information via front-end or user interface

Bar-code scanning

Content delivery

Personalisation involves gathering of user-information and delivering appropriate content to meet specific user needs aligned to user profile

Electronic bulletin boards, through portals is knowledge distributed as needed by different applications

Content evolution

Knowledge creation, combining new sources of knowledge, optimize feedback loops and re-apply, re-create

Data mining and learning tools

Content upload

Upload documents in various formats into the knowledge repository

Operations manual in.pdf format

Content validation

Validation and approval of content prior to making it available generally

Site administrator or editor

Context sensitivity

Solution should be able to understand the context of the knowledge requirement and tailor response accordingly

Should be able to understand and respond differently between animal reproduction and document reproduction

Creation

Refers to generation of new knowledge through thinking or reasoning

Brainstorming

Customisation

Configuration and set-up of solution aligned to organizational processes, requirements and architecture

Branding

Date and time stamp

Refers to date and time knowledge was committed to knowledge base

-

Distributed architecture

Ensures that the knowledge management application can be adapted to the size, application and architecture configuration of an organization

-

continued on following page

33

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Table 10. Continued Externalization

Refers to the connection of information source to information source and creating interrelationships; integration of organizational interdependencies

Focuses on explicit knowledge and provides a means to capture and organise this knowledge into a knowledge repository

Flexibility

Solution should be able to handle knowledge of any form as well as different subjects, structures, taxonomies and media

If knowledge seeker wants to learn about gramophone records, it should supply knowledge on the technology as well as purchasing trends and examples of famous recordings

Hardware platform independent

Application should be scalable and applicable to oganisational specific hardware configuration

-

Heuristic

Solution should constantly learn about its users and the knowledge it possesses as it is used i.e. continually refine itself as a user’s pattern of research is tracked by the system. Its ability to provide a knowledge seeker with relevant knowledge should therefore improve over time

If the solution responds to many requests on a particular subject, it should learn how to assist multiple users in more depth on that subject

Indexing

Handles content management according to context of organization, corporate taxonomy

Corporate taxonomy as knowledge map supported by classifying and indexing tools

Intermediation

Refers to the connection of people to people i.e. bring together those who are looking for a certain piece of knowledge and those who are able to provide this piece of knowledge

Primarily positioned in the area of tacit knowledge based on its interpersonal focus

Internalisation

Refers to the connection of explicit knowledge to people or knowledge seekers

Involves extraction of knowledge from the external repository and subsequent filtering ensuring greater relevance to knowledge seeker

Knowledge gap identification

Allows knowledge user to identify areas of the knowledge repository that is utilised significantly vs. underutilisation, as well as to identify areas where more content can be uploaded and populated in the knowledge repository

-

Knowledge harvesting

Pro-active facilitation of harvesting and capturing of ideas, knowledge, expertise

Knowledge harvesting workshops and focus groups, defining tangible knowledge and capturing it

Knowledge objects

Data is facts, raw numbers. Information is processed / interpreted data. Knowledge is personalised information. Knowledge is an object of structured information, un-structured information, insight, facts, practical and theoretical experience, as well as best practice to be stored and manipulated.

KMS will not appear radically different from existing IS, but will be extended toward helping in user assimilation of information. Role of IT involves gathering, storing and transferring knowledge.

Multi-language support

Refers to user specification of preferred language for user interface

-

Process

Knowledge is a process of applying expertise.

Role of IT is to provide link among sources of knowledge to create wider breadth and depth of knowledge flows.

Refresh data and information

Update of knowledge repository as new data and information becomes available

Mobile handset manual

Responsiveness

Knowledge must be available whenever it is needed with almost immediate retrieval and presentation cycles

Different time zone applicable in global companies

Scalability

Refers to independence of solution to size of organization

Major corporate vs. small and medium enterprise (SME)

Search and Retrieval

Primarily concerned with enhancing the interface between the user and information / knowledge sources, user-friendliness and learning agility

Help users better understand the information and knowledge available by providing subject-based browsing and easy navigation

Security

Have to address physical and logical security since knowledge is such a valuable asset

Implemented using inherent mechanisms in each tool or by using specific tools in addition to the existing system

continued on following page 34

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Table 10. Continued Storing

Support knowledge creation through exploitation, exploration and codification

Technology enabled store or knowledge repository that can support less structured information

Suggestive

Solution should be able to deduce what the knowledge seeker’s knowledge needs are

Suggest knowledge associations the user is not able to do

System learning agility

Refers to how easy it is to transfer the skill of using the system to the users

-

Taxonomy

Organise knowledge repository in a defined way in order to retrieve

Medical term taxonomy

Timeliness

Knowledge is available whenever it is needed.

Eliminates time-wasting distribution of information just in case it might be required

User-friendly user interface

Refers to ease with which user interface may be applied and whether interface usage is intuitive

-

User-sensitive

Solution should be able to organise the knowledge in the way most useful to the specific knowledge seeker

Should give knowledge relevant to knowledge seeker’s current knowledge level, facilitating easier understanding

Various distribution bearers

Refers to utilization of different mechanisms for distributing knowledge or handling knowledge requests

Mobile phone Short Message Service (SMS) notification

Workflow enabled

Connects people in different ways supporting increased work performance and productivity

Notify users when any changes were made to the knowledge repository component that they are interested in

agement or the optimization of an organization’s existing technologies in achieving knowledge management objectives. These ideas could be explored further and a comprehensive checklist and process can be designed to facilitate this in organizations today. Holm, Olla et al. (2006) suggest that a process must be followed in order to create a knowledge management system architecture. The objectives and overall strategy of the knowledge management system must be compiled first after which requirements for individual groups in the organization must be established (Holm, Olla, Moura, & Warhout, 2006; Marwick, 2001). Individual knowledge management tasks can be derived from the requirements that need to be structured in such a way that it provides a successful course of action for the organization (Holm, Olla, et al., 2006; McManus, Wilson, & Snyder, 2004). The next step is to define the services, e.g. capturing tacit expertise and expert directories required as services to integrate processes, people and systems. The final step after the services architecture

has been defined is to delineate the system architecture according to a layered approach building on to already existing infrastructure and services (Holm & Olla, et al., 2006).

Conclusion Various dimensions of knowledge, namely explicit knowledge as well as implicit and tacit knowledge exist. Information becomes knowledge when it is retained as suitable representations of the relevant knowledge and when the value can be increased through analytical thought processes and by transforming knowledge into competencies, replicating know-how in the process. In order to optimally use its know-how, organizations must gain an understanding of the source and nature of knowledge in the organization to create a strategic knowledge solution for knowledge work and to foster continuous innovation. Such a strategic solution is as much about innovation, the knowledge management process, people and culture in

35

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

an organization as it is about the technology that optimally supports it. An understanding of explicit, implicit and tacit knowledge in organizations, the modes and context of conversion of knowledge and the technologies used in this conversion are tactical approaches to knowledge creation. A strategic knowledge-creation solution encompasses all of these steps in one seamless and complete procedure for knowledge work, and these requirements must be considered in the design of a KM system architecture. KM tools are enhancements of existing technologies although true KM technologies differ in several aspects from traditional technologies based on the nature of knowledge and KM as discussed in the previous section. Some of these aspects include an understanding of the context of knowledge, organization of knowledge in the way most useful to the knowledge seeker, capability of the solution to constantly learn about its users and the ability to deduce what the knowledge seeker’s knowledge needs are. Other aspects include access to sources of knowledge rather than the knowledge itself, support in user assimilation of information and providing effective search and retrieval mechanisms in locating relevant information. A variety of software tools are available providing support to KM systems through four main functions, namely the association of people to people, the association of information source to information source, the association of explicit knowledge to knowledge seekers and the association of knowledge to process. KM systems share many basic features although a specific KM system would be informed by the specific organization. The set of characteristics obtained from the literature and from the research participant interviews are such a list of common, basic features that KM solutions share. This characteristic set can be utilised in two ways: the first is as a specification of the requirement of a KM system before technology is acquired, and the second way is to evaluate existing technolo-

36

gies for compliance to KM specific applications or to assess suitability before purchasing new technology. The character of KM is about people, systems and processes in building core competencies through managing knowledge reserves. It supports enhanced learning and understanding through provision of explicit and implicit knowledge and aids the assimilation of information. KM is concerned with knowledge flows and the process of creation, sharing and distributing knowledge through organised access to content. It is inherently linked to the sharing of knowledge between individuals, who are not necessarily collocated, by means of collaborative processes creating new knowledge and aiding innovation. Technology is a key enabler of KM and enhances intellectual capital by supporting the development of individual and organizational competencies. It aids the gathering, storing and transferring of knowledge by providing access to sources of knowledge and knowledge itself through user-friendly capture and effective search and retrieval mechanisms enabling continuous innovation.

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Key Characteristics Relevant for Selecting Knowledge Management Software Tools

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Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Nonaka, I., & Takeuchi, H. (1995). The knowledge creating company. New York: Oxford University Press. Nonaka, I., Toyama, R., & Byosiere, P. (2001). A theory of organizational knowledge creation: Understanding the dynamic process of creating knowledge. In Dierkes, M., Antal, A. B., Child, J., & Nonaka, I. (Eds.), Handbook of organizational learning & knowledge (pp. 491–517). New York: Oxford University Press. O’Leary, D. E. (1998). Enterprise knowledge management. IEEE, March, 54-61. Offsey, S. (1997). Knowledge management: Linking people to knowledge for bottom line results. Journal of Knowledge Management, 1(2), 113–122. doi:10.1108/EUM0000000004586 Salisbury, M. W. (2003). Putting theory into practice to build knowledge management systems. Journal of Knowledge Management, 7(2), 128–141. doi:10.1108/13673270310477333 Senge, P. M. (1990). The fifth discipline: The art and practice of the learning organization. New York: Doubleday. Stewart, T. A. (1997). Intellectual capital: The new wealth of organizations. New York: Bantam Doubleday Dell. Sveiby, K. E. (1997). The new organizational wealth. San Francisco, CA: Berrett-Koehler. Tsai, C. H., & Chen, H. Y. (2007). Assessing knowledge management system success: An empirical study in Taiwan’s high-tech industry. Journal of American Academy of Business, Cambridge, 10(2).

Vandaie, R. (2007). Developing a framework to describe the interaction of social and intellectual capital in organizations. Journal of Knowledge Management Practice, 8(1). Vequist, D. G., & Teachout, M. S. (2006, May). A conceptual system approach for the relationship between collaborative knowledge management and human capital management. Collaborative Technologies and Systems, 2006, 150–156. Wessels, P. L., Grobbelaar, E., & McGee, A. (2003). Information systems in the South African business environment (2nd ed.). Durban, South Africa: LexisNexis Butterworths. Westhuizen, J. V. D. (2002). Building horizontal companies: The job KM has come to finish. Convergence, 3(3), 92–95. Whitman, M. E., & Woszcynski, A. B. (2004). The handbook of information systems research. London: Idea Group Publishing. Wilson, L. T., & Frappaolo, C. (1999). Implicit knowledge management: The new frontier of corporate capability [Electronic version]. Knowledge Harvesting Inc. Retrieved DATE, from http:// www.knowledgeharvesting.org /papers.htm Wilson, L. T., & Snyder, C. A. (1999). Knowledge management and IT: How are they related? IT Professional, 1(2), 73–75. doi:10.1109/6294.774944 Wind, J. Y., & Main, J. (1998). Driving change: How the best companies are preparing for the 21st century. London: Biddles Ltd. Yu, S. H., Kim, Y. G., & Kim, M. Y. (2004, January). Linking organizational knowledge management drivers to knowledge management performance: An exploratory study. Paper presented at the 37th Hawaii International Conference on System Science, Hawaii.

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Chapter 3

Knowledge-Based Diffusion in Practice: A Case Study Experience Hilary Berger University of Wales Institute Cardiff, UK Paul Beynon-Davies Cardiff University, UK

ABSTRACT This chapter uses a case study to consider how development methods shape information systems practice and how organizations adapt, deploy, and use such knowledge in situ. The authors explore how an information system development method (ISDM) acting as a de-contextualized “knowledge bundle” is diffused and infused within an organization through the process of contextualization. The case study looks at a regional government project responsible for the distribution of European Community (EC) monies through agricultural grants and subsidies. A new IT/IS system was designed and developed to improve the administration and management of the EC’s agricultural policy across the region. A longitudinal research project was conducted over three years and was situated within the project environment. It involved a sustained period of fieldwork (nine months of intensive observations), and data was collected through 126 semi-structured interviews, shadowing of key participants, and informal discussions and conversations. Secondary data involved an in-depth and systematic analysis of published literature, project documentation, and artifacts. The authors consider how the structure and culture of organizations affect implementation and processes of diffusion and infusion.

INTRODUCTION Our discussion is grounded in case material collected as part of an ethnographic study of a large-scale information system development project. Within this project an agile development DOI: 10.4018/978-1-60566-701-0.ch003

method promoted by external vendors initially experienced problems in deployment amongst organizational actors. Over time however the development method was adapted and used successfully by project participants. We consider how and why this transformation occurred. The main findings are that the success of ISDMs is influenced by the inherent structure and culture

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of the host organization, the actors involved and the IS development activities. The ISDM adopted was clearly tempered by the relationship between the development approach and the nature of the organization, although the attitudes and behaviors inherent in the organization hindered the diffusion process. Thus, knowledge management crucial to the adoption or rejection of technology is subjective and can be influenced by the various actors involved and the related social system and environment have a significant impact upon the unfolding ISDM process. The lesson here for knowledge based diffusion is that the presence of inherent antecedents and characteristics that present areas of risk may be mitigated through a cultural acclimatization of both the environment and of key stakeholders involved. It is important to acknowledge some of the fundamental principles and practices for knowledge diffusion relative to the work place. Knowledge diffusion is the adaptation of knowledge across a broad range of business contexts, and it allows for the communication of ideas. It is the process of knowledge diffusion that enables the right information to be disseminated to the right person at the right time. Knowledge can be diffused through a diversity of mechanisms (dialogue, discussion, manual or electronic method) and is be broadly categorized as implicit [i.e. can be readily gathered], explicit [i.e. documented] and tacit [i.e. individual ‘know-how’ gained through experience]. Decision-making requires a relevant blend of these knowledge types. However the diffusion of knowledge depends on the interpretation and communication by the various stakeholders involved. It requires understanding and effort that does not result in a loss of meaning or validation of the knowledge itself (Martinez-Brawley & Emilia, 1994). Although humans as social animals may transfer knowledge, whether new or old, through their social interactions within the work place, both IT and IS and the development of information system play an important role in the diffusion process.

Information system development methods (ISDMs) specify an approach for developing an Information Technology System within its larger Information System. Typically, an ISDM encompasses a model of the ISD process, a set of development techniques, a documentation method, a perception of how these would fit into the development process and a philosophy of assumptions about what constitutes information, an IS and the place of IS within organizations (Beynon-Davies, 2002). They play a central role in the creation, adaptation and renewal of an organizations’ IS infrastructure. Thus strategically they have a significant affect on an organizations performance (Beynon-Davies & Williams, 2003). The current turbulent business environment combined with the continual growth, the rapid change, the dynamic nature, and the increasing complexity of organizational knowledge reflects a need for new types of efficiency based on adaptability and innovativeness (Clarke & Staunton, 1989; Hollingsworth, 1991). The implementation of an ISDM can be regarded as an instance of both technological as well as an organizational innovation (Veryard 1987). The diffusion of ISDMs is a key example of knowledge-based diffusion (Newell et al., 2000; Beynon-Davies & Williams, 2003). In the knowledge-based perspective, an ISDM is considered as a de-contextualized ‘knowledge bundle’ that needs to be contextualized and unbundled such that it becomes relevant to an organizations own rationale. ISDMs are typically perceived by practitioners as packets representing ‘best practice’(Beynon-Davies & Williams, 2003). However, literature suggests that organizations rarely implement an ISDM as specified. In some cases no specific ISDM is used at all. However practitioners often utilize existing ISDMs by adapting development practices to respond to the exigencies and situational requirements of the particular organization, and the specific IS/IT development project they are concerned with (Button & Sharrock 1993; Mustonen-Ollila et al., 2004). This may prove problematic for a number of

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reasons. Indeed Jones & King (1998) put forward two instances of IS development projects where the adoption of an agile ISDM became difficult. Although adaptation was ultimately successful in one, in the other it proved counter to the culture of the host organization and failed. This chapter focuses on understanding the diffusion experience of a particular ISDM through examination of a case study. We examine the implementation of a UK government IS/IT development project where a RAD type ISDM approach was unbundled and contextualized (Beynon-Davies & Williams, 2003). We consider the relationship between the shape of the ISDM itself as a ‘knowledge bundle’ and key facets of the organization such as structure and culture (Fichman, 1992; Gharavi et al., 2004). The case study illustrates how the success of agile ISDM [RAD] development approach adopted was influenced by the interplay between the RAD development activities, and how the project was affected by both the structure and culture of the organization and the actors involved (Light & Papazafeiropoulou, 2004). We see how the situational antecedents and characteristics of the potential adopters impacted on the unfolding adoption process (Gharavi et al., 2004; Kishore & McLean, 1998). Use of the actual experiences and commentary from project participants adds meaningful insight and validity to the conclusions drawn. Thus we aim to increase the understanding of how development methods shape the community of practice in the IS development domain, and, in particular, how organizations adapt, deploy and use such knowledge in situ and consequently how technology assessment and implementation can be progressed and advanced (Fichman, 1992). The chapter begins by introducing an understanding of diffusion of innovation, knowledge based diffusion and ‘knowledge bundles’. The case study is described and illuminated through use of a knowledge-based conceptual focus. Then consideration is given to the problems of the ISDM diffusion experienced in practice. Finally we review the lessons from the

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case to enable better understanding of the practical application of ISDMs within organizations. The real-life context, experiences and commentary from individuals directly involved in the project provides meaningful insight and validity to the conclusions drawn.

THEORETICAL BACKGROUND In this section we examine the most commonly accepted definition of diffusion, the role of communications in knowledge diffusion and explore how knowledge is bundled within ISDMs. We look at the diffusion of technological innovation and discuss the adoption process in relation to an organizations culture, information infrastructures and the stakeholders involved which all influence what, and how an innovation is adopted and its eventual success or failure.

Diffusion of Innovation Everett Rogers (1983, 2003) puts forward the most commonly accepted definition of diffusion. He defined it as the process by which an innovation is communicated through certain channels over time among the community of potential users of a social system. His interpretation emphasized that communication links are essential to drive the technology adoption. The acquisition of knowledge to assimilate and deploy the technology also plays a central role. However such a broad focus on the way communication channels, and thought leadership influence adoption does not necessarily ‘illuminate the network mechanisms by which variables and constructs interact and become important during adoption’ (Hovorka & Larsen, 2006, P.160). Diffusion describes the process by which an innovation is taken on board across a population of organizations. It begins with an initial awareness of the innovation, and progresses through a series of stages to formal adoption and full-scale development. Innovation

Knowledge-Based Diffusion in Practice

can de defined as an ‘idea, practice or object that is perceived as new by an individual or other unit of adoption’ (Rogers, 2003, p.11). It is the decisions taken within a company in order to make full use of a new idea that characterizes an innovation diffusion adoption. The idea may well exist elsewhere but it must be new to, and both adopted and implemented within the organization. For large scale operations where Roger’s (2003) Diffusion of Innovation Theory is combined with understanding an organizations culture and information infrastructures, then implementation challenges can be managed (Mustonen-Ollila & Lyytinen, 2004). Beynon-Davies (2002) describes the adoption of an ISDM as a diffusion process in which development approaches are promoted, taken up, adapted and used within organizations. Diffusion theory provides a useful perspective on one of the most persistent and challenging topics in the IT field, namely how to improve technology assessment and implementation (Fichman, 1992). A review of the diffusion literature by Fichman (1999) emphasizes that it is the context in which the adoption occurs that influences what, and how innovation is adopted. Indeed the process of adoption is a function of an individual organizations’ strategic choice and encompasses both the internal (culture, structure) and external boundaries (professional, legal) that relate to the specific circumstances involved (Mustonen-Ollila et al. 2004). Fichman, (1992) suggests that at project level achieving a correct fit between the implementation characteristics and the implementation strategies can determine adoption success (1992, p.197). This view is supported by Gharavi et al., (2004) who explain further that success or failure may well depend, in part, on how well management handles the changes resulting from strategic choice and the measure of fit to the organization, as well as any new environmental conditions.

Diffusion of Technical Innovation The diffusion of technological innovations can be considered either as an overtly rational process or subject to social forces i.e. interpretive (Beynon-Davies & Williams, 2003). Interpretive approaches are concerned with the social construction of technology, and emphasize the way in which technologies are ‘configured’ during the diffusion process by the parties involved. In other words the adoption or rejection of technology is affected by the various actors, or relevant social groups involved such as professional bodies and their conflicting ideas or requirements, as well as the related social system and environment that impact upon the unfolding adoption process (Beynon-Davies & Williams, 2003; Kishore & McLean, 1998). However there is some argument that within a rational perspective emphasis is placed on the innovation itself rather than upon any social influences that impede or facilitate the process of adoption (Light & Papazafeiropoulou, 2004). Thus Rogers’ theory (1983) has been criticized for not taking into account the particularities of other actors and complex information technologies (Iacono & Kling, 1996; Lyytinen & Damsgaard, 2001). In this view, the adoption or rejection of technology is thus subject to many factors and multiple interpretations. It is the situational antecedents and characteristics of the potential adopters that impact on the diffusion of innovation in association with the specific social system and environment of the adopting organization. All of which affect the unfolding adoption process (Gharavi et al., 2004; Kishore & McLean, 1998). Rogers (2003) refers to four general approaches of the diffusion of innovations (innovation decision process theory, individual innovativeness theory, rate of adoption theory and perceived attributes theory) where five attributes are specified upon which an innovation is judged (complexity, trialability, observability, relative advantage and compatibility - Fichman 1999). Further research (Moore & Benbasat,

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1991), has expanded to include voluntariness and result demonstration (Valier et al., 2004); and intention to use put forward by Van Slyke et al., (2002). Literature uses terms such as institutionalization, routinization and incorporation to denote the final stage in the adoption of innovations. However, Kishore & McLean (1998) draw a distinction between success of adoption (when an innovation is successfully adopted and used by most/all of adoption units within a community of potential adopters) and success from adoption (realization of the potential benefits) - The former being a prerequisite of the latter. They suggest that an ‘innovation will become institutionalized, routine and incorporated in the organization when two conditions are met; firstly, when most or all of the individual members of the adopting organization utilize the innovation such that it then becomes an integral part of their regular work-routine and secondly, when the use of the innovation is fully and completely used routinely. Thus when an innovation is implemented across a large number of potential adopters it is said to be diffused, and if it is used by its adopter(s) in a full and complete manner, then it can be said to have infused. Although they do not specifically define success in their context they draw a distinction between diffusion as the breadth of use amongst the community of potential adopters, and infusion as the depth of use of the features and functions from the situational perspectives as two dimensions of the success of adoption of IS innovation in situ.

Knowledge-Based Diffusion and ‘Knowledge Bundles’ As mentioned above the acquisition of knowledge in the diffusion process plays a central role. Knowledge-based diffusion between and within organizations is where a process of complex knowledge, ideas and technical processes are bundled together and packaged in particular ways such that technology suppliers can provide

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solutions for organizational problems (Newell et al., 2000). Beynon-Davies & Williams (2003) propose that an ISDM should be treated as a ‘knowledge bundle’ because they act as devices for communication and adoption of IS development practice. Accordingly technology suppliers are able to represent such bundles as ‘best-practice’ fixes that can be contextualized across a range of contexts. However, the increasing uncertainty and dynamic nature of current business environments necessitates an ISDM that embodies the situational social structure and culture (economic, political, social and technological aspects) in the adopting organization. More specifically an environment that extends adaptability and flexibility, responds to speed and scalability (Baskerville et al., 2001, 2005), and can mitigate the risks of unexpected and perhaps unprecedented business changes (Sharifi & Zhang, 2000). However, Coughlan & Macredie (2002) point out that it is important to recognize that an ISDM may not necessarily map directly onto an organizations’ culture, rationality or the context of its specific users. Nevertheless, when knowledge arrives in an organization it has to be unbundled and contextualized relevant to the specific situation. This process may prove problematic for a number of reasons. For example, an organization may lack an established knowledge-base of people and skills for understanding and applying the ‘technology’, or difficulties of internal networking may prevent people with the requisite knowledge and skills of being involved. In such cases consultants may frequently be called on to manage the knowledge implementation process.

THE ISDM ADOPTED RAD/IAD (AGILE) The aim is to understand the way in which a commercial agile ISDM (a RAD approach called Iterative Application Development - IAD) was adopted and applied within the case study setting

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as an example of knowledge based diffusion in practice. RAD is one of a number of development methodologies that sit under the umbrella of ‘agile’ development. Others include SCRUM (Schwaber & Beedle, 2002), Extreme Programming (Avison & Fitzgerald, 2006), Dynamic System Development Methodology (DSDM, 2001). The term agile development is used to emphasize the lightweight methods and frameworks where high value is placed on human roles and relationships such as developer/client collaboration and team spirit rather than on tools and processes (Cockburn, 2002; Highsmith, 2002; Miller & Larson, 2005). History has shown how the more traditional structured methods of development such as the ‘Waterfall Model’, are no longer effective for the increasingly volatile, and dynamic nature of current business environments. Consequently, more agile approaches to development have evolved to provide a more flexible approach. RAD, an agile development method, is an iterative and incremental development approach that compresses the analysis, design, build and test phases of the development life cycle into short, iterative development cycles. The nature of the iterative development cycles means that RAD can accommodate the growing uncertainty and increasingly volatile nature of current development environments. This provides flexibility

within the development arena that is receptive to change. The key features include user-driven requirements gathering activities, fast and authoritative decision-making, and the co-operation and collaborative of all participants within the development arena. Joint Application Development (JAD) workshops are used as a mechanism for requirements gathering. This involves small integrated teams of developers, key users and other stakeholders working together within tight timescales to prioritize business needs so that delivery deadlines are met. It is this intensive user involvement, consensus and rapidity of authoritative decision-making that are crucial for the achievement of development and delivery schedules and thus critical for project success. In 1994 the DSDM (Dynamic Systems Development Method) Consortium established nine fundamental principles (see Table 1.) that are considered to represent a RAD framework. These principles were aligned to the case study setting to confirm that a RAD/IAD development approach was in fact used and the extent of its application during the 3 year project. Table 1 illustrates the degree to which the agile development approach was applied during the three year project. It clearly shows that the initial measure of ‘fit’ between the IAD approach utilized and DSDM principles within the develop-

Table 1. DSDM principles applied to the case study project DSDM PRINCIPLES Active user involvement Empowered decision-making Integrated testing during lifecycle Frequent delivery of products Iterative and incremental delivery

Case Study Setting Actual involvement during the different development stages i.e. requirements negotiation, system design, testing and so on. Implied through the iterative and flexible nature of the development approach and the development schedules.

Case Study Project Year 1

Year 2

Year 3

Yes/No

Yes

Yes

No

Yes

Yes

Yes

Yes

Yes

No

Yes/No

Yes

No

Yes/No

Yes

Yes

Yes

Yes

Requirements Catalogue

Yes

Yes

Yes

Fitness for business purpose

Consensus of stakeholders

No

Yes

Yes

Stakeholders co-operation/collaboration

Internal and external

No

No

No

Changes are reversible High-level requirements

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ment environment was lower than anticipated by both the Developers and the Clients. However, over the life of the project it is evident that the RAD/IAD type application evolved. This reflects both an acclimatization of the environment to the approach, and a familiarization of the approach within the environment. However an area of apparent difficulty refers to problems experienced with inflexibility of the EC as an external stakeholder but this is not the focus of the case study.

CASE STUDY SETTING The case study concerns the implementation of an integrated IS/IT system for a UK Regional Government Department (the Client organization). The structure and culture of the Client organization is best described as bureaucratic in that it is strongly hierarchical, highly procedural and risk averse. Work is conducted in a highly regulated and control-oriented manner, supported by clear management lines of responsibility within a ‘perceived’ blame culture (Carnell, 2003; Hofstede, 2003; Morgan, 1986; Weber, 1964). The Client organization is responsible for managing the administration and expenditure of a number of European Commission (EC) agricultural grants and subsidies. Management of the grants and subsidy schemes is highly specialized requiring specific sets of skills and domain knowledge by individual business managers adhering to EC legislation, guidance and control. Individual Business Managers were responsible for the business needs and administration of the individual grant and subsidy schemes and were deemed to have ownership of the business processes involved. Thus they were identified as the key knowledge holders and decision makers who held the necessary understanding and detailed business knowledge that was required for the Generic Process Model of the new IS/IT system. Therefore, by tradition the Business Managers had acquired an inherent sense of ownership of their

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key business processes and this generated a level of legitimate power and control borne from their actual working practices. In bureaucratic cultures ‘position power is a predominant form of power’ (Carnell 2003, P. 232) and this is relevant to the behaviour of the business managers within the development arena. This made sharing knowledge across processes and reaching consensus as to the shape of new integrated process model difficult. An internal evaluation of the existing IT system and quality of service revealed poor customer satisfaction and an increasing inability to meet the changing business requirements. The case for a new IT system was put forward and approved. The aim of the new system was to move away from the previous individually driven scheme administration procedures towards a Generic Process Model which integrated the core processes of separate agricultural schemes. Consequently project development was broken into development modules that involved process definitions relevant to the development stages of the Generic Process Model. The business case specified the need for an integrated set of re-designed and standardized processes that facilitate automated data capture, data validation and speedier payments to customers. As previously mentioned development of the new IS/IT System was outsourced to a commercial company (the Developers) who adopted their own in-house commercial agile information systems development method (ISDM) - Iterative Application Development (IAD). This has been described and justified in the above section. It is a de-contextualized ‘knowledge bundle’ which underwent a process of contextualization within the organizational setting. The Developers believed that this was particularly suited to volatile nature of situational environment. The aim was to provide early visibility of the system being developed with the potential to incorporate user feedback, and the flexibility to handle new and changing requirements. A co-operative and collaborative working milieu was anticipated.

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The project environment remained within a central location where both the project participants of the organization and the outsourced Developers were co-located on the same site for the duration of the project. A project structure was established consisting of a Senior Management Board and teams of integrated developers and organizational participants working within a pre-defined reporting structure. These teams consisted of agricultural scheme managers, business managers/ stakeholders, agricultural administrative personnel, and the outsourced IT specialists (analysts, designers, developers, testers). The application of IAD involved a sequence of short, time-boxed iterative development cycles that adhered to a ‘fit for purpose’ philosophy rather than building 100% of business needs. The Developers were provided with a Requirements Catalogue at procurement that formed the basis and scope of their planned development activities. Figure 1 presents aspects of the life-cycle model adopted in the IAD ISDM and applied within the case study project. It can be seen that IAD adopts both notions of iterative development and incremental delivery. The Developers envisaged completing the initial development work during stage 2 and then revising and modifying the system to incorporate new business needs during iterations in stages 3 and

4. However, the iterations did not occur as intended. Stage 1 was completed as planned but towards the end of stage 2 the EC imposed a directive that radically changed major requirements of the system. Thus development on the case study project was halted and a revised project emerged (i.e. version 2, year 2 onwards) to accommodate the new development direction. This case study concentrates on the initial development project over its’ three year duration. Developers imported their customary technique of JAD workshops for requirements gathering purposes. The analysis of case study materials applies perspectives from Rogers (2003) and Van de Ven (1986), which emphasize the issues of antecedents and characteristics, in order to ascertain the extent of the success of adoption and success from adoption of the ISDM in situ, more specifically, the breadth and depth of the knowledge diffusion and management (Kishore & McLean, 1998).

ANALYSIS OF THE CASE STUDY A central tenet of RAD/IAD development is the iterative process that necessitates speedy decisions to support development schedules. However ‘Bureaucracy is the enemy of speed’

Figure 1. Planned iterative development stages of the IAD development approach

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Martin (1991, P. 128), bureaucratic structures are unsuited to highly complex, dynamic business processes (Carnell 2003). Fixed procedures and static working patterns are not beneficial in a volatile and changing business environment. Thus there is recognition that complex issues cannot be brought under unilateral control (Crozier 2004). In agreement Highsmith states ‘…trying to build collaborative practices into a rigid hierarchical, control-oriented culture would be folly’ (2002, P. 128). Yet when questioned the Developers and the Client Department both believe that, although the journey has been difficult, the agile development approach has been successful for this case study project, particularly in light of its evolving and volatile nature. Success was acknowledged by Senior Management who reported that organizational effort required to process the schemes is considerably lessened, and that operating costs had been significantly reduced. Project documentation reports savings of £1.6m of benefit that represents the ‘value for money’ criterion against which the project’s success was measured (Berger, 2007). In this section the case study project is examined from two perspectives. Firstly, the exploration of the inherent antecedents and characteristics that influenced the level of diffusion of the RAD/IAD development approach adopted, and secondly examination of the key factors that influenced the success and/or failure of the ISDM adopted.

The Level of Diffusion of the ISDM A number of factors impacted significantly upon the successful diffusion of the IAD approach. Key areas of concern affecting the diffusion process were the antecedents of user involvement, requirements negotiation and decision-making procedures. Additionally, the JAD workshops used for requirements negotiation proved unexpectedly challenging. It was these factors that were influenced by the nature of the organizational culture, the inherent working patterns and characteristics of the organizational people, and

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the agile development approach. For example during requirements negotiation activities, the attitudes and behaviour of a number of the business participants involved was counter productive to the speed and consensus in the decision-making processes that is crucial for agile development. User involvement, requirements negotiation and decision-making are examined below.

User Involvement For this case study user involvement proved significant as an antecedent that influenced the application of principles specified in the IAD development method. Agile approaches necessitate regular and prolonged user involvement, user availability and high levels of commitment from those involved. It was clear that in the early stages of the development, although Business Managers were assigned to development teams it proved difficult to insulate them from their daily ‘Business as Usual’ activities. This was frustrating for the Developers who commented,‘… also you find that you do sometimes go into meetings and there are a lot of project people, a lot of development people and no business people or very few’ (Developer 17). The business people were not always available to participate when required. The Client Project Manager commented ‘it is often those people maintaining the day-to-day activities, who were the least likely to be available, but who were the key knowledge contributors. I think we had this gap … the business expertise at that time we needed them they were all off doing other things to keep the organization running. So we suffered really not having the right expertise available to us and, necessarily that expertise has to be available on demand. That is the conflict, people have their jobs to do’. It seems that during the planning stages although the need for user participation was recognized little thought had gone into the maintaining of their day to day activities during their involvement with the project.

Knowledge-Based Diffusion in Practice

The consequences of this were two-fold. Firstly, key domain knowledge was sometimes lacking in design sessions. Senior Manager (2) reflected ‘One of the things that we’ve learnt … is the need for their (Business Managers) involvement during analysis’. Secondly, user commitment was affected because people had to fulfill their daily work tasks and this impacted on the ultimate user acceptance of the system. Thus in the early stages business motivation and commitment were reduced at a time when the foundations of the project were being created. The Developers found this particularly frustrating, one commented ‘One of the things that seemed to happen before, probably nearer the start, was trying to get information (from Business Managers) by a certain deadline and if it wasn’t there by that deadline there were implications on all the other deadlines’ (Developer 11). As a consequence the planned IAD development cycles did not iterate as anticipated from the start and this affected the project’s development and progress. One view put forward by the Business Managers is that perhaps senior management placed too much faith in the anticipated expertise and experience of Developers at project inception. They were expected to understand and take on board a wide range of highly complex requirements that were characteristic of the agricultural grants and subsidy schemes. As a result the substance of the system being developed did not materialize early enough in the project. In fact one the of the Developers Managers commented ‘I remember them (Developers) saying, scratching their heads and saying that they didn’t know that these things (agricultural schemes) inter-linked … and they were scratching their heads and trying to find a technical solutions’. Similarly Business Manager (6) said ‘… you could see the faces of developers, the penny’s dropping as to how complex some of these schemes really are. The disbelief in their faces when they find out what the EC are actually obliging us to do’. It was evident in the design / development workshops that when the

key knowledge holders were not able to attend due to other commitments it proved difficult to progress development forward, and subsequently delays occurred.

Requirements Negotiation and JAD Workshops As is often the case the volatile nature of the development environment meant that it was not possible to fully define the continually changing business needs. Consequently the Client organization produced a high level ‘To Be’ vision of the business requirements that conceptualized the Generic Process Model from which a Requirements Catalogue was formulated prior to procurement and given to the outsourced developers. The Developers used JAD sessions as the mechanism for negotiating the crucial business knowledge underlying the new IT system. JAD is a systems development technique involving workshops at which both developers and business stakeholders articulate system requirements, negotiate and prioritize said requirements to develop system specifications. JAD workshops are a key aspect of the IAD development approach. Utilized throughout the development process they form a fundamental part of the iterative development cycles. The essence is to get users involved in structured meetings to make fast decisions, around which development activities evolve. The integrated team environment is aimed at breaking down communication barriers, and increasing the level of trust and confidence between developers and business people. Typically JAD sessions consist of small and diverse teams of 4-8 key people, comprising developers, users and other stakeholders empowered to make decisions for a rapidity of development within the short iterate cycles that optimize speed, unity of vision and purpose of the development process. The aim is to produce documented business requirements within a set timeframe, i.e. a time-box. Thus project control is achieved by applying fixed

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timescales to project activities and when slippage occurs, requirements are prioritized and descoped to meet the specified timeframe sacrificing functionality if necessary rather than by extending the deadlines and incurring project delays. Therefore JAD sessions need careful management to ensure that the JAD groups are productive and reach a consensus otherwise their benefit is lost. However all participants describe early JAD sessions as ‘difficult’. In the first instances, the size of these joint JAD sessions was larger than the typical 4-8 people characteristic of JAD. Large gatherings of 12+ people were assembled in an attempt to involve representatives from all business areas and this meant that the sessions were less productive than expected. Problems also experienced with the level of attendance by key business knowledge holders committed to their ‘Business as Usual’ activities compounded this situation. Additionally, participants were used to line management procedures and they felt outside their ‘comfort zones’ in such JAD sessions. They were unwilling to openly voice opinions in front of their colleagues, particularly if senior managers were present. This was problematical for the Developers who reported that ‘There’s definitely an attitude of not wanting to criticize your boss … that would just be a comment that is not of the same opinion of your boss, that seems to be perceived as a criticism so there isn’t that openness of being able to comment or speak their mind’ (Developer 6), and ‘…having 20 people to a workshop where only one person speaks and they happen to be the most senior person in the room isn’t helpful’ (Developer 1). This problem was particularly visible in development meetings where difficulty arose in the prioritizing and subsequent scheduling of scheme development work. In scheduled meetings those business managers present believed their own priorities to be paramount and although required to make decisions, did not feel able to do so if they felt it was counter to their own individual agendas. Developer (21) commented ‘… actually

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what happened was everybody was still saying ‘my priority is first, mine’s the first’, from 5-6 different Business Leaders.’ The Business Managers had their individual agendas related to their specific scheme/grant management, for example ‘I had to promote my business needs so I could do my job’ (Business Manager 7). A further difficulty related to this issue was getting agreement from the managers about what was core to development and what was secondary. There is evidence that for some managers cosmetic changes to the system were as important as getting a fundamental aspect of the system working. For example ‘I think it’s very difficult to keep the Business on track during meetings, they do tend to wander off and try to solve every single little problem’ (Developer 17). The inability to make decisions about business needs was a key concern for the Developers who needed prioritization of development work to meet time-boxed development deadlines. Consequently, little team identity, unity or spirit developed, causing lack of trust between developers and business stakeholders that was not conducive to collaborative working environment, nor the promotion of consensus in decision-making activities, as discussed in the following section.

DECISION-MAKING A key problem experienced that had a noticeable impact on development, and which can be measured in terms of missed delivery deadlines, relates directly to former, inherent bureaucratic behaviour i.e. decision-making. The fast, authoritative decision-making from development teams necessary for agile development was not achieved in many aspects of the project environment. The inherent organizational protocols embedded in the bureaucratic tradition of the Client organization were a significant antecedent shaping the way in which this particular ISDM was adapted for use within the project environment.

Knowledge-Based Diffusion in Practice

The bureaucratic nature of the development setting engendered a perceived ‘Blame Culture’ environment. This is borne out by the Client Project Manager who commented ‘The ability to make effective decisions is a cultural aspect of the Department in that people don’t particularly want to make decisions because they don’t want the blame attached to them if something goes wrong’. In other words decision-making was restrained and limited in terms of the hierarchical role structure of the organization. Business Managers were rarely prepared to make decisions they thought were outside their direct area of responsibility. This was particularly evident in situations that were outside their own domain of business experience. They preferred to refer such decisions up the chain of command. Thus they were often reluctant to make decisions in workshops. For example in meetings they would leave, defer or just avoid decisionmaking leaving issues unresolved. Comments from Developers support this ‘…they (Business Managers) were empowered to make decisions but they just couldn’t. It was very frustrating we were trying to meet deadlines but this, well it just made it very difficult’ (Developer 21). JAD sessions necessitate rapid and timely decision-making practices that facilitate ‘fit for purpose’ development to satisfy core business needs. In other words, decision-making needs to be fast, authoritative and open to compromise. This proved difficult to achieve because such characteristics ran counter to the conventions of decision-making operated by Business Managers in their normal work patterns. Although they had been especially empowered to make decisions, the Business Managers found it difficult to make such rapid decisions in the joint design sessions. For the greater part they preferred to defer decisions up the hierarchical management structure. Project commentary confirms this ‘…the difficulty in the workshops was that people didn’t want to make decisions or couldn’t’ (Business Manager 7). Such behaviour affected the ability of the Developers to meet their development and delivery schedules

and the delays experienced cascaded throughout development and ultimately influenced the success of the project. Consequently, even though the project consisted of integrated development teams there is a perception that these managers were still working with mindsets of their former scheme-specific behaviour. Empowerment is not enough, there has to be a willingness to make important critical business decisions. Empowered decision making is inhibited by hierarchical cultures. Morgan (1997) proposes that ‘the limits of ‘empowerment’ are usually quickly felt as people run into the constraints imposed by the existing hierarchy’ (P. 169). Empowerment focuses on people rather than the process. Traditionally, within bureaucratic environments it is the time-horizons of the inherent culture that determine the speed decisions can be made. In contrast, the decision-making model specified in IAD focuses on activities and objectives rather than people and behaviour. Therefore the success of ISDM adoption was influenced by the interplay between activities specified in the development method and the structure and culture of the organization. Also an ISDM can be affected negatively by the behavior and attitudes of organizational personnel so they impact upon the overall trajectory of a project (Beynon-Davies & Williams, 2003; Jones & King, 1998).

CONCLUSION The chapter has explored knowledge-based diffusion against an empirical case study in which an agile ISDM [RAD/IAD] promoted by an outsourcing IT supplier was unbundled and contextualized within an organizational context. It supplies rich detail as to what ‘un-bundling’ means in practice to better understand the practical application of ISDMs within organizations. However, literature suggests that the unbundling and contextualization of an ISDM may prove problematic in situ particularly within highly centralized and bureaucratic

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organizations. This was clearly the situation of the case study. The application of the IAD approach promoted by an outsourcing IT supplier was clearly tempered by the relationship between the development approach and the nature of the organization. Difficulties were experienced during the process of unbundling and contextualization. We can say that the ISDM innovation described in the case only partly diffused in the early stages of the project amongst project participants. Although the developers, senior managers, and to a certain extent business personnel were receptive to adopting the practices of the ISDM, in its early stages infusion of some of the features of the ISDM was less than straightforward and proved difficult for a number of Business Managers. The attitudes and behaviours characteristic of conventional practices of the organization were initially counter with those necessary for the effective application of the ISDM adopted. Thus knowledge management crucial to the adoption or rejection of technology is subjective and can be influenced by actors and their contrary ideas or requirements. Additionally, the related social system and environment have a significant impact upon the unfolding adoption process. We examined how the success of ISDM adopted was influenced by the inherent structure and culture of the host organization, the actors involved and the IAD development activities. Key areas of concern were the difficulties associated with the management of user involvement and decision-making processes necessary for the effective operation of JAD workshops. We can emphasize how the organization’s culture influenced people’s behaviour. The inherent organizational protocols played a significant role in determining the working patterns, behaviour and attitudes of those involved. Problems experienced reflect the nature of the traditional hierarchicaldriven business policies and procedures governed through knowledge management and management hierarchy. As a consequence the climate of trust, co-operation, collaborative and flexible working

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practices necessary for consensus and authoritative fast decision-making was not present and this had significant practical implications. It is these elements that are crucial if iterative development of knowledge based systems is to succeed. This has implications for practice because governmental environments that tend to be bureaucratic in nature exhibit similar characteristics in terms of their structure and culture (Clegg et al. 1997). However, any development project, particularly of the scale of the one described, evolves in terms of its interaction with the holding organization. There is evidence in our case of an evolving rate of diffusion and infusion of aspects of the ISDM which initially proved problematic. As the project matured and development evolved through the subsequent second project environment (year 3 onwards), Business Managers appear to have begun to infuse and accept some of the initial, problematic elements of the ISDM due to, we believe, a familiarization with the development approach. Indeed interviewees report a greater acceptance of the need for collaboration and acknowledgement to practice effective decisionmaking. A senior manager reflects ‘I think over the last 12 months it has become more effective but as usual you learn as you are going along’ (Senior Manager 13). We should hence not assume that the diffusion and infusion of an ISDM is a necessarily linear, all-or-nothing process. The difficulty in making the transition from the previous bureaucratic behaviour of referring decision-making up the line management hierarchy is attributed to the bureaucratic nature typical of most Government departments. This may partly explain some of the difficulties experienced with IAD or agile practices more generally. However, in a large-scale project environment there is scope for a wider range of stakeholder involvement and greater time for practices to diffuse and infuse within the holding organization. A lesson here for knowledge based diffusion is that the presence of inherent antecedents and characteristics that present areas

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of risk may be mitigated through a cultural acclimatization of both the environment and of key stakeholders involved. The authors would like to thank the outsourced developers and all organizational participants of the IS development project for their time and contributions. The authors would like to thank the outsourced developers and all organizational participants of the IS development project for their time and contributions. We would also like to thank the National Assembly of Wales for funding this research.

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Button, G., & Sharrock, W. (Eds.). (1993). Practices in the work of ordering software development: The discourse of negotiation (pp. 159–180). Oxford: Pergamon. Carnell, C. A. (2003). Managing change in organisations. London, UK: Prentice Hall. Clarke, P. A., & Staunton, S. (1989). Innovation in technology and organization. London: Routledge. Clegg, C., Aztell, C., Damodarant, L., Farbey, B., Hull, R., & Lloyd-Jones, R.,…. & Tomlinson, C. (1997). Information technology: A study of performance and the role of human and organization factors. Ergonomic, 40(9), 851–871. doi:10.1080/001401397187694 Cockburn, A. (2002). Agile software development. Boston: Addison Wesley.

Baskerville, R., & Pries-Heje, J. (2001). A multiple-theory analysis of a diffusion of information technology case. Information Systems Journal, 11, 181–212. doi:10.1046/j.1365-2575.2001.00106.x

Coughlan, J., & Macredie, R. D. (2002). Effective communications in requirements elicitation: A comparison of methodologies. Requirements Engineering, 7(2), 47–60. doi:10.1007/s007660200004

Baskerville, R., Pries-Heje, J., & Ramesh, B. (2007). The enduring contradictions of new software development approaches: A response to ‘Persistent Problems and Practices in ISD.’. Information Systems Journal, 17, 241–245. doi:10.1111/j.1365-2575.2007.00236.x

Crozier, M. (2004, September). Theatres of innovation: Political communication and contemporary public policy. Paper presented at the Australasian Political Studies Association Conference, (ACIS), Adelaide.

Berger, H. (2007). Agile Development in a bureaucratic arena: A case study experience. International Journal of Information Management, 27(6), 386–396. doi:10.1016/j.ijinfomgt.2007.08.009 Beynon-Davies, P. (2002). Information systems: An introduction to informatics in organisations. Basingstoke, UK: Palgrave. Beynon-Davies, P., & Williams, M. D. (2003). The diffusion of information systems development methods. The Journal of Strategic Information Systems, 12, 29–46. doi:10.1016/S09638687(02)00033-1

DSDM Consortium. (2001). Dynamic systems development method. Retrieved September 25, 2001, from http://www.dsdmna.org/en/about / principles.asp Fichman, R. G. (1992). Information technology diffusion: A review of empirical research. In proceedings of the 13th International Conference on Information Systems (ICIS), Dallas, TX (pp. 195-206). Atlanta, GA: AIS. Fichman, R. G., & Kemerer, C. F. (1999). The illusory diffusion of innovation: An examination of assimilation gaps. Information Systems Research, 10(3). doi:10.1287/isre.10.3.255

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Gharavi, H. Love, P.E., & Sor, R. M. (2004). Diffusion of innovation: An institutional perspective. Presented at the Australasian Conference on Information Systems (ACIS), Tasmania. Highsmith, J. (2002). Agile software development ecosystems. London: Addison Wesley. Hofstede, G. (2003). Cultures and organisations: Software of the Mind. London: Profile Books Ltd. Hollingsworth, J. R. (Ed.). (1991). The logic of coordinating American manufacturing sectors (pp. 35–74). New York: Cambridge University Press. Hovorka, D. S., & Larsen, K. R. (2006). Enabling agile adoption practices through network organizations. European Journal of Information Systems, 15, 159–168. doi:10.1057/palgrave.ejis.3000606 Iacono, S., & Kling, R. (Eds.). (1996). Computerization movements and tales of technological utopianism, computerization and controversy: Value conflicts and social choices. Chichester, UK: Wiley. Jones, T., & King, S. F. (1998). Flexible systems for changing organizations: Implementing RAD. European Journal of Information Systems, 7, 61–73. doi:10.1057/palgrave.ejis.3000289 Kishore, R., & McLean, E. R. (1998). Diffusion and infusion: Two dimensions of ‘success of adoption’ of IS innovations. In Proceedings of the 4th Americas Conference of the Association on Information Systems (AMCIS), Baltimore, MD (pp. 731-733). Atlanta, GA: AIS. Light, B., & Papazafeiropoulou, A. (2004). Reasons behind ERP package adoption: A diffusion of innovations perspective. In Proceedings of European Conference of Information Systems (ECIS), Turku, Finland. Retrieved from http:// www.informatik.uni-trier.de/~ley/db/conf/ecis/ ecis2004.html

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Lyytinen, K. J., & Damsgaard, J. (2001). What’s wrong with the diffusion of innovation theory. In Ardis, M., & Marcolin, B. (Eds.), Diffusing software product and process innovation. Dordrecht, Netherlands: Kluwer. Martinez-Brawley, E. E., & Emilia, E. (1994). Retrieved September 25, 2008, from www.eric.ed.gov/ ERICWebPortal/ recordDetail?accno=ED390619 Miller, K. W., & Larson, D. K. (2005). Agile software development: Human values and culture. IEEE Technology and Society, 24(4), 36–47. doi:10.1109/MTAS.2005.1563500 Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. doi:10.1287/isre.2.3.192 Morgan, G. (1986). Images of organization. London: Sage Publications. Mustonen-Ollila, E., & Lyytinen, K. J. (2004). How organizations adopt information systems process innovations: A longitudinal analysis. European Journal of Information Systems, 13, 35–51. doi:10.1057/palgrave.ejis.3000467 Newell, S., Swan, J. A., & Gallier, R. D. (2000). A knowledge-focused perspective on the diffusion and adaptation of complex information technologies. Information Systems Journal, 10(1), 239–259. doi:10.1046/j.1365-2575.2000.00079.x Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). New York: The Free Press. Schwaber, K., & Beedle, M. (2002). Agile software development with SCRUM. Hemel Hempstead, UK: Prentice Hall. Sharifi, H., & Zhang, Z. (2000). A methodology for achieving agility in manufacturing organizations. International Journal of Operations & Production Management, 20(34), 96–512.

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Valier, F. M., McCarthy, R. V., Aronson, J. E., & O’Neill, H. (2004). The province of diffusion of innovations: Usable social theory for information systems research. In Proceedings of Americas Conference on Information Systems, New York (pp.1441-1445). Atlanta, GA: AIS. Van de Ven, A. H. (1986). Central problems in the management of innovation. Management Science, 32, 590–607. doi:10.1287/mnsc.32.5.590

Van Slyke, C., Lou, H., & Day, J. (2002). The impact of perceived innovation characteristics on intention to use groupware. Information Resources Management Journal, 15(1), 5–12. Veryard, R. (1987). Implementing a methodology. Information and Software Technology, 29(9), 469–474. doi:10.1016/0950-5849(87)90003-6 Weber, M. (1964). The theory of social and economic organization. New York: The Free Press.

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Chapter 4

Deploying Knowledge Management in R&D Workspaces Won-Chen Chang National Cheng Kung University, Taiwan Sheng-Tung Li National Cheng Kung University, Taiwan

ABSTRACT The active and effective management of valuable knowledge is widely believed to be a core competency for solidifying the competitive advantage of an organization. Whether knowledge management (KM) is a new idea or just a recycled concept per se both managerial and academic campuses have sought a vast array of KM strategies, solutions, frameworks, processes, barriers and enablers, IT tools and measurements over the past decade. Although there are many KM studies for both public and private sectors, most of them focus on the practice of international companies and western experiences, relatively few cases are reported on KM deployment and implementation in the Chinese community, especially for knowledge intensive research and development (R&D) institutes whose missions are to serve traditional industries. To reveal some of the accomplishments gained in the Asia-Pacific region, this chapter presents and discusses the lessons learned from a particular case study in fostering the KM initiative and system in a research-oriented institute serving the metal industry.

KNOWLEDGE ASSETS IN R&DORIENTED ORGANIZATIONS R&D plays a fundamental role in the competitiveness of technological innovation. These R&D processes can primarily be seen as information transformation processes, transforming information about client orders, market demand and DOI: 10.4018/978-1-60566-701-0.ch004

technological advancement into product and process designs (Drongelen et al., 1996). In the case of R&D organizations, knowledge workers synthesize tangible and intangible resources to create value-added knowledge-based products as their major outputs. These knowledge assets are indexed in terms of consultancy, innovative products, expert reports, and intellectual properties. The majority of professional knowledge and expertise frequently originates in the context and

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Deploying Knowledge Management in R&D Workspaces

activity of research projects and industrial services implementations. In a project-based engineering firm, there are three main aspects of knowledge: technical, entrepreneurial and project management knowledge (van Donk & Riezebos, 2005). From another point of view, types of project knowledge can be viewed as: knowledge about projects, in projects and from projects (Damm & Schindler, 2002). Knowledge for R&D work exists in various forms and sources as indicated in Table 1. The ability to manipulate R&D knowledge highly depends on the type of knowledge source and form. For instance, internal-explicit knowledge is easy to collect and manage, while external-tacit knowledge requires a lot of efforts to acquire and maintain. Accordingly, when an organization wishes to incorporate KM, the first step is to implement knowledge audit to identify the sources of R&D knowledge and decide the management priority. In practice, Paraponaris (2003) further indicates that, for R&D process, knowledge could be viewed as a stock of regular object inventories to explore the potential for innovation. Nevertheless,

the transfer of implicit knowledge among individuals is another story. Knowledge acquisition is not a matter of ‘copy, paste and save’ between individuals or teams with the knowledge to those without it (Sapsed et al., 2000). Knowledge sharing networks, (i.e. communities of practice) provide a common purpose and effective links allow for repeated interactions that create knowledge spillovers based on shared knowledge creation. Moreover, accumulation of personal knowledge in each individual is not totally equivalent to accumulation of embedded knowledge in the organization. In other words, the implicit characteristics for tacit knowledge and collective knowledge in know-how and business service experiences, and a culture that is unwilling to share within an organization, make knowledge transformation, accumulation and sharing difficult (Szulanski, 1996). Milliou examined the impact of R&D information on innovation incentives and welfare (Milliou, 2004). If members in such research teams can agilely and correctly acquire and assimilate organizational knowledge assets that are already

Table 1. Forms and sources of R&D knowledge (Adapted from Parikh (2001) Modified with additions by Chang (2008)) Internal

External

Tacit

Experiences/judgments* Insights/intuitions/beliefs* Educational background Cultural background Intra-organizational relationships Unwritten rules of thumb History and stories Master technicians Experts/researchers

Industry experts/consultants Industry best practices Communities of professions* Inter-organizational relationships Consumers Academic researchers Informal social networks* Other research organizations

Explicit

Organizational databases Information systems File systems Standard operating procedures Discussion minutes/trails Design and prototypes Product manuals Own patents Training courses* Machine/equipment* Manufacturing processes*

Trade publications External databases Benchmarking matrices Others’ patents Competitors’ products and manuals Academics research articles Specifications and design manuals Seminars and conferences* Standards Regulatory guidelines and governmental policies*

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known to other organizational units, they can accelerate tasks and improve the quality of outputs. For example, Booz Allen & Hamilton employed a ‘knowledge engine’ to better distill and deploy the firm’s ideas and learning (Burgelman, et al., 2004). There are still many immeasurable parts of knowledge assets, some R&D organizations have tried to issue intellectual capital reports in terms of human capital, structural capital and relational capital (Leitner & Warden, 2004). These organizations found, during the measurement, that they learned more about their knowledge production processes and explored some productive uses of knowledge-based assets.

KM IN R&D-ORIENTED ORGANIZATIONS In the context of R&D, the barriers to promoting institutional knowledge cycle are mainly decided by the choices made in the organization structure and R&D strategy, which in turn depend on the business strategy and other functional strategies within the organization (Drongelen, et al., 1996). Common KM barriers and enablers found in R&D-oriented organizations are reviewed in Table 2. Most of the barriers and enablers are similar with those found in many companies. In brief, issues on culture, infrastructure and technology are the three main concerns for implementing KM in R&D-oriented organizations. In the past decade, many R&D-oriented organizations have paid great attention to incorporate KM into their strategic management and routine practices. From review of literature, we summarize some of the KM efforts including initiatives and strategies in R&D-oriented organizations, as shown in Table 3. The focus of KM varies with the characteristics of R&D-oriented organizations. For instance, some organizations are concerned about knowledge integration and management with external partners, while others might be more interested in managing R&D outputs of projects and securing the pass-on of expertise knowledge. 58

CASE BACKGROUND In recent years R&D organizations have encountered many intensified challenges including increase of domain complexity, evolution of technology, competition for research funds and management of new operation modes, i.e. serving the needs of industry more effectively so as to raise private funds from industries. Thus, competition through knowledge exploitation, transfer and leverage are reaching a new paradigm in this sector. In reaction to these structural changes, some government sponsored non-profit research-oriented organizations in Taiwan have voluntarily begun to incorporate new instruments for managing knowledge-related activities more effectively and efficiently. These R&D organizations, including Industrial Technology Research Institute (ITRI), Taiwan Textile Research Institute (TTRI), Institute for Information (III), and Metal Industries Research & Development Centre (MIRDC) provide diversified professional R&D and information services for the government and local industry. In addition, they are paying to improve the production, productivity and performance of knowledge and its management. In view of the history of KM development within these R&D organizations, the first KM initiative was introduced into a joint industry market research project - Industrial Technology and Information Services (ITIS). This project was sponsored by the Ministry of Economic Affairs and has been operated jointly by eighteen R&D organizations since the early 90s. In the beginning, a Web-based knowledge bank was formed in 1999 to effectively manage industrial information collected by these eighteen institutes and later, they tried to build a cross-institute KM framework facilitated by a famous international consulting firm. However, the project-based KM idea was not successfully implemented due to two obstacles: first, the KM framework proposed by the international consulting firm was based on their own KM experiences acquired from a single organization

Deploying Knowledge Management in R&D Workspaces

and the characteristics of group dynamics within a single organization and cross-institutes were different in terms of power structure, culture and fellowship. Second, it was hard for the project members to get equivalent support from their mother institutes for contributing their time and spending as the contributions these project members made to the cross-institute KM community could not be acknowledged to the full extent by an individual institute. In other words, the disposition of each institute toward the KM initiatives varied, therefore the recognition of a psychology contract, i.e. the expectations that members have about each

other in return, among participants was in vain as no one was willing to actively participate and take it seriously. Undoubtedly, a KM community that lacks comparable knowledge contributions from each party is destined to be a failure, but in spite of the downfall, this pilot plan did ignite sparks for these institutes to take further KM action. For instance, in 2000, ITRI began its KM journey by forming a KM interest group and in the next year kicked off an organizational-wide KM scheme focused on constructing a ‘competency network.’ Six areas including white light LED, nanotechnology, water world, mobile information appliances,

Table 2. Common KM barriers and enablers found in R&D-oriented organizations (Source Chang 2008) R&D oriented cases

KM barriers

KM enablers or success factors

British Telecom Labs (Warren & Graham, 2000)

Not mentioned

Building the network

KM in research and development (Armbrecht, et al., 2001)

Not mentioned

Culture, infrastructure, technology

R&D cooperation project of Nokia, Airbus France and Airbus Germany (Barnard & Poyry, 2004)

Lack of good tools Differences between groups Changes in economic and business situation making people unwilling to share Lack of personal contacts Lack of time& high workloads

Adequate KM tools and procedures People involved making a conscious effort for sharing The presence of people who facilitate sharing and become personally acquainted with other groups, thus enhancing trust.

Role of tacit knowledge in innovation processes of small technology companies (Koskinen & Vanharanta, 2002)

Bureaucracy

Coaching type of leadership Engaging technology companies and their customers in interactive learning and effective sharing of tacit knowledge

A survey focused on strategies and tactical moves employed by CKOs from 22 companies in the Geneva Knowledge Forum (Raub & Wittich, 2004)

Unable to target key actors Using the hierarchy to put pressure on resistors Overemphasizing IT aspects and putting IT in the driver’s seat Fostering knowledge network Purchasing ready-made KM solutions in the market Excessive reliance on outside experts for KM implementation Delivering a purpose Presenting KM as a management fashion Keeping KM vague or avoiding KM terminology together

Targeting key actors Aligning KM with contributions from key functional units Gaining support from line managers and top management Fostering knowledge network Identifying and leveraging existing KM initiatives Establishing networks with outside KM practitioners Delivering a purposeful message Adapting the message to different target groups Focusing on business value Communicating KM ‘quick wins’

French national project for the automotive industry (Barthès & Tacla, 2002)

In R&D projects, there is simply no extra time to organize a complete knowledge capitalizing cycle.

Not mentioned

Enhancing knowledge sharing – case studies of nine companies in Taiwan (Hsu, 2006)

Not mentioned

CEO’s commitment on: Continuous learning initiatives Performance management systems which motivate employee knowledge sharing Information disclosure to create climate of sharing

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innovative R&D and human resources were chosen to pilot run the six communities of practice. In 2003, MIRDC also pioneered its KM journey by initiating a KM plan for a limited number of project members and a few months later expanding to an organization-wide KM portal. To establish a set of effective practices for managing workspace knowledge, MIRDC tackled its KM challenges by activating changes in organizational learning and sharing, and introducing an integrated KM portal to enhance productivity of knowledge work (Chang et al., 2004). Founded in 1963 by the United Nations and transferred to the Taiwanese government in 1968, MIRDC has positioned itself for researching and developing the leading technology of the metal industry and related industries in Taiwan. After 42 years of development, MIRDC has blossomed into the third largest research organization supervised by the Department of Industrial Technology,

Ministry of Economic Affairs of Taiwan. The Centre has devoted itself to innovative R&D and has been granted nearly 300 patents, it carries out hundreds of R&D projects and offers an array of managerial and technological services to the government and industrial communities. MIRDC has now positioned itself on firm ground for the transition of technology from basic research to purposeful applications and in terms of scale, it can be rated as mid-scale among worldwide R&D organizations. The annual turnover was around US$26-28 million from 2000 to 2005 and has increased to US$40 million in 2007. The total number of employees in 2007 was around 572, 29 with PhD qualifications and 275 with Master Degrees, the majority of them well educated and with an average experience of 11 years, forming a large talent pool for R&D services to Taiwanese industries.

Table 3. A review of KM initiative and strategies adopted by R&D organizations (Source Chang 2008) Cases

KM initiatives

Bridge people with people

Bridge people with system

British Telecom Labs (Warren & Graham, 2000)

Generate and disseminate technology trend and comparative R&D information

Regular research audits in groups allow cross-fertilization of tacit knowledge

Build a central database and intranetbased directory Use groupware Adopt automated patent classifier

R&D cooperation project of Nokia, Airbus France and Airbus Germany (Barnard & Pöyry, 2004)

Develop procedures and tools for facilitating and improving KM processes among partners

Not mentioned

Build the Knowledge portal, i.e. WISE Easy access to all documents, tools and people Provide knowledge object (KO) annotation to enhance tacit knowledge transfer and knowledge generation trace

Samsung Advanced Institute of Technology (Sohn, 2004)

Develop and secure Samsung’s leadingedge position in key technology areas

A careful combination of a KM system and reinforcement mechanism Adopt ‘knowledge intensive (KI) staff meetings’ integrated into formal problem solving process to form extensive social network across technology boundaries

‘Praise Ground’ website that address activities or behavior of knowledge sharing, creation, collaboration and problem solving

French national project for the automotive industry (Barthès & Tacla, 2002)

Develop a means of managing the knowledge created in complex high priority urgent R&D projects efficiently.

Not mentioned

Groupware and agent are integrated to offer an agent-supported portal for collaborative R&D work.

Enhancing knowledge sharing – case studies of nine companies in Taiwan (Hsu, 2006)

Not mentioned

Tutor and lecturer development program Workshop and forum Knowledge modularization

Not mentioned

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Prior to the implementation of the KM plan, very few efforts were undertaken to identify, capture and transfer the knowledge assets within MIRDC. Workspace knowledge, including R&D expertise, domain know-how, best practices, project documents, administration and customer information, was not systematically collected and organized as all the information was scattered in various departments, different directories and separate databases. As a result, engineers often complained about the difficulties in acquiring and accessing necessary information and knowledge that existed in the centre, and were hard to find. In order to be innovative in R&D performance, the knowledge utilization activities should not only apply existing knowledge but should also transform it into new and creative knowledge, which provides competitive advantage. The centre had to discover a routine practice to enhance its knowledge cycle, driving the initiatives of KM in MIRDC.

FIVE STAGE KM APPROACH ADOPTED BY MIRDC Previous studies indicated that IT solutions can efficiently facilitate explicit knowledge access and utilization, while implicit knowledge sharing and transformation is decided by people-to-

people interactions (Gold, et al., 2001; Huber, 1991). From our survey, MIRDC believes that the organizational management strategy, application of IT solutions, cultivation of an innovative and sharing culture, and gaining support at all levels are the major enablers of KM deployment as raised in knowledge auditing. Undoubtedly, this means that KM manipulation is a sophisticated and multi-disciplinary task. Building internal alliances can pave the way to initialize a KM plan. Thus, MIRDC organized a cross-departmental task force consisting of supervisors, IT personnel and human resource managers to handle KM planning, coordination and implementation. In addition, in our review, MIRDC was clearly aware that if it wished to succeed in promoting KM, a cohesive and evidence-based deployment framework had to be developed beforehand. Therefore, through a synthesis of previous concepts and best practices (Holsapple & Joshi, 1999; Rubenstein-Montano, 2001; Maier and Remus, 2003), MIRDC proposed a KM approach, which is further summarized as a five-stage KM approach including auditing, planning, execution, evaluation and reinforcement, shown in Figure 1. MIRDC attempted to synergize previous frameworks and developed a more exhaustive KM deployment framework that not only highlights KM key concerns and drivers but also includes detailed assignments, check points of

Figure 1. Five-stage KM approach and critical tasks involved (Source Chang (2008))

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quality control/quality assurance and resource allocation.

KM AUDITING Varied modes of operation within organizations require different types of knowledge assets as well as KM systems. This activity is the groundwork of KM, which aims to clarify and examine the basic questions of KM initiatives. Therefore, the major work of the KM auditing stage is to conduct a status quo survey across various workspaces in the centre. The survey conducted in 2003 primarily focused on the identification of needed knowledge assets, the current KM practices in each department, what gaps existed in KM, and consensus of KM vision and mission throughout the organization. Once these knowledge assets were identified and represented in the form of an organizational K-map, managers were provided with a whole picture of knowledge resources and knowledge gaps. The KM task force conducted two internal studies within three months in 2003, a questionnaire and a focus study group. Firstly, an open-ended questionnaire was sent to all employees to elicit comments and information about the current KM

practice, type of knowledge assets, and expectations of KM objectives. Sixty-one replies were collected. A summary response analysis report regarding overall KM needs was prepared as background information for the focus group study, which was held five times and eight to ten crossdepartment employees were invited each time. Through KM auditing, MIRDC found that the intrinsic KM needs come from the occurrence of a ‘knowledge vacuum’, one of the greatest fears of organizational management, which refers to the losing of proper possession or the inability of unified government over organizational knowledge. After thoroughly examining the management status of explicit and tacit knowledge, MIRDC found that a crisis of knowledge vacuum did exist for both types of knowledge that could be worsened if no actions were taken. Table 4 summarizes the KM gaps/barriers found in knowledge audits. First of all, the management of explicit knowledge was problematic as the majority of it (see Table 5) was dispersed and fragmented, due to the inherent individualism of the various departments. This explicit knowledge could be treated as a series of knowledge objects (KOs) and was supposed to be easy to handle but did not receive enough attention to be effectively managed as assets.

Table 4. Summary of KM gaps/barriers found in knowledge audits (Source Chang (2008)) Aspects

KM gaps/barriers

General issues

Lack of KM strategies and their alignment with organizational strategies Management support is not strong Researchers lack time and have high workloads, thus inhibiting their participation in KM activities KM activities are not tightly integrated with workflow The necessity awareness of KM among employees is poor

Explicit knowledge

Knowledge is dispersed and management of explicit knowledge is problematic and unsystematic Hard to reach distributed databases and lack of KM portal Lack of on-line collaboration platform Search efficiency for explicit knowledge objects is low Management of PKO, such as MS PowerPoint files, is insufficient. No clear regulations and procedures guiding the production, sharing and accumulation of explicit knowledge\

Implicit knowledge

Rigidity of bureaucratic structure categorized by technology fields and each department operated as profit-center Cross department R&D networking is inadequate so that new knowledge creation, i.e. innovation, is slow Internal expert skills directory is not available Lack of mechanisms and incentives to encourage implicit knowledge sharing

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KM PLANNING This activity defines the objectives, scope, strategies and approaches of the KM plan. Prior to implementing KM, one elementary issue is how to integrate it with the organization’s vision and mission. For the sake of attaining KM goals and closing the KM gaps, two simple and straightforward strategies were formulated; ‘bridging people with systems’ and ‘bridging people with people’ as shown in Table 6. By applying the ‘Plan-Do-Check-Action Cycle’ the KM deployment framework was thoroughly configured in the planning stage to ensure every KM effort would be implemented and coordinated in an appropriate way.

KM EXECUTION This activity represents an effort to realize the goals of KM in routine practice. Even though

knowledge sharing and information exchange is part of the Taiwanese culture and is a strong advantage over U.S. organizations (Stankosky, 2005), knowledge sharing and transfer does not spontaneously happen in a R&D organization as employee motivation for knowledge exchange is certainly influenced by national cultural norms. Hsu (2006) indicated that Chinese people tend to divide their ‘gainsharing’ circle into an ‘ingroup’ and an ‘outgroup’, and only the ‘ingroup’ can effortlessly enjoy knowledge sharing. Furthermore, influenced by Chinese culture, employees in Taiwan are often conservative when expressing their opinions because they were taught to respect corporate power structures and behavioral norms, and that managers’ knowledge is superior to that of employees (Pun K. et al., 2000). As a result, challenges like relation circles, evaluation anxiety and power barriers need to be faced when organizations in Taiwan wish to encourage knowledge sharing in KM practices (Hsu, 2006). KM is a series of long-term systematic processes

Table 5. Various forms of KO identified from knowledge auditing (Source Chang & Li (2007)) Origins

Forms of knowledge objects

R&D works

Patent documents, research logs, research reports, manuals, experiment records, engineering drawings, technical specifications and project management notes

Education and training

Training reports, best practices, lessons learned, policy and strategy declarations, norms and rules

Customer account

Customer profiles, customer contact records, proposals, bid files, market analysis reports and marketing plans

Table 6. KM visions, strategies and goals of MIRDC (Source Chang & Li (2007)) KM visions Enriched knowledge, diversified learning, interesting work, concrete impartation, faster processing, and quality output. KM strategies

KM goals

Bridging people with systems

1. A centralized knowledge repository, mainly powered by Lotus Notes, is used to store all knowledge objects as identified in knowledge auditing. 2. Establish a KM portal to connect individual information systems which are dispersed at different sites.

Bridging people with people

1. Develop industry-focused communities of practice to call for a horizontal network platform that links employees to enable knowledge sharing. 2. Link KM with collaborative R&D process management.

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and to combine them with synergies, people and tools, MIRDC established a sophisticated KM deployment framework as shown in Figure 2. In order to tackle the knowledge vacuum existent in MIRDC and avoid the KM gap, this framework covers the assignments, steps and control points for systematically managing KM progress by developing a series of ‘small steps’ within the five stages to deliberately manage the KM plan. For example, the first step of KM planning is to propose the KM plan to management in order to gain support and resources. The major tasks in the KM execution stage can be divided into two groups. For the KM task force, their job mainly focused on developing the KM portal, facilitating organizational change and managing the KM progress. For example, to facilitate organizational change, the following actions were undertaken: formulate the KO output and submission procedures in a rules and regulations system, arrange five training seminars to

present the KM concept and KM portal, and hold activities to promote the KM portal. The primary issues to be communicated in the trainings were: the kind of KM that was going to be carried out, the functionalities of the KM portal and how personal benefit associations were made. Parallel to the task force actions, the knowledge workers in R&D teams and administrative units were obliged to submit KO in response to the rules and regulations. This was particularly effective since most R&D projects were sponsored by the government and all R&D outputs had to be as requested in the contracts. All the R&D outputs and related reports were recorded as part of the employee’s evaluation system. Nevertheless, MIRDC generated the administration databases for human affairs, accounting, procurement and internal news postings so that employees were free to access those databases to search for useful information. In the planning stage the management also found that horizontal knowledge exchange and

Figure 2. KM Deployment Framework in MIRDC (Source Chang & Li (2007))

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communications for creating a total solution to the clients were inadequate and inefficient due to a rigid hierarchical structure with the technology classifications. To initiate lateral communications and knowledge sharing across disciplines, MIRDC established six industry-focused communities of practice in 2005 and 2006 respectively. These areas were precision and micro parts/assembly, moulds and dies, transportation vehicle parts/assembly, flat panel display, motor and export-oriented metal fabricated products. However these communities did not work well and additional actions were taken to enhance collaborative innovation. Two adjustments are argued in later sections. The promotion of KM awareness and initiatives to all departments influences the success of KM implementation in MIRDC, hence the task force has spent great efforts to promote KM awareness and educate employees about KM portal features. The successful introduction of the KM portal can be attributed to the following promotional activities and incentive programs: •

•

• •

•

•

To encourage novice users to use the KM portal. The task force held a KM portal knowledge seeking game and offered rewards (convenience store coupons), nearly a quarter of the total employees participated; Periodically published KM e-news to keep employees/staff aware of KM progress and the latest updates; Modified organization rules and regulations to bring changes to work habits; Communication with managers to gain basic support for auditing the KO’s production; Invitation of two R&D teams to test running an ontology-enabled document management and collaboration system by rewarding a free notebook to the team; Encouraging sharing of non work-related postings such as recreational topics, internet jokes, inspiring stories and quotations

to create an informal and receptive KM culture. The three-layered architecture of MIRDC’s KM portal is illustrated in Figure 3. The infrastructure services layer integrates various internal information systems and collaboration systems including the MIS system, Lotus Notes system, homepages of Websites, library system, E-learning system and individual file systems. The middle knowledge services layer aims to promote the knowledge cycle. Thus, knowledge services including workflow management, document management, search engine and K-map are integrated to support such processes. The upper presentation services layer benefits users by providing access to a unified application for KM processes. The KM portal uses a Java-enabled Web interface for easy browsing and accessing of information and knowledge. Personal alert messages from workflow management systems and operational databases are delivered to meet the specific needs of a user whenever he/she logs onto the portal.

KM EVALUATION This stage refers to the activity of examining the impact of KM implementation on the organization and the performance of the KM system. A thorough assessment of KM solutions usually involves evaluating the extent to which knowledge cycles are supported. Practically speaking, MIRDC did not take this KM plan from an academic perspective, thus it was unable to conduct a sophisticated evaluation for both the bridging people with systems strategy and the bridging people with people strategy. Alternatively, it assessed the success of KM through a user satisfaction survey for the KM portal system. These evaluations were important as they enabled the assessment of the effectiveness and usability of a developed KM system (Nagi and Wat, 2005).

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First of all, the IT engineers tried to enhance the system stability based on user feedback. Later the IT managers interviewed heavy users in the first quarter of 2004 after introducing the KM portal and collected comments about how the portal could be further improved to fulfill their needs. Accordingly, in the second quarter of 2004, IT engineers added new functionalities to make it as robust as possible, which included on-line administration approval, personal workflow alerts and on-line booking. A user opinion survey was then conducted by asking several open questions about user perceptions of the KM portal, which 72 users responded. The results showed that most users were satisfied with the improved system that offered the following benefits in facilitating knowledge work and knowledge flows across the organization: •

•

Almost all staff users enjoyed the convenient single sign-on and integrated workflow functions such as MIS and Notes online approval in the KM portal; More centralized knowledge storage, and more efficient knowledge discovery across dispersed databases facilitated by a search

•

•

•

engine. For instance, most engineers and managers felt a great ease in browsing various technical and training reports and then posting articles of interest; The on-line personalized workflow information for daily work could give timely reminders for users and improve work-efficiency. Integration of personalized daily workflow and routine practices such as project alerts, accounting alerts and facilities arrangement with the KM portal is a dominant function to the sustainability of the KM portal. Any information management system that is isolated from routine practices is expected to fail in the long run; (4)Users were satisfied with the daily industry e-news provided by a Web spider, which gave a quick glance at the everyday news. The news headlines were then stored in the database and could be searched for future R&D work; Employees located at the MIRDC headquarters and in two regional offices in different cities were happy with the simultaneous administrative announcements without solicitation.

Figure 3. Three-layered architecture of MIRDC’s KM portal (Source Chang & Li (2007))

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The growth of the KM portal usage is another factor to evaluate. The initial daily usage count was nearly fifty and after improving the KM portal functionalities based on user opinions, the average daily user count gradually grew as shown in next section. This evidence supports that the popularity of the new KM system was decided by the effective integration with the existing tools and culture. A rough estimate of the general cost of the KM deployment program throughout the organization in time and operational resources is given in Table 7. Total man power spent on the KM deployment program was estimated about 18 man-months including the time spent by the task force, IT engineers and employees. Expenses

spent on commercial software and KM portal construction was around US$62,000. This activity concerns the continuous improvement based on user responses and availability of new tools. The final stage in the manipulation of KM consists of monitoring progress and making adjustments accordingly on an ongoing basis. Following the introduction of the KM plan, more sophisticated KM portal functionalities (bridging people with systems) and knowledge sharing activities (bridging people with people) were implemented every few months, as indicated in Figure 4, to make the KM portal even more popular. These reinforcements consist of the linkage with daily workflow as work proceeds (e.g.

Table 7. Estimate of general costs of the KM deployment (Source Chang & Li (2007)) Types of cost Manpower

Expenses

Items

Spending

Time of cross-department task force spent throughout the deployment process

10 man-months

Time of internal IT engineers spent on configuring and setting up the IT platform

5 man-months

Others (time of employees spent on interviews and questionnaire)

3 man-months

IT software expenses (mainly for search engine, web spider and project workflow groupware)

US$30,000

Portal development by external IT service provider

US$32,000

Figure 4. Portal functionality replenishments & usage growth (Source Chang (2008))

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providing on-line administration approval and project report alerts), customer directory and personal messages (e.g. unknown work leaves, unchecked travel expenses), and automatic email publishing on the KM portal. These reinforcements echoed user feedback and encouraged users to interact with the system and other people, significantly increasing the average daily usage of the portal as new functions were added. As suggested by Barthès and Tacla (2002), the KM system must contain all the administrative and organizational knowledge deemed to be necessary for smoothing the user’s work. We found similar evidence from MIRDC experiences. By the first quarter of 2006 the average daily usage reached 130 which indicated that roughly one third of the employees in the offices would log onto the KM portal to support their daily work. Perhaps this is not such a great achievement in comparison with other famous international research organizations, but at least it demonstrates that MIRDC has successfully embedded the application of the KM portal into their organization. This case revealed that accessible central document management, integrated workflow system with personal alerts and on-line information sharing are essential to KM.

examine the commonality and specificity of the KM deployment framework adopted by MIRDC, we conducted a literature review. There are three types of KM frameworks with varied factors focused and questions concerned, i.e. system approach, step approach and hybrid approach as illustrated in Figure 5. Based on our analysis, the MIRDC’s KM deployment framework was designed in an attempt to fully answer the question of why, what, when, where, who and how of KM. This is analogous with the hybrid approach which covers the overall perspectives of KM and is generally applicable in different workspaces. On the other hand, considering the specific nature of the project operation in the MIRDC context, the common communication protocols existing in MIRDC such as specifying assignments of KM task forces, control points and resource allocation are embedded in the deployment framework which allows managers to closely watch over the progress of KM and make adjustments when necessary. Sharing and teamwork are common disciplines in the Taiwanese education system. Consequently, Taiwanese knowledge workers are more open to sharing thoughts and ideas, especially if they are encouraged to communicate (Stankosky, 2005).

KM DEPLOYMENT IN R&D CONTEXT

Figure 5. An analysis of KM implementation frameworks (Source Chang (2008))

Having reviewed the KM developments in MIRDC, this section will deliver the overall managerial implications for the KM field. The KM deployment framework is an enhanced approach in terms of applying process quality assurance and quality control concepts in KM implementation. Compared with other KM arguments which are mainly focused on the knowledge spiral or knowledge cycle (Holsapple & Joshi, 2002), the five-stage approach offers a holistic view of KM manipulation activities and serves as a clear and reliable guideline to assure that every step of KM implementation is properly undertaken. To

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One primary solution to enhance knowledge flow between employee and across departmental boundaries is the structuring of social network (Dahl and Pedersen, 2005). However, not all social contacts diffuse knowledge. Our study observed that a rigid hierarchical R&D structure due to technology segmentation in MIRDC actually inhibited the dynamics of the knowledge cycle across domains and between departments, making it no longer able to meet today’s business demand for total solutions in one-stop. MIRDC found that a parallel collaborative R&D structure with a focused industry interest across departments would streamline the sharing, exploitation and leverage of knowledge assets, thus to enhance the transfer of tacit knowledge, sharing should become a standard activity, not only for short-term projects. This is the reason why MIRDC started formulating six industryfocused communities of practice since mid 2005 in the hope of blending together individuals who have varying expertise and know-how. However it was observed that the organizational climate change to a more interactive one was slower than expected. One main reason was the lack of strong support from managers across departments and limited resources allocated to these loosely coupled communities. To tackle this problem and promote knowledge innovation across domains and among engineers, MIRDC transformed some of the communities into industry-focused work teams, i.e. ‘Mission Offices’, to solidify the value system of teambuilding. This change can be viewed as an evolution of the social network structure for solidifying interactive levels between employees. Necessary budget was allocated to these ‘Mission Offices’ to recruit specialists from various departments so that they could integrate their diversified knowledge and make the greatest contributions and produce innovations in the industry service. In addition, previous studies revealed that the acquisition and transfer of tacit knowledge can be enhanced by action learning. This is a process

through which participants learn with and from each other by mutual support, advice and questioning, as they work on real issues or practical problems while carrying out real responsibilities under real conditions (Koskinen and Vanharanta, 2002). Therefore, MIRDC strengthened another new mechanism of bridging people with people called ‘Pioneer & Innovation Program’ by allocating more research funds. This program was initiated to encourage cooperation among crossdepartmental engineers by submitting collaborative innovation ideas about new technologies or new applications of existing technologies. The budget supported the realization of the approved ideas, then knowledge exchange among engineers and new ideas began to boom. For instance, in 2005 the quantity of conception-type proposals (max. sponsorship US$30,000/each) and explorationtype proposals (max. sponsorship US$180,000/ each) was 24 and 9 respectively, and it increased to 44 and 14 in 2006. It is found that the ‘Pioneer & Innovation Program’ serves as a KM interaction platform for conceptualizing innovative ideas and discussing their feasibility by including other parts of the organization in the process. Meanwhile, through proper horizontal networking KM can help to identify and address gaps in the firm’s core technology area and explore emerging areas. It was observed that engineers were more likely to contribute their ideas under funding support and engaged in a sense of purposeful competition and operated under a project-based scheme. This contrasted with the original undisciplined and free knowledge share in KM communities, which often produced unproductive results. To deal with technological complexity and rapid change, this case illustrated that the fostering of ‘co-opertition’ (a combination of co-operation and competition) in a R&D context is a key to boosting the value of intellectual capital. Pure competition, i.e. profit center mode, will tend to deter individuals from sharing knowledge, even if it will lead to new knowledge and to greater rates

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of innovation. Thus we agree with Brännback (2003) that competition and cooperation usually co-exist in a network and enable the formation of Ba across different ontological dimensions. We also found that what MIRDC has conducted is analogous to the work and findings of Díaz-Díaz, et al. (2006). They identified two tacit knowledge mechanisms, namely technological alliance as channels for the transfer of knowledge, and the inter-firm mobility of engineers that would stimulate generation and integration of new organizational knowledge. Job changes help to build social networks across groups of firms by bridging the gaps between them (Dahl and Pedersen, 2005). Additionally, Möller and Svahn (2004) suggested that by constructing a strategic-innovation net and developing functional responsibilities, the collectivist and individualist could be merged and communication barriers between employees overcome. Suitably flexible and united task communication in an environment with a fixed reward and control structure has been proved to successfully create a more comprehensive and interactive information flow among team members (Brown and Eisenhardt, 1995; Kpskinen & Vanharanta, 2002), which is beneficial to creative works.

Figure 6. KNG Bridged by P&I Program & Mission Offices (Source Chang (2008))

Figure 6 illustrates the knowledge network gap (KNG) between departments bridged by ‘Pioneer & Innovation Program’ and ‘Mission Offices’. As a result, by interweaving two horizontal networking instruments, i.e. ‘Mission Offices’, and ‘Pioneer & Innovation Program’, with the vertical management hierarchy, and by adopting departmental knowledge sharing quantity as a rating factor in the yearly performance evaluation, MIRDC has successfully tackled the KM challenges in the Chinese context like relation circles, evaluation anxiety and power barriers, as indicated previously. This study further compared MIRDC’s KM networks with other literatures as illustrated in Figure 7. The formation of ‘Mission Offices’ naturally incur job changes and inter-firm mobility, while ‘Pioneer & Innovation Program’ acts like a technological alliance and a strategic innovation net.

AN INTEGRATED VIEW OF KM DYNAMICS It is widely perceived that technology alone does not offer a complete solution to manage organizational knowledge; an extensive change at behavioral, cultural and organizational level is needed to make KM successful (Barnard & Pöyry, 2004). Socio-technical perspective is another KM approach to understand the interweaving of social and technical factors in the way people work and adapt. Pan & Scarbrough (1998) claimed that a successful implementation of KM should comprise three socio-technical components: •

•

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Infrastructure – refers to the technical components such as hardware and software that enables the communications and interactions between people, Info-structure – incorporates a set of formal rules and norms governing exchanges and sense making between people;

Deploying Knowledge Management in R&D Workspaces

•

Info-culture – involves the background knowledge embedded in social relations and work group process plus core values and beliefs influencing employees’ willingness to exchange knowledge and help solve problems.

Snowden (2003) also discusses that the engineering approaches have become a hygiene factor in KM implementation. Furthermore, Hsu’s study (2006) suggested a stage model of knowledge sharing in the Chinese context. At first, organizational knowledge sharing can be supported with IT tools and networking at a time when a company seeks to achieve knowledge centralization and modularization. Once this routine is established and self-normalizing, the company is changed from the role of manager to facilitator, which encourages a sharing climate. Consequently, based on our observation of MIRDC’s experiences and reviewed literature, we argue that different angles of KM approaches and organizational behavior studies are analogous with each other. Herzberg’s two factor theory (Herzberg, Mausner & Snyderman, 1959) states that peoples’ attitudes about work and the social-technical KM perspective can be mapped harmoniously. Figure

8 presents the integrated view between MIRDC’s KM practices, two factor theory and the socialtechnical view of KM. For KM enforcement, we argue that IT tools act as hygiene factors which can create KM dissatisfaction, but their presence alone does not motivate or create satisfaction. On the other hand, a sharing culture, trusting relationships and respect based on personal knowledge contributions are motivating factors that determine KM advancement and achievement. Therefore, KM practitioners should be aware of that under high IT conditions (hygiene factors) and low sharing cultures (motivating factors), employees often have few complaints but are not highly motivated, while low IT availability and high sharing culture represents a situation where KM involvement is exciting and challenging, but complaints about supporting IT conditions occur. Regarding the KM social-technical perspective, the three components play different roles. Info-structure serves as the ground rules and standards that guide the process of KM implementation. The infrastructure of KM has higher impacts on explicit knowledge management and lower impacts on implicit knowledge management,

Figure 7. Comparison of mechanisms of KM networks (Source Chang (2008))

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Deploying Knowledge Management in R&D Workspaces

Figure 8. MIRDC’s KM approach, T-F theory & S-T perspective (Source Chang (2008))

while info-culture primarily decides the degree of networking between knowledge workers, which leads to the openness of sharing critical knowledge stored in the minds of people. This leads to a schema of the interaction between MIRDC’s approach to KM, mapped onto Herzberg’s ‘Two Factor Theory’ (T-F theory) and a social-technical perspective (S-T perspective) as shown in Figure 8.

KM FAILURE ANALYSIS AND LESSONS LEARNED Successful implementation of KM in MIRDC is very much dependent on the promotion of KM awareness within the organization and the implementation of a good reward mechanism to encourage contributions from engineers. Table 8 reviews the KM gaps and barriers found in knowledge audits and related ‘remediation’ ap-

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proaches adopted by MIRDC. In order to keep all parties involved, various activities such as focus group meetings, weekly KM news and competitive incentive programs are important to encourage user participation. Meanwhile, nonstop improvements on the KM portal and KM process play an important role to show the organization’s commitment to continuous growth in this area. The usage growth trend of the KM portal clearly proves the Taiwanese saying: ‘Employees follow what their leaders do, not what they say’. Except for the transformation of failed communities of practice to ‘Mission Offices’ and ‘Pioneer & Innovation Program’, during the process of KM deployment in MIRDC, there are some KM efforts that were not successfully implemented as expected: •

On-line R&D project collaboration: failure reasons may be owed to project engineers

Deploying Knowledge Management in R&D Workspaces

Table 8. Review of KM gaps/barriers and approaches adopted by MIRDC (Source Chang (2008)) Dimensions

KM gaps/barriers

MIRDC’s approach

General issues

Lack of KM strategies and their alignment with organizational strategies Management support is not strong Researchers lack time and have high workloads, thus inhibiting their participation in KM activities Necessity awareness of KM among employees is poor

Develop KM strategies and policies Gain fund from project leaders Integrate KM portal with daily administration workflow

Explicit knowledge

Knowledge assets have not been clearly defined Management of explicit knowledge is problematic and unsystematic Hard to reach distributed databases and lack of KM portal Lack of on-line collaboration platform

Conduct knowledge audit and focused group discussion Use Lotus Notes as the central knowledge repository Connect distributed databases through a KM portal Invite two R&D teams to test run a project collaboration platform Develop a presentational KO management prototype system Outsource an intelligent Chinese search engine Formulate KM regulation and assign responsibilities

Management of presentational KO, such as MS PowerPoint files, is problematic. Search efficiency for explicit knowledge objects is low No clear regulations and procedures guiding the production, sharing and accumulation of explicit knowledge Implicit knowledge

•

•

Rigidity of bureaucratic structure categorized by technology fields and each department operates as profit-center Cross department R&D networking is inadequate so that new knowledge creation, i.e. innovation, is slow Expert skills and customer directory are not available Lack of mechanisms and incentives to encourage implicit knowledge sharing

working in proximity, non-user-friendly collaboration platform and utilization of collaboration platform not obliged; KM strategies not intimately aligned with organizational strategies: After launching the KM portal, engineers enjoyed ever handy knowledge sharing and access, however the alignment between KM pursuit and organizational performance remains vague. One major reason is the weak commitment from top management; The transfer of organizational tacit knowledge, i.e. passing on the know-how and philosophy behind the engineering drawings, cannot be systematically measured and secured.

There are two more lessons we have learned from this case. The first one relates to the politics among key stakeholders. After reporting the KM

KM promotion activities

Firstly formulate six industry-focused communities of practice, then replaced by mission offices Start the Pioneer & Innovation program Partly supported by KM portal Enhanced by mission office and Pioneer & Innovation program

plan, the KM task force found that top management does not seem to plan to allocate enough resources as expected. In a wish to launch the KM plan and establish the KM portal, the task force communicated with two general project leaders to improve document management, patent management and on-line project collaboration, and the leaders were persuaded and endowed half of the budget. In other words, in a hierarchical structure paralleled with intensive project operations, i.e. a matrix system, there are different ways to acquire the resources you need to accomplish the goals. The second lesson learned relates to the package of KM activity. Perhaps it is better not to loudly advocate the term KM to enforce the management of knowledge because in many situations, KM might seem dangerous to employees in terms of job security and extra workload. Like the formation of ‘Mission Offices’ and ‘Pioneer & Innovations Program’ in MIRDC, they were not launched and

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announced by the name of KM, however they actually trigged lateral knowledge interactions, circulation and creation.

CONCLUSION In conclusion, this case study contributes to the KM research in three aspects. First of all, this chapter reviews the development of KM among Taiwanese R&D organizations, contributing to a field which has not been extensively studied in previous literature. Secondly, this case reveals a very sophisticated deployment process for managing existing knowledge in the R&D workspace that could be very useful for other organizations in guiding and implementing a KM plan. Finally, this case echoes other studies which have identified that the organization structure can create significant effects on KM achievements. Many researches have revealed that the diffusion of knowledge between firms, departments and teams can take place either through formalized collaboration or through informal social network (Dahl & Pedersen, 2005). Social networks among R&D engineers carry knowledge across organization boundaries. Several factors limit the scale and depth of social networks and communities including frequency of communication, physical proximity, mutual disclosure of information and mutual trust, etc. The case study emphasizes the perspective that a rigid hierarchical R&D structure through technology segmentation should also incorporate a parallel R&D collaboration structure that is cross-departmental and industry-focused to obtain horizontal networking and interactive knowledge sharing. A parallel R&D collaboration can actively enforce the occurrence of information trading, the exchange of information between employees working for different interests in the organization. With respect to the commonality and specificity of the KM deployment framework, based on the characteristics of our case, we suggest that the

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KM experiences gained from MIRDC might be generally applicable to other institutes that have similar organization structure and routine operation mode, i.e. technology-segmented organization structure, project-based operation and non-profit orientation.

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Chang, W., Chuang, S., & Li, S. (2004). A case study of adopting knowledge management systems for research-oriented organizations: MIRDC’s experience. Proceedings of the Knowledge Management Conference in Asia Pacific, Taiwan, 128-135. Chang, W. C., & Li, S. T. (2007). Fostering knowledge management deployment in R&D workspaces: A five-stage approach. R & D Management, 37(5), 479–493. doi:10.1111/j.14679310.2007.00484.x Dahl, M. S., & Pedersen, C. Ø. R. (2005). Social networks in the R&D process: The case of the wireless communication industry around Aalborg, Denmark. Journal of Engineering and Technology Management, 22, 75–92. doi:10.1016/j.jengtecman.2004.11.001 Damm, D., & Schindler, M. (2002). Security issues of a knowledge medium for distributed project work. International Journal of Project Management, 20(1), 37–47. doi:10.1016/S02637863(00)00033-8 Díaz-Díaz, N. L., Aguiar-Díaz, I., & Saá-Pérez, P. (2006). Technological knowledge assets in industrial firms. R & D Management, 36(2), 189–203. doi:10.1111/j.1467-9310.2006.00425.x Drongelen, I. C. K., Weerd-Nederhof, P. C., & Fisscher, O. A. M. (1996). Describing the issues of knowledge management in R&D: Towards a communication and analysis tool. R & D Management, 26(3), 213–229. doi:10.1111/j.1467-9310.1996. tb00957.x

Holsapple, C. W., & Joshi, K. D. (1999). Description and analysis of existing knowledge management frameworks. Proceedings of the 32nd Hawaii International Conference on System Sciences, Hawaii. New York: IEEE. Holsapple, C. W., & Joshi, K. D. (2002). Knowledge manipulation activities: Results of delphi study. Information & Management, 39, 477–490. doi:10.1016/S0378-7206(01)00109-4 Hsu, I. (2006). Enhancing employee tendencies to share knowledge: Case studies of nine companies in Taiwan. International Journal of Information Management, 26, 326–338. doi:10.1016/j.ijinfomgt.2006.03.001 Huber, G. P. (1991). Organizational learning: The contributing processes and the literatures. Organization Science, 2(1), 88–115. doi:10.1287/ orsc.2.1.88 Koskinen, K. U., & Vanharanta, H. (2002). The role of tacit knowledge in innovation processes of small technology companies. International Journal of Production Economics, 80, 57–64. doi:10.1016/S0925-5273(02)00243-8 Lee, S. M., & Hong, S. (2002). An enterprise-wide knowledge management system infrastructure. Industrial Management & Data Systems, 102(1), 17–25. doi:10.1108/02635570210414622 Leitner, K., & Warden, C. (2004). Managing and reporting knowledge-based resources and process in research organizations: Specifics, lessons learned and perspectives. Management Accounting Research, 15, 33–51. doi:10.1016/j. mar.2003.10.005

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Maier, R., & Remus, U. (2003). Implementing process-oriented knowledge management strategies. Journal of Knowledge Management, 7(4), 62–74. doi:10.1108/13673270310492958 Milliou, C. (2004). Vertical integration and R&D information flow: Is there a need for firewalls. International Journal of Industrial Organization, 22, 25–43. doi:10.1016/S0167-7187(03)00090-0 Möller, K., & Svahn, S. (2004). Crossing east-west boundaries: Knowledge sharing in intercultural business networks. Industrial Marketing Management, 33, 219–228. doi:10.1016/j.indmarman.2003.10.011 Nagi, E. W. T., & Wat, F. K. T. (2005). Fuzzy decision support system for risk analysis in ecommerce development. Decision Support Systems, 40, 235–255. doi:10.1016/j.dss.2003.12.002 Pan, S. L., & Scarbrough, H. (1998). A sociotechnical view of knowledge-sharing at Buckman Laboratories. Journal of Knowledge Management, 2(1), 55–56. doi:10.1108/EUM0000000004607 Paraponaris, C. (2003). Third generation R&D and strategies for knowledge management. Journal of Knowledge Management, 7(5), 96–106. doi:10.1108/13673270310505412 Parikh, M. (2001). Knowledge management framework for high-tech research and development. Engineering Management Journal, 13(3), 27–33. Pun, K., Chin, K., & Lau, H. (2000). A review of the Chinese cultural influences on Chinese enterprise management. International Journal of Management Reviews, 2(4), 325–338. doi:10.1111/14682370.00045 Raub, S., & Wittich, D. V. (2004). Implementing knowledge management: Three strategies for effective CKOs. European Management Journal, 22(6), 714–724. doi:10.1016/j.emj.2004.09.024

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Rubenstein-Montano, B., Liebowitz, J., Buchwalter, J., McCaw, D., Newman, B., & Rebeck, K. (2001). SMARTVision:Aknowledge-management methodology. Journal of Knowledge Management, 5(4), 300–310. doi:10.1108/13673270110411724 Sapsed, J., Bessant, J., Partington, D., Tranfield, D., & Young, M. (2000). From IT to teams: Trends in the management of organisational knowledge. Paper presented at the R&D Management Conference: Wealth from Knowledge: Innovation in R&D Management, Manchester, UK. Snowden, D. (2003). Innovation as objective of knowledge management, Part 1: The landscape of management. Knowledge Management Research & Practice, 1, 113–119. doi:10.1057/palgrave. kmrp.8500014 Sohn, J. H. D. (2004). Rewarding KM performances at SAIT. KM Review, 7(4), 8–9. Spiegler, I. (2000). Knowledge management: A new idea or a recycled concept? Communications of the AIS, 3(14), 1–23. Stankosky, M. (2005). Creating the discipline of knowledge management. Burlington, MA: Elsevier. Szulanski, G. (1996). Exploring internal stickiness: Impediments to the transfer of best practice within the firm. Strategic Management Journal, 17(Winter Special Issue), 27-43. van Donk, D. P., & Riezebos, J. (2005). Exploring the knowledge inventory in project-based organizations: A case study. International Journal of Project Management, 23, 75–83. doi:10.1016/j. ijproman.2004.05.002 Warren, P., & Graham, D. (2000). Knowledge management at BT Labs. Research Technology Management, 43(3), 12–17.

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Chapter 5

Innovation in New Technology and Knowledge Management:

Comparative Case Studies of its Evolution during a Quarter Century of Change Sean Tung-Xiung Wu Shih Hsin University, Taiwan

ABSTRACT The research on which this chapter is based monitors the evolution of IT innovations and their effect on human emotions, including longitudinal influential factors, and examines some of the resulting syndromes, which are termed Computer Fear Syndrome (CFS) and User Alienation Syndrome (UAS). The research involves an analysis of the empirical data derived from several case studies and concludes with a funnel model that explains appropriate management action and puts forward new ideas for developing knowledge management systems in a variety of organizations that may alleviate or prevent such syndromes in the work place.

Introduction This comparative research depicts and explains the evolution of organizational innovation through the adoption and deployment of information systems. There are two cases that have been carefully and thoroughly investigated for years. The first is the M Company, one of the top two computers groups in Taiwan, which developed corporate publishing systems in 1988. The second was the C Company, the largest telecommunications group in Taiwan,

which brought in knowledge management (KM) systems for corporate training in 2004. There are differences in the objects and the objectives of innovation between the two cases. Technically, the corporate publishing systems are desktop based; relatively compact local working groups while the knowledge management systems are web-based, with more sophisticated, boundaryless environments. The goals of the former systems were to reduce labor and increase sufficiency of production. The purposes of the latter intended to share intelligence and to encourage creativity

DOI: 10.4018/978-1-60566-701-0.ch005

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Innovation in New Technology and Knowledge Management

through collaboration. In the early days of computerization, the Computer Fear Syndrome (CFS) was a conceptual threat while the User Alienation Syndrome (UAS) may be a subconscious threat at present. It is worthy of note that there are more similarities in the processes of innovation of the current case with the first case that occurred more than a quarter of a century ago. The fundamental corporate decision setting is also the same as before. Management, as always, has to comply with innovative ideas, investment and risk at the same time. The goals of expected efficiency whether in physical profits or in mental productivity have also remained the same over the years. The individual user’s behavioral factors involved in implementation and results of innovations have also always needed to be identified. The organizational factors in management actions are worthy of constant re-examination. Yes, there are many intriguing, even novel, variables that may affect innovations, according to the vast body of related literature. However, how many of these are fundamentally influential? This research attempts to reveal, by selective quantitative and qualitative evidence from very fruitful resources, which factors are changeable and which are likely to stay in place for a very long time. The author/researcher concludes with several statements that may be helpful for those who want to adopt new technology, especially, in acquiring knowledge management systems in the future. Discussion of the measurement of the efficiency of knowledge management or latent, unstructured psychological constructs have also been amended according to the specific requirements of the present. The researcher of this study was the Director in charge of the Innovation Project for the M Company and a consultant for the C Company.

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Case 1: M Company The Problem Around the mid 1980’s, business began to seriously consider bringing in PC-based information systems to replace labor intensive work.1 One of management’s great concerns was that of the Computer Fear Syndrome (CFS). CFS referred to computer anxiety or negative attitudes toward adopting computing work and predicted that employees with CFS would performance poorly. (Wu, 1995). When computers “invaded” human life, some researchers argued as to whether the computer was “a threat or a promise?” (Cherry, 1971) They thought that senior persons or novices who were used to a traditional work environment would express negative attitudes towards computers. If they began to learn computing tasks, they would be slower and make more errors than new employees. The Fear Syndrome, generated by an anxiety of failure, might also limit their performance. (Caldeira &Ward, 2001) (Shneiderman, 1980). So, management had two choices: First, if the CFS did widely exist; they would have to recruit new, young employees and let the current workers go. Second, if the CFS did not really cause significant harm, they would need to provide training programs for current employees and educate them about the EUC environment. The CFS was also cautiously perceived and discussed in Taiwan. Hung and Xu (1988), based on a survey of government organizations, found that staff employees who reported themselves as having the CFS was 20%. However, the range of the CFS that was recognized and evaluated by supervisors was bewilderingly greater, from 4.4% to 48.2%. Since the evidence was inconclusive, the reaction of different companies varied. The publishing business used to be a highly labor intensive industry, and was interested in computerization. (Bjorn-Andersen, Earl, Holst & Blunden, 1982) (Young, 1988). Thus, the U Company, one of the top two publishing companies in Taiwan, was the

Innovation in New Technology and Knowledge Management

first one to launch the computerized publishing process around 1985. (Boldt, 1987) It decided to hire a brand new crew to learn the EUC process and to work for the Department of Typesetting and Composition. It did not even bother to ask current employees if they wanted to change. Because of this decision, the U Company had to transfer current employees to other jobs, such as security, or to pay for their layoffs. Obviously, the cost of this policy was enormously expensive. There were, and are, not many companies that could afford to make such a move. (Wu, 1984b, 1984a). The M Company made a different decision.

Case Description The M Company was one of the top two computers groups in Taiwan whose business covered systems integration, PC manufacturing, distribution and other related information services. The H Company was one of the M Company’s subsidiaries and ran business in cultural and educational fields. It published a magazine on computers, provided computer training programs, and ran media campaigns for its clients. In 1987, the Board of Directors of the M Company determined to spin off the H Company and let it extend its business independently. The researcher, who used to be the Vice Director of the Computerization Project of the U Company, was invited to become the Chief Executive of the H Company and took responsibility for the H Company’s innovations. The researcher proposed that the M Company and the H Company work together to develop Desktop Publishing (DTP) Systems for the H Company’s operation. The idea was that if the new systems were to be successful in meeting the H Company’s mission requirements, it would create a new DTP total solution and initiate a new market of corporate/personal publishing. (Wu, 1985). Examining the U Company’s experience, its computerization design could be categorized as a “Simulation Model” that maintained the con-

ventional organization and kept both its Editorial Department and Composition Department. The publishing processes had remained the same; the writers and editors stayed on, using old forms of manual operation. Compared to the U Company, the H Company’s design was a “Reengineering Model” that altered all processes and entirely removed the Composition Department. It was expected that the writers/editors would cover writing, typesetting, page design and composition at the same time. This project also wanted to answer the following questions: 1. How can we measure the evaluation of the innovation of DTP? 2. What is the extended evidence of the CFS on IT innovation? 3. What are the factors that may affect the IT innovation? An academic research project was designed and conducted along with the Product Planning Project. (Wu, 1995)

Methodology Overview We examined the idea of User Psychology that attempted to establish theories on computer users in the early days of the birth of the PC. It presented a distinct field of the “Human Aspects of Computing” Study both in computer and management sciences after the 1980’s. (Moran, 1981) (Ramsey, 1979). Researchers have observed plenty of variables that might affect, even determine users’ behavior. Among them, Newell’s (1972) user’s behavior formula and Moran’s (1981) follow up especially drew our attention. After a thorough and careful discussion, we chose two sets of dependent variables (DV) to evaluate the efficiency of DTP. Since time and errors were always employed as users’ performance indicators by information

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systems designers (Card, Moran & Newell 1980; Shneiderman, 1979; Walther & O’Neil, 1974), one set of DVs was users’ Performance measured by their work efficiency (speed) and quality (errors) on specific assignments. The second set of DVs was users’ Adoption Behavior measured by their psychological acceptance and practice preference. In practice, we had tested many independent variables (IDV) and demographic variables. Many of them contributed little influence on the DV. For this reason, we ruled out those variables in this final report. We kept two, which are also commonly recognized by previous researchers, for deliberation. These IDVs were users’ knowledge (about the system) and users’ motivation (to participate in the innovation). In order to observe the CFS, we designed a quasi-experiment to compare the performance of current employees with that of new users.

Quasi-Experiment Design This DTP was developed from September 1987 and was launched in August 1988. Then, the following quasi-experiment design was summarized as Table 1. A group of seventeen employees of the Editorial Department of the M Company was designated to be the quasi-experimental group, as Group E. Two control groups with the same number of persons as Group E were comprised of college students who had no past experience of traditional publishing. Students from computers and information departments were assigned to

Group C, while students from liberal arts departments were in Group A. All groups took the same training courses and same tests before and after training. Word processing (WP) and desk top publishing (DTP) were defined as the core tasks of the computing work. All groups’ performances were measured by a final assignment at the end of the training program. Users would be required to input and compose two scripts into one section using a specified layout with WP and DTP. In spelling languages, to input alphabet does not sound a problem, but it requires certain special skills to input Chinese characters. It was also more difficult to compose page layout at that time.

Measurement Performance was measured by users’ speed and errors during their final assignment. WP speed was measured by average words typed during test times. DTP’s speed was measured by minutes required for finishing the task. Error was measured by wrong operations during the whole task. Users’ knowledge was measured by a “UKAT” test, which was designed by the Technical Advisory Committee of this Project and modified by pilot studies, with an internal reliability Alpha =.4817. Users’ motivation was measured by “MLS” psychometric scales, tested by pilot studies, with an internal reliability Alpha =.7419.

Table 1. Summary of the Quasi-experiment Design Pretest Time

Posttest

Sep.1988Feb.1989

Group

E, C, A

Measurement

Knowledge Motivation

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Training

E, C, A

Online Work

Evaluation

Mar.1989Nov.1989 E, C, A Performance 1. WP 2. DTP Acceptance Preference

E

E Innovation Experience 1. By scorers 2. By users

Innovation in New Technology and Knowledge Management

In-Depth Personal Interview This procedure recorded users’ points of view from their self-report. Each user of the three groups was interviewed after the final assignment to reveal their attitude change of acceptance for and preference of the new DTP, or of and for the old manual tools. Group E was interviewed again six months after their online work. Three scales and questionnaires were employed: • • •

Acceptance scales: with internal reliability Alpha =.7558. Preference scales: with internal reliability Alpha =.6812. Innovation Project questionnaires: overall opinions about users’ CFS, attitude change, and innovation experience in open-ended form, only for Group E.

Participant Observation and Scorer Evaluation This method provided the researcher’s point of view and evaluation on users’ behaviors. The researcher and an associate, who was the Administrative Manager of the M Company, worked together to observe and score Group E users’

adoption by structured scales. The observation period, according to the Innovation Theory, was divided into two stages. Stage 1 was before the end of training. Stage 2 was six months after the online work. Scorer reliability of stage 1 was.7550 (p