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Knowledge Management

Measuring Knowledge Management in the Business Sector FIRST STEPS

This book offers a synthetic view of the results of the first systematic international survey on knowledge management carried out by national statistical offices in Canada, Denmark, France and Germany.

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Measuring Knowledge Management in the Business Sector

Co-published with Statistics Canada.


Knowledge management involves any activity related to the capture, use and sharing of knowledge by an organisation. Evidence shows that these practices are being used more and more frequently and that their impact on innovation and other aspects of corporate performance is far from negligible. Today, there is a recognition of the need to understand and to measure the activity of knowledge management so that organisations can be more efficient and governments can develop policies to promote these benefits.

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Measuring Knowledge Management in the Business Sector FIRST STEPS

© OECD, 2003. © Software: 1987-1996, Acrobat is a trademark of ADOBE. All rights reserved. OECD grants you the right to use one copy of this Program for your personal use only. Unauthorised reproduction, lending, hiring, transmission or distribution of any data or software is prohibited. You must treat the Program and associated materials and any elements thereof like any other copyrighted material. All requests should be made to: Head of Publications Service, OECD Publications Service, 2, rue André-Pascal, 75775 Paris Cedex 16, France.

Measuring Knowledge Management in the Business Sector: First Steps

ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT STATISTICS CANADA

ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT Pursuant to Article 1 of the Convention signed in Paris on 14th December 1960, and which came into force on 30th September 1961, the Organisation for Economic Co-operation and Development (OECD) shall promote policies designed: – to achieve the highest sustainable economic growth and employment and a rising standard of living in member countries, while maintaining financial stability, and thus to contribute to the development of the world economy; – to contribute to sound economic expansion in member as well as non-member countries in the process of economic development; and – to contribute to the expansion of world trade on a multilateral, non-discriminatory basis in accordance with international obligations. The original member countries of the OECD are Austria, Belgium, Canada, Denmark, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, the United Kingdom and the United States. The following countries became members subsequently through accession at the dates indicated hereafter: Japan (28th April 1964), Finland (28th January 1969), Australia (7th June 1971), New Zealand (29th May 1973), Mexico (18th May 1994), the Czech Republic (21st December 1995), Hungary (7th May 1996), Poland (22nd November 1996), Korea (12th December 1996) and the Slovak Republic (14th December 2000). The Commission of the European Communities takes part in the work of the OECD (Article 13 of the OECD Convention).

STATISTICS CANADA Statistics Canada, Canada's central statistical agency, has the mandate to "collect, compile, analyse, and publish statistical information relating to the commercial, industrial, financial, social, economic and general activities and condition of the people of Canada." The organisation, a federal government agency, is headed by the Chief Statistician of Canada and reports to Parliament through the Minister of Industry. Statistics Canada provides information to governments at every level and is a source of statistical information for business, labour, academic and social institutions, professional associations, the international statistical community, and the general public. This information is produced at the national and provincial levels and, in some cases, for major population centres and other sub-provincial or "small" areas. The Agency fosters relations not only within Canada but also throughout the world, by participating in a number of international meetings and professional exchanges. Statistics Canada conducted the pilot survey on Knowledge Management Practices as part of an international initiative headed by the Centre for Educational Research and Innovation (Organisation for Economic Co-operation and Development). Canada was the lead country piloting the survey. Other countries that in 2001 undertook pilot surveys or questions based on the contents of the Knowledge Management Practices' questionnaire were Denmark, Germany and France Publié en français sous le titre : Mesurer la gestion des connaissances dans le secteur commercial : premiers résultats © Organisation for Economic Cooperation and Development (OECD), Paris and Minister of Industry, Canada, 2003 Permission to reproduce a portion of this work for non-commercial purposes or classroom use should be obtained through the Centre français d’exploitation du droit de copie (CFC), 20, rue des Grands-Augustins, 75006 Paris, France, tel. (33-1) 44 07 47 70, fax (33-1) 46 34 67 19, for every country except the United States. In the United States permission should be obtained through the Copyright Clearance Center, Customer Service, (508)750-8400, 222 Rosewood Drive, Danvers, MA 01923 USA, or CCC Online: www.copyright.com. All other applications for permission to reproduce or translate all or part of this book should be made to OECD Publications, 2, rue André-Pascal, 75775 Paris Cedex 16, France.

FOREWORD

Foreword

A

t the start of the 21st century, there is a recognition of the need to understand and to measure the activity of knowledge management (KM) so that organisations, and systems of organisations, can do what they do better and so that governments can develop policies to promote these benefits. Facing such new emerging practices, economists, management scientists and statisticians have not yet much systematic evidence. Among the various categories of knowledge-related investments (education, training, software, R&D, etc.), KM is one of the less known, both from a quantitative and qualitative point of view, as well as in terms of costs and economic returns. Thus, there is certainly a need to know more on this new knowledge-based activities; on the current state of KM as an organisational process within various kinds of companies and sectors; on the variety of methods and tools that are developed; and on the economic effects of KM practices that are actually observed. To achieve those objectives, the Center for Educational Research and Innovation (OECD) and Statistics Canada have set up a working group com prising representatives from the statistical offices of Canada, France, Italy, the Netherlands and Sweden and representatives from research bodies in Australia, Denmark, Germany and Ireland. The working group has met four times since February 2001, in Copenhagen, Ottawa, Paris and Karlsruhe. A questionnaire was devised during the course of the four meetings and the information deriving from the first pilot studies was discussed. This questionnaire includes a survey on the use of 23 KM practices and is complemented with questions on incentives for using KM practices, results, responsibilities, etc. The questionnaire includes many informal management practices in order to accommodate how micro-firms are managing knowledge. For countries willing to carry out their own national surveys, two kinds of strategies were possible: either implementing the whole survey as a pilot study or lodging few questions on KM in an existing and regular questionnaire, such as the Community Innovation Survey. While the first option gives the opportunity to really test the KM questionnaire and to collect information related to a large range of issues and problems, the second option has proven to be very useful for countries where starting a new survey is a difficult task for administrative, political or technical reasons.

MEASURING KNOWLEDGE MANAGEMENT IN THE BUSINESS SECTOR – ISBN 92-64-10026-1 – © OECD/MINISTER OF INDUSTRY, CANADA, 2003

3

FOREWORD

This book presents a synthetic view of the results of the surveys carried out in Canada, Denmark, France and Germany, as well as statistical analysis about various issues dealing with KM and a policy discussion. This foreword cannot be closed without stressing the extent to which producing this book has itself been a successful experiment in knowledge management. Especially involved were two teams that were geographically very far apart: the OECD team (D. Foray, K. Larsen, S. Vincent-Lancrin) and the Statistics Canada team (M. Bordt, L. Earl and F. Gault). The teams built up an impetus which was greatly aided by E. Kremp, S. Lhuillery and J. Mairesse (France), J. Edler and F. Meyer-Krahmer (Germany), W. Strømsnes (Denmark), C. Noonan (Ireland), G. Perani (Italy), S. Nousala (Australia), S. Pronk (Netherlands), L. Prusak (United States), J. Morgan and P. Quintas (United Kingdom) and A. Sundström (Sweden). All of them deserve thanks.

The book is published on the responsibility of the Secretary-General of the OECD.

4


TABLE OF CONTENTS

Table of Contents Part I Frameworks Chapter 1.

Measurement of Knowledge Management Practices Dominique Foray and Fred Gault .................................................... 1.1. Introduction ..................................................................................... 1.2. Knowledge Management: What is New?...................................... 1.3. Knowledge Management as a Topic for Empirical Studies: Opening another Black Box ........................................................... 1.4. From Good Case Studies to Systematic Surveys ........................ 1.5. Why, How and So What? ................................................................ 1.6. Knowledge Management Surveys ................................................ 1.7. Three Main Tasks of a Knowledge Management Survey ........... 1.8. A Brief History of the OECD-Statistics Canada Project and a First Look at the Results ...................................................... 1.9. Outline of the Book ......................................................................... Bibliography ...............................................................................................

Managing Knowledge in Practice Paul Quintas ..................................................................................... 2.1. Introduction...................................................................................... 2.2. Key Knowledge Processes ............................................................... 2.3. Getting Knowledge Management Started .................................... 2.4. Limits and Potentials of Technological Solutions ....................... 2.5. Knowledge Capture ......................................................................... 2.6. Knowledge Sharing.......................................................................... 2.7. Auditing and Exploiting Intellectual Capital................................ 2.8. Cross-boundary Knowledge Acquisition and Integration.......... 2.9. Conclusions ...................................................................................... Bibliography ...............................................................................................

11 12 13 16 18 19 21 22 23 24 26

Chapter 2.


29 30 34 35 36 38 40 42 44 48 50

5

TABLE OF CONTENTS

Part II Country Reports Chapter 3.

Are we Managing our Knowledge? The Canadian Experience Louise Earl ....................................................................................... 3.1. Highlights ......................................................................................... 3.2. Introduction ..................................................................................... 3.3. Survey Background/Overview ........................................................ 3.4. Definition of Knowledge Management ........................................ 3.5. Knowledge Management Practices in Use ................................... 3.6. Reasons Why Knowledge Management Practices Were Adopted .................................................................................. 3.7. Knowledge Management Practices Most Effective for Improving Workers’ Skills and Knowledge ............................ 3.8. One Quarter of Firms Had Dedicated Budgets for Knowledge Management ......................................................... 3.9. Knowledge Management – Important Business Practices ........ Annexes ...................................................................................................... Bibliography .............................................................................................. The Management of Knowledge in German Industry Jakob Edler ....................................................................................... 4.1. Introduction: Filling Knowledge Gaps on Industrial Knowledge Management in Germany .......................................... 4.2. Methodology: The Sample .............................................................. 4.3. The Employment of KM Practices in German Industry ............. 4.4. What Kind of KM Practices ............................................................ 4.5. The Driving Forces of Knowledge Management: Motivation Patterns in German Industry...................................... 4.6. Effects of Knowledge Management .............................................. 4.7. The Institutionalisation of KM and its Meaning for the Use of Knowledge Management ............................................ 4.8. Knowledge Management and its Role within Innovation Management ................................................................ 4.9. Concluding Summary: Only First Steps towards Filled Gaps ... Annexes ...................................................................................................... Bibliography ..............................................................................................

55 56 57 57 58 59 64 67 69 72 76 85

Chapter 4.

6

89 90 92 94 95 98 104 108 109 112 116 118


TABLE OF CONTENTS

Chapter 5.

The Promotion and Implementation of Knowledge Management – A Danish Contribution Anja Baastrup and Wenche Strømsnes ........................................... 5.1. Introduction ..................................................................................... 5.2. Some Overall Results ...................................................................... 5.3. Measuring, Controlling and Documenting Effectiveness .......... 5.4. Inspiration for Top Managers – Content and Process ................ 5.5. What can Top Management Expect from the Environment? ... 5.6. Further Research ............................................................................. Annexes ..................................................................................................... Bibliography ..............................................................................................

Knowledge Management, Innovation and Productivity: A Firm Level Exploration Based on French Manufacturing CIS3 Data Elizabeth Kremp and Jacques Mairesse ........................................... 6.1. Introduction ..................................................................................... 6.2. Diffusion of Knowledge Management ......................................... 6.3. Complementarity of Knowledge Management Practices .......... 6.4. Knowledge Management and Innovation ................................... 6.5. Knowledge Management and Productivity ................................. 6.6. Conclusion ....................................................................................... Annex ......................................................................................................... Bibliography ..............................................................................................

119 120 121 125 127 130 131 134 141

Chapter 6.

Knowledge Management: Size Matters Louise Earl and Fred Gault ............................................................... 7.1. Introduction ..................................................................................... 7.2. Practices ........................................................................................... 7.3. Reasons for Using KM Practices .................................................... 7.4. Results of Using KM Practices ....................................................... 7.5. Incentives to Use KM ...................................................................... 7.6. Moving from Micro to Large .......................................................... 7.7. Intensity of KM Use ........................................................................ 7.8. Specific KM Applications ............................................................... 7.9. What was Learned? ........................................................................ 7.10. Where Next? .................................................................................... Annex ......................................................................................................... Bibliography ..............................................................................................

143 144 146 151 152 159 161 164 168

Chapter 7.


169 170 172 174 176 177 178 178 178 181 181 183 186

7

TABLE OF CONTENTS

Part III Methodological Aspects Chapter 8.

A Word to the Wise – Advice for Conducting the OECD Knowledge Management Survey Louise Earl and Michael Bordt ......................................................... 8.1. Introduction ..................................................................................... 8.2. Questionnaire Content ................................................................... 8.3. The Questions ................................................................................. 8.4. Conducting the Survey ................................................................... 8.5. Analysing and Reporting the Results ........................................... 8.6. Conclusions ..................................................................................... Bibliography ...............................................................................................

Chapter 9.

Knowledge Management Practices Questionnaire OECD ...............................................................................................

189 190 190 191 196 199 201 203

205

Conclusion

8

D. Foray and F. Gault ............................................................................................

213

List of Authors ....................................................................................................

219


PART I

Frameworks


ISBN 92-64-10026-1 Measuring Knowledge Management in the Business Sector © OECD/MINISTER OF INDUSTRY, CANADA, 2003

PART I

Chapter 1

Measurement of Knowledge Management Practices by Dominique Foray and Fred Gault

This chapter puts this survey on knowledge management practices in the historical perspective of surveys in the domain of R&D, technology and innovation. It shows to what extent this survey is of a different nature as compared with the available surveys on knowledge management and it highlights the value added of this new one. Finally it provides a brief history of the OECD-Statistics Canada project at the origin of the survey.


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I.1. MEASUREMENT OF KNOWLEDGE MANAGEMENT PRACTICES

1.1. Introduction This is a book about measuring the practices associated with knowledge management and interpreting the findings. It is new empirical work and one of the objectives of bringing together and publishing contributions from a number of OECD member countries, now, is to set the stage for improved measurements and more comprehensive findings that can be compared across national and cultural boundaries. This is a work in progress. However, the book is not just about surveys and data, it is about understanding a set of practices that are being used by firms and public institutions, especially the larger ones, to do better what they do. The use of knowledge management practices in the first decade of the 21st century is beginning to attract the same interest in the international policy community as did the use of advanced technologies in the 1980s, and the engagement of the firm in the activity of innovation in the 1990s. Of course, the reason for this interest is the identification of best practices, and their economic and social context, with a view to sharing them, and making more organisations work better, as separate organisational units, and as part of an economic and social system. Th e discussion beg ins w ith wh at is meant by ‘knowl edg e management’. Knowledge management (KM) covers any intentional and systematic process or practice of acquiring, capturing, sharing and using productive knowledge, wherever it resides, to enhance learning and performance in organisations. 1 These investments in the creation of “organisational capability” aim at supporting – through various tools and methods – the identification, documentation, memorization and circulation of the cognitive resources, learning capacities and competencies that individuals and communities generate and use in their professional contexts. Practices, like formal mentoring, monetary, or non monetary, reward for knowledge sharing and the allocation of resources to detect and capture external knowledge, are examples of knowledge management. Knowledge management is, therefore, a matter of using a category of practices which are difficult to observe and manipulate and sometimes are even unknown to those who possess them. This is a challenge for firms, more familiar with the management and accounting for fixed capital. However, evidence shows that these practices are being used more and more frequently and that their effect on innovation and other aspects of corporate

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performance is far from negligible (de la Mothe and Foray, 2001). The adoption and implementation of knowledge management practices may be seen as a critical stage in the corporate move towards corporate integration into what is more and more a knowledge-based economy. At the start of the 21st century, there is a recognition of the need to understand and to measure the activity of KM so that organisations, and systems of organisations, can do what they do better and so that governments can develop policies to promote these benefits. Facing such new emerging practices, economists, management scientists and statisticians have little systematic evidence on which to base analysis. Among the various categories of knowledge-related investments (education, training, software, R&D, etc.), KM is one of the less well known, both from a quantitative and qualitative point of view, as well as in terms of costs and economic returns. As a result, there is certainly a need to know more about: these new knowledge-based activities; the current state of KM as an organisational process within various kinds of companies and sectors; the variety of methods and tools that are being developed; and, the economic effects of KM practices that are actually observed.

1.2. Knowledge Management: What is New? Larry Prusak – a world expert on knowledge management – likes to say that like Monsieur Jourdain who spoke in prose, and was not even aware of that, companies have always managed knowledge. But the need for knowledge management as a systematic strategy is becoming far more urgent for the following reasons. Firstly, some of the older practices buried in human resources and employment policies, which helped in knowledge management, no longer work. For example, the memorisation and transmission of tacit knowledge has always been ensured by internal institutions (the craft guild, the internal labour market) and external organisations (professional networks), in which this was an essential function. However, these institutions have largely disappeared or find themselves in profound crisis. For instance, in some large companies, a new engineer was hired a year before the old one retired in order to ensure that knowledge was passed on in the context of an extended master-student relationship. In such cases, the conditions were propitious for ensuring that the professional community itself ensured the memorisation and transmission of knowledge from one generation to the next. However, the system was so costly that it is rarely used. These days, a young engineer arrives a few weeks before the old one passes on the reins. Naturally, the transmission of knowledge is partial. As a result, the old system for transmitting new knowledge management practices has to be replaced by


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one, which might, for instance, be based on a codification of knowledge that would enable a new arrival to use this written memory as a learning program (instruction manuals, maintenance documents, expert systems). Other practices no longer work. The principle of lifelong careers and longterm attachment to the company led to a kind of common destiny between the employee and his/her company. From that point on, the individual’s knowledge was an almost integral part of the company’s intellectual heritage. Here again, recent developments in terms of turnover, mobility and flexibility make it necessary to invent new forms of knowledge retention – again, through either codification or the implementation of strong legal mechanisms to protect the company’s intellectual heritage, or through human resources policies that are better suited to maintaining skills. Secondly, the imperative of innovation as a condition of business survival has forced the introduction of explicit forms of knowledge management. The cost of missing the boat on an innovation (bypassing and ignoring a “good idea”) becomes enormous. We no longer have the luxury of missing out on one or two innovations. Thus, it becomes essential to introduce planned strategies for the collection and documentation of ideas and suggestions by employees. In addition to this type of knowledge management, processes for stimulating creativity become essential. Thirdly, the extension of knowledge markets, the dissemination of information technologies and new methods for the evaluation of intangible assets are three characteristics of the new economy which require the introduction of explicit knowledge management methods. The expansion of markets for knowledge. The increase in the rate of patent applications, the impressive growth in revenues arising from the granting of licences and the explosion in costs associated with intellectual property settlements are all indicators of the current development of the “knowledgebased market economy” (Arora, Fosfuri and Gambardella 2001). Yet, knowledge markets are, by definition, inefficient markets (Teece 1998). Buyers and sellers are not well informed about the commercial opportunities (no one knows who has what or who wants what). There are problems associated with revealing the characteristics of the product. Intellectual property rights, even though they can reduce the first two difficulties, are fragile, uncertain and heterogeneous. The product (or consumption) unit is not clear. Knowledge is sold neither by weight nor by size! At this point, knowledge management can be interpreted as an effort to create less inefficient market conditions. From this point of view, intellectual property policies clearly form part of knowledge management. The use of ICTs as an opportunity to increase productivity. The productivity paradox can be expressed very simply as the delay between the appearance of

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new knowledge tools and instruments and the persistence of old forms of organisation. It then becomes a matter of moving to a higher level of systematising organisational skills and procedures. The management of knowledge, particularly in terms of the codification of procedures, is central to these changes (Steinmueller 2000). The importance of intellectual capital measurement and evaluation (to attract venture capital or to build a partnership). It appears that the stock market valuation of a company increasingly depends on the value of its intangibles. Here again, the management of knowledge involves techniques for the identification and quantification of intangibles in terms of the company’s knowledge base (Masoulas 2000). Fourthly, the understanding of the phenomena pertaining to learning and the transmission of knowledge is increasing; this, in turn, provides an op p ortuni ty t o f org e new t ool s an d new t e ch niq ue s o f kn ow le dg e management. The management of knowledge, as an activity, requires project engineering in the form of tried and true tools and techniques which have themselves been built on the basis of general advances in the economics and management of knowledge, as a discipline. Yet, since the work of Nonaka, Prusak, Teece, von Hippel and many others, there has been significant progress in these disciplines, which has provided an opportunity to understand better the field and, thereby, the possibility of new tools. Just as progress in scientific instrumentation makes it possible to observe phenomena that were previously invisible, progress in the innovation sciences introduces a world that had previously been ignored. The exploration of this universe makes it possible to improve our understanding of the process of knowledge production, transmission and use and, in the end, provides new operational opportunities. Finally, beyond this economic and managerial line, some sociologists argue that each age of capitalism has to provide those who participate in the economic activity (specifically for senior managers and engineers) reasons to get excited and motivated. Thus, the knowledge management argument is certainly a central part of the new system of argument and representation, capable of renewing the grounds for motivation for those who participate in the capitalist enterprise (Boltanski and Chiapello 1999). All these reasons are discussed in a recent book on knowledge management in the innovation process (de la Mothe and Foray 2001) and in the next Chapter by Paul Quintas.


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1.3. Knowledge Management as a Topic for Empirical Studies: Opening another Black Box The production of detailed data on innovation-related activities and the improvement of the economic analysis of innovation are parallel trends, which have always been in mutual reinforcement and dependence. The OECD has been centrally involved in both trends, particularly playing a key role in the design of new indicators, as the theory of innovation has developed, and then in the systematic collection, interpretation and use of data at an international level. This process – dealing with theoretical and empirical advances – consists of opening one black box …after another!2 Thus, the first generation of indicators [see, for instance, the works by Mansfield (1968) and Griliches (1957)], focused on the visible inputs to innovation – such as the expenditure on, and human resources devoted to, R&D as well as the patents and publications resulting from the R&D. The OECD has been engaged in this work, playing a key role in producing and revising the Frascati family of manuals. These manuals are all works in progress, introducing new indicators and developing those already in use. The second generation of indicators addressed the activity of innovation, or the introduction to the market of a new or significantly improved product, or of a new or significantly improved process to production. As well as the activity, there were also linkage measure (sources of innovation) and measure of economic and social outcomes. Such set of indicators and analysis permits entry to the black box of the innovation process. It is related to the “interactive” model of innovation [see Kline and Rosenberg (1986), Teece (1989) and von Hippel (1988)] that emphasises the diversity of possible innovation paths within an organisation, the importance of the various design activities and the predominance of feedback loops. It is also related to the observation of a diversity of sectoral patterns of technical change (Pavitt 1984) and to the increasing interest of economists in the appropriation strategies of companies (“patent or trade secret?”) as well as to the interest for the detailed analysis of the links between the scientific knowledge base and the innovation process. Surveys on technological appropriation – followed by the surveys on university & industry relations – and then surveys on innovation, based on the Oslo Manual, are expressing a fine and detailed representation of the innovation processes and aim at providing data for supporting systematic analysis at a high level of detail and complexity. Again, the OECD plays, in collaboration with Eurostat, a significant role. However the first and second generation indicators are largely influenced by a strong “science and technology” focus. The light that these indicators shed on innovation is therefore more relevant for some enterprises and sectors than for others. In

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certain cases they are satisfactory – the cases of sectors characterised by a centrality of science and technology – but in others these indicators illuminate an almost empty stage. However, a second black box appears within the process of innovation showing the need for a third generation of indicators. Innovation consists obviously in the production of new (theoretical or practical) knowledge, which is generated intentionally (R&D) or non intentionally (learning by doing), and which is shared, modified, recombined and introduced to the market. The seminal references are probably Nonaka (1994) and Davenport & Prusak (1998) in the field of management science and David (1993), Nelson (1992), von Hippel (1994) in the field of economics. Such a new representation of innovation – as a process of knowledge production, mediation and use (OECD 2000a) – opens suddenly an extremely broad field of investigation by moving the emphasis away from technological change towards organisational change. What kinds of stylised facts are to be discovered in this new black box? Firstly, people learn within their professional context. They carry out experiments during the regular production of goods and services. They generate knowledge, while it is not the main motivation of the activity. “Innovation without R&D” is, thus, an activity with considerable impacts. These impacts, however, are likely to vary depending on whether the knowledge generated remains invisible and ignored, or is articulated and shared (Adler and Clarke 1991, Argote et al. 1990, Cantley and Sahal 1980, Pisano 1996, von Hippel and Tyre 1995). Secondly, learning processes are “situated” and knowledge is “sticky”. The development of a situated perspective highlights the importance of the physical context of learning. This context is an essential component in the process. This is why an engineer will pay frequent visits to a user in order to settle a technical problem. Such an understanding of the situational nature of learning provides an opportunity to design principles of location and “optimal mobility” for experts as a function of the operational stages (Tyre and von Hippel 1997). Thirdly, establishing an “organisational memory” is a critical factor for innovation and learning. It can be properly developed through efficient methods of documentation, codification, storage and search or through the implementation and maintenance of strong inter-personal networks of knowledge (Hansen et al. 1999, Steinmueller 2000). Fourthly, the absorption capabilities as well as the strategies of connection to external networks of knowledge and external sources of innovation (users, suppliers, science and technology) are key factors (Cockburn and Henderson 1994, Hicks 1995). At this level there are conflicts between the requirements of searching for information (for which there


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would be an advantage in building a system of weak ties, i.e. distant and infrequent connections) and transferring knowledge [for which it is necessary to build a system of strong ties (Hansen 1999)]. Fifthly, there is a strong relation at the firm level between economic performances stemming from the use of new ICTs and the evolution of workplace practices and training (Brynjolfsson and Hitt 2000). Finally, an efficient intellectual property policy is not only a matter of patent application and of infringement prevention. IP also concerns protected commercial secrets and codified know how (often called proprietary information), such as technical drawings, training, maintenance and operating manuals. Managing this part of intellectual property is difficult and often this information has not been collected or combined and remains poorly identified in the firm (Arora 1995). In short, the management of knowledge is now a key factor in promoting innovations in organisations both by private companies and to some extent by public authorities.

1.4. From Good Case Studies to Systematic Surveys In opening this new black box, one can observe a quite depressing situation: the main item – knowledge – is not observable and thus not measurable (Carter 1996, Henderson and Cockburn 1994, Jaffe 1999). Questions could be raised about the meaning of the direct measurement of a stock of knowledge (say of IBM to be compared with the stock of knowledge of Monsanto). Several obstacles hinder, or even prevent, undertaking such measurements (Machlup 1984). There is the difference between knowledge of "that which is known" and knowledge as “the state of knowing”. There are, moreover, the difference between knowledge of enduring significance and knowledge of merely temporary, quickly vanishing relevance; the difference between knowledge important for many and knowledge of interest to only a few. Thus as soon as one goes beyond a single mind or memory, the problem of additivity arises. While measuring the stock of physical capital is a colossal task, measuring the stock of knowledge capital seems, thus, virtually impossible. Even limited to current science and technology indicators, this measurement will be introduced only if techniques for dealing with the question of obsolescence are developed. Moreover, does the measurement of a stock of knowledge have any meaning if problems pertaining to its location and access are not taken into account? An even more difficult task would be to measure flows of knowledge or the share of the stock of knowledge that enters into the economy during a given period. Measurement of embodied diffusion (i.e. the introduction into production processes of elements incorporating a new technology) and of dis-embodied diffusion (i.e. transmission of

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knowledge in the form of patents licenses or know-how) are the two aspects that today are relatively well under control. But here again, they cover only a small part of the knowledge flows. The building of “proxies” will, thus, be at the centre of any investigation. But building good proxies requires fine and detailed case studies, providing the basis for future and systematic works. The good news is that such case studies are happening. A few examples have already been mentioned. All these works encourage the launching of programs to develop indicators and to collect data about learning processes and knowledge management. It is fair to mention that empirical studies are far more advanced in one portion of the new black box, and these advances deal with organisational changes, the adoption of new workplace practices and impacts of these changes on performance (OECD 2000b). Such works have been strongly pushed by the discussions dealing with the so-called “productivity paradox” problem (raising the argument that the potential of the new ICTs for productivity gains is great but there are many factors impeding, at least in the medium term, the productivity growth).

1.5. Why, How and So What? The why type of question deals with the various rationales that private companies are showing to explain the (costly) implementation of a KM policy. These rationales are the following: ●

Making better use of what already exists within the organisation and outside. This is a static efficiency principle aiming at not “re-inventing the wheel”, improving corporate memory and knowledge sharing, evaluating competencies in order to create best practices, and capturing external knowledge;

●

Solving co-ordination problems which arise because of the increasing complexity and modularity of products and systems;

●

Increasing opportunities for innovation (through recombination, synergy, or transfer);

●

Transforming the stock of knowledge into a direct source of value (through the use of intellectual property management, licensing, and other means of transfer);

●

Attracting talents.

While the two first objectives are of particular relevance for large companies and organisations, the three others are of value for any entity in the modern economy.


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The how type of question deals with the issue of creating and implementing a coherent KM strategy, meaning that a main logic has to be decided and a set of compatible practices have to implemented in this framework. It is useful to differentiate between two main knowledge management strategies (Hansen, Norhia and Tierney 1999): ●

Personalization: knowledge remains in its tacit form and is closely bound to the person who developed it; it is shared primarily through person-toperson contact. To make this strategy work, companies invest heavily in networks of people (mobility, culture of bilateral interaction). In a sense, this strategy is simply another form of the traditional “internal labour market” as a powerful mechanism for capitalizing on, transferring and sharing knowledge. It relies on the logic of expert economics. Both the problem and the knowledge are unique, and the service is expensive and time-consuming;

●

Codification: knowledge is transformed so that it can be stored in databases and then easily accessed and used by anyone in the company; while codification involves high fixed costs, it enables agents to perform a number of operations at a very low marginal cost. This model is appropriate for firms or organisations that deal repeatedly with similar problems. For them, the efficient reuse of codified knowledge is essential, because their business model is based on fast and cost-effective service, which an efficient system of knowledge reuse provides. Firms or organisations that follow a codification strategy rely on this. Once a knowledge asset – software or manual – is developed and paid for, it can be used many times by many people at very low cost, provided it does not have to be substantially modi fied at each use. Re-use of know ledg e s aves work, reduces communication costs and makes it possible to take on more projects;

Of course, all firms and organisations use both strategies, but the hypothesis is that those that excel focus on one and use the other in support. Hansen, Norhia and Tierney (1999) see an 80-20 split: 80% of their knowledge management follows one strategy, 20% the other. Those that try to excel at both risk failing at both. The argument is that the selection of a particular knowledge management strategy must reflect the firm’s or organisation’s business model, which relies either on knowledge reuse or on unique problems and expertise. Interesting for a survey is that various dimensions of knowledge management will differ, depending on the firm’s main strategy. There is thus an issue to identify consistent set of practices based on a dominant KM logic. The so what type of question deals with the fundamental problem of the benefits to be expected: active price competitiveness (process innovation, productivity), technological competitiveness (product innovation) and market

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power. It is also a matter of identifying what are the most important “intangibles” to show up for a company. Those most important intangibles being closely related to the KM strategy (personalisation and social network or codification and ICT systems) selected.

1.6. Knowledge Management Surveys The lack of systematic evidence for KM activities is due to the fact that very few large scale surveys have been carried out.3 Surveys that have been done have the following attributes: ●

they are multi-sectoral and international;

●

they are mainly addressed to large companies; and;

●

they do not make any data linking with existing data bases of R&D, innovation, employment, and so forth.

While providing useful insights on KM practices,4 the results are difficult to interpret for several major reasons. Firstly, there is still considerable instability and ambiguity in the meaning of the various concepts dealing with knowledge (consider for example the instability of the notions of tacit and codified knowledge, knowledge and information, knowledge and competence, and expert systems). Researchers, experts and statisticians are nowadays in the same position as researchers and statisticians interested in working on R&D over fourty years ago. The historical analogy with the emergence of statistical works on R&D has, however, some limitations: R&D expenditures (and personnel) are easily quantifiable, while we have no clearly defined equivalents for knowledge management. The absence of a systematic terminology based on clear and widely shared category increases dramatically the sensitivity of responses to subjective perceptions and idiosyncratic understanding of “what is KM?” The effect of such ambiguity and lack of stable categories is amplified by the fact that KM methods and processes are not yet (and perhaps will never be) associated with the same departmental or functional budget throughout firms and organisations. KM strategies can be implemented and funded by the R&D, ICT, human resource & training or customer service sales department within a company. Thus, people with different “cultural background” can have a highly different representation of “what is KM?” and “what are the KM issues in the company?”. We can note that this is a problem, which is minimised in the case of a R&D survey, which is addressed in principle to R&D people. Secondly, because there was no previous experience in national statistical offices in OECD countries of doing KM surveys, the existing surveys done by other organisations cannot make the link between data on


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KM practices and the common economic performance and innovation indicators. These surveys limit, thus, the scope of questions about performance to subjective perceptions of the benefits (expected and “actually realised”). Works on these issues tend, therefore, to be “self referential” in the sense that they are not validated by external economic criteria, such as revenues or profits. There is, thus, a need for various tasks that could be achieved through the design, implementation and exploitation of an international survey carried out by national statistical offices or in close co-operation with them.

1.7. Three Main Tasks of a Knowledge Management Survey The first task is to build a systematic database on KM practices. Such a database should ideally include information on six broad classes of questions: ●

Adoption and implementation of KM practices;

●

Reasons for using/non using KM practices;

●

The sources which prompted the development of these practices;

●

The actual benefits and consequences;

●

The financing of a KM policy;

●

General indicators.

The second task should be to use the unique opportunity offered by “official surveys” carried out at the national level to link the KM databases with data coming from other sources (R&D, innovation, enterprise surveys). This task covers not only the technical aspect but also the analytical one. There will be, for example, hypotheses about the types of linkage that could be tracked between R&D data, innovation data, and KM data. At a first glance, it could be considered that variations in: ●

R&D intensity;

●

innovation intensity;

●

types of innovation;

●

appropriation strategies (patent, secrecy, lead time, complementary asset); and,

●

sources of innovation and information (internal, users, universities, suppliers) should be related to various KM strategies and practices. This is, however, a rather uncertain conjecture which is discussed in the Chapter by Elizabeth Kremp and Jacques Mairesse.

The third task should be to exploit an indirect effect of the survey, which is to contribute to the stabilisation of meanings and to the standardisation of the terminology of KM strategies and practices through an international exercise. The design of a questionnaire achieved by an international group of well-

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recognised experts and the use of this questionnaire in various contexts (national, sectoral) can have substantial spill-over elements as it can contribute largely to the stabilisation of basic categories and to the development of a common language on knowledge practices. This follows the practice of the OECD R&D and innovation.

1.8. A Brief History of the OECD-Statistics Canada Project and a First Look at the Results Following the OECD High-Level Forum on knowledge management in Ottawa in September 2000, a working group was set up, comprising representatives from the statistical offices of Canada, France, Italy, the Netherlands and Sweden and representatives from research bodies in Australia, Denmark, Germany and Ireland. The working group met four times in 2001, in Copenhagen, Ottawa, Paris and Karlsruhe. A questionnaire was devised during the course of the four meetings and the information emerging from the first pilot studies was discussed. This questionnaire includes a survey on the use of 23 KM practices and is complemented with questions on incentives for using KM practices, results, responsibilities, etc. The questionnaire includes many informal management practices in order to accommodate how micro-firms are managing knowledge. On the other hand, it does not focus very much on the ICT infrastructure. For countries willing to carry out their own national surveys, two kinds of strategies were possible: either implementing the whole survey as a pilot study or lodging a few questions on KM in an existing and regular questionnaire, such as the Community Innovation Survey. While the first option gives the opportunity to really test the KM questionnaire and to collect information related to a large range of issues and problems, the second option has proven to be very useful for countries where starting a new survey is a difficult task for administrative, political or technical reasons. To date, four pilot studies have been carried out, to which this book is largely devoted. The Canadian study (by Statistics Canada) covered 348 respondent firms of varying size (from 9 employees upwards), belonging to 7 different sectors. The German study (Fraunhofer ISI) covered 497 firms of varying size (from 1 employee upwards), belonging to 7 different sectors. The Danish study (CFL) covered 61 firms of varying size (from 1 employee upwards), belonging to all sectors of the economy. The French study (SESSI) adopted the second strategy, which was to merge four questions on knowledge management in the CIS3 survey. This allowed a very large number of firms to be covered (5100 firms with a response rate of 85%). It is to be noticed that Japan adopted more recently the same strategy – lodging four questions on KM in the Japanese National Innovation Survey 2003. Results will be available in Autumn 2003.


23


Some of the most interesting findings to emerge from these pilot studies are the following: ●

KM practices have spread across the economy, just as technology diffuses;

●

KM practices are implemented to deal with a great variety of objectives (static efficiency, innovation, co-ordination);

●

Size matters: firms manage their knowledge resources differently, depending upon their size, and with little regard to industrial classification;

●

KM practices matter for innovation and productivity performance;

●

Cluster of practices: although this is a bit premature to make this kind of statement, cluster of practices makes it possible to see the two main strategies: codification and personalisation;

●

Survey respondents showed a high level of interest, which in fact increases as the size of the firm grows.

All of these results show that the measurement process is possible and this is both good news and an exciting challenge for statistical offices and econometricians.

1.9. Outline of the Book In the next chapter knowledge management in practice is presented with examples from case studies. Chapters 3 to 6 deal with country reports. The Canadian, German, Danish and French cases are successively developed. The data collected in each national survey are not presented using the same structure. On the contrary each Chapter is built on a specific structure, which best related the data to the national circumstances and particularities. The reader will also note the differences between the Canadian, German and Danish surveys based on a pilot study (full use of the questionnaire on a limited sample of companies) and the French survey based on the introduction of few questions about KM in a large scale survey (CIS3). Chapter 7 addresses the relation between scale and KM practices on the basis of the Canadian data. Chapter 8 provides some “best practices” insights for those considering conducting the OECD survey. Rather than providing a manual that specifies the exact process required to conduct, analyse and report the survey, this chapter aims at advising the prospective KM survey manager. Chapter 9 presents the most recent version of the basic questionnaire. The concluding chapter is devoted to a first look at the policy implications of the survey results as well as some indications about the next steps.

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Notes 1. This definition is drawn from Scarbrough, Swan and Preston (1999). 2. Although this metaphor is, perhaps, not fully appropriate because the next black box discovered within the one that is being explored is not necessarily smaller than the one that “contains” it. 3. There was a French official survey “Les compétences pour innover”, carried out in 1997 by the statistical office of the Ministère de l’Economie. This survey, however, does not strictly focus on KM practices (SESSI 1998, Lhuillery 2001). 4. For instance, the survey undertaken by KPMG consulting provides many interesting results on the current state of KM. It covers 423 organisations, in several OECD countries, which belong to 9 different sectors KPMG (2000). See also Arthur Andersen (2000), Cranfield School of Management (2000) and XEROX (2000).


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Bibliography Adler, P. and K. Clark (1991), “Behind the learning curve: a sketch of the learning process”, Management Science, vol.37, n°3. Argote, L. et al. (1990), “The persistence and transfer of learning in industrial settings”, Management Science, vol.36, n°2. Arora, A. (1995) “Appropriating rents from innovation: a historical look at the chemical industry”, in Albach & Rosenkranz (eds.) Intellectual Property Rights and Global Competition, Wissenschaftszentrum, Berlin. Arora, A., A. Fosfuri and A. Gambardella (2001), Markets for Technology, MIT Press, Harvard. Arthur Andersen (2000), Le Knowledge Management en France, résultats de l’enquête 2000, Arthur Andersen, Paris. Boltanski, L. and E. Chiapello (1999), Le nouvel âge du capitalisme, Gallimard. Brynjolfsson, E. and L. Hitt (2000), “Beyond computation: information technology, organisational transformation and business performance”, Journal of Economic Perspective, 14. Canibano, L., M. Garcia Ayuso, and P. Sanchez (2000), “Accounting for intangibles: a literature review”, Journal of Accounting Literature, Vol. 19. Cantley, M. and D. Sahal (1980), “Who learns what? A conceptual description of capability and learning in technological systems”, IIASA Research Report, December. Carter, A.(1996), “Measuring the performance of a knowledge-based economy”, in OECD, Employment and Growth in the knowledge-based economy, Paris. Cockburn, I. and R.Henderson (1994), “Managing innovation in the information age”, Harvard Business Review, January-February. Cranfield School of Management (2000), The state of the art of knowledge management in Europe, Cranfield School of Management, Cranfield. Davenport, T. and L. Prusak (1998), Working Knowledge, Harvard Business School Press, Boston. David, PA. (1993), “Knowledge, property and the system dynamics of technological change”, in the Proceedings of the World Bank Annual Conference on Development Economics. de la Mothe, J. and D. Foray (2001), Knowledge management and the innovation process, Kluwer Academic Press, Boston. Griliches, Z. (1957), “Hybrid Corn: An Exploration in the Economics of Technological Change”, Econometrica, 25(4). Hansen M., N. Nohria and T. Tierney (1999), “What’s your strategy for managing knowledge?”, Harvard Business Review, March-April. Hansen, M. (1999), “The search-transfer problem: the role of weak ties in sharing knowledge across organisation subunits”, Administrative Science Quarterly, 44. Henderson, R. and I. Cockburn (1994), “Measuring competence? Exploring firm effects in pharmaceutical research”, Strategic Management Journal, Winter, Special Issue, 15.

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Hicks, D. (1995), “Published papers, tacit competences and corporate management of the public/private character of knowledge”, Industrial and Corporate Change, vol.4, 2. Jaffe, A. (1999), “Measuring knowledge in the health sector”, paper presented at the OECD/NSF high-level forum, Measuring Knowledge in Learning Economies and Societies. Kline, S. and N. Rosenberg (1986), “An Overview of Innovation”, in R. Landau and N. Rosenberg (eds.), The Positive Sum Strategy: Harnessing Technology for Economic Growth, National Academy Press, Washington DC. KPMG (2000), Knowledge Management Research Report 2000, KPMG Consulting, London. Lhuillery, S. (2001), “Managing Surveys on Technological Knowledge: The French Experiences in the Nineties”, in J. de la Mothe and D. Foray (eds.), Knowledge Management in the Innovation Process, Boston, Kluwer Academic Press. Machlup, F. (1984), Knowledge, its creation, distribution and economic significance, Princeton University Press, vol.III. Mansfield, E. (1968), Industrial research and technical innovation, W.W.Norton & Co, New York. Masoulas, B. (2000), “Managing organisations’ knowledge and intellectual capital”, Economic & Financial Computing, Winter. Nelson, R.R. (1992), “What is ‘commercial’ and what is ‘public’ about technology, and what should be?”, in Technology and the Wealth of Nations, N.Rosenberg, R.Landau & D.C.Mowery (eds), Stanford University Press. Nonaka, I. (1994), “A dynamic theory of organisational knowledge creation”, Organisation Science, vol.5,1. OECD (2000a), Knowledge management in the learning society, OECD, Paris. OECD(2000b), “The changing workplace: trends, links with economic growth and policy implications”, OECD working paper, Paris. Pavitt, K. (1984), “Sectoral patterns of technical change: towards a taxonomy and a theory”, Research Policy, 13. Pisano, G. (1996), “Learning-before-doing in the development of new process technology”, Research Policy, 25. Scarbrough, H., J. Swan and J. Preston (1999), Knowledge management: a literature review, Institute of Personnel and Development, London. SESSI (1998), “Les compétences pour innover”, Chiffres clés Référence, Sessi, Ministère de l’Economie, Secrétariat à l’Industrie, Paris. Steinmueller, E.W. (2000), “Learning in the knowledge-based economy: the future as viewed from the past”, in the Proceedings of the Conference in honour of Paul A.David, May, Turin. Teece, D. (1989), “Inter-organisational requirements of the innovation process”, Managerial and Decision Economics, Special Issue. Teece, D. (1998) “Capturing value from knowledge assets”, California Management Review, vol.40, n°3.


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Tyre, M. and E. von Hippel (1997), “The situated nature of adaptive learning in organisations”, Organisation Science, vol.8, 1. von Hippel, E. (1988), The sources of innovation, Cambridge University Press. von Hippel, E. (1994), “Sticky information and the locus of problem solving: implications for innovation”, Management Science, vol.40, 4. von Hippel, E. (1998), “Economics of product development by users: the impact of ‘sticky’ information”, Management Science, vol.44, 5. von Hippel, E. and M.Tyre (1995), “How learning by doing is done: problem identification in novel process equipment”, Research Policy, 24. XEROX (2000), The Knowledge Scanning Workbook, Fuji Xerox.

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PART I

Chapter 2

Managing Knowledge in Practice by Paul Quintas

This chapter draws on case studies and real-world examples to illustrate knowledge management in practice. We relate current knowledge management (KM) practice to the wider context of existing knowledge processes in organisations. We note that the processes of knowledge creation, sharing and application have been central to organisational activity for centuries, and that there are differences in perceptions of knowledge management between different cultural traditions. Key issues addressed include the social nature of knowledge processes, start-up strategies for KM initiatives, the role of technology, knowledge capture and sharing, intellectual capital measurement, and cross-boundary processes. Some lessons are drawn from organisations’ experiences to date.


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I.2. MANAGING KNOWLEDGE IN PRACTICE

2.1. Introduction The surge of interest in “knowledge management” (KM) in the West from the mid 1990s is even more evident in organisational practice than it is in the plethora of academic articles, books and conferences on the subject. Profound changes in the economy and business environment at the end of the twentieth century prompted organisations of all types to rethink the nature of the resources and capabilities that generate advantage.1 Resources might now include intellectual capital and enhanced consideration of intangible assets, as well as knowledge itself. Focus on capabilities prompted interest in key processes such as knowledge creation, knowledge sharing, learning, and the exploitation of intellectual property. Pre-1995 “knowledge management” initiatives in firms such as BP, Chevron, Shell, Hewlett Packard, Buckman Labs and Xerox, and the pioneering of intellectual capital reporting in Skandia (1994), pre-date the academic KM publishing boom (see Figure 2.1).

Figure 2.1. Growth in Knowledge Management Literature No. of knowledge management articles on ABI/Inform database 700 600 500 400 300 200 100 0 1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997 1998 1999 Year of publication

Source: Gordon and Grant (2002)

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We should of course acknowledge that the literature on knowledge, viewed from an economics and organisational perspective, has a rather longer history than this “KM” phenomenon suggests. From Adam Smith in the 18th century to Alfred Marshall in the 19th and Frederick Heyek and Edith Penrose in the early and mid 20th, the awareness of the economic importance of knowledge and its centrality to organisations has been emphasised, if not fully articulated. As Penrose wrote: Economists have, of course, always recognized the dominant role that increasingly knowledge plays in economic processes but have, for the most part, found the whole subject of knowledge too slippery to handle. (Penrose, 1959, p. 77) Nevertheless it is undeniably the case that, in practice, people have effectively managed knowledge from the earliest incarnations of the organisation. There is a serious issue here as to what is the new subject of interest in real-world organisations. Much of the previous “managing of knowledge processes” has been informal and unremarked, and certainly not labelled as “knowledge management”. The case studies of Honda, Matsushita and other firms in Nonaka and Takeuchi’s influential book The Knowledge Creating Company (1995) were not examples of designated “knowledge management” initiatives but rather descriptions of actual knowledge processes of knowledge sharing, knowledge combination, and so on. These were identified post hoc as examples of knowledge being managed. Similarly, story-telling has recently been “discovered” as being alive and well and providing knowledge sharing in many organisations. Conversely (and ironically) many so-called “knowledge management” initiatives and tools that emerged in the late 1990s were less concerned with real knowledge issues than the informal or existing processes that are not so labelled. The example of communities of practice illustrates real management of knowledge without the “KM label”. As has been pointed out by Spender, Brown, Wenger, Baumard and others, knowledge has a social dimension – it may be created and held collectively. People who share work experiences, problem agendas and have similar learning opportunities form communities of practice (Lave and Wenger 1991). Wenger (2000) defines a community of practice (CoP) as a social learning system, united by joint enterprise, mutually recognised norms and competence, with shared language, routines and stories. Crucially, a community of practice is most often an informal grouping. It may be unrecognised (Scarbrough 1996) or ignored or taken for granted (Baumard 1999) in the organisation. So too it may transcend organisational boundaries, including people in several organisations who hold experiences in common. CoP members act as resources for each other, “exchanging


31


information, making sense of situations, sharing new tricks and ideas” (Wenger, 1998, p. 47). In Xerox, photocopier engineers were observed working together on a problem machine, communicating like jazz musicians, exchanging truncated phrases and able to communicate non-verbally because of shared experience, shared learning, shared understandings (Brown and Duguid 1991). CoPs therefore represent oasis within which knowledge processes function naturally. Formal management styles may be at odds with the informality of CoP processes, and indeed attempts to formally manage CoPs from outside may undermine them. Baumard (1999) identifies three CoPs in the Australian airline Qantas: the pilots and their retinue, the financial group, and the marketing group. Each of these communities has their own language, which as Baumard emphasises, indicates different interpretations of reality. Qantas’ top-down management style favours documents, manuals and computerised information, whereas the CoPs favour less explicit circulation of knowledge: “... communities of practice, conjectural knowledge and repertories of thought inscribed in practice are all tacit.” (Baumard, 1999, p. 135). The Qantas communities refused to use a new computer-based “knowledge management system” introduced from outside the CoPs. The CoPs examples show that, unsurprisingly, knowledge processes function and work well without the “KM” label, and indeed attempts to formally introduce KM may adversely affect these more natural processes. Also, formal KM may be less concerned with knowledge than it is with information. A key point here is that the concept of knowledge invites us to move beyond the rather safer and certainly easier ground of data and information management. As Spender (1996) has pointed out, there is little point in introducing such a complex concept as knowledge into management thought and practice if we do not take seriously the characteristics of knowledge that make it special, and distinguishable from information. This realisation calls into question so-called “knowledge management” practice that focuses wholly on technology, codification or commodified off-the-shelf “solutions”. Prusak makes a similar point when he says, “if you spend more than one-third of your knowledge budget of technology … then it becomes a technology project and not a knowledge project” (Prusak 2001, p. 156). T h e a dva n t a g e of a n e n h a n c e d f o cu s o n kn ow l e d g e p rov i d e s opportunities for new thinking, both about and within organisations. To ignore the transformational potential of a knowledge perspective is to miss an opportunity. In this regard it is valuable to emphasise knowing as a process. This counters the tendency, as is common in the West, to think about knowledge as a “thing” or commodity that can easily be moved around, managed and traded. An alternative approach, focused on knowing as

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process, is more practical than it might at first appear, as this definition of knowledge management from the Xerox Corporation illustrates: Knowledge management is the discipline of creating a thriving work and learning environment that fosters the continuous creation, aggregation, use and re-use of both organisational and personal knowledge in the pursuit of new business value. (Cross, 1998, p. 11) The Xerox definition is strongly process and action oriented. It does not emphasize knowledge resources and assets, as many definitions and indeed initiatives do. Rather, it focuses on the processes of creating new knowledge and actively doing things with it. It is not surprising that perceptions of knowledge differ between cultures. Grossly simplifying a more complex geographical and epistemological variation, Western and Eastern traditions differ in their views of the extent to which knowledge can be separated from the knower. Even within European cultures there are differences in conceptualisations of knowledge, as is reflected in the language we use to discuss it. For example, the English language may be accused of being deficient in having only the one word – knowledge – when, for example, French makes a distinction between connaître and savoir, and German between kennen and wissen. Differences in language reflect the fact that knowledge itself is conceptualised differently in different contexts, and we should not underestimate the challeng es of seeking universal definitions and vocabularies (Cohen, 1998). Nevertheless, managers and organisations increasingly have to operate across cultural and other boundaries, and an awareness of difference is essential. A European survey of knowledge management among 100 European business leaders (Murray and Myers, 1997) revealed some interesting cultural differences. In France, more than anywhere else in Europe, nearly a quarter of business leaders believed you can’t create any processes to help you manage knowledge. It is simply a matter of “management ability”. In Germany, more than four out of five respondents already considered their organisation to be good at encouraging staff to share knowledge and to bring forward new ideas. In the UK the main knowledge management focus was to exploit and control the knowledge that companies believe they already have. Most remarkably, almost a quarter of UK respondents said that creating new knowledge was not a key priority, compared with only 1% in Germany. The international and multi-cultural approach adopted in this book is an attempt to begin to develop a comprehensive account of knowledge management that goes beyond the limitations of a mono-culture perspective.


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2.2. Key Knowledge Processes Some would argue that having knowledge is a defining characteristic of human beings and therefore it is inconceivable that we could have human activity without knowing and knowledge. Perhaps, then, all organisational processes involving humans are knowledge processes. Certainly it may be argued that all activity in organisations is “knowledge based” to some extent, and therefore all workers are “knowledge workers”, up to a point, and all tasks performed by humans is essentially “knowledge work”. The counter view is that certain types of work are more knowledge intensive than others. Machlup (1962) demarcated the “knowledge economy” from the rest, Drucker (1969) coined the phrase “knowledge workers” and Reich (1991) refers to the rise of “symbolic analysts” – distinguishing those who deal with concepts from those who work with physical materials. In all of these post-industrial accounts knowledge processes are argued to be intensifying. Here we will focus on a number of key processes that are central to the management of knowledge in organisations. Generic processes, especially communication and learning, underpin many of the more focused processes, such as knowledge sharing, acquiring, integrating, mapping, and capturing etc. It is revealing that arguably the most important process – that of knowledge creation – is often ignored or forgotten by the “KM” professionals. We can see differing priorities in the variety of ways that firms approach their knowledge management initiatives. For the majority of firms in the West, the priorities are the “capture” of employees' knowledge, exploitation of existing knowledge resources or assets, improved access to expertise (i.e. improved “know-who”), transferring knowledge between projects, and building and mining knowledge stores. Examples of early initiatives include Nat West Markets’ knowledge directory, and Teltech's mapping networks of experts (Davenport 1997). Ernst & Young, Andersen Consulting, and other companies developed firm-wide IT systems for document sharing with the aim of sharing codified best practice and increasing re-use (Hansen et al., 1999). McKinsey consultants and Bain & Company put greater effort into support for networking and people-to-people links (ibid.). Skandia focused on measuring and auditing intellectual capital and intangible assets (Skandia, 1996). Dow Chemical, Glaxo Welcome (pharmaceuticals) and Integra Life Sciences (health care) target the improved management and exploitation of intellectual property rights (IPR). Like many organisations, UK Post Office Consulting (POC) launched a cluster of knowledge management projects in the late 1990s, including: ●

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knowledge sharing (targeted on communicating, learning, reviewing, capturing and sharing knowledge);



●

use of stories to communicate experience (targeted on transferring learning);

●

after-action reviews (capturing learning from experience);

●

intelligent agents (identifying specific and tailored information or contacts);

●

people database (providing access to expertise);

●

expert interviews (capturing expertise);

●

learning from mistakes (surfacing and capturing learning in a non-blame culture, avoiding costly repetition); and

●

expert masterclasses (sharing expertise).

(Quintas et al 1999) Adopting a knowledge focus also generates new business models and opportunities. Consultancy firms realise their business is entirely a knowledge business and seek to commodify their knowledge as a product. New business opportunities spring up for knowledge brokers and for “talent” agents who represent knowledge workers in sectors where expertise is in great demand. The software services company ICL found that their knowledge management expertise generated a new line of business and revenue stream.

2.3. Getting Knowledge Management Started In this section we focus on how new initiatives labelled “knowledge management” or KM (i.e. espoused KM) are started within organisations. The beginnings of formal KM may be located anywhere in the organisation and may be bottom-up or top-down. Often the formal starting point was the appointment of a chief knowledge officer. IT professionals predominate in leading many early KM initiatives. Who is driving an initiative matters, as it has been demonstrated in relation to organisational learning programmes in 3M and Coca-Cola. Different groups championing and steering the programme (in these cases the HR department, and the technical experts in R&D) have different priorities and objectives, and the programmes may be markedly different. However, the realisation that there are serious people-management and cultural challenges associated with “capturing” the knowledge of employees, or influencing the ways people deal with or share knowledge, has led to greater involvement of HR professionals. Time and time again we hear the realisation dawning that it is the “soft issues” that determine knowledge processes. Knowledge management was not adopted in a vacuum, and most organisations in the early 1990s already had ongoing initiatives in areas such as continuous improvement, quality management and business process reengineering. Some early adopters of the KM label re-badged existing initiatives, and consultancy firms re-labelled management consultancy


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methods. Some KM programmes were explicitly developed out of existing programmes, such as Texas Instruments best-practice knowledge sharing programme which emerged from their quality programme TI-BEST, and Dow Chemical’s knowledge programme developed out of their Intellectual Asset Management programme. BP’s KM programme began with a project called Virtual Teamworking. Many KM initiatives are driven by board-level and CEO interest, and a common approach is to set up a centralised office to coordinate KM developments, usually accompanied by the appointment of a KM champion, titled “chief knowledge officer” (CKO) or similar. The software and systems company ICL’s CEO appointed Elizabeth Lank as Director of ICL’s Knowledge Management Programme in 1996. In this case the KM champion’s role was to head-up a programme with finite duration. The task was to embed knowledge management practice within all parts of the organisation, after which the central role would be superfluous. Bottom-up KM often starts with a small core of interested and active enthusiasts, as is the case in both Siemens and BT. Pilot projects are valuable low-cost / low risk ways of proving the viability of a KM approach and gaining experience, and they provide a demonstrator to be evaluated and replicated.

2.4. Limits and Potentials of Technological Solutions “It is worth remembering that the music is in the pianist, not the piano.” (Jim Marsh, Knowledge Director, Post Office Consulting) By the early 1990s there was growing awareness that business information systems were not capturing the knowledge that managers use in their work, as noted by the former head of Information Technology (IT) research for Ernst & Young: ...evidence from research conducted since the mid-1960s shows that most managers don’t rely on computer-based information to make decisions. … managers get two-thirds of their information from face-toface or telephone conversations; they acquire the remaining third from documents, most of which come from outside the organisation and aren’t on the computer system. (Davenport, 1994, p. 121) It is ironic that many subsequent so-called KM approaches have been base d on inform ation technology. While codified know ledg e is also information, much human knowledge cannot be codified and remains inaccessible to information technology. You cannot share a violinist’s knowledge (i.e. learn to play the violin) by listening to a CD or to a lengthy verbal explanation of the technique. Practice is required. Also, availability of information does not mean knowledge is being communicated (if a text is in Japanese it is information that is meaningless to a non-Japanese speaker).

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Certainly information and communications technologies (ICT)s have potential to support communication and information flows, and the vast expansion of information available via the internet is an undeniable resource. However studies of organisations that have adopted an IT-driven approach to KM show that the use of ICTs must be framed within a strategy that addresses other, fundamental factors. Many organisations introduced new IT systems as part of, and in some cases the totality of, their “knowledge management” initiative. One such was the UK Defence Evaluation and Research Agency (DERA), an organisation of some 10 000 engineers, scientists and strategists.2 DERA had a KM program from 1994. The initial KM strategy was devised by IT professionals and essentially IT-driven. The four themes of the strategy were (1) technology, (2) processes, (3) people & behaviour and (4) content, prioritised in that order. DERA had an intranet in place in 1995. By 1998 the lack of success with this prompted the introduction of “culture & behaviour” initiatives in 1998, and a second knowledge management strategy – this time devised by a team including librarians as well as IT specialists, was published in February 1999. DERA introduced KNet, a database intended to help DERA staff network more easily. It aimed to provide information on who in DERA has expertise on a given topic, and details about them and how to contact them. KNet was accessible by all staff, who could update their own records on-line. It was also designed to hold information on expert contacts outside DERA. Though recognised externally as a model system of its type, it has emerged that KNet did not work well. The database categories were found inappropriate by users, who were also reluctant to enter their own data. Only 10% of staff entered any data, until management mandated this with financial incentives, causing friction. A further major KM system, the Knowledge Store, introduced in 2001, intended to provide easy access to all types of explicit knowledge or information. It included intuitive navigation maps, search tools, software agent support, cross-linking between content libraries, and integration of existing web sites and applications. Anyone in DERA could publish almost any type or for m at of i nf orm atio n. Th e Know le dg e Store was a m aj or development, reportedly employing 80% of UK Oracle developers at one time. The problem in practice was that people would not publish and share their information, principally because the organisational culture was resistant and protective, not least because DERA was divided into business units that were in competition to make profits. Additionally, the organisational culture instilled an aversion to sharing knowledge – it was simply not the way people had learned to behave. In the words on one insider: “people don’t share - so everything collapses” (Thornton 2001). Similar cultural barriers were experienced in other organisations, such as IT company ICL (Mackay 2001).


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Following the break-up of DERA a new KM strategy emerged within the emergent Defence Science & Technology Laboratory (Dstl, which employed around 3000 people). Drawing on the DERA KM experience the Dstl strategy was based on reversed priorities: (1) people, behaviour, culture, (2) content, (3) processes and (4) technology & tools. The DERA experience had shown a KM initiative requires the enthusiastic co-operation and input of all staff within a supportive culture. DERA had a culture of secrecy, internal competition and lack of trust. This had to be changed to an environment that encourages and rewards the sharing of information, knowledge and skills, including successes and failures. We can summarise the lessons that emerge from DERA and the many organisations with similar experiences as suggesting that: ●

technology should not drive knowledge management practice, it has a supporting role;

●

ICTs can only deal with knowledge in so far as it can be represented or codified – this does not include tacit experiential human knowledge;

●

social, cultural and process issues, and in some cases structural barriers, constrain the contribution of technology to any KM programme.

For organisations seeking to better manage their knowledge, it seems that the use of ICTs should be focused on connectivity – providing c o m mun ica t i on s y st e m s t h a t l in k h um a n s t og e t he r – rat h e r t h a n concentrating on the capture and representation of human knowledge. There is therefore significant potential in “groupware” and other innovative communications technologies, but organisations must also create conditions of trust where individuals feel encouraged to share their ideas, opinions and knowledge.

2.5. Knowledge Capture Realisation that people in organisations possess knowledge that is not codified has prompted formal “knowledge capture” initiatives in many organisations. Codified knowledge is information, which may be stored and re-used by others, providing they can understand its meaning. Whereas some types of knowledge may be readily captured and codified, other forms of knowledge are less amenable. All organisations possess a great deal of experience-based knowledge which is learned implicitly and internalized by individuals. Much of this experiential knowledge is tacit knowledge. Almost a century ago Frederick Taylor not only recognised the presence of individual experiential knowledge, but also the potential value in attempting to capture this knowledge and make it available to the organisation. A central part of Taylor’s “scientific management” was concerned with identification of

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exemplary expertise possessed by key individuals and attempting to codify this in order to make it transferable and replicable. His description of the role of (knowledge) managers finds echoes today: The managers assume the burden of gathering together all of the traditional knowledge which in the past has been possessed by the workmen and then of classifying, tabulating, and reducing this knowledge to rules, laws, and formulae (Taylor, 1911, quoted in Braverman, 1974, pp. 32, 36) There are fundamental issues that must be considered in relation to knowledge capture, whether the approach is Taylorism or more modern equivalents. First, not all knowledge held by individuals is codifiable – “we know more than we can tell” (Polanyi 1966). Therefore knowledge is neither wholly open to capture or to transfer to others via language. Important forms of knowledge, from the mundane (riding a bicycle) to the exotic (playing a violin) can only be gained by experience. Further, knowledge is contextspecific – it is created in relation to specific time and specific social, technical, market and locational contexts. The downstream use of codified knowledge requires meaning to be interpreted, in a different context from that in which the knowledge originated. In one consultancy organisation, the KM programme included a knowledge capture component, within which a major initiative aimed to capture knowledge from an overseas project. Individuals who had worked on the overseas project were interviewed by a team trained in “knowledge interview” techniques. Verbal responses were written down and the resulting texts were made available in electronic form to the organisation. There were some problems about the “ownership” of the captured knowledge, triggering conflicts between competing divisions within the wider organisation. There were also some concerns about confidentiality which led to sensitive texts being restricted. More important, the knowledge capture process, although thorough in its approach to interview training, did not attempt to involve any potential users of the captured knowledge. The process assumed that deployment of the knowledge would occur later. Unfortunately any “deployment” of the captured knowledge was thwarted by internal political conflicts over ownership. It is also interesting to note that the knowledge capture team did not themselves appear to treat this first project as a learning exercise. Even though their objective was transferring learning from (someone else’s) project to (someone else’s) project, there was no reflexive consideration of their own activity; that is, no structured attempt to capture and share the learning from their own knowledge capture process.


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This example suggests that capturing knowledge “in a vacuum” – i.e. without knowing anything about the use context, or indeed the users – is problematic. The commonly practised sequential model clearly has disadvantages that bringing together the knowledge sources and potential users would alleviate.

2.6. Knowledge Sharing Above we looked at knowledge capture and suggested that this must be seen within a broader context that includes knowledge use. This broader context is essentially the process of sharing knowledge. Within this heading we find initiatives aimed at transferring knowledge, learning between projects and sharing best practice. It is significant that the widely quoted Knowledge Creating Company (Nonaka & Takeuchi 1995) places much emphasis on knowledge sharing without particularly emphasising the barriers and problems experienced by many organisations in this area. The key factor here is that the companies studie d by Nonaka and Takeuchi we re Japanese, w ithin which the organisational culture minimises the barriers to knowledge sharing traditionally experienced in many Western organisations. Knowledge sharing implies learning, since learning is a process of acquiring knowledge. However the focus of KM initiatives tends to be more on the source of knowledge, capture and codification (see Section 2.5) and the measures to link these with potential recipients. The learning process of the recipient is largely assumed to be unproblematic. Linking measures include knowledge directories and intranets which enable seekers of information to identify and then contact people with specific knowledge. Consultancy firms such as PricewaterhouseCoopers and Andersen Consulting built best practice databases intended to share knowledge and reduce “reinventing the wheel”. In Section 2.4 we identified some challenges associated with technological solutions. In the case of one global consultancy firm, a major commitment to KM and sharing knowledge had mixed results. KM is both centrally and locally funded, and every new recruit undergoes a one day KM induction process. All staff is assessed by their colleagues on how well they share knowledge. The firm’s strategy was also heavily IT focused. A range of IT tools were developed including knowledge repositories, national and global intranets, an extranet, online news updates, and access to a range of online databases. However the existence of departmental silos inhibited knowledge sharing and users found that information on the intranet, divorced from context, was not useful. The people and time commitments required to embed KM processes proved to be more extensive than management had

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anticipated. It became clear that technological solutions were not sufficient, and that the people management and “soft issues” are key. Volunteer knowledge champions were enlisted to facilitate the knowledge sharing processes. These were given time to establish relationships with their internal clients. Internal case studies were used as exemplars of how things can work well. Fundamentally, the organisation recognised that the key was to work with the natural human processes and preferences for communication. For example, the first thing anyone wants to do when looking for information is ask a colleague – the conclusion was that the KM processes should work with these natural processes instead of against them. Like this consultancy firm, other Western companies have introduced incentives for knowledge sharing. Software company Lotus Development (a division of IBM) allocates 25 per cent of its overall performance evaluation points among its customer support staff for knowledge sharing. Other organisations have introduced bonus schemes for rewarding knowledge sharers. As we saw above, some organisations (such as DERA) use financial persuasion targeted at employees who won’t post information on a knowledge sharing system. It seems that many Western companies have to work hard to achieve the culture of support, fairness, trust and reciprocity that is required if knowledge sharing is to be embedded. Many organisations seek to identify and share best practice knowledge. Jerry Junkins, the CEO of Texas Instruments in the 1990s, said “We cannot tolerate having world-class performance right next to mediocre performance, simply because we don’t have a method to implement best practices.” (Johnson, 1997). In 1994 TI implemented TI-BEST (Texas Instruments Business Excellence Standard) programme to create databases of best-practices. In time the database contained over 500 best practice examples. However TI, with 60000 employees, recognised that knowledge sharing is a process that requires management and support. They designated around 200 staff to act as facilitators to support the process of knowledge sharing. These devoted 30 to 50% of their time making links and facilitating knowledge transfer across the organisation. They also ran company-wide knowledge ShareFairs, seminars, and established an annual “Not Invented Here, But I Did It Anyway” award. According to TI the benefits from sharing best practice accrued by 1997 were equivalent to “one free fab (semiconductor) plant” (Johnson, 1997). Similarly BP has had some success in sharing knowledge between projects. In this case the knowledge gained in bringing the Andrew oil-field onstream was transferred to the team developing the Schiehallion oil-field. The knowledge sharing process was facilitated by a member of the Andrew team becoming part of the knowledge management group, and working with the Schiehallion team. Shared learning between the projects was reported as saving BP over USD 50 million in start-up drilling costs (Skyrme 1999).


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Different organisational cultures determine rather different experiences. Knowledge sharing is vital in the World Bank, which has world-class expertise in many fields of knowledge distributed across the planet, and relevant to economic development in many countries. The World Bank has highlighted knowledge inequalities and damaging “knowledge gaps” between countries. Countries like South Korea have prospered by exploiting agricultural and technical knowledge, and countries like Ghana have not (World Bank 1998). As part of its KM programme the World Bank has created IT networks that enable field workers facing problems in one part of the world to learn from similar situations faced elsewhere (Denning, 1998). For example, in August 1998 Pakistan required urgent advice on premature failure in road surfaces, and experience in an alternative construction technology. The request was posted on the Bank’s network which has a section for transport and related queries. This elicited helpful responses with lessons learned from experience in Asia, Australia and Africa. Pakistan had a solution to the problem within days whereas previously it would have taken weeks. Moreover, this experience was written-up as a case study for further use, posted in the relevant knowledge base on the World Bank system. The example suggests that knowledge can be shared between different contexts provided the recipients can make sense of it and relate it to their own specific context.

2.7. Auditing and Exploiting Intellectual Capital The importance of intellectual capital to economic competitiveness has been explicitly recognised for over 150 years (e.g. Senior, 1836). At the level of the firm, accountants have long attempted to place a value on intangible assets under the headings “goodwill” and brands, and also to an extent to value intellectual property, especially copyrights and patents. In the early 1990s a number of companies pioneered the development of intellectual capital auditing and reporting, with a view to capturing a wider and deeper data on intellectual resources and capabilities. The Swedish financial services company Skandia developed an audit tool – the Skandia Navigator – based on the Balanced Scorecard. Other methods include the IC Index (Roos & Roos 1997) and The Intangible Assets Monitor (Sveiby 1997). Intellectual capital auditing and reporting is now used by many organisations internally as an input to strategy formulation and externally as a means of indicating organisational performance (e.g. Systematic 2002). There is also renewed focus on intangible assets spurred by notions of post-industrial economics where wealth is no longer created by production of physical goods. The classic example is Microsoft’s ownership of the de facto standard for personal computer operating systems software MS.DOS and Windows. This standard (rather than the software itself) is the intangible asset

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that by the early 1990s drove the stock market to value Microsoft as being worth more than IBM (in terms of market capitalization).

The Skandia Navigator Annually since 1994 Skandia have published an intellectual capital report as a supplement to their annual report (e.g. Skandia 1994). Skandia is attempting to account for many of those assets it believes are “hidden” in traditional accounting policies. Starting in 1989 Skandia initially developed a methodology for identifying business indicators, grouped within a range of focus areas that were considered vital for Skandia’s future. This evolved into the Skandia Navigator, which identifies five areas of focus: financial, customer, human, process, and renewal and development (Skandia, 1994). As shown in Figure 2.2, human focus is seen to interact with all major areas of concern. There is also a chronological flow from top to bottom – financial indicators focus on past performance whereas the lower indicators aim to provide a focus on the future. Within the focus areas the different divisions within Skandia identify specific IC metrics. For example, in its “customer focus” area of concern Skandia reported on “number of contracts”, “points of sale”, “number of fund managers” and “number of funds” for the years 1992–94 inclusive. For the “human focus” area of concern it reported “number of employees”, “number of managers” and “training expense per employee” (Skandia, 1994, p. 19). These have been reduced over time from what was at first a long list. Skandia believes that the data gathered on a host of elements that were usually left unmeasured “result in a more systematic description of the company’s ability and potential to transform intellectual capital into financial capital” (Skandia, 1994, p. 7).

Figure 2.2. Skandia Navigator

Financial loss

Customer focus

Human focus

Process focus

Renewal and development focus Source: Skandia


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The IC Index The IC Index (Roos & Roos 1997) is intended to show how effectively an organisation is utilizing its intellectual capital. As in the other methods, the IC Index identifies key areas of focus that are vital for the organisation. Unlike other methods, which seek a balance across different types of indicators, the IC measures are combined in order to give an index of overall performance or efficiency. Table 2.1 is an example of the indices and metrics.

Table 2.1. Examples of Indices in an IC Index Hierarchy Relationship capital index

Human capital index

●

Growth in number of relationships

●

Fulfillment of key success factors

●

Growth in trust

●

Value creation per employee

●

Customer retention

●

Training efficiency and effectiveness

●

Distribution channels productivity and quality

●

Efficiency

●

Ability to generate new business

●

Effectiveness

●

Ability to generate good products

●

Key success factors utilization

●

Growth

●

Distribution efficiency

●

Ability to improve productivity

Infrastructure capital index

Innovation capital index

Source: Skyrme, 1998, p. 68

The process of developing such an index requires negotiations to identify the appropriate areas of concern and the relevant measures associated with them. This process is key for all IC methods. The discussions around the measurement system are as important as the measures themselves, since the development of an appropriate language is crucial to identifying what is important for a particular organisation. A related approach focuses on the better management and exploitation of companies’ existing intellectual capital. The Dow Chemical Company is a pioneer in managing intellectual capital assets. Dow’s approach included classification of the knowledge within the company and an evaluation of the worth of the intellectual assets. An early initiative established an intellectual capital measurement programme aimed at understanding how well knowledge resources were being utilized within the company (Smith and Irving, 1997).

2.8. Cross-boundary Knowledge Acquisition and Integration Arguably no firm has ever been independent in knowledge terms, but it is certainly the case today that all organisations are increasingly dependent on external sources of knowledge. The complexity and pace of change in markets and technologies makes it impossible even for the largest organisations to

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cover all potential developments and to grow knowledge capabilities across all potentially relevant areas. Increasingly knowledge is accessed and shared across cultural and national boundaries as organisations and markets become international. Cross-boundary knowledge transactions also apply to boundaries within organisations, between functional specialisms and between disciplines. Much new knowledge is created outside the corporate boundary, so organisations must develop absorptive capacity (Cohen and Levinthal 1990): the capability to access and assimilate new knowledge from external sources. Knowledge interdependence creates new management challenges resulting from the risks and difficulties of knowledge transactions across boundaries. So too, the development of new products, systems and services increasingly requires the integration of knowledge from many disciplines (Pavitt 1998). The ability to share knowledge across functional and disciplinary boundaries presents particular challenges since different communities and disciplines may have little common ground for shared understandings.3 The primary cross-boundary knowledg e transactions for many organisations are with customers and suppliers, and indeed “understanding the customer” is now a mainstream (knowledge acquisition) priority. For example, office furniture manufacturer Steelcase puts much effort into understanding its customers. Not satisfied with surveys and other feedback methods, Steelcase uses video techniques to observe the users of its products at work in offices, airports and hotels. The result is award-winning furniture and modular office workstations (Skyrme 1999). Knowledge transactions within the supply chain take many forms. Whereas market transactions for bought-in discrete products and services may require little ability to acquire and share knowledge, joint R&D or new product development requires a degree of inter-penetration of organisational knowledge processes. As Cohen and Levinthal (1989) point out, one of the main reasons why firms invest in R&D is to track external developments. Adding this to R&D’s role in knowledge creation, we can see that R&D is t h e r e f o r e a n o t h e r l o n g - e s tabl i s h e d c o m po n e n t o f o rg a n i s a ti o n al management of knowledge. Such inter-penetration of organisational boundaries presents challenges. In particular, organisations have to manage the paradox of having open knowledge boundaries for new knowledge a c q u i s i t i o n w h i l s t p ro t e c t i n g w h a teve r k n ow l e d g e e n s u re s t h e i r competitiveness and survival. Formula One car racing provides an extreme example. F1 Grand Prix Engineering (pseudonym) manufactures and races Formula One cars. Continuous technological innovation is an absolute requirement. Race by race, every car that competes will have prototype features – there are always


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differences from the previous race. Innovation is primarily focused on incremental improvement based on feedback from testing and racing which provides huge amounts of data. Periodically F1 GPE or rival teams develop more radical or step-change innovations. The latter are often associated with changes in the regulations (i.e. the “formula”) or inputs from different knowledge domains, e.g. orthogonal knowledge from computing, aerospace or new materials developments, allied to creative ideas from key engineers. Individual engineering brilliance is at a great premium, and key engineers are sought after as competitively as the drivers. The quality and rapid availability of data and information, and rapid lea rn in g , are paramou nt. Kn ow le dg e sharing with tyre an d brak e manufacturers is vital. For F1 GPE these are the main suppliers apart from the engine manufacturer. Both tyres and brakes are, like the cars, in continuous development throughout a season, and often tyre technology determines the outcome of a race. Their manufacturers depend on steams of accurate data from F1 GPE. There is a mutual dependency and relationship of trust since these suppliers are also working for rival teams. This means that the tyre and brake manufacturers must have “Chinese walls” that isolate and protect the knowledge gained from different F1 teams. For their part F1 GPE have to maintain and develop their own absorptive capacity in tyre and brake technology in order to maximise the advantage they can gain from these everdeveloping components. F1 GPE has to maintain capabilities to continuously create competitive advantage which is difficult to replicate. Whereas the addition of an aerodynamic feature on a car can be seen (i.e. it is explicit) and copied by other teams, many improvements are less visible, and indeed the capability to continuously innovate and stay ahead of the opposition is in large part due to tacit processes. Such capability is hard to copy, as is illustrated by another example, that of Chaparral Steel. The CEO is happy to tour competitors through the Chaparral plant, showing them “almost everything and we will be giving away nothing because they can’t take it home with them” (Leonard 1995, p. 7). A further example illustrates the challenges of integrating knowledge from different domains. SouthTech (pseudonym) is a long established manufacturer of environmental monitoring equipment which it supplies to defence and security agencies throughout the world. The company’s core technologies are a complex integration of electronics and chemical processes. In this case we focus on the design, development and manufacture of portable chemical agent monitors. SouthTech routinely has to provide its suppliers with knowledge in order to enable them to meet its requirements, and materials supplied have to be

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further worked upon by SouthTech to achieve required performance. This means that SouthTech has to maintain levels of knowledge capabilities that encompass and even exceed those of its suppliers in their own fields. New knowledge is introduced into the company by individuals, and through the literature, through licensed technology and through technological systems, like CAD. The firm is conscious that its culture and management must encourage staff to be receptive and to accumulate the knowledge breadth that will add robustness to the company’s knowledge base. While SouthTech generally has to retain and continuously develop all the required knowledge in-house, exceptions to this have occurred. In one key project the need to grow in-house knowledge about miniaturisation was removed by substituting a modular product from elsewhere. SouthTech then only needed to know about the interfaces, but not the internal workings of the “black-box” module. As Pavitt et al (2000) note, with reference to manufacturing, the degree of interpenetration of organisational boundaries is in part a function of whether the supplied components can be modularised and the pace of change is relatively low. SouthTech is very protective of its knowledge base. However defence contracts require contractors to hand over detailed logbooks of all their development activities. SouthTech meets this requirement for data without passing on anything of value: “we don’t ship understanding. Understanding is not for sale!” This again illustrates the limitations of codified knowledge, also confirmed by a remarkable insight into a solution to the problem of systems integration in complex systems. A key individual, who we will refer to as Brown, left the firm during the product innovation process. He passed to his successors CAD drawings and 50 assembled prototype products exposing manufacturing issues. However the successor team couldn’t progress the development because even with these starting points they didn’t understand the data or the lessons from the prototypes. These required interpretation in the light of the tacit knowledge accumulated in Brown’s head. Moreover they couldn’t integrate all the complex component technologies conceptually. This task was exceptionally difficult because the component technologies spanned nuclear physics, electronics and chemistry. SouthTech were forced to re-hire Brown as a consultant. He was aware of his own ability to conceptualise the whole product, and the lack of that ability elsewhere: “I had remained in total control, and had short-circuited all the knowledge flow problems of a conventional design & manufacture company. (SouthTech) does not have the advantage of integration within one mind”. In this case the ability to integrate (i.e. manage) knowledge was concentrated in one person, rendering SouthTech highly dependent and vulnerable. A challenge for “KM” is therefore to develop distributed capabilities as well as achieving cross-boundary knowledge integration.


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2.9. Conclusions This chapter has barely scratched the surface of the practice of espoused knowledge management, let alone the real practices associated with knowledge that are not labelled “KM”. In particular we have not explored the process of knowledge creation, or the lessons learned concerning that vital area – the management of people. Even the finest data and “knowledge capture” systems cannot substitute for human knowledge: If NASA wanted to go to the moon again, it would have to start from scratch, having lost not the data, but the human expertise that took it there last time. (Brown and Duguid 2000, p. 122) What we have attempted to do is cover the main current areas of espoused KM practice, giving examples of real world activity, and drawing some of the lessons from these. The self-reported benefits from KM are legion: ●

Sharing of best practices and lessons learned led to avoidance of costly mistakes and “reinventing the wheel” (General Motors), saved millions of dollars a year (Chevron);

●

Making expertise available throughout the company using video conferencing at off-shore oil platforms minimised downtime and speededup problem solving (BP Amoco);

●

Development of learning networks improved the rate of innovation (Schlumberger);

●

Customer feedback direct into the computer network, and access to expertise throughout the organisation, lead to more innovative customer solutions (Buckman Laboratories).

(Skyrme, 1999) Though less widely reported, it is apparent that many organisations have learned through false-starts and ill-conceived KM initiatives, and a number of consistent themes have emerged. The first is the obvious point that knowledge is managed in organisations whether or not it is labelled “KM”, and indeed we should bear in mind that much formally labelled KM compares unfavourably with these informal practices. This brings us to the second point: that the introduction of knowledge as a concept poses qualitatively different questions from an information agenda. “We’re overrun with information, but we’re dying for lack of knowledge” (Strategic Planning Director of Qantas quoted in Baumard 1999, p. 133). Third (and relatedly) technology cannot deal with tacit knowledge, and should not drive any KM strategy, although it has potential primarily as a communications medium, shifting the emphasis to “connectivity” rather than “knowledge capture”. Fourth, knowledge processes are social processes, and again and again we find that organisational culture determines knowledge practices. In this regard

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many Western organisations in particular may begin from disadvantaged positions. Fifth, the tendency to focus on explicit knowledge and treat knowledge as a “thing” or commodity leads to an impoverished approach. Rather, we should see knowing is a dynamic process: “knowing is to interact with and honour the world using knowledge as a tool” (Cook and Brown 1999). These conclusions draw upon case studies, published literature and surveys of the last few years, underpinned by a conceptual framework rooted in a much longer history. However, this book contains new findings from recent knowledge management surveys conducted in selected OECD countries (Canada, Denmark, Germany), or from knowledge management questions added to existing surveys, as in the cases of France and Japan. These pilot studies have given rise to the questionnaire and guidelines in Chapter 9 and the book points forward to the next round of country experience. As the questions have been tested in different countries, there is now potential for internationally comparable insights into the rich practices of knowledge management which also transcend cultural differences.

Notes 1. The structural economic changes that drove many organisations in the late 20 th century to seek to implement knowledge management initiatives are discussed in Quintas (2001). 2. The DERA / Dstl case study draws on Thornton (2001). 3. The communication and coordination problems stemming from the divisions of knowledge across supply chains and networks of organisations are explored in Quintas (2002).


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Bibliography Baumard, P. (1999), Tacit knowledge in organisations, Sage, London. Braverman, H. (1974), Labour and Monopoly Capitalism: The Degradation of Work in the Twentieth Century, Monthly Review Press, New York. Brown, J. S. and P. Duguid (1991), “Organisational learning and communities of p r a c t i c e : t o w a r d s a u n i f i e d v i ew o f w o r k i n g , l e a r n i n g a n d organisation,”Organisation Science, Vol. 2, No. 1, pp. 40–57. Brown, J. S. and P. Duguid (2000), The Social Life of Information, Harvard Business School Press, Boston, Mass. Cohen, D. (1998), “Towards a knowledge context: report on the first annual UC Berkeley forum on knowledge and the firm,” California Management Review, Vol. 40, No. 3, pp. 22–39. Cohen, W. M. and D.A. Levinthal (1989), “Innovation and learning: two faces of R&D,” Economic Journal, Vol. 99, 569-596. Cohen, W. M. and D.A. Levinthal (1990), “Absorptive Capacity: A New Perspective on Learning and Innovation,” Administrative Science Quarterly, Vol. 35, pp. 128-152. Cook, S.D.N. and J.S. Brown (1999), “Bridging Epistemologies: The Generative Dance Between Organisational Knowledge and Organisational Knowing,” Organisation Science, Vol. 10, No. 4, pp. 381-400. Cross, R. (1998), “Managing for knowledge: managing for growth,” Knowledge Management, Vol. 1, No. 3, pp. 9–13. Davenport, T. H . (1994), “Saving IT’s soul: human-centred inform atio n management,” Harvard Business Review, March–April, pp. 119–31. Davenport, T. H. (1997), “Ten principles of knowledge management and four case studies,” Knowledge and Process Management, Vol. 4, No. 3, pp. 187–208. Denning, S. (1998), “The importance of leadership: gaining active support for a knowledge initiative,” paper presented at Knowledge Summit ’98 Conference, 56 November, London. Drucker, P. F. (1969), The Age of Discontinuity: Guidelines to Our Changing Society, Heinemann, London. Gordon, R. and D. Grant (2002), “Knowledge Management or the Management of Knowledge: Why People Interested in Knowledge Management Should Read Foucault,” in S. Clegg; P. Booth; T. Clarke and F. Sominan (eds.), Deciphering Knowledge Management, Springer-Verlag, New York. Hall, M. (2003), “Codification of knowledge in the organisation: knowledge management in the UK Post Office,” unpublished PhD Thesis, The Open University, Milton Keynes, UK. Hansen, M. T., N. Nohria and T. Tierney (1999), “What’s your strategy for managing knowledge?” Harvard Business Review, March–April, pp. 106–116. Johnson, C. (1997), “Leveraging knowledge for operational excellence,” Journal of Knowledge Management, Vol. 1, No. 1, pp. 50-55. Lave, J. and E. Wenger (1991), Situated Learning: Legitimate Peripheral Participation, Cambridge University Press, Cambridge.

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Leonard, D. (1995), Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation, Harvard Business School Press, Boston. Machlup, F. (1962), The Production and Distribution of Knowledge in the United States, Princeton University Press, Princeton, NJ. Mackay, G, (2001), “ICL’s Cafe Culture,” Knowledge Management, Vol. 4, No. 5, pp. 14-17. Murray, P. and A. Myers (1997), “The facts about knowledge,” Information Strategy, Sept., pp. 31–33. Nonaka, I. and H. Takeuchi (1995), The Knowledge-Creating Company, Oxford University Press, Oxford. Pavitt, K. (1998), “Technologies, Products and Organisation in the Innovating Firm: What Adam Smith Tells Us and Joseph Schumpeter Doesn't,” Industrial and Corporate Change, Vol 7 No 3 pp. 433-452. Pavitt, K., S. Brusoni and A. Prencipe (2000), “Knowledge Specialisation, Organisation and the Boundaries of the Firm,” BPRC Conference on Knowledge Management: Concepts and Controversies, 10-11 February, Warwick. Penrose, E. T. (1959), The Theory of the Growth of the Firm, Basil Blackwell, Oxford. Polanyi, M. (1966), The Tacit Dimension, Routledge & Kegan Paul, London. Prusak, L. (2001), “Practice and Knowledge Management,” in J. de la Mothe and D. Foray (eds.) Knowledge Management in the Innovation Process, pp. 153-158, Kluwer Academic Press, Boston, Quintas, P. (2001), “Managing knowledge in a new century,” in S. Little, P. Quintas and T. Ray (eds.) Managing Knowledge, pp. 1-14, Sage, London. Quintas, P. (2002), “Implications of the Division of Knowledge for Innovation in Networks,” in J. de la Mothe and A.N. Link (eds.) Networks, Alliances and Partnerships in the Innovation Process, pp. 135-162, Kluwer Academic Press, Boston. Quintas, P., J. Jones and A. Demaid (1999), An Introduction to Managing Knowledge, Unit 1 of Open University Business School’s Managing Knowledge programme, The Open University, Milton Keynes, UK. Reich, R. (1991), The Work of Nations: Preparing Ourselves for 21st-Century Capitalism, Simon and Schuster, London. Roos, J. & G. Roos (1997), “Valuing Intellectual Capital,” FT Mastering Management, No. 3, July-August, 6-10. Scarbrough, H. (1996), Business Process Re-design: The Knowledge Dimension, Warwick ESRC Business Processes Resource Centre, University of Warwick. Senior, N. (1836) reprinted (1971), An Outline of the Science of Political Economy, Allen and Unwin, London. Skandia (1994), Visualizing Intellectual Capital in Skandia, Supplement to Annual Report, Stockholm. Skandia (1996), Value Creating Processes, supplement to 1995 Annual Report. Skyrme, D. (1998), Measuring the value of knowledge, Business Intelligence Ltd., London.


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Skyrme, D. (1999), Knowledge Management in Practice, Unit 12 of Open University Business School’s Managing Knowledge programme, The Open University, Milton Keynes, UK. Smith, C. and R. Irving (1997), Knowledge Management, Institute of Management Foundation, Corby. Spender, J. C. (1996), “Making knowledge the basis of a dynamic theory of the firm,” Strategic Management Journal, Vol. 17, Winter Special Issue, pp. 45–62. Sveiby, K.E. (1997), “The Intangible Assets Monitor,” Journal of Human Resource Costing and Accounting, Vol. 2, No. 1, pp. 25-36. Systematic (2002), “Intellectual Capital Report,” www.systematic.dk/pdf-files/ ICReports/ICR.pdf. Taylor, F. W. (1911), Principles of Scientific Management, Harper, New York. Thornton, S. (2001), “Knowledge Management in Dstl,” presentation at Knowledge Management in Practice workshop, 12 October, The Open University, Milton Keynes,. Wenger, E. (1998), Communities of Practice, Cambridge University Press, Cambridge. Wenger, E. (2000), “Communities of practice and social learning systems,” Organisation, Vol. 7, No. 2, pp. 225–46. World Bank (1998), Knowledge for Development, The World Bank, Washington DC.

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PART II

Country Reports



PART II

Chapter 3

Are we Managing our Knowledge? The Canadian Experience by Louise Earl

This chapter introduces the findings from the Canadian pilot Survey on Knowledge Management Practices that was conducted in the fall of 2001 as part of the international initiative headed by the Organisation for Economic Co-operation and Development. While presenting detailed results from the questions on the survey, the chapter also highlights some interesting findings that suggest that the majority of firms were managing some aspect of their knowledge. Findings imply firms are employing knowledge management practices strategically to improve their competitive performance. Knowledge sharing, creation, generation and maintenance are perceived as important to a firm's productivity. Firms also found that knowledge management practices effectively improved worker skills and knowledge and suggested that more knowledge management practices would be employed due to loss of key personnel.


55

II.3.

ARE WE MANAGING OUR KNOWLEDGE? THE CANADIAN EXPERIENCE

3.1. Highlights The pilot Survey on Knowledge Management Practices was conducted in the fall of 2001 as part of an international initiative headed by the Organisation for Economic Co-operation and Development. The pilot survey accomplished two objectives. It demonstrated that the use of knowledge management practices in firms could be identified and it provided the findings described in this paper. This survey sampled firms in five sub-sectors of the North American I n d u s tr i a l C l a s s i f i c a t i o n S y s t e m : f o re s t ry a n d l o g g i n g ; ch e m i c a l manufacturing; transportation equipment manufacturing; machinery, equipment and supplies wholesaler-distributors; and management, scientific and technical consulting services. According to the data, a majority of firms in these five sub-sectors were managing some aspect of their knowledge. Nine out of 10 used at least one of 23 business practices related to knowledge management, which involves any systematic activity related to the capture and sharing of knowledge by the organisation. Not surprisingly, service industries had the highest average number of practices in use. These industries depend to a great extent upon marketing the application of the knowledge of their workers. On average, firms in all five sub-sectors used 11 knowledge management practices. This ranged from a high of 14 used by firms in management, technical and scientific consulting services, to 10 used by firms in machinery and equipment supplies wholesaler-distributors. Findings suggest that firms are employing knowledge management practices strategically to improve their competitive performance and productivity. Half the firms in the five sub-sectors reported that the critical reason they used knowledge management practices was to improve the competitive advantage of the firm. About 30% of firms said they used such practices to increase efficiency by using knowledge to improve production processes. About 23% reported that their aim was to train workers to meet strategic objectives of the firm, and another 23%, to integrate knowledge within the firm.

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II.3. ARE WE MANAGING OUR KNOWLEDGE? THE CANADIAN EXPERIENCE

Knowledge sharing, creation, generation and maintenance are perceived as important to a firm’s productivity. Almost nine out of 10 firms reported that the most effective result of using knowledge management practices was improving worker skills and knowledge. The second most effective result was increased worker efficiency and/or productivity. Firms viewed the loss of key personnel as the main trigger for implementing more knowledge management practices, followed by the loss of market share.

3.2. Introduction Today more than ever, knowledge matters. 1 New terms related to knowledge, often not clearly defined, are creeping into everyday vocabulary. There is the idea of the knowledge-based economy and knowledge-based industries (OECD, 1999). 2 We have knowledge workers. Academics study knowledge-based enterprises.3 Firms and organisations are concerned about knowledge loss (Cross and Baird, 2000; and Brown and Duguid, 2000). And business strategists advise of the need to leverage knowledge resources (Bartlett and Ghoshal, 2002; Zack, 1999; and Quinn, 1999). Knowledge has long been recognised as “power” and pundits are persuaded that this “power” intensifies when it is shared (Stehr, 2001; and de la Mothe and Foray, 2001). Understanding how and whether Canadian firms and organisations are actively applying management practices to their knowledge was a primary objective of the pilot Knowledge Management Practices Survey, 2001 (KMPS).

3.3. Survey Background/Overview The pilot Knowledge Management Practices Survey was conducted in the fall of 2001 with a sample of five sub-sectors of the North American Industrial Classification System (NAICS)(Statistics Canada, 1998): forestry and logging (NAICS 113); chemical manufacturing (NAICS 325); transportation equipment manufacturing (NAICS 336); machinery, equipment and supplies wholesalerdistributors (NAICS 417) and management, scientific and technical consulting services (NAICS 5416) (Table 3.1). The questionnaire was mailed to 407 firms of which 348 or 86% responded. Taken together these firms represent an estimated 5 245 enterprises in these five sub-sectors. (For more information on the survey, see Annex 3.3 – Methodological Notes)


57

II.3.


Table 3.1. Distribution of Weighted Sample by Sub-sector and by Firm Size Five Sub-sectors and Firm Size Sub-sectors Forestry and Logging

Distribution % 100% 11% A1

Chemical Manufacturing

9% A

Transportation Equipment Manufacturing

10% A

Machinery, Equipment and Supplies Wholesaler-Distributors

52% B

Management, Scientific and Technical Consulting Services

18% B

Workers in Canada

100%

Less than 50 workers

82% A

50 - 249 workers

13% A

250 - 499 workers

2% A

500 - 1,999 workers

2% A

2,000 and more workers

1% A

1. Data quality indicators are described in Annex 3.3 – Methodological Notes. Source: Statistics Canada

3.4. Definition of Knowledge Management Many experts from different disciplines have defined knowledge management in many ways (Earl and Scott, 1999). For the purpose of the pilot Knowledge Management Practices Survey, “knowledge management involves any systematic activity related to the capture and sharing of knowledge by the organisation.” Respondents indicated whether they used or planned to use 23 business practices related to knowledge management. And the vast majority (93%) of firms or organisations is using at least one of the knowledge management practices listed.

Non-Users of Knowledge Management Practices Non-users of the knowledge management practices comprised a very small but important component of the five sub-sectors at 7% (See Annex 3.1 for more information on non-users). Firms or organisations of less than 50 workers represented the majority (88% and 59% for firms with less than 20 workers) of non-users of knowledge management practices. This result is in keeping with Larry Prusak’s work on knowledge management (Prusak, 2001; Cohen and Prusak, 2001; Davenport and Prusak, 1998; and Lesser and Prusak, 2000). Prusak commented that the need for knowledge management practices rose with firm size and that those firms with less than 250 employees were less likely to employ these business practices.4 The Knowledge Management Practices Survey’s results suggest that for Canada, firms begin to employ more knowledge management practices when they attain at least 100 workers (Figure 3.1).

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Figure 3.1 Average Number or Knowledge Management Practices in Use by Employment Size Group Employment size 2 000 and more 500 to 1 999 250 to 499 100 to 249 50 to 99 1 to 49 0

Source:

5

10

15

20 Average number

Statistics Canada

3.5. Knowledge Management Practices in Use For the purposes of this paper, users of knowledge management are defined as those firms that indicated they used at least one knowledge management practice from the list shown in Table 3.2. The sub-sector that had the highest average number of practices in use was not surprisingly in the services sector. (See Annex 3.2 – Definitions) Firms in services depend to a great extent upon marketing the application of the knowledge of their workers. On average, management, technical and scientific consulting services firms used 14 of the knowledge management practices. Machinery and equipment supplies wholesaler-distributors had the lowest average number of practices in place at 10. Overall the average number of knowledge management practices in use was 11 for the five sub-sectors.


59

II.3.


Table 3.2. Knowledge Management Practices in Use and the Proportion of them that were Recently Adopted – Users of Knowledge Management Practices In Use %

Per cent of the Practices in Use Since 1999

Knowledge management practices were a responsibility of managers and executives

94% A

13% B

Knowledge management practices were explicit criteria for assessing worker performance

35% B

27% C

Knowledge management practices were a responsibility of non-management workers

34% B

21% C

Knowledge management practices were a responsibility of the knowledge officer or knowledge management unit

22% B

25% C

Firm captured and used knowledge obtained from other industry sources such as industrial associations, competitors, clients and suppliers

92% A

9% B

Firm captured and used knowledge obtained from public research institutions including universities and government laboratories

Knowledge Management Practices Leadership

Knowledge Capture and Acquisition

43% C

13% C

Firm dedicated resources to detecting and obtaining external knowledge and communicating it within the firm

43% C

18% C

Firm encouraged workers to participate in project teams with external experts

41% B

25% C

Training and Mentoring Firm encouraged experienced workers to transfer their knowledge to new or less experienced workers

82% C

9% B

Firm provided informal training related to knowledge management

81% B

17% B

Firm encouraged workers to continue their education by reimbursing tuition fees for successfully completed work-related courses

63% C

4% B

Firm offered off-site training to workers in order to keep skills current

51% C

20% B

Firm provided formal training related to knowledge management practices

32% B

16% B

Firm used formal mentoring practices, including apprenticeships

28% B

43% C

Used partnerships or strategic alliances to acquire knowledge

68% B

20% C

Policies or programs intended to improve worker retention

66% B

24% C

Values system or culture intended to promote knowledge sharing

59% C

31% C

Written knowledge management policy or strategy

36% C

39% C

Workers shared knowledge by preparing written documentation such as lessons learned, training manuals, good work practices, articles for publication, etc. (organisational memory)

44% B

24% C

Workers shared knowledge by regularly updating databases of good work practices, lessons learned or listings of experts

41% B

34% C

Workers shared knowledge in collaborative work by project teams that are physically separated (“virtual teams”)

17% B

26% C

Knowledge sharing was rewarded with monetary incentives

32% B

35% C

Knowledge sharing was rewarded with non-monetary incentives

36% B

30% C

Policies and Strategies

Communications

Incentives

Note: Users are defined as having used at least one of the knowledge management practices listed. The percentage of practices adopted since 1999 is calculated by dividing the total of practices in use since 1999 by total in use. Source: Statistics Canada

60



The Most Popular Knowledge Management Practices The users of knowledge management practices in the five sub-sectors indicated that almost every firm (94% A) looked to its managers and executives to be responsible for providing knowledge management leadership (see Table 3.2). For just 13% (B) of managers and executives this was a recently adopted practice. Firms also showed their marked inclination towards capturing and using knowledge obtained from other industrial sources. 5 Again this popular practice that could include business environment scanning and market research was only recently adopted by 9% (B) of firms using the practice.

Table 3.3. Percentage of Firms by Sub-sector that were Capturing and Using Knowledge Obtained from Other Industry Sources – Users of Knowledge Management Practices Sub-sector

In Use %


100% A


96% A


89% A

Forestry and Logging

81% A


73% A

Note: Users are defined as having used at least one knowledge management practice. Source: Statistics Canada

Every firm in management, scientific and technical consulting services using at least one knowledge management practice actively captured and used knowledge obtained from other industry sources such as industrial associations, competitors, clients and suppliers (Table 3.3).6 Transportation equipment manufacturing firms were the least likely to employ this knowledge management practice at 73% (A). The two next most popular knowledge management practices in use fell under training and mentoring. This section of practices indicates how firms develop, transfer and retain the knowledge of their workers.7 Training and mentoring practices included formal and informal training that encouraged the development of new knowledge or skills in workers as well as the transfer of work experiences between new and experienced workers (Dixon, 2000; Cross and Israelit, 2000; and Baird, Deacon and Holland, 2000). While some of these practices, such as apprenticeships, have been used for hundreds of years, their continued use emphasises the importance of transferring and sharing knowledge in the workplace. Not all workplace skills can be put down in writing (codified) and distributed through documentation (Denning, 2001). Some skills and knowledge are shared and transferred through practical application or "doing". Four-fifths of firms encouraged experienced workers to


61

II.3.


transfer their knowledge to new or less experienced workers. This is clearly a long-standing practice since only 9% (B) of firms adopted it after 1999. Providing informal training on knowledge management practices was also widespread – four-fifths of firms reported using it. The higher proportion of recent adopters of this practice (17% B) perhaps indicates a recent rising awareness of knowledge management practices by firms in the five sub-sectors. Machinery, equipment and supplies wholesaler-distributors firms were the least likely to employ this knowledge management practice at 72% (C) (Table 3.4).

Table 3.4. Percentage of Firms by Sub-sector that Encouraged Experienced Workers to Transfer Their Knowledge to New or Less Experienced Workers – Users of Knowledge Management Practices Sub-sector

In Use %


98% A


96% B


92% A


88% A


72% C

Note: Users are defined as having used at least one knowledge management practice. Source: Statistics Canada

The Least Used Knowledge Management Practices Interestingly, collaborative work on project teams that were physically separated (“virtual teams”) was the least popular knowledge management practice with under one fifth of firms using this practice to share knowledge. For about one quarter of the firms using virtual teams, this was a recent practice. The second least popular practice for knowledge sharing and transfer were formal mentoring programs including apprenticeships. The low popularity of this practice is striking due to the long-standing practice of using apprenticeships in some industries and trades and perhaps in this instance reflects the sub-sectors sampled. For instance, one half of forestry and logging firms used this practice as opposed to one out of five firms in the machinery and equipment supplies wholesaler-distributors sub-sector. Also, mentoring has become much more noticeable in the business press recently and this may have influenced the higher recent adoption rate for mentoring practices – 43% (C).8 (Stone, 1999; Shea, 1999; and Bell, 1996, have all written manuals on mentoring.)

Firms Are Turning to Communications Practices Having and requiring good documentation and making these materials available is recognised as being vital to maintaining high quality work

62



standards (Field, 2001). Accessing the lessons learned by others as well as good work practices helps to prevent firms from repeating errors while allowing new project teams to build on the work of their predecessors (Dixon, 2000; and Baird, Deacon and Holland, 2000). As the results indicate, in 44% (B) of firms workers prepared written documentation such as lessons learned, training manuals, and good work practices. These activities taken together assist firms in developing their organisational memory. For almost one quarter of firms that are developing their organisational memories through documentation (or codification of knowledge) this was a new practice. And one-tenth of users not already codifying their knowledge indicated that they intended to put the practice in place in the next 24 months. Updating databases of good work practices, lessons learned or listings of experts is another method of creating organisational memory, usually electronically. Over 40% of users indicated their use of updating databases. Suggesting a growing interest in this type of practice, for over one third of the firms that updated databases of good work practices recently introduced this practice.

Knowledge Acquisition – Always Vital Sharing knowledge and information generated from work within the firm is one method that firms use to manage their knowledge. Another important aspect of managing knowledge is acquiring it from outside of the firm. This can be done through hiring of new employees, an aspect of knowledge management that was not covered by the Knowledge Management Practices Survey as well as by capturing knowledge generated elsewhere. Obtaining knowledge from public research institutions, dedicating resources to obtaining external knowledge and encouraging workers to participate in project teams with external experts were less frequently used methods of knowledge acquisition. As opposed to the nine tenths (using at least one knowledge management practice) of firms that regularly captured knowledge from other industry sources, about four tenths obtained knowledge from public research institutions. And this was a new practice for 13% (C) of firms looking to public research institutions for knowledge. The findings are quite similar for firms that dedicated resources to obtaining external knowledge with 43% (C) participating and 18% (C) of the firms participating indicating that they recently introduced the practice.

Culture Backed by Policies Important to Knowledge Management Firms in the five sub-sectors generally believed that their corporate cultures or value systems encouraged knowledge sharing and two-thirds had policies or programs in place that were intended to improve worker retention. Churn rates for firms – employee turnover – are topics of many investigations


63

II.3.


(Sunter, 2001; Bowlby, 2001; Picot and Dupuy, 1996; and Picot, Heisz and Nakamura, 2001). Retirement and a seasonal business cycle are some of the natural causes of employee turnover. And for the most part, firms know and plan for their business cycles and employee retirement (Hamdani, 1996). In a hot market in which workers with specialised skills are in high demand, churn rates can sky rocket (Catt and Scudamore, 1997; and Kaye and Jordon-Evans, 1999).9 The results of the Knowledge Management Practices Survey indicate that firms in the five sub-sectors are anticipating the need to formally plan the retention of employees. Worker retention policies could in part reflect the costs to firms associated with new hires ranging from providing basic orientation programs to the time and productivity lost while employees learn how to do their new tasks efficiently. Using partnerships or strategic alliances specifically to acquire knowledge was a fairly common knowledge management practice for firms with almost 70% participating. Of interest, this high rate may reflect the importance that this strategy played with small firms of less than 50 employees.

Leadership from Management and Executives and the Lack of Rewards As already stated, in most firms, knowledge management practices were a responsibility of managers and executives. However, a small percentage of firms had a knowledge management unit or knowledge officer with responsibility for knowledge management practices. About one third of the firms explicitly assessed worker participation in knowledge management as part of their performance reviews. The firms in the five sub-sectors also very rarely gave monetary or nonmonetary incentives as rewards for knowledge sharing. The lack of rewards combined with the low level of assessment as part of performance reviews could perhaps indicate that knowledge management practices including knowledge sharing are expected work behaviours and therefore do not require formal recognition. Finally, a low proportion of the firms had adopted written knowledge management policies or strategies.

3.6. Reasons Why Knowledge Management Practices Were Adopted This section looks at the importance users of at least one knowledge management practice attribute to reasons for using knowledge management practices (Table 3.5).

64



Table 3.5. Reasons for Using Knowledge Management Practices Reasons Knowledge Management Practices Were Used

Very effective

Effective

Very effective or effective Sub-total

Somewhat or not at all important Sub-total

Improve competitive advantage of firm

50% C

43% C

93% A

7% A

Train workers to meet strategic objectives of the firm

23% B

58% C

81% C

19% C

Improve worker retention

13% B

61% B

74% B

26% B

Help integrate knowledge within the firm

23% B

49% C

72% C

28% C

Increase worker acceptance of innovations

10% B

61% C

71% C

29% C

Increase efficiency by using knowledge to improve production processes

30% B

39% C

69% C

31% C

Identify and/or protect strategic knowledge present in firm

18% B

47% C

65% C

35% C

Promote sharing or transferring knowledge with clients or customers

20% B

41% C

61% C

39% C

Improve sharing or transferring of knowledge with partners in strategic alliances, joint ventures or consortia

13% B

45% C

57% C

43% C

Protect the firm from loss of knowledge due to workers’ departures

17% B

36% C

53% C

47% C

Improve the capture and use of knowledge from sources outside the firm

14% B

37% B

51% B

49% B

Ease collaborative work of project or teams that are physically separated (i.e. different work sites)

7% B

20% B

27% B

73% B

Note: Percentage is calculated for knowledge management practitioners (used at least one knowledge management practice). Source: Statistics Canada

Improving Competitive Advantage Critical to Half of the Firms As expected, half of the firms asserted that improving the competitive advantage of the firm to be a critical reason to use knowledge management practices; in fact less than 10% of the firms found this reason of little importance. Increasing efficiency by using knowledge to improve production processes placed second as a critical reason to use knowledge management practices at 30% (B). It was followed closely by training workers to meet strategic objectives of the firm (23% B) and integrating knowledge within the firm (23% B). These findings suggest that firms are employing knowledge manag ement practices strategically to im prove th eir competitive performance and productivity.10


65

II.3.


Firms Did Not Employ Knowledge Management Practices to Ease Work of Virtual Teams The high proportion of firms that viewed easing collaborative work of projects or teams that are physically separated as unimportant is striking in relation to the other reasons listed. However, this latter finding is in keeping with the low proportions of firms that encouraged workers to participate in virtual teams or on project teams with external experts. Large firms with more than 2,000 workers in Canada were more likely (72% B) to find this reason of importance than small firms of less than 50 workers (21% B) showing the importance of firm size to working in virtual teams. What is interesting is that although almost every firm captured and used knowledge obtained from other industry sources and about four tenths captured knowledge obtained from other external sources, only half felt that it was important to improve their ability to capture and use of knowledge from external sources. This may suggest that some of the knowledge capturing and acquisition practices are quite entrenched in the firms and as such not viewed as candidates for improvement. This is probably true for firms that indicated they regularly captured and used knowledge obtained from other industry sources such as industrial associations, competitors, clients and suppliers. About half of these firms indicated that improving knowledge capture and use was important or critical. For firms capturing and using knowledge obtained from public research institutions, however, the improvement of the capture and use of knowledge from sources outside the organisation was critical to 29% (C) and important to 39% (C). And those firms that dedicated resources to knowledge acquisition most found improving external knowledge capture and use to be of importance; in fact for 29% (C) it was critical and 51% (C) important.

Firms of at least 50 Workers Found Increasing Efficiency Most Important to using Knowledge Management Practices Firms with at least 50 workers in Canada rated increasing efficiency by using knowledge to improve production processes as the most important or critical reason for using their sets of knowledge management practices. Small firms of less than 50 workers, however, rated improving their competitive advantage as the most important or critical reason for using their sets of knowledge management practices (93% A) with increasing efficiency rating seventh at 64 % (C). Of interest, three of the five sub-sectors rated increasing efficiency as their most important reason for employing their sets of knowledge management practices. For machinery equipment and supplies wholesaler distributors rated improving the competitive advantage of their firms as the most important or critical reason to use knowledge management practices (97% A) with just half finding that improving efficiency was important. On the other hand, firms in management, scientific and technical consulting services

66



found that integrating knowledge within the firm was the most important or critical reason to use knowledge management practices (99% A) with increasing efficiency tying with three other practices for third at 93% (B).11

3.7. Knowledge Management Practices Most Effective for Improving Workers’ Skills and Knowledge Knowledge management practices were considered most effective for two human resources-oriented results. The most effective result of using knowledge management practices was improving worker skills and knowledge – 88% (A) (Table 3.6). The second most effective result was increased worker efficiency and / or productivity. These results suggest that knowledge sharing, creation, generation and maintenance are perceived as important to firm productivity. Knowledge management practices were also very effective or effective at creating a client-oriented firm. Almost four out of five firms indicated that the knowledge management practices they used were very effective or effective at increasing the adaptation of products or services to client requirements as well as improving client relations.

Table 3.6. Effectiveness of Results of Using Knowledge Management Practices

Using knowledge management practices:

Very Effective and Effective – Sub-total

Somewhat Effective and Not at all Effective – Sub-total

Improved skills and knowledge of workers

88% A

12% A

Improved worker efficiency and / or productivity

80% B

20% B

Increased the adaptation of products or services to client requirements

78% B

22% B

Improved client or customer relations

76% B

24% B

Increased knowledge sharing horizontally (across departments, function or business units)

65% C

35% C

Helped add new products or services

64% B

37% B

Improved the involvement of workers in the workplace activities

63% C

36% C

Increased knowledge sharing vertically (up the organisational hierarchy)

52% C

48% C

Improved corporate or organisational memory

51% C

48% C

Increased the ability to capture knowledge from other business enterprises, industrial associations, technical literature, etc.

49% C

50% C

Increased flexibility in production and innovation

44% B

55% B

Prevented duplicate research and development

34% C

65% C

Increased the number of markets (more geographic locations)

33% C

68% C

Increased the ability to capture knowledge from public research institutions including universities and government laboratories

22% B

77% B

Note: Percentage is calculated for knowledge management practitioners (used at least one knowledge management practice). Source: Statistics Canada


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Knowledge Management Practices Not Very Effective for Increasing Capture of Knowledge from Public Research Institutions Overall, almost four out of five firms indicated that knowledge management practices were not very effective at increasing the capture of knowledge from public research institutions. This result, however, indicates the low propensity of the firms to capture and use knowledge from public research institutions. When the results are viewed for firms actually capturing knowledge from public research institutions, then the picture changes with 46% (C) of these firms finding the practice either very effective or effective. This indicates that firms could answer these questions for their own set of practices. This could also hold true for the low level of effectiveness for preventing duplicate research and development. Some firms may have responded “not at all effective” due to the fact that they do not undertake research and development. Finally, while knowledge management practices were considered effective for client-orientation, they were not considered effective for increasing markets by adding more geographic locations. Again this may reflect the nature of the sub-sectors sampled, that firms served local markets or that the firms had not expanded their number of markets.

Large Firms Found that Knowledge Management Practices Led to Increased Horizontal Knowledge Sharing, Improved Worker Efficiency and Skills In Canada, firms in the five sub-sectors with more than 2 000 workers using knowledge management practices found that these practices were effective or very effective at increasing horizontal knowledge sharing, improving worker efficiency and improving workers’ skills and knowledge (all rated first at 87% A). Adding new products and services and increasing flexibility in production and innovation ranked second for large firms of more than 2 000 workers (both at 81% B). The high ranking for horizontal sharing may indicate the perceived need for this type of practice in large firms as opposed to small firms (less than 50 workers) – 63 % (C) that indicated they found their set of knowledge management practices were effective or very effective at increasing knowledge sharing horizontally. Small firms on the other hand rated improved skills and knowledge of workers as the most effective result at 92 % (B). And across the sub-sectors improving workers’ skills and knowledge rated first, ranging from a high of 96% (C) for firms in management, scientific and technical consulting to a low of 71% (A) in the logging and forestry sub-sector finding this practice effective or very effective.

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Executive Management Teams Responsible for Knowledge Management in Firms As already noted almost every firm in the five sub-sectors looked to its managers and executives for knowledge management leadership (see Table 3.2). And, just over two-thirds of the firms also ascribed the overall direct responsibility for knowledge management practices in place in the firm to their executive management teams. While the executive management team had the responsibility for knowledge management, a very low proportion of firms indicated that they measured the effectiveness of the their firm’s knowledge management practices. Management (95% A) was also almost always a source that triggered the introduction of the set of knowledge management practices in place in the firms. Other important sources for the knowledge management practices in place were suppliers (50% B) and customers or clients (42% B). One third of firms used strategic partners (33% C) and competitors (34% C) as sources of knowledge management practices. These findings are in keeping with the low usage rate of capturing and acquiring knowledge from external sources such as public research institutions.

3.8. One Quarter of Firms Had Dedicated Budgets for Knowledge Management Just one quarter of firms using knowledge management practices had dedicated budgets or spending for these practices. Firms that did not have budgets indicated that they did not expect to have dedicated budgets or spending within the next 24 months. These findings are in keeping with low proportion of firms that indicated they had knowledge management units or officers and the high proportion of firms that looked to management and executives for leadership and for the ultimate responsibility for the knowledge management practices in place. Obviously the practices in place in the firms had to be funded from other budgets that could include human resources, marketing and information communications technology. This could help explain why dedicated spending on knowledge management practices increased with firm size (Figure 3.2).

Almost No Resistance Recorded to the Implementation of Knowledge Management Again, the firms indicated that they encountered very little resistance to the implementation of their sets of knowledge management practices. This result could in part indicate that resistance to implementation of knowledge management practices was not an issue for the firms in the five sub-sectors. In the very few firms that experienced resistance, the group most likely to resist were non-management workers and department was production.


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Figure 3.2. Proportion of Firms with Dedicated Spending or Budgets for Knowledge Management Practices by Worker Size Group – Users of Knowledge Management Practices In Canada 2 000 and more workers

500-1 999 workers

250-499 workers

50-249 workers

Less than 50 workers 0 Source:

10

20

30

40

50

60 %

Statistics Canada

Loss of Key Personnel Would Trigger Firms to Use More Knowledge Management Practices Firms viewed the loss of key personnel as the main trigger for implementing or implementing more knowledge management practices. This is not surprising given the fact that three-quarters of the firms indicated that the reason they had implemented knowledge management practices was to improve worker retention. However, just one-half indicated that the reason they used knowledge management was to protect the firm from loss of knowledge due to workers’ departures. This seeming contradiction could indicate that the firms surveyed had not experienced loss of workers but were prepared to plan for such a contingency. Losing market share placed second followed by difficulties in capturing workers’ undocumented knowledge (know-how) as triggers for implementing more knowledge management practices (Table 3.7). The importance given to these triggers may indicate that firms were prepared to put into place mechanisms to control knowledge loss and therefore to protect themselves competitively.

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Table 3.7. Incentives to Implement Knowledge Management Practices

Incentives to Implement Knowledge Management Practices

Total Response

Users of Knowledge Management Practices

Loss of key personnel and their knowledge

77% B

79% B

Loss of market share

57% B

61% B

Difficulty in capturing workers’ undocumented knowledge (know-how)

38% B

40% B

Information overload problems with the firm or organisation

32% B

34% B

Use of knowledge management tools or practices by competitors

27% B

29% B

Difficulties in incorporating external knowledge

13% B

13% B

Source: Statistics Canada

Of interest, firms of different sizes that used at least one knowledge management practice rated the incentives to use knowledge management practices differently. For firms of less than 250 workers, loss of key personnel was stated as a reason to introduce new or more knowledge management practices by four-fifths of firms. And this reason in terms of popularity by far out-stripped the other reasons. However, for firms of 250 and more workers, loss of key personnel while still rating as a most important reason for introducing new or more knowledge management practices, clustered much more closely to two other reasons: loss of market share and difficulty in capturing workers’ undocumented knowledge (know-how) (Table 3.8).

Table 3.8. Selected Reasons to Use More or to Implement Knowledge Management Practices by Firm Size – Users of Knowledge Management Practices Loss of key personnel and their knowledge

Loss of Market Share

Difficulty in Capturing Workers’ Undocumented Knowledge (know-how)

Less than 50 workers

79% B

64% C

35% C

50-249 workers

83% B

44% C

56% C

250-499 workers

57% A

59% A

59% A

Users of knowledge management practices Worker Size Group

500-1,999 workers

72% A

43% A

72% A

2,000 and more workers

59% B

49% B

42% B

Note: all size groups reflect workers in Canada only. Source: Statistics Canada

T he ord e ring of reasons to introduce n ew or mo re know le dg e management practices was similar across the five sub-sectors (Table 3.9). However, firms in the machinery, equipment and supplies wholesalerdistributor sub-sector showed a higher tendency to cite loss of key personnel and loss of market share as reasons to introduce knowledge management


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practices than firms in the other sub-sectors. While some firms in forestry and logging expressed concern over the economic viability of their sector, loss of market share was considered by less than one-third a reason to introduce knowledge management. This suggests that these firms may have decided to look to other devices to protect their market shares.

Table 3.9. Selected Reasons to Use More or to Implement Knowledge Management Practices by Sub-sector – Users of Knowledge Management Practices Loss of key personnel and their knowledge

Loss of Market Share

Difficulty in Capturing Workers’ Undocumented Knowledge (know-how)


88% C

77% C

29% C


69% A

29% A

44% A


68% A

52% A

46 %A

64% A

54% A

57% A

Users of knowledge management practices Sub-Sector

Chemical Manufacturing Source: Statistics Canada

3.9. Knowledge Management – Important Business Practices The results of this pilot Knowledge Management Practices Survey indicate that most firms are managing some aspect of their knowledge. At present it appears that firms are more actively managing the transfer and sharing of knowledge within the firm and external knowledge that could directly bear on their markets. Knowledge management practices are seen as important tools in improving firms’ competitive advantage and as a manner to unite workers in the goals of firms’ strategic objectives. In fact, the majority of reasons found to be most important to the firms show a slant towards internalising knowledge and protecting the knowledge in place. Very few of the practices in use or the reasons or results of using the knowledge management practices indicated a strong willingness on the part of firms to share their knowledge with competitors or between work-sites. It must be taken into account that not all firms surveyed would have multiple work-sites so creating virtual teams or easing collaborative work between projects that were physically separated may not have been applicable. However, horizontal sharing of knowledge ranked within the top four results of using knowledge management practices for firms. Firms are adopting knowledge management practices. Knowledge obviously matters to these firms. Firms’ strengths appear to be internalising their knowledge and their weakness may be not looking outside for sources of

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knowledge and expertise. The results of the Knowledge Management Practices Survey indicate that firms in different industries and of different employment size groups manage their knowledge resources in differently. Twenty years ago, similar results were shown for the adoption of advanced technologies. Now it is important to know more about how those technologies are being used, especially the information communication technologies (ICTs). Knowledge management practices are a significant application with policy implications and both economic and social impacts. This is a step towards understanding better how and why firms are using selected management practices to do better what they do.

Acknowledgements. This report provides data from the first release of the pilot Knowledge Management Practices Survey, 2001. Canada owes the success of its statistical system to a long-standing partnership between Statistics Canada, the citizens of Canada, its businesses, governments and other institutions. Accurate and timely statistical information could not be produced without their continued cooperation and goodwill. The publication of this report was made possible through the contribution of many people, first and foremost amongst whom are our respondents. The members of the working group on Knowledge Management Surveys in the Private Sector, especially the Centre for Educational Research and Innovation at the Organisation for Economic Cooperation and Development, Wenche Strømsnes, Center for Ledelse (Copenhagen), Jakob Edler, Fraunhofer Institute for Systems and Innovation Research (Karlsruhe), and Larry Prusak, Institute for Knowledge Management (Boston) all made immeasurable contributions to the development of the survey questionnaire. The following people at Statistics Canada freely gave their time and expertise to the success of the survey: Fred Gault, Michael Bordt, Iain McKellar, Yves Morin, Brian Nemes, Claude Beaudoin, Joel D’aoust, Linda Gorman, and Mary-Ann ClarkeWilkinson. This report would not have been possible without the assistance of Guy Sabourin, Adele St.Pierre, Al Short, Nicholas Lavigne, John Flanders and Claire Racine-Lebel. Finally, the constant assistance and encouragement of Dominique Foray and Fred Gault made working on this project a pleasure.


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Notes 1. Canada’s Innovation Strategy, 2002, has two major texts: Knowledge Matters: Skills and Learning for Canadians and Achieving Excellence: Investing in People, Knowledge and Opportunity. The latter “recognises the need to consider knowledge as a strategic national asset. It focuses on how to strengthen our science and research capacity and on how to ensure that this knowledge contributes to building an innovative economy that benefits all Canadians.” The former “recognises that people are a country’s greatest resource in today’s global knowledge-based economy.” (abstracts) 2. One direction that the Working Party on Statistics of the Committee on Industry and Business Environment, Organisation for Economic Co-operation and Development is taking is to study knowledge-based industries. 3. For example: the Queen’s School for Business has a Centre for studying Knowledge-Based Enterprises. The Conference Board of Canada has annual conferences on knowledge management. Recently Federated Press announced its three-day conference on knowledge management in government. And the fifth World Congress on Intellectual Capital was hosted by McMaster Business School and the Centre for Management of Innovation and New Technology Research in Hamilton in January 2002. At this conference topics such as intellectual capital, knowledge management, innovation, organisational learning, and knowledge assets were discussed. 4. Notes taken from conversations with Larry Prusak, February 2001. 5. W. Cohen and R. Levinthal (2000) argued that “the ability of a firm to recognize the value of new, external information, assimilate it, and apply it to commercial ends is critical to its innovative capabilities.” This ability they labelled its absorptive capacity. (p. 39) There is an entire body of work on organisational learning and absorptive capacity that relates directly to acquiring, capturing and using knowledge from sources outside of firms. 6. R. Miller (2001) in “Bringing Tradeshow Knowledge to the Desktop” provided a case study about integrating customer queries and concerns from trade shows into work processes at Uniqema. He concluded that this process was applying “business intelligence in real time” (p. 33). 7. S. Brelade and C. Harman (2001) discussed in depth the role of human resource departments in knowledge management. They stated “it’s only through the acquisition of knowledge by individuals and their willingness to apply it for the benefit of the organisation that competitive advantage and service excellence can be achieved.” (p. 30) For them, human resources needed to play an active role in implementing rewards and recognition strategies for knowledge sharing, designing employee retention, recruitment and succession plans, developing training programs oriented towards knowledge management and in general understanding the role of knowledge in the organisational culture. 8. Victor Newman (2002) discussed the role that retired employees played in Pfizer’s knowledge transfer and retention plans. Retired employees are invited to return to share their experiences and knowledge with current incumbents thus ensuring that less knowledge is let “walk out the door” (p. 17). Knowledge transfer mechanisms in place at Pfizer are intended to “help someone become competent in the shortest period of time by concentrating on the most relevant areas of knowledge.” (p. 15)

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9.

The Knowledge Management Review Vol. 4 Issue 6 addresses the question of knowledge retention from many angles. Charles Seeley’s (2002) “Knowledge Preservation in Turbulent Times” as well as the section “Briefings: Facing the Reality of Knowledge Attrition” discuss knowledge retention techniques firms are using. These techniques include: “alumni” programs, “exit interviews”, and retention plans for the highly mobile younger workers sometimes known as “free agents” with their “my way perspective” and the middle-aged “balance careerists” for whom work-life balance is a priority. Understanding these human resource issues are all important to ensuring the competitive well being of firms as knowledge leakage is costly.

10. The Survey of Innovation 1999 gave firms the opportunity of rating objectives of their innovations. Four of these objectives related to productivity. Of the four objectives, 63% (C) logging firms found increasing production capacity of moderately or high importance; for reducing labour costs it was 55% (B); and reducing production time for 51% (C) and finally 47% (B) for improving production flexibility. 11. For management, scientific and technical consulting services firms the order of reasons using knowledge management practices was: 1. Integrating knowledge within the firm or organisation (99%B); 2. Improving the competitive advantage of the firm (96% B); 3. Improving the capture and use of knowledge from sources outside the firm or organisation (93% B); 4. Training workers to meet strategic objectives of the firm (93% C); 5. and Increasing efficiency by using knowledge to improve production processes (93% B).


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Annex 3.1.

Non-Users of Knowledge Management Practices Non-users – forestry and logging comprised one-third The forestry and logging sub-sector had by far the largest proportion of nonusers of knowledge management practices at one-fifth of the firms in the industry. In fact these firms comprised over one-third of all of the non-users. In the fall of 2001 the softwood logging industry was pre-occupied with the softwood lumber dispute with the United States. In fact, one respondent noted: “We are in the forest industry. Does not apply to us. Get us back to work.” According to the Survey of Innovation 1999, about four out of ten logging firms were innovators.1 Innovators were defined as firms that introduced new or significantly improved products or processes from 1997 to 1999 (see Annex 3.2 – Definitions). Just over one third of logging firms introduced new processes. While these rates are in keeping with the results from the five-selected natural resource sub-sectors, they lag those of the manufacturing sub-sectors. In fact, four out of five manufacturing firms were innovators with two thirds of manufacturers introducing new or significantly improved processes (Table A3.1.1). The lower process innovation rate of the logging industry suggests that this industry might also be less likely to introduce new management practices.

Table A3.1.1. Percent of Innovative Firms during the Period 1997-99, Survey of Innovation 1999 Selected Sub-sectors

Innovators

Product Innovators

Process Innovators

Logging

41% B

22% B

35% B

Coal Mining

50% A

33% B

33% B

Metal Ore Mining

47% B

21% A

47% B

Non-Metallic Mineral Mining

42% B

32% B

33% B

Electric Power Generation, Transmission and Distribution Manufacturing (Total) Source: Statistics Canada

31% B

23% B

19% B

80% A

68% A

66% A

Firms in forestry, fishing and hunting sector also recorded lower than average rates of organisational and technological change between 1998 and 2000 (see Annex 3.2 – Definitions). The average rate of organisational change for the private sector was 38% and 44% for technological change. Firms in the other

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sectors recorded higher rates of change for both organisational and technological change (Table A3.1.2). The lower than average introduction of organisational change rate for the forestry, fishing and hunting sector together with the low innovation rate for logging to some extent confirms the suggestion that the forestry and logging sub-sector may not introduce new management practices.

Table A3.1.2. Percentage of Firms Introducing Organisational and Technological Change, Selected Sectors, 1998-2000 (Survey of Electronic Commerce and Technology, 2000) Sectors

Private Sector Forestry, Fishing and Hunting

Organisational Change

Technological Change

% of Firms

Reliability*

% of Firms

38%

B

44%

Reliability* B

23%

C

27%

C

Manufacturing

50%

B

51%

B

Wholesale Trade

46%

C

45%

C

Professional, Scientific and Technical Services

40%

B

59%

B

* For an explanation of the reliability codes see: Annex 1 in Earl “Innovation and Change in the Public Sector: A Seeming Oxymoron” Statistics Canada, Catalogue No. 88F0006XIE02001. Source: Statistics Canada

Comments about the survey from small firms Comments from some small firms indicated that the Knowledge Management Practices Survey was not pertinent to them. These examples are all from firms of less than 50 employees: “We are a very small with 6 office staff. All scalers (a job in the forestry) work on their own.” “Sending this survey to a company of our size is a waste of everyone’s time” (20-49 workers). “Better off to leave surveys to bigger companies” (1-19 workers). And “Nous sommes juste une petite enterprise avec cinq personnes au bureau et vingt personnes dans le niveau du production: cela ne s’applique pas à notre entreprise on est trop petit” (20-49 workers). Finally, “We are a very small family-owned and operated business. Formal policies and procedures do not apply” (1-19 workers).2

Notes of the Annex 1. For more information from the Survey of Innovation 1999 results see “Innovation in Canadian Manufacturing: National Estimates”, June 2001 by Susan Schaan and Frances Anderson (catalogue no. 88F006XIE No. 10). 2. Schuetze (2001) commented that knowledge management in small firms, while important, these firms may not understand the term and concepts. He suggested that “for these firms knowledge management presents problems of another kind, in particular finding relevant information and know-how from outside the firm, and absorbing and applying it to the firm’s business” (p. 98). The Knowledge Management Practices Survey specifically addressed some of these issues by including formal and informal practices as well as targeting firms with at least 10 employees.


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Annex 3.2.

Definitions Industrial sub-sectors C h e m i c a l M a n u fa c t u r i n g ( N A I C S 3 2 5 ) : T h i s s u b s e c t o r co m p r i s e s establishments primarily engaged in manufacturing chemicals and chemical preparations, from organic or inorganic raw materials. Exclusion(s): Establishments primarily engaged in: ● field processing of crude petroleum and natural gas (211, Oil and Gas

Extraction) ● Beneficiating mineral ores [212, Mining (except Oil and Gas)] ● Processing

crude petroleum and coal (Petroleum and Coal Products Manufacturing)

● Smelting and refining ores and concentrates (331, Primary Metal

Manufacturing) Forestry and Logging (NAICS 113): This subsector comprises establishments primarily engaged in growing and harvesting timber on a long production cycle (of ten or more years). Long production cycles use different production processes than short production cycles, which require more horticultural interventions prior to harvest, resulting in processes more similar to those found in the Crop Production subsector. Consequently, Christmas tree production and other production involving production cycles of less than ten years are classified to the Crop Production subsector. Industries in Forestry and Logging specialize in different stages of the production cycle. Reforestation requires production of seedlings in specialized nurseries. Timber production requires natural forests or suitable areas of land that are available for a long duration. The maturation time for timber depends upon the species of tree, the climatic conditions of the region, and the intended purpose of the timber. The harvesting of timber, except when done on an extremely small scale, requires specialized machinery unique to the industry. The gathering of forest products, such as gums, barks, balsam needles and Spanish moss, are also included in this subsector. Machinery, Equipment and Supplies Wholesaler-Distributors (NAICS 417): This subsector comprises establishments primarily engaged in wholesaling farm, lawn and garden machinery and equipment; construction, forestry, mining and industrial machinery, equipment and supplies; computers and communication equipment and supplies; and other machinery, equipment and supplies.

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Management Scientific and Technical Consulting Services (NAICS 5416): This industry group comprises establishments primarily engaged in providing expert advice and assistance to other organisation on management, environmental, scientific and technical issues. Exclusion(s): Establishments primarily engaged in: ● providing expert advice and assistance to other organisations on architectural

and engineering issues (5413, architectural, Engineering and Related Services); ● providing expert advice and assistance to other organisations on interior,

industrial and graphic design issues (5414), Specialised Design Services); and ● providing expert advice and assistance to other organisations on information

technology issues (5415, Computer Systems Design and Related Services). Transportation Equipment Manufacturing (NAICS 336): This subsector comprises establishments primarily engaged in manufacturing equipment for transporting people and goods. The industry goods are based on the various modes of transportation – road, rail, air and water. Three industry groups are based on road transportation equipment – for complete vehicles, for body and trailer manufacture and for parts. Establishments primarily engaged in rebuilding equipment and parts are included in the same industry as establishments manufacturing new products. Exclusion(s): Establishment primarily engaged in: ● manufacturing equipment designed for moving materials and goods on

industrial sites, construction sites, in logging camps and other off-highway locations (333, Machinery Manufacturing).

Innovation related terms Innovators: Includes both product innovators and process innovators (defined elsewhere) either in combination or uniquely. Product Innovators: Offered a new product (good or service) that was new to the firm whose characteristics or intended uses differed significantly from products previously offered by the firm. And / or offered a significantly improved product (good or service) of an existing product whose performance has been significantly enhanced or upgraded. A complex product which consists of a number of components or integrated subsystems may be improved by partial changes to one of the components or subsystems. Changes to your firm’s existing products which are purely aesthetic or which only involve minor modifications are not to be included. Process Innovators: Introduced new production/manufacturing methods, procedures, systems, machinery or equipment that differed significantly from the firm’s previous production/manufacturing processes. And / or introduced significantly improved production/manufacturing processes that involved significant changes to existing processes that may be intended to produce new or significantly improved products (goods or services) or production/ manufacturing processes.


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Knowledge management related terms Knowledge Management: Knowledge management involves any systematic activity related to the capture and sharing of knowledge by the organisation. Knowledge Management Users: Firms that indicated they are using at least one of the knowledge management practices listed in question 1 of the Knowledge Management Practices Survey and in Table 3.2. ● Knowledge Management Non-Users: Firms that indicated that they are not using

at least one of the knowledge management practices listed in question 1 of the Knowledge Management Practices Survey and in Table 3.2. ● Number of full-time equivalents: “Full-time equivalents” represents the number

of person-years. ● Recently Adopted: Indicates the proportion of practice in use that was adopted

since 1999. ● Workers: The term “workers” includes regular workers (employees) as well as

managers, executives, partners, directors, and persons employed under contract.

Organisational change related terms Organisational change is defined by a positive response to this question from the Survey of Electronic Commerce and Technology, 2000: “During the last three years, 1998 to 2000, did your organisation introduce significantly improved organisational structures or implement improved management techniques?” Technological change is defined by a positive response to this question from the Survey of Electronic Commerce and Technology, 2000: “During the last three years, 1998 to 2000, did your organisation introduce significantly improved technologies?”

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Annex 3.3.

Methodological Notes Questionnaire development Statistics Canada conducted this pilot survey on Knowledge Management Practices as part of an international initiative headed by the Organisation for Economic Co-operation and Development. Canada was the lead country piloting this survey. Other countries that in 2001 undertook pilot surveys based on the contents of the Knowledge Management Practices’ questionnaire were Denmark and Germany. The questionnaire for the Knowledge Management Practices Survey was designed by the Science, Innovation and Electronic Information Division of Statistics Canada in collaboration with: the Centre for Educational Research and Innovation (Organisation for Economic Co-operation and Development); the Ministry of Trade and Industry and the Center for Ledelse (Denmark); the Fraunhofer Institute for Systems and Innovation Research (Germany); Service des études et des statistiques industrielles and Institut national de la statistique et des études économiques (France); the Office of National Statistics (the United Kingdom); Innovazione tecnologica e ricerca scientifica (Italy); Statistics Netherlands (the Netherlands); Statistics Sweden (Sweden); and the Institute for Knowledge Management (United States of America). Statistics Canada undertook cognitive testing of the questionnaire through extensive interviews with individual firms in both official languages to ensure that the questions were well understood. Feedback from respondents was incorporated into the questionnaire design.

Survey content Statistics Canada between September and December 2001 conducted the pilot survey. The questionnaire presented in this volume is a revised version of the questionnaire initially tested by Statistics Canada. The survey is based on inuse / planned-use identification of a series of business practices related to knowledge management. These practices are grouped/categorised as follows: policies and strategies; leadership; incentives; knowledge capture and acquisition; training and mentoring; and communications. Respondents that indicated that any practice listed in the first question was “In Use” (In Use Before 1999 or Used Since 1999) continued to the next section. Respondents not using any of the practices moved (skipped) to question 10 – “Incentives to Use”.


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Questions 3-9 (referring to the previous questionnaire) captured the reasons, results, effectiveness and responsibility for using knowledge management practices. Also included in this section were questions on the sources of knowledge management practices, spending dedicated to knowledge management and resistance to using knowledge management practices. All respondents answered questions 10-14. Question 10 related to incentives to use knowledge management practices. Question 11 provided employment structure information for the firm. Questions 12-14 were administrative questions for response burden issues, improvements to the questionnaire and to determine if the results were of interest to the respondents.

Data reliability The reliability of the data has been assessed using the following convention: Code A

Rating Very good

Standard Error < 2.5%

B

Good

> 2.5% and < 7.5%

C

Good to poor –use with caution

> 7.5% and < 15.0%

D

Very poor –may not be acceptable

> 15.0%

Success of the survey The Knowledge Management Practices Survey was a pilot survey. Its first objective was to confirm that the questionnaire, which had undergone extensive cognitive testing with potential respondents and revisions based upon feedback, worked. That is, it was able to distinguish between firms on the basis of their use of knowledge management practices. The overall response rate and the response rates for individual questions suggest that the questionnaire made sense to respondents. The analysis demonstrated that firms could be distinguished on the basis of their use of knowledge management practices. For these reasons, the survey was deemed to have satisfied the criteria to determine its success.

Collection methodology The primary objective of this survey was to determine which practices Canadian businesses used to support the sharing, transfer, acquisition and retention of knowledge and if they found these practices to be effective. The KMPS used samples from the Annual Survey of Manufacturers (ASM) and the Unified Enterprise Survey (UES). Preliminary contacts took place around September 12, 2001 and the mailout started on September 24, 2001. Follow-ups were carried out starting on October 14, 2001.

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Since it was a pilot survey, the coverage of Canadian enterprises is limited to the following activity sectors: ● Forestry and Logging (113) ● Chemical Manufacturing (325) ● Transportation Equipment Manufacturing (336) ● Machinery, Equipment and Supplies Wholesaler-Distributors (417) ● Management, Scientific and Technical Consulting Services (5416)

Survey frame In order to reduce the response burden of the questionnaire, existing surveys were used as a survey frame. Thus, the 1999 Annual Survey of Manufacturers (ASM) was considered for sectors 113, 325 and 336 while the 1999 Unified Enterprise Survey (UES) was used for sectors 417 and 5 416. Financial and production data are available from these surveys.

Sampling Given that existing samples were used, a two-stage survey was developed. For the first stage level, you must refer to the documentation in the ASM and UES to understand the sample stratification, allocation and selection process. It should be noted that the statistical unit of these surveys is the establishment. The KMPS information was collected from enterprises with at least 10 employees and revenue of USD 250,000 or more. A mailing of about 400 questionnaires was desirable. Based on the combined rate of 21% for nonrespondents, out-of-scope units and inactive units, the size of the sample was set at 510 enterprises. At the second stage, the units of interest were responding enterprises from the ASM and UES with at least 10 employees and revenue of USD 250,000 or more. The establishments in these two surveys were grouped at the enterprise level. The activity sectors (5) and the size of the enterprises (10-49, 50-199, 200 and more employees) were used for the purposes of stratification. The distribution of these 510 enterprises was done in such a way that the Coefficients of Variation (CVs) are similar for all strata. A simple random sampling was carried out for each of them.

Verification and imputation All questionnaires confirmed as completed passed through a verification and imputation system. As one of the objectives was to evaluate the questionnaire, minimal imputation took place. In general, verification was limited to ensuring that the responding values were valid and that the question skips were respected. In cases identified as incorrect, the following actions were carried out: ● imputation of a value from a donor for questions identified as mandatory, ● imputation

of a non-response code for questions identified as non-

mandatory.


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Donors were selected randomly according to certain characteristics (hot deck) and independently for each of the questions. Groups of donors were assembled based on their characteristics: ● Group I: same province, same activity sector and same category - number of

employees (question 11), ● Group II: same activity sector and same category - number of employees

(question 11), ● Group III: same activity sector and category grouping - number of employees

(question 11). For each value to be imputed, an attempt was made to find a donor in the Group I’s. If no donor was found there, donors from Group II’s were used, and so on.

Response rate ● After preliminary contact, the distribution of the response codes for the 510

enterprises was as follows: ● 407 enterprises suitable to receive a questionnaire, ● 48 non-respondent enterprises (refusal, no contact, ...), ● 51 out-of-scope enterprises, ● 4 inactive enterprises.

Of the 407 questionnaires sent out, the distribution of the response codes is as follows: ● 348 enterprises with a complete questionnaire, ● 58 enterprises with an incomplete questionnaire or non-respondents, ● 1 out-of-scope enterprise.

The response rate for the survey is about 76.5% (348/455).

Estimation As mentioned earlier, the statistical units of the first stage are for enterprises whereas the second stage is for establishments. To produce estimates at the enterprise level, the weight share method was used. All the estimates were produced using Statistics Canada’s Generalized Estimation System (GES). For the formulas used in variance calculations, please refer to the GES documentation.

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Bibliography Baird, R. S. Deacon and P. Holland (2000), “From Action Learning to Learning from Action: Implementing the After Action Review” in R. Cross and S. Israelit (eds). Strategic Learning in a Knowledge Economy: Individual, Collective and Organisational Learning Process, (Resources for the Knowledge-Based Economy Series), Butterworth-Heinnemann, Woburn, pp. 185-202, Bartlett, C.A. and S. Ghoshal (2002), “Building Competitive Advantage Through People”, MIT Sloan Management Review, Cambridge, MA, Vol. 43, No. 2. pp. 34-41. Bell, C. R. (1996), Managers as Mentors: Building Partnerships for Learning, BerrettKoehler Publishers, San Francisco. Bowlby, G. (2001), “The Labour Market: Year-end Review”, Perspectives on Labour and Income, Statistics Canada, Cat. No. 75-001XPE, Vol. 13, No. 1. pp. 9-19. Brelade, S. and C. Harman (2001), “How Human Resources Can Influence Knowledge Management”, Strategic HR Review, Melcrum Publishing, London, Vol 1, Issue 1. pp. 30-33. Brown, J.S. and P. Duguid (2000), “Balancing Act: How to Capture Knowledge Without Killing It”, Harvard Business Review, Boston, MA, May-June 2000, pp. 73-80. Catt, H. and P. Scudamore (1997), Solving Skills Shortages: How to Recruit and Retain Skilled People, Kogan Page Limited, London. Cohen, D. and L. Prusak (2001), In Good Company: How Social Capital Makes Organisations Work, Harvard Business School Press, Boston. Cohen, W. M. and D.A. Levinthal (2000), “Absorptive Capacity: A New Perspective on Learning and Innovation” (reprint of 1990 article) in R. Cross and S. Israelit (eds), Strategic Learning in a Knowledge Economy: Individual, Collective and Organisational Learning Process, Resources for the Knowledge-Based Economy Series, Butterworth-Heinnemann, Woburn, pp. 39-67. Cross, R. and L. Baird (2000), “Technology is Not Enough: Improving Performance by Building Organisational Memory”, MIT Sloan Management Review, Cambridge, MA, Vol 41 No. 3. pp. 69-78. Cross, R. and S. Israelit (eds) (2000), “Introduction: Strategic Learning in a Knowledge Economy: Individual, Collective and Organisational Learning Process”, Strategic Learning in a Knowledge Economy: Individual, Collective and Organisational Learning Process, Resources for the Knowledge-Based Economy Series, Butterworth-Heinnemann, Woburn, pp. vii-xvii. Davenport, T. H. and L. Prusak (1998), Working Knowledge: How Organisations Manage What They Know, Harvard Business School Press, Boston. de la Mothe, J. and D. Foray (eds) (2001), “Conclusion”, Knowledge Management in the Innovation Process, Kluwer Academic Press, Boston, pp. 217-225. Denning, S. (2001), The Springboard: How Storytelling Ignites Action in Knowledge-Era Organisations, Butterworth-Heinemann, Woburn. Dixon, N. M. (2000), Common Knowledge: How Companies Thrive by Sharing What They Know, Harvard Business School Press, Boston. Earl, L. (2002), “Innovation and Change in the Public Sector: A Seeming Oxymoron”, Statistics Canada, Cat. No. 88F0006XIE02001, Working Papers Series No. 1, Science, Innovation and Electronic Information Division, Ottawa.


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Earl, M. J. and I. A. Scott (1999), “Opinion: What is a Chief Knowledge Officer”, MIT Sloan Management Review, Cambridge, MA, Vol 40 No. 2. pp. 29-38. Field, D. (2001), “Sharing on the London Underground”, Knowledge Management Review, Melcrum Publishing, London, Vol 4, Issue 6. pp. 12-13. Hamdani, D. (1996), “The Temporary Help Service Industry: Its Role, Structure and Growth”, Services Indicators, Statistics Canada: Catalogue No. 63-016-XPB, pp. 1-19. Human Resources and Development Canada (2002), “Knowledge Matters: Skills and Learning for Canada”, Catalogue No. SP-482-02-02. Industry Canada (2002), “Achieving Excellence: Investing in People, Knowledge and Opportunity”, Catalogue No. C2-596/2001. Kaye, B. and S. Jordan-Evans (1999), Love’em or Lose’em: Getting Good People to Stay, Berrett-Koehler Publishers, San Francisco. Knowledge Management Review (2002), “Briefings – Facing the Reality of Knowledge Attrition”, Knowledge Management Review, Melcrum Publishing, London, Vol 4, Issue 6, pp. 8-9. Lesser, E. and L. Prusak (2000), “Communities of Practice, Social Capital and Organisational Knowledge" in E.L. Lesser, M.A. Fontaine and J.A. Slusher (eds) Knowledge and Communities, Resources for the Knowledge-Based Economy Series, Butterworth-Heinnemann, Woburn, pp. 123-131. Miller, R. (2001), “Bringing Tradeshow Knowledge to the Desktop: Sharing Conference Knowledge with your Workforce”, Knowledge Management Review, Melcrum Publishing, London, Vol 4, Issue 4, pp. 30-33. Newman, V. (2002 Interview) (2002), “Transferring High-value Knowledge at Pfizer: Retaining Knowledge to Improve Decision-Making”, Knowledge Management Review, Melcrum Publishing, London, Vol 4, Issue 6, pp. 14-17. OECD (1999), Science, Technology and Industry Scoreboard 1999: Benchmarking Knowledge-based Economies, Paris. Picot, G. and R. Dupuy, (1996), “Job Creation by Company Size Class: Concentration and Persistence of Job Gains and Losses in Canadian Companies”, Analytical Studies Branch, Statistics Canada, Cat. No. 11F0019MPE, Research Paper No. 93. Picot, G., A. Heisz and A. Nakamura (2001), “Job Tenure, Worker Mobility and the Youth Labour Market During the 1990s”, Analytical Studies Branch, Statistics Canada, Cat. No. 11F0019MPE, Research Paper No. 155. Prusak, L. (2001), “Practices and Knowledge Management” in de la J. Mothe and D. Foray (eds) Knowledge Management in the Innovation Process, Kluwer Academic Press, Boston, pp. 153-158. Quinn, J.B. (1999), “Strategic Outsourcing: Leveraging Knowledge Capabilities”, MIT Sloan Management Review, Cambridge, MA, Vol 40 No. 4. pp. 9-21. Schaan, S. and F. Anderson (2001), “Innovation in Canadian Manufacturing: National Estimates”, Statistics Canada, Cat. No. 88F0006XIE No.10, Working Papers Series No. 10, Science, Innovation and Electronic Information Division, Ottawa. Schuetze, H. G. (2001), “Knowledge Management in Small Firms: Theoretical Perspectives and Evidence” in de la J. Mothe and D. Foray (eds), Knowledge Management in the Innovation Process, Kluwer Academic Press, Boston, pp. 97-122.

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Seeley, C. P. (2002), “Knowledge Preservation in Turbulent Times”, Knowledge Management Review, Melcrum Publishing, London, Vol 4, Issue 6, p. 5. Shea, G.F. (1999), Making the Most of Being Mentored, Crisp Publications, Menlo Park, CA. Statistics Canada (1998), North American Industry Classification System, Canada, 1997, Statistics Canada, Catalogue No. 12-501-XPE. Stehr, N. (2001), “The Grammar of Productive Knowledge” in de la J. Mothe and D. Foray (eds), Knowledge Management in the Innovation Process, Kluwer Academic Press, Boston, pp. 193-203. Stone, F.A. (1999), Coaching, Counselling and Mentoring, American Management Association, New York. Sunter, D. (2001), “Demography and the Labour Market”, Perspectives on Labour and Income, Statistics Canada, Cat. No. 75-001XPE, Vol. 13, No. 1. pp. 28-39. Zack, M.H. (1999), “Managing Codified Knowledge”, MIT Sloan Management Review, Cambridge MA, Vol 40 No. 4, pp. 45-58.


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PART II

Chapter 4

The Management of Knowledge in German Industry by Jakob Edler

This article summarises an empirical study on knowledge management (KM) in German industry building upon the answers of 497 enterprises out of seven sectors – including service sector – to a broad KM questionnaire. It followed the general pattern of the OECD core questionnaire, and included an additional analysis of innovation management aspects. The analysis shows that KM in Germany meanwhile is a broad, horizontal task that has diffused widely and cannot any longer be confined to ICT-related tools. Although KM practices are spread widely, KM is still a rather uncoordinated, spontaneous endeavour rather than a systematically organised and strategically guided management task. It is shown that the institutionalisation of KM and the number of KM practices is systematically related to size while the sector difference, with the notable exception of the by far most active service companies, is rather limited. While the motivations to use KM are broad, three basic families stick out: internal integration of knowledge, human resource development and capture and control. The most important effects of KM are functional (human resources and market success) rather than restricted to KM aims such as organisational memory or knowledge capture. Interestingly, while overall a higher degree of organisational institutionalisation, i.e. in centralised KM functions, has a positive impact on the effects of KM, it may also have negative effects on the capability to exchange knowledge with the environment. Finally, it is fair to say that any innovation management is somehow linked to KM. The relation between innovation activities and KM activities is obvious, especially as for the implication of the ability to capture external knowledge for the innovation process.


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THE MANAGEMENT OF KNOWLEDGE IN GERMAN INDUSTRY

4.1. Introduction: Filling Knowledge Gaps on Industrial Knowledge Management in Germany While the conceptual academic literature on the management of k now ledge has itself re cently be co me al m os t un man ag eable, 1 th e management literature on the topic already is. In view of this abundance, this contribution is both modest and ambitious. It is modest as it does not seek to th eorise about knowledge management and add yet another conceptualisation. It is ambitions, as it seeks to build a more solid basis for speculating about knowledge management in providing a new kind of data to the German discussion. It presents analyses and interprets the findings of a KM survey conducted among German companies from seven sectors in the context of the OECD endeavour to map the KM of industries within the OECD.2 The study therefore used the broad definition of knowledge management according to which knowledge management (KM) involves any activity related to the capture, use and sharing of knowledge by the organisation. In this context, the study must be understood as an exploratory endeavour, and thus the empirical findings might very well contribute to further conceptualisation of KM in the future. For German industry – as for the industry of many other countries – a survey applying a broad concept of KM and covering a wide range of sectors was overdue for several reasons. First of all, almost all empirical work done on KM practices in Germany is based on case studies (e.g. Willke 1998).3 As many of these case studies are limited to one key aspect of KM, i.e. ICT-based approaches (Bach et al. 1999, Bach et al 2000), even the aggregate of case studies cannot provide a general picture of KM in Germany. Secondly, the existing studies – and this is true not only for German companies – are focused mainly on the internal KM processes and somehow neglect the interface between internal and external knowledge sources and knowledge processing. However, one central premise of this article is that due to a number of reasons – growing complexity, interdisciplinarity, economies of speed, interorganisational co-operation etc. – internal knowledge generation is under pressure and must increasingly integrate external knowledge quickly and smoothly.4 A third open question regarding our understanding of KM in German industry is if KM means different things in different sectors and for different company sizes. There is only one survey that, next to a couple of European

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II.4. THE MANAGEMENT OF KNOWLEDGE IN GERMAN INDUSTRY

firms, mainly includes German companies (Heisig/ Vorbeck 2001). This very valuable work is limited to some 140 German companies and therefore does not differentiate the answers according to different sectors and sizes. Only from the response rate did the authors find indications that – in very general terms – KM is apparently used more broadly in certain industries – such as chemistry and pharmaceuticals, consulting, automobiles, ICT and mechanical engineering (Heisig/ Vorbeck 2001, p. 121). Furthermore, although it has been shown that the usage of KM practices correlates with size, i.e. KM is used more widely in larger companies,5 for Germany a broad empirical analysis is still lacking. In short, there are severe knowledge gaps regarding KM in German industry. It is the aim of this article to contribute to filling these gaps. Our broad definition of KM means that we see KM at work not only as the management of codified information with the help of IT processes, but as an ensemble of practices ranging from IT solutions for internal storage and communication of data to training and mentoring, from KM strategy plans to practices of knowledge acquisition. In view of this broad notion of KM, there are five underlying research dimensions that guided the survey and its analysis: 1. Usage: How widely are the various KM practices used and how dynamic is the diffusion of these instruments? 2. Motives: What are the driving forces to employ KM practices, and can we find certain key drivers that define different types of KM? 3. Effects: What are the effects attributed to the usage of KM practices? 4. Institutionalisation: Is KM institutionalised within the companies organisationally and/or financially and what effects does institutionalisation have? 5. Innovation: What is the relation between KM in general and innovation management? Is KM a central element of innovation management, if yes, in which sense? All but the last dimension are integral parts of the country cases elaborated in this volume. The innovation process dimension is an amendment for the German survey,6 in order to test the hypotheses that innovation is increasingly managed by using KM, respectively integrated into the KM of companies. Especially absorbing and integrating external knowledge is increasingly important and it needs to be shown if this may be even a prime driver for KM in the first place. The added value of this dimension – by comparison with existing analysis based on innovation surveys7 – results from the fact that for the first time, the practices to absorb knowledge for innovation purposes are put into context of the management of knowledge in companies in general.


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II.4.


After a short introduction of the methodology, especially the sampling, the structure of this article is guided by the above mentioned five thematic building blocks, providing necessary interlinkages and concluding with overarching lessons.

4.2. Methodology: The Sample Due to our lack of systematic knowledge on the sectoral and size influence as regards KM, it is extremely important for the understanding of the following analysis to characterise the sample. 497 firms answered the questionnaire adequately, which is 14.22% of the total sample of 3 495 companies that were randomly drawn.8 This response rate is very satisfying compared to other nonmandatory business surveys in Germany. In addition to the questionnaire, a non-response analysis was conducted, to which 410 companies answered.9 The main survey was prepared by a preceding pilot study with a smaller sample that served the purpose to optimise the questionnaire and to get a feeling for response behaviour of companies. The company sample consists of companies from seven sectors, covering a broad range of traditional industries, as well as knowledge intensive-sectors such as biotechnology and pharmaceuticals and, above all, a large sample of service companies (see Table 4.1). The sample of the service sector has deliberately been drawn larger than the others in order to be able to analyse service companies vis-à-vis companies from manufacturing industries. Our premise here is that service companies rely even more than manufacturing companies on the knowledge of their employees as well as their organisation and use KM differently. In selecting service companies we have focused on four sub-sectors of the service sector which are rather knowledge intensive.10

Table 4.1. Company Sample and Response Rate – Sectoral Distribution NACEa 24 (except 24.4) 24.4 Internal Database 27-29 34-35 30-32 74 (selection)b

Sector Chemical (except pharmaceutical and biotech.) Pharmaceuticals Biotech Mechanical Engineering Vehicles (including transport equipment) Electrical Engineering/ Electronics (ICT) Business-related services No sector/company name given Total

N 409 344 612 395 394 614 727 3 495

sample

rate (%)

48 31 76 51 36 61 160 34 497

11.7 9.01 12.42 12.88 9.14 9.95 22.01 14.22

a: The classifications of sectors in NACE are identical with those of ISIC REV3, except for pharmaceuticals which is 242x in ISIC REV3 rather than 24.4 (NACE). b: See text for details as for service sub-sectors. Source: Fraunhofer ISI Survey 2002

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The size of the various randomly selected sector samples has been defined following the experiences of the pilot survey. Somewhat surprising was the low response rate by the pharmaceutical companies and the very high response rate of the service companies. However, this might be interpreted as a first indication of the (low) importance of KM for these companies. In any case, the resulting sample is large enough for sectoral differentiation. The same is true for the size distribution of the responding sample. Figure 4.1 shows that for the whole sample three of the four groups are represented very similarly, and even the group of larger and largest companies (over 2 000 employees) is big enough for an in-depth analysis. The size distribution shows significant dif ferences between sectors, which is important for the analysis. The service and especially the biotechnology sector are dominated by smaller companies, while the pharmaceutical sector is dominated by companies with more than 250, but less than 2 000 employees and the remaining four sectors are dominated by companies with more than 250 employees, including very large enterprises.

Figure 4.1. Size Distribution of the Sample (%): Total and Sectors 1-49

50-249

250-1 999

2 000+

60

50

40

30

20

10

0 Chemistry

Source:

Mechan. engin.

Electr. engin.

Vehicles

Pharmac.

Biotech.

Services

Total

Fraunhofer ISI Survey 2002


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II.4.


4.3. The Employment of KM Practices in German Industry The aggregated picture Following our broad definition of KM given above, the companies were asked about their usage of 19 different instruments. On aggregate, the employment of these instruments differs according to the size and the sector of companies. As for the size, the finding of Prusak (2001) and others can be somewhat confirmed: the larger the company, the higher the average number of KM instruments used (Figure 4.2), i.e. the greater the need for broad KM.11

Figure 4.2. Average Number of KM Practices Used-size 0-49 (N = 121)

50-249 (N = 150)

250-1 999 (N = 139)

2 000 + (N = 64)

Total (N = 497) 2.0 Source:

2.5

3.0

3.5

4.0


The pattern for the sectors is less clear cut (Figure 4.3). Only two sectors stand out while the rest show a very similar average number of KM practices. Apparently our hypothesis that KM is more important for service sectors is confirmed, at least for the service sub-sectors we have selected, which are business-related and knowledge-intensive (see above). These service companies on average employ almost 13 out of the 19 instruments we asked about, although the sector sample consists mainly of SMEs. Exactly the opposite pattern is true for the vehicle sector, here the sample is characterised by large companies, still the average number of KM practices is lowest.

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Figure 4.3. Average Number of KM Practices Used-sector 13

12

11

10

9 Chemistry Source:

Mechan. engin.

Electr. engin.

Vehicles

Pharmac.

Biotech.

Services


4.4. What Kind of KM Practices? To understand the relative importance of the different major lines of KM practice, we have grouped the 19 instruments into four broad categories (see Table 4.2): (1) communication, (2) training and mentoring, (3) policies and strategies and finally (4) knowledge capture and acquisition. While in many KM analyses the communication practices, mainly ICT-based, are the focus, Table 4.2 shows that the weight in our broad approach has been set differently. This reflects various premises underlying the study. First, ICT and ICT-related communication is important, but should not be misunderstood as the major or even sole dimension of KM. The legitimacy of this approach has been supported by the non-response analyses. Only 9% of the 410 companies participating to the non-response survey indicated that for them KM is largely ICT based documentation and sharing of knowledge, rather than a broad approach (see Annex 4.1).12 Second, the importance of human resources as the carrier and transmitter of knowledge is growing, both as related to KM practices (training for KM) as well as other functional knowledge that needs to be shared with others. Third, it is crucial to learn if the companies are systematically dedicated to KM, i.e. if they have formulated KM strategies, if they have an appropriate value system, etc.13 Fourth, there are indications that, in order to cope with the growing dynamics and complexity of knowledge development, companies increasingly have to rely on knowledge that cannot – for various reasons – be produced within the company itself. In fact, in some cases the acquisition of external knowledge has been defined as crucial for the persistence of an efficient evolution and innovative capacity of companies. The study seeks to


95

II.4.


test this hypothesis, for if it were true, it would have severe implications for KM, since it would have to cope systematically with the complex knowledge environment and link its fruits to the internal knowledge circulation.14

Table 4.2. Percentage of Companies Using Selected KM Practices – Total Samplea Practice

in usec

before 99 d

13

Regularly updating databases of good work practices, lessons learned or listings of experts

57

36

18

25

7

Preparing written documentation such as lessons learned, training manuals, good work practice etc. (organisational memory… )

69

6

10

12

Facilitating collaborative work by projects teams that are physically separated (“virtual teams”)

59

33

12

29 75

Rankb

plan not in use

Communication

85

Training and Mentoring 17

Providing formal training related to KM practices

16

11

9

16

Providing informal training related to KM

34

21

12

54

15

Using formal mentoring practices, including apprenticeships

39

26

7

55

4

Encouraging experienced workers to transfer their knowledge to new or less experienced workers

93

78

3

4

5

Encouraging workers to continue their education by reimbursing tuition fees for successfully completed work-related courses

90

79

2

8

2

Offering off-site training to workers to keep skills current

95

84

2

4 60

Policies and Strategies 19

Having a written KM policy or strategy

23

10

18

14

Having a values system or culture promoting knowledge sharing

45

30

18

37

10

Using partnerships or strategic alliances to acquire knowledge

68

50

6

26

11

KM within responsibility of top management

61

44

11

27

18

Monetary or non-monetary incentives

30

21

12

59

Knowledge Capture and Acquisition 1

Using knowledge obtained from other industry sources

97

89

0

3

6

Using knowledge obtained from public research institutions

88

78

2

9

9

Dedicating resources to obtaining external knowledge

70

56

5

25

2

Using the Internet to obtain external knowledge

95

57

2

3

8

Encouraging workers to participate in project teams with external experts

81

65

4

14

a: percentage of all companies answering the respective question. b: instruments ranked according to the percentage of companies using them (decreasing order). c: total percentage of companies using the practice, no matter when they introduced it. d: percentage of companies having introduced the practice before 1999. Source: Fraunhofer ISI Survey 2002

To avoid missing the big picture by diving into the level of single instruments right away, these four categories can first be looked at in aggregate. To do so, an index from 0 (no use of any practice) to 1 (all practices used) was calculated for each of the four clusters of practices. Table 4.2 shows impressively that the capture and acquisition of knowledge are most widely used, confirming

96



the hypothesis that external knowledge acquisition is becoming an increasingly important task and a major pillar of the competitiveness of companies. The second most widely used cluster are the mainly ICT-based communication practices, followed by the human resource instruments. Interestingly, for the German companies KM is a practical reality that is not yet guided by related corporate strategy, policies, cultures and commitments. Most interestingly, this pattern of high emphasis on capture and acquisition on the one hand and the low emphasis on policy and strategy is true for all sectors and for all size groups, the differences at the level of instrument clusters are almost negligible. The persistence of this pattern is even more striking, considering the differences in the degree of employment of KM practices between the sectors and especially the size clusters demonstrated above.

Individual KM practices: highlights and lowlights On the level of single practices the picture is of course not as clear cut (see Table 4.2 above). First of all, from the eight individual practices used by more than 80 % of the companies, four are related to knowledge capture, three to training, only one to communication and none to KM strategies. The two most popular practices, measured by the percentage of companies using them, are the use of knowledge obtained from other industrial sources and the use of the Internet (capture), followed by off-site training, inter-personal knowledge transfer and work-related formation (training), using knowledge from public research (capture), written documentation (communication) and encouraging collaboration with external experts (capture). At the low end, out of the six practices used by less than one third of the companies, three are related to policies and strategies (appropriate value system, incentives and written KM strategy),15 three stem from the training category. It is clear that in contrast to general training practices KM practices geared towards the build-up of KM capabilities are not broadly established, in fact only 16% of the companies have a formal KM training – which is the lowest rate of use.

The dynamics of the diffusion of KM: Strategic – but limited However, what about the dynamics of the diffusion of KM practices in recent years in view of the increased importance KM has received in business management literature and conference circles?16 Here we concentrate only on the most dynamic tendencies within German industry. Not surprisingly, the usage of the Internet has diffused most in German industry lately (38 % out of the 95 % using it now have introduced this only recently). Secondly, there is a growing need to integrate knowledge across organisational borders and distances, be it from inside or outside the company, as indicated by the increased importance of – first – attempts to ease collaboration of teams that are physically separated and – second – inter-firm partnerships to capture knowledge. Thirdly, there is a diffusion of ICT-based KM solutions, as the


97

II.4.


updating of databases has greatly gained importance. And finally, KM has increasingly become a responsibility of top management, since 40% of the companies who indicate that they have placed KM within the responsibility of top management have done so only within the last three years. And where will they go from here? The companies were also asked which practices they plan to introduce in the coming 24 months (Table 4.2). The signals are mixed. On the one hand, there is a large share of companies that plan to organise their KM more comprehensively, as 18% of the companies indicate to foster an appropriate value system or culture and another 18% plan to formulate a written KM strategy. In addition, the rather low share of companies that have an informal KM training will grow by 12%. At the same time, the tendency to employ ICT-based databases and to ease collaboration across distances remains. On the other hand, however, this development should not be overrated, especially as for policies and strategies and as for KM-related human resource instruments there seems to be a stable and large portion of German companies that will continue to do without.

Striking uniformity in usage patterns Space does not permit presentation of the differentiation for sector and size at the level of single instruments. A comparative analysis has shown that across the board the differences are very minor. Strikingly, the similarities of patterns at the level of categories is mirrored at the level of instruments. Especially at the low end of practices there are almost no differences, especially the distribution of policies and strategies is low for all sectors and size groups. The sectors deviating most from this general pattern are mechanical engineering with a special focus on human resource practices in use, and electrical engineering, a sector that is apparently prepared to undertake comprehensive, strategic KM in the near future. Finally, the stronger usage of KM by large companies in general – shown above – is also characterised by a different pattern, as very large companies lay much more emphasis on the acquisition of knowledge from outside the company [especially from research institutes (95%)], with 88% of them dedicating resources to do so.

4.5. The Driving Forces of Knowledge Management: Motivation Patterns in German Industry Three main drivers to employ KM on the level of single instruments What are the most important reasons for German companies to use KM? Can we see a pattern of motivation? In line with the broad understanding of KM, the motivations to use KM practices are manifold. The companies were asked to rate the importance of 19 different motivations on a scale from 1 (extremely important) to 6 (not important at all). Table 4.3 indicates the motives in the order of decreasing importance for the whole sample. There are

98



eight most important motives for which more than 75% of the companies have attributed an importance 1 or 2 (top two boxes).17 The single most important driving force to employ KM practices is apparently the sharing and integration of knowledge among the workforce within the company, represented by the two most important single motive (transfer to new workers, integration of knowledge) plus the support for intra-company collaboration across distances. The second most important driving force, made up of three out of the top eight variables (motive 3, 6, 8), is rather defensive. Many companies rate the importance of stock taking of knowledge and its protection as highly important. This reflects the increasing fluctuation of the workforce as well as the growing importance of knowledge as a strategic asset. Finally, the companies grasp the opportunity provided by KM tools for the upgrading of their workforce internally, as KM is a major tool for human resource development (motive 4, 5). In short, German industry is employing KM driven by three major purposes: internal integration and internal transfer of knowledge, taking stock and protection from loss of knowledge and the improvement of the workforce.

Table 4.3. Motivations to Use KM, Whole Sample Rank

Motive

top twoa

meanb

1

To accelerate and improve the transfer of knowledge to new workers

91

1,64

2

To help integrate knowledge within your firm or organisation

86

1,75

3

To protect your firm or organisation from loss of knowledge due to workers’ departure

82

1,77

4 5

To encourage managers to share knowledge as a tool for professional promotion of their subordinates To train workers to develop their human resources

80 77

1,91 2,00

6

To identify and/or protect strategic knowledge present in your firm or organisation

76

1,95

7

To ease collaborative work of projects or teams that are physically separated

75

2,03

75

2,06

69 68

2,29 2,21 2,28

8

To capture workers’ undocumented knowledge (know-how)

9

To ensure that knowledge resident in all international work sites is accessible to the entire firm or organisation

10

To train workers to meet strategic objectives of your firm or organisation

11 12

To help managers to focus their attention to key information To improve the capture and use of knowledge from sources outside your firm or organisation

67 67

2,22

13 14

To increase worker acceptance of innovations To avoid information overload problems within your organisation

65 59

2,30 2,45

15

Following merger or acquisition to help integrate knowledge within your new firm or organ

16

To promote sharing and transfer of knowledge with suppliers

47 47

2,92 2,75

17

To improve sharing or transferring of knowledge with partners in strategic alliances, joint ventures or consortia

37

3,05

18

To promote sharing and transfer of knowledge with customers

36

3,07

19

To update your firm or organisation on KM tools or practices used by competitors

31

3,23

a: top two indicates the percentage of companies who have rated one or two on the scale from 1 (extremely important) to 6 (not important at all). b: scale ranging from one (extremely important) to 6 (not important at all). Source: Fraunhofer ISI Survey 2002


99

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The low end: Capture rather than share The motives at the low end confirm the result that for the German companies KM is still very much connected with internal knowledge stock and flow, including the integration of knowledge – or information – obtained from external sources. The sharing of knowledge with actors external to the organisation is of low relevance, three out of the four least important motives are about the sharing of knowledge with customers, suppliers and cooperation partners. This marks an important characteristic of the relation with the outside world if it comes to KM. While the practices used to obtain knowledge from outside are rather prominent and important (see Table 4.3 above) and while using the environment as a knowledge source gets at least a medium mean value and is an important motive for two thirds of companies (motive 12), the inclination to actually integrate the internal circulation of knowledge with the relevant environment is weak. As we will see below, sharing knowledge with the environment is still accompanied and hampered by the fear of giving away critical knowledge (Chapter 6).

Clusters of motives This first rough overview points towards a certain pattern of KM motivation. However, the picture is still too complex to interpret the major lines of KM drivers, especially when it comes to the comparison along the sector and size dimension. In order to define a clear set of basic motivations to utilise KM practices, we conducted a principal component analysis with varimax rotation to aggregate connected groups of variables. The resulting reduced set of factors will both be easier to interpret and can be used for an aggregated comparison between sectors and size groups. Table 4.4 indicates the five factors – which have an Eigenvalue above 1 – that have been extracted and their respective factor loadings. Together, the five components underlying these factors explain around 60% of the total variance (see Table A4.2.1 in Annex 4.2). The factor explaining most of the variance (15%) encompasses variables that all describe the overall operative function of KM practices regarding human resources (see Table 4.4, performance of the management and workforce etc.). Factor 2, explaining almost 13% of the variance, describes the capture and protection of knowledge, it is strategic in a more defensive sense. The factors 3 to 5 are all concerned with the sharing and integration of knowledge. Factor 3, explaining a bit more than 12%, encompasses the vertical knowledge transfer in the market. Factor 4 can be labelled as knowledge integration across interfaces within (!) the company, while finally, factor 5 earmarks the integration of knowledge in very general terms.

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Factor 1

2

3 4

5

Description Operational and instrumental motivation geared towards human resources

Knowledge capture (including external) and control

Vertical knowledge transfer in the market Transfer and sharing of knowledge across interfaces within the company and with close partners.

Internal integration of knowledge

Imp.a

Major Variable

Factor loading

11

To help managers to focus their attention to key information

10

To train workers to meet strategic objectives of your firm or organisation

0,535 0,694

5

To train workers to develop their human resources

0,572

4

To encourage managers to share knowledge as a tool for professional promotion of their subordinates

0,722

13

To increase worker acceptance of innovations

0,633

19

To update your firm or organisation on KM tools or practices used by competitors

0,661

12

To improve the capture and use of knowledge from sources outside your firm or organisation

0,471

3

To protect your firm or organisation from loss of knowledge due to workers’ departure

0,737

6

To identify and/or protect strategic knowledge present in your firm or organisation

0,645

8

To capture workers’ undocumented knowledge (know-how)

0,771

14

To avoid information overload problems within your organisation

0,503 0,785

16

To promote sharing and transfer of knowledge with suppliers

18

To promote sharing and transfer of knowledge with customers

0,786

15

Following merger or acquisition to help integrate knowledge within your new firm or organisation

0,754

9

To ensure that knowledge resident in all international work sites is accessible to the entire firm or organisation

0,839

7

To ease collaborative work of projects or teams that are physically separated (i.e. different work sites)

0,775

17

To improve sharing or transferring of knowledge with partners in strategic alliances, joint ventures or consortia

0,564

2

To help integrate knowledge within your firm or organisation

0,781

1

To accelerate and improve the transfer of knowledge to new workers

0,793

Principal Component Analysis - varimax rotation with Kaiser-normalisation, Kaiser-Value 0,86, Barlett’s test of sphericity 2953,348, p=0.000 a: Ranking of importance for the single variable (see Table 4.3). Source: Fraunhofer ISI Survey 2002

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Table 4.4. Definition of Factors: Motivation for KM (varimax rotated factor loadings)

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This result of the factor analysis is highly interesting, as it almost exactly confirms the intellectual clustering of motivation variables that we grouped hypothetically ex ante.18 It stresses the fact that the companies have clearly distinguishable types of motivations to use KM. Furthermore, it may lead us to think about KM in terms of clear cut categories rather than analyse the whole ensemble of possible motivations – but still stick to our broad understanding of KM. On the basis of this factor analysis, we clustered the original motivation variables according to “their factor” (Table 4.4) and calculated the overall mean values for these factors as for their importance. The result more or less confirms what we already interpreted above (Figure 4.4). The most important motivation is the internal knowledge integration, followed by capture and control, the human resource dimension and the transfer and sharing of knowledge across organisational interfaces within the company (multiple sites) and/or with close partners (factor 4). Of least importance is the vertical external knowledge transfer in the market (see Figure 4.4). Strikingly again, this order of motivation clusters is the same no matter the size, the only exception being the transfer across internal borders and with close partners which – due to more fragmented structures – is more important for larger companies.

Figure 4.4. Importance of Cluster of KM Motives - Size Human resource

Capture/Control

Vertical transfer (market)

Transfer/Sharing (internal interfaces, partners)

Internal integration

Total motivation

1

2

3

4 1-49

50-249

250-1 999

Above 2 000

1 = extremely important, 6 = not important at all Source:

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The rather uniform pattern of motivation is also true for the sectors (Figure 4.5), as they show all the same order of motivation types. Still, there are some important sectoral differences in two dimensions: external vertical sharing of knowledge (market) and knowledge transfer across borders within the company respectively with close partners. Apparently, the service companies are – in relation to other motives – driven rather weakly by the need for sharing of knowledge with customers and suppliers. This is somewhat counter-intuitive, as service companies, especially the knowledge-intensive ones that we included in the service sector, are dependent upon the exchange of knowledge. This might indicate that service companies are not driven by the need to exchange knowledge with their environment that much, but rather capture the necessary information needed to deliver their specific service. The little relevance of sharing knowledge with the environment is also true for the chemical companies, which – in addition – indicate least importance of transfer of knowledge across intra-company interfaces or with close partners. According to our survey data, the chemical companies seem to be least open to letting their knowledge circulation come in touch with outside actors. The opposite is true for the pharmaceutical and, to a lesser extent, for electronic companies, for which the sharing of knowledge with external partners, especially vertically (market) is significantly more important.

Figure 4.5. Importance of Cluster of KM Motives - Sectors Human resource Vertical transfer (market) Internal integration

Capture/Control Transfer/Sharing (internal interfaces, partners) Total motivation

1

2

3

4 Chemistry

Mech. engin.

Electr. engin.

Vehicles

Pharmac.

Biotech.

Services


Fraunhofer ISI Survey


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4.6. Effects of Knowledge Management Success of KM: Functional, but again limited How effective are the companies in employing KM? As yet, indicators for KM are still to be defined. A recent project funded by the European Commission has only started to work on guidelines for the reporting of intangibles in companies, which should, as a working programme, include indicators for knowledge management practices (Calvo/Sánchez Munoz 2002). However, the empirical findings on the actual practices of companies to measure and even report on their intangibles and the related management practices are extremely poor, the majority of companies, although often reporting on their activities as part of the knowledge economy, do not have measuring practices and reporting systems, and those who do are rather reluctant to disclose them. Secondly, our knowledge of the relative impact of KM on certain business indicators we might have is still rather poor. 19 Therefore, up to now effects of KM cannot– beyond the level of case studies – be measured systematically. The simple solution chosen in our study was to ask those responsible what they think about how effective the ensemble of their KM – not single instruments – are. The German survey asked for nine possible effects on a range between 1 (extremely effective) and 6 (not effective at all). The effects are presented in Table 4.5 in decreasing order of magnitude. First of all, there is a strong correlation between the number of practices used and the effects reported. The more practices are employed the higher the score for effects. 20 Secondly, KM is most effective when it comes to the improvement of human resources and the direct market effects, although the related motivations are rated rather low. Table 4.5 indicates that two of the three top rated effects are human resource effects (skills, productivity). This is interesting, since the improvement of human resources is not the most important driving factor for KM (see above). Thirdly, the single biggest effect (adaptation in the market), as well as number 4 and 5 (Table 4.5), are directly linked to the market success of companies. Again, we have seen that the company at the same time rates the motivation for external transfer or sharing of knowledge with clients very low. In other words, the companies either see no necessity to share and transfer knowledge with their clients in order to meet their needs properly, or they are reluctant to do so.21 The fact that they still rate the market effect as high rather points to the general effects obtained through the efficiency gains of internal mechanisms of KM. Fourthly, the direct KM effects (capture of knowledge and the improvement of the organisational memory), are rated low. It would be interesting to find out, through more qualitative research, why these direct KM effects are rated lower than the functional effects (human resources, market). One explanation – as indicated above – might be that the companies simply have no measurement,

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maybe not even a feeling for their KM abilities, and thus are not able to assess the effects in the first place. Furthermore, the limitations of the direct KM effects (capture, memory) might point towards the slow reaction of the companies to a KM culture that needs to be institutionalised in order to be effective. The functional KM effects (HR, market), on the other hand, are traditional dimensions that might very well have improved through KM, however, KM on that level is only one explanatory variable among many other managerial tasks, and effects hard to attribute.

Table 4.5. Effects of KM – Whole Sample Type of effecta

Effect

top twob

meanc

Market

Increased our adaptation of products or services to client requirements

73

2,07

Human Res.


73

2,08

Human Res.

Improved worker efficiency and productivity

69

2,12

Market

Helped us add new products and services

61

2,34

Market

Improved the relation to customers and/or clients

59

2,38

Organ. Mem.

Improved the memory of our organisation

57

2,47

Organ. Mem.

Helped avoid duplicating R&D activities

53

2,55

Capture

Increased our ability to capture knowledge from other businesses

51

2,56

Capture

Increased our ability to capture knowledge from public research instit.

38

2,87

a: ex ante, intellectual clustering of effects; Organ. Mem. = Organisational Memory b: top two indicates the percentage of companies who have rated one or two on the scale from 1 (extremely effective) to 6 (not effective at all). c: scale ranging from one (extremely effective) to 6 (not effective at all). Source: Fraunhofer ISI Survey 2002

Effects differ by size, not by sector To compare sectors and size groups the nine factors need again be grouped to reduce complexity. Again a principal component analysis has been conducted, which resulted only in two factors, one for the two variables “capture”, one for the rest of the variables. Therefore, the nine variables have been grouped intellectually in the four clusters already indicated above (market and customer relations, human resources, organisational memory and capture). The overall mean values have been calculated for these clusters. Not surprisingly, for the whole sample the effects on human resources are biggest, followed by the market effect, while the direct KM functions organisational memory and capture of knowledge from outside were rated considerably lower, especially knowledge capture is – relatively speaking – rather poor. As the practices to capture knowledge are used rather broadly (see above), there is obviously ample room for improvement as for their effectiveness.


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Figure 4.6. Cluster of KM Effects - Size Capture Market and customer relation

Human resource Organisational memory

1

2

3

4 1-49

50-249

250-1 999

Above 2 000


Fraunhofer ISI Survey

What is somewhat surprising, however, is the rather uniform pattern as to the different size groups, the order of effects and the mean value are the same (Figure 4.6). The only obvious deviation from the general pattern is the fact that large companies report a higher average score as for effects on the organisational memory; a second, minor, deviation is a very low score for the effect on knowledge capture from outside for the second biggest group of companies. This high degree of uniformity in effect patterns between size groups agrees with the uniformity in the motivation dimension (see above). Consequently, the need for and the results of drivers to employ KM do not systematically differ with size. While size does not matter much, the sector makes a difference regarding the prevalence of the impacts of KM (Figure 4.7). The variation is rather small in the two most effective functional dimensions human resources and market. In all sectors except for mechanical engineering, the companies are most effective in promoting their human resources through KM practices; the latter is rated second for all but mechanical engineering and pharmaceuticals. However, there are considerable differences as regards effects to be seen within the knowledge capture dimension. Apparently, there are three sectors that severely lag behind in their ability to capture knowledge from outside the company (chemicals, mechanical engineering and vehicles), while the knowledge-intensive biotechnology sector is situated best. Finally,

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the sectors differ considerably in their ability to build up and improve organisational memory. Here the pharmaceutical sector is by far the most effective (mean 2.2), while the vehicle sector – again – lags behind most.

Figure 4.7. Cluster of KM Effects - Sectors Capture Market and customer relation

Human resource Organisational memory

1

2

3

4

Chemistry

Mech. engin.

Electr. engin.

Vehicles

Pharmac.

Biotech.

Services



To sum up the effects of KM as reported by German companies: first the challenges and opportunities posed by a sector rather than the company size influence the effect of KM. Secondly, while the human resources dimension is not the key driver, the effects related to human resource are rated highest across the board. Third, there is a striking mismatch as regards motivation and effects as for the relation with the environment. On the one hand, the companies report high market effects of their KM activities, but these are accompanied by rather low motivation to share knowledge with clients. Knowledge sharing with customers is not regarded as a priority for companies in order to reach market objectives. The opposite is true for the effects as regards the capture of external knowledge. Although the use of practices to do so is distributed very widely (see above, Table 4.2) and the motivation is at least of medium importance, the effects are reported to be rather low. As said before, the companies have recognised the importance to capture external knowledge, but the processes are still to be improved considerably.


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4.7. The Institutionalisation of KM and its Meaning for the Use of Knowledge Management Different levels of dedication To fully trace the organisational design of KM in our broad understanding would not be possible in a survey, given the multitude of practices and their complex interplay. What can be done, however, is to identify the institutional commitment to KM. Three proxies for institutionalisation – or dedication – as regards KM have been asked about: (1) dedicated budget for KM, (2) organisational unit or a specific manager mainly responsible for KM and – as additional question in the German questionnaire – (3) the responsibility for KM at the top management level. For the whole sample, top management responsibility is by far most important, indicated by more than 60% of the companies, while a quarter of the companies have a dedicated budget and slightly less a functional unit or responsible manager for KM (Figure 4.8 left box).22 Regarding the institutional commitment, it is obviously the size that matters rather than the sector (Figure 4.8). There is a negative correlation between size and top management responsibility and a positive correlation for specific KM functional units and size on the other hand. This is of course to a large degree structurally determined, as the functional differentiation, especially for a relatively horizontal task like KM, is more difficult – or less necessary – for small companies. Therefore, it is hard to assess the explanatory share of the dedication for KM as compared to the minor necessity for small companies to create functional units for each specific task. The sectoral patterns (Figure 4.8) therefore reflect the size distribution of the sectors – with the notable exception of the pharmaceutical sector, which contains a very large share of companies with more than 250 employees, but still shows a very high level of top management responsibility. Given the size bias for top management responsibility and functional units, the dedicated budget might be a better proxy for the institutionalisation of KM. The connection between KM budgets and size is not as clear cut, as the two middle categories show rather similar values. The sector distribution shows, first, the overall importance of KM for the service companies and, second, the importance of budgets for – next to the service sector – the knowledge-intensive pharmaceutical and biotechnological sectors in which it is apparently necessary to invest strongly in intellectual capital.

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Figure 4.8. Institutionalisation of KM Unit exists

Budget exists

Top management responsibility Services

Overall

Biotech. 2 000+

Pharmaceutical Vehicles

250-1 999

Electr. engin. 50-249 Mechan. engin. 1-49

Chemical 0

10 20 30 40 50 60 70 80

0

10 20 30 40 50 60 70 80

Percentage of companies having institutionalised KM by the measures indicated Source:


Institutional commitment matters – but may backfire Do the three different forms of organisational dedication towards KM make a difference to the effects of KM? To find out, for all three different forms just discussed we conducted a comparison of mean values for the nine effect variables with the help of T-Tests. Top management commitment makes the biggest difference. The mean values for all effects are higher for companies with top management responsibility for KM with a high statistical significance.23 Companies with a dedicated budget for KM also report higher values for all effects; however, only five out of nine effects are significant at the level of at least 10%. While most effects are higher if functional units or key managers are mainly responsible for KM, the effects for the management of knowledge interfaces with the environment (capture of knowledge from public research institutes, relation to customers and suppliers) are lower.24 The centralisation of KM through organisational units in fact may hamper the openness to the outside world, as the interface function itself is reduced to – or can be delegated to – a core KM group rather than placed within the responsibility of the whole workforce. While this might improve the central overview on external effects – and support the control function – it reduces the number of possibilities for exchange with the environment.

4.8. Knowledge Management and its Role within Innovation Management Innovation and Knowledge Management: brothers in arms As a consequence of three major trends, a comprehensive analysis of KM must take into consideration the meaning of KM for the innovation process.


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First, there is no doubt that the capacity to innovate is the major precondition to stand the competitive pressure, and companies are increasingly geared towards efficiency gains in order to speed up innovation and maximise the realisation of its innovation potential. Second, the catch word of “knowledge economies” points to the fact that the importance of knowledge for competition as well as innovation has grown. Consequently, and the analysis so far supports it, strategic and especially operative management is increasingly employing KM tools. Thirdly, companies are more and more at their limits when it comes to providing the necessary input for innovation, especially as for innovation based on in-house research and development (R&D). What is increasingly asked for is the absorption of knowledge from external sources and integrate it within the knowledge stock and flow of the company. Together, these three trends make the connection of KM and innovation management sensible, if not indispensable. One key hypothesis derived from these considerations is that there is a relationship between the employment of KM and the innovation activities. To do so, the responding sample can be grouped into innovators (N=294) and non-innovators (N=203) as for products and into process innovators (N=90) and process non-innovators (N=380). In addition, the companies indicated if they do R&D (N=267) or not (N=222).25 For all three “innovation” dimensions we compared the mean value as for the usage of KM, the motivation for and effects of KM.26 While for the motivation and the effect dimension there is no statistically significant relation, for the usage of KM practices (as clustered above, see (Table 4.2) the relationships are positive and significant. In terms of our four clusters of KM practices, the product innovators show a significant positive relationship for the two clusters communication and, extremely significant, knowledge capture.27 Successful innovation for the market therefore has to do with the ability to store and communicate knowledge internally and, above all, to tap into the knowledge sources outside the company. For the process innovation the relationship is even stronger, the process innovators use significantly more KM in each of the four KM clusters than non innovators.28 Without claiming causality, we can nevertheless conclude that the ability to change processes goes hand in hand with the willingness to employ KM practices broadly. Finally, companies that do R&D are also more active in employing KM, both as for the total number and as for the two clusters communication and capture.29

Growing meaning of knowledge absorption The special relation between capture and acquisition instruments on the one hand and innovation respectively R&D activities on the other hand confirms a hypothesis made earlier according to which the absorption of

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external knowledge is a key activity for innovators. This conclusion can further be qualified. 3 0 First of all, the companies rated sources for technological knowledge 31 outside the company as more important (mean 1,93) than sources within the company (2,12).32 Furthermore, for two thirds of the company the meaning of external technological knowledge has grown in the past, and slightly more expect it to grow in the future. This means that for the whole sample the question is not if they need KM as interface management, but how they get what they need from outside. The reasons to utilise external technological knowledge are certainly numerous, but the two most important ones point towards the two most pressing needs of companies: speed and lack of appropriate human resources (Table 4.6).

Table 4.6. Importance of Reasons to Capture External (Technological) Knowledge – Mean Valuesa quick adaptation (N=387)

2.39

not sufficient human resource internally (N=387)

2.53

internal generation too costly (N=368)

2.74

knowledge needed is too broad (N=364)

2.78

knowledge needed is too specific (N=357)

2.89

a: 1= extremely important, 6 not important at all Source: Fraunhofer ISI Survey 2002

Apparently, the German industry has realised the relation between the growing complexity and need to absorb knowledge on the one hand and KM. Those companies that sense a grown meaning of external knowledge in the past or a growing meaning in the future use KM practices overall significantly more than those who do not.33 A last result from the analysis of the absorption of external technological knowledge points towards a characteristic problem of KM in general. Asked for the importance of obstacles to absorb external technological knowledge, by far the most important reason are internal reservations to give away own sensitive know how (Table 4.7). This is in line with the finding above that the sharing of knowledge with external actors is a weak motive in general. Given the growing need to integrate knowledge flows with external partners, procedures and institutions to foster trust, cultures to foster openness and adequate regulations for intellectual property are to stay on the agenda.


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Table 4.7. Importance of Obstacles to Capture and Use of External Technological Knowledge - Mean Valuesa Reservations about giving away own sensitive know-how (N=393)

2,80

Lack of procedures to discover external knowledge (N=348)

3,54

Other industrial firms not willing enough to co-operate (N=335)

3,60

Costs of search too high (N=353)

3,90

Reservations about becoming dependent on external knowledge (N=372)

3,91

Resistance in search for or implementation of ext. knowledge from own R&D personnel (N=335)

4,01

Scientific institutes are not appropriate partners (N=320)

4,10

We do not have any (great) need (N=262)

4,10

a: 1= extremely important, 6 not important at all Source: Fraunhofer ISI Survey 2002

4.9. Concluding Summary: Only First Steps towards Filled Gaps Do we have a clearer idea on how, why and to what effect German companies manage their knowledge now? The big picture behind all detailed analysis presented – which certainly must and will be driven further – shows some major trends in our data set that justify this conclusion. The broad understanding of knowledge management, as an ensemble of very different types of practices, driven by diverse motives and being effective on different levels, is fully justified. Knowledge Management is a broad, horizontal task that has diffused widely. Not even the non-response analysis showed a strong diffusion of the idea that KM needs to be focused around ICT applications. Knowledge management practices are very diverse, the most important category employed are instruments to capture and acquire knowledge from ex ter n al so urce s. At the sam e tim e, howeve r, K M is stil l a rathe r uncoordinated, spontaneous endeavour rather than a systematically organised and strategically guided management task, even if the policy dimension has been indicated as the most dynamic for the future. The number of KM practices is systematically related to size while the sector difference, with the notable exception of the by far most active service companies, is rather limited. Even more striking, the diffusion pattern of different practices is very similar for different sectors and sizes, with the notable exception of the very large companies which employ more strategic and systematic approaches than the rest. Apparently, KM is not only a horizontal task within companies, but the challenges of KM are similar across the whole range of industries. This is confirmed by the motivation patterns, that again are very similar for all groups analysed. The motivations to use KM are broad, but three basic families stick out: internal integration of knowledge, human resource development and capture and control. The functional effects on human resources and market (respectively customer relations) are rated higher than the effects related to

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knowledge management effects in a more narrow sense (organisational memory and knowledge capture). The institutionalisation of KM is strongly dependent on company size. However, it was shown that higher degree of organisational institutionalisation, i.e. in centralised KM functions, may also have negative effects on the capability to exchange knowledge with the environment. Finally, it is fair to say that any innovation management is somehow linked to KM. The relation between innovation activities and KM activities is obvious, especially as for the implication of the ability to capture external knowledge for the innovation process. What these major findings make clear, above all, is the necessity to go on analysing KM in industry. The relations among the many variables for which data have been collected must be analysed more intensively. Furthermore, aggregated data must always be checked with qualitative findings on the basis of existing case studies. In addition, we must go on comparing countries and sectors. A prime line of future work, however, must be the conceptualisation of a framework that enables us to measure the effects of KM much more accurately than we can based on estimates by respondents or idiosyncratic case studies. Only if we know systematically what the benefits of KM practices and strategies are can we take the next steps, such as, for example, the development of uniform guidelines and frameworks as for the analysis and employment of KM.

Acknowledgements. With the indispensable support of Rainer Frietsch for the statistical analysis. I am also indebted to Michael Bordt from StatCan and to Dominique Foray from OECD/CERI for their extremely valuable comments on a first draft. Any mistakes and inconsistencies remain of course within the full responsibility of the author.

Notes 1. To mention only a couple of key studies and analyses: OECD (1999), LeonardBarton (1995), Prusak (1997), Davenport/ Prusack (1998), de la Mothe, J./ Foray, D. (2001) Willke (1998), Den Hertog/ Huizenga (2000), Calvo/ Sanchez Munoz 2002). 2. The study on which this article is based was made possible by the Donors’ Association for the Promoting of Sciences and Humanities in Germany who fully funded it, and by the willingness of the German Federal Ministry to officially support it. We are deeply grateful to both institutions. 3. Some German cases can also be found in Mertins et al. 2001.


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4. In contrast to the German private companies, public research institutions as a major source of external knowledge for companies, have been analysed again and again in order to improve their ability to transfer knowledge. Recently see Schmoch et al. (2000); Edler, Schmoch (2001). 5. See for example Prusack 2001 and Earl in this volume. 6. The French example provided in this volume (Kremp/Mairesse) has also connected the innovation dimensions and KM practices. While the German study has inserted selected innovation questions into the broad KM survey, Kremp/Mairesse have inserted selected KM questions into a broad industry and innovation study (CIS3). 7. For Germany see Janz et al. 2001; Janz 2000 and Janz/Licht 1999. 8. The most distinguished German company database Hoppenstedt, which classifies on NACE basis, was used for all sectors except for Biotechnology, since Biotechnology is not yet clearly defined as a NACE code. The list of biotech companies was constructed at Fraunhofer ISI three years ago. The survey itself was conducted in spring and summer 2002, all companies received two reminders. 9. See annex for a short description of the non response analysis. 10. Market/opinion research (Nace 74.13), strategic and PR company consulting (74.14), architecture and engineering services (74.20), technical, physical and chemical expertise, consultation (74.30). 11. The relationship between number of employees and number of practices used is statistically significant at the level of 1 per cent, Spearman coefficient 0.14. 12. That is of course not to say that ICT based KM approaches do make no sense, however, one should be aware that they are only part of the picture. 13. In this section the German study has expanded the OECD core questionnaire and added the questions on top management responsibility, respectively incentives (see Table 4.2). 14. An early recognition of this has been made by Barabaschi (1993), a former manager of a large Italian company in the electronics sector. 15. This is true although 60% of the companies indicate that KM lies within the responsibility of top management (as was asked additionally in the German questionnaire). Apparently this high institutionalisation has not yet led to formalised KM policies. 16. The companies were asked to indicate if they had introduced a practice they use before 1999 or if they have used it since 1999.

17. Top two category reflects the percentage of companies who indicated a value lower than 3 on the scale from 1 (extremely important) to 6 (not important at all). 18. Ex ante, these motivations were grouped into the following five categories: knowledge sharing and integration (S/I), knowledge capture and control (CC), information management (IM), human resource management (HR) and external (ext.) This latter category simply asked for the motivation to update the company on KM of the competitors. These ex ante classifications differ from the factors resulting from our factor analysis only in two respects, first, the two variables from the information management category (IM) are no part of the operational motivation (help managers focus their attention on key

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information) respectively – and somewhat counter-systematic – the capture and control motivation (avoid overload problems). Second, the broad ex ante category of integration and sharing of knowledge has been differentiated into the three factors 3 to 5. 19. One recent example of measuring effectiveness of KM is given by Kremp/ Mairesse (Chapter 6) in a study on French industry. They show that there is statistically significant correlation between usage of KM and labour productivity. Their basis is the linkage of questions on KM practices and data stemming from the regular French industry survey panel. 20. We conducted a Chi-Square test, for which an index of overall usage was constructed and the sample was grouped into those companies that employ not more than 50% of the instruments (N=128) and those who employ more than half the instruments. The total effect was calculated building the overall mean value on the scale from 1 (extremely effective) to 6 (not effective at all). Three groups reporting high (mean below 1.5), medium (mean between 1.5 and 3) and low effectiveness (mean above 3) were built. The resulting cross table was tested, correlation showed high significance on the 1% level. 21. One major reason for the reluctance to acquire technological knowledge is, as mentioned above and shown below (Chapter 6), fear of losing critical knowledge, the same might be true for knowledge sharing with clients. 22. A further indication of a rather low formal commitment is the fact that only 16% of the companies provided formal training related to KM (see above, Table 4.2). 23. Significance below 1%, the only exception being the avoidance of duplicate efforts in R&D, where the significance is below 5%. 24. Statistical significance of 10%. 25. Product Innovators are defined as companies that in the period from 1999 to 2001 had a share of turnover with new or considerably improved products above 10%. Process Innovators have introduced a new internal process within the same period. 26. By means of a T-Test. 27. Level of significance for communication is 10%, for capture it is 1%. 28. Level of significance is below 1% for all categories. 29. To exemplify the magnitude of differences: The companies that are active in R&D on average employ 4,4 KM instruments of the five instruments grouped within the category capture, those without R&D employ 3,8 instruments. 30. A deeper analysis of this dimension, including sectoral and size differentiation, will be provided in a second, extended version of this analysis and will contain the importance and usage of sources for external technological knowledge, methods to monitor external technological knowledge and the level of knowledge about the knowledge external to the company. 31. Technological Knowledge was introduced as the interest here was on the R&D and innovation dimension rather than organisational or market knowledge. In the questionnaire it was defined by ways of examples, containing “knowledge on technologies, methods, scientific results etc“. 32. On the scale 1=extremely important, 6=not important at all. 33. Especially, again, in the communication and capture clusters.


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Annex 4.1.

Non-response The non-response analysis served the purpose of testing the relevance of the overall topic and to ask if companies had a totally different understanding of KM. 410 companies sent back the non-response, meaning that altogether 907 companies responded to the survey. Table A4.1.1 below gives the possible answers that were formulated (multiple responses possible) and the counts as well as percentage of responses and cases. One can see that the broad understanding of KM was no major problem for the companies asked, only very few indicated that they followed a narrow, ICT focused KM approach. Furthermore, there are only very few companies that do not have KM at all but plan to introduce it. That means that KM is already started, or is not considered at all. The most important reasons for not participating – next to the practical ones time and principle objections to surveys – is that in many companies there is KM at place, but it is distributed, loosely connected and not systematically managed. 86 companies, out of more than 900 companies who answered to the survey, indicated that KM plays no role whatsoever and is not on the agenda either. While it is clear that most of those non-users of KM might have not answered in the first place, the percentage below 10% indicates that KM – one way or the other – is an important topic in the German industry.

Table A4.1.1. Non-response Analysis, N=410 Count

Percentage of responses

cases

Reasons related to KM KM is a horizontal task within the responsibility of every manager, therefore systematic statements for KM as such are hard to make

99

17,4

24,1

KM plays no major role and there are no plans to build up systematic KM

86

15,1

21

KM is a major task of our ICT management (databases, information systems) and not as road as in the definition given in the questionnaire

37

6,5

9

KM plays no major role, but a build up of systematic KM is planned

14

2,5

3,4

General reasons, not KM related Answering takes too much time No participation for principle reasons Other reasons (company dissolved etc.)

124

21,8

30,2

93

16,3

22,7

116

20,4

28,3

Multiple answers possible Source: Fraunhofer ISI Survey 2002

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Annex 4.2.

Components Factor Analysis Motivation Table A4.2.1. Factor Loadings and Contribution to Explain Variance Component

Rotated sum of squared loadings

% of the variance

cumulative % 15,9

total 1

3,02

15,9

2

2,457

12,9

28,8

3

2,396

12,6

41,4

4

1,802

9,5

50,9

5

1,588

8,4

59,3

Source: Fraunhofer ISI Survey 2002


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Bibliography Bach V. et al. (1999), “Business Knowledge Management”, Praxiserfahrungen mit Intranet-basierten Lösungen, Springer, Heidelberg u.a. Bach V. et al . (2000), “Business Knowledge Management” in der Praxis, Prozessorientierte Lösungen zwischen Knowledge Portal und Kompetenzmanagement, Springer, Heidelberg u.a. Barabaschi, S. (1993), “Managing the Growth of Technological Information”, in Rosenberg et al. (ed.), Wealth of Nations, Stanford University Press, Stanford, pp. 407-434. Calvo, L. and M. Sanchez Munoz (2002), “Guidelines for Managing and Reporting on intangibles”, Intellectual Capital Report, Madrid. Davenport, T. and L. Prusak, (1998), Das Praxisbuch zum Wissensmanagement, Landsberg/Lech: verlag moderne industrie. de la Mothe, J. and D. Foray (eds) (2001), Knowledge Management in the Innovation Process, Kluwer Academic Press, Boston. Den Hertog, F. and E. Huizenga (2000), The Knowledge Enterprise. Implementation of Intelligent Business Strategies, Imperial College Press, London. Edler, J. and U. Schmoch (2001), “Wissens- und Technologietransfer in öffentlichen Einrichtungen”, in ifo-Schnelldienst, 4/54, pp. 18-27. Heisig, P. and J. Vorbeck (2001), Benchmarking Survey Results, in Mertens et al. (2001), Knowledge Management. Best Practices in Europe, Springer-Verlag, Heidelberg, pp. 97-126. Janz, N. et al. (2001), Innovationsverhalten in der Deutschen Wirtschaft. Indikatorenbericht zur Innovationserhebung 2001, Mannheim. Janz, N. (2000), “Quellen für Innovationen: Analyse der ZEW-Innovationserhebungen 1999” in Verarbeitenden Gewerbe und im Dienstleistungssektor, Janz et al., ZEW Dokumentation, Mannheim, Nr. 00-10, (2001). Janz, N. and G. Licht, (1999), “Innovationsaktivitäten der deutschen Wirtschaft”, ZEW Wirtschaftsanalysen, Baden-Baden, Bd. 41. Leonard-Barton, D. (1995), Wellsprings of Knowledge. Building and Sustaining the Sources of Innovation, Harvard Business School Press, Massachusetts. Mertens, K. et al. (2001), Knowledge Management. Best Practices in Europe, SpringerVerlag, Heidelberg u.a. OECD (1999), Knowledge Management in the Learning Society, Paris. Prusak, L. (1997), Knowledge in Organisations, Butterworth-Heinemann, Boston u.a. Prusak, L. (2001), “Practices and Knowledge Management” in de la J. Motte, J. and D. Foray (eds), Knowledge Management in the Innovation Process, Kluwer Academic Press, Boston, pp. 153-158. Schmoch, U. et al. (2000), Wissens- und Technologietransfer in Deutschland, Studie für das BMBF, Fraunhofer IRB-Verlag, Stuttgart. Willke, H. (1998), Systemisches Wissensmanagement, Lucius & Lucius, Stuttgart.

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PART II

Chapter 5

The Promotion and Implementation of Knowledge Management – A Danish Contribution by Anja Baastrup and Wenche Strømsnes

This chapter presents the results of the Danish pilot study. It first offers a look at what the survey shows on where to place responsibility and which activities seem to be most effective. Secondly, the most significant results from the Danish study are comprised into a set of guidelines for top management. Thirdly, the chapter looks at what can be expected from the environment when implementing knowledge management.


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THE PROMOTION AND IMPLEMENTATION OF KNOWLEDGE MANAGEMENT – A DANISH CONTRIBUTION

5.1. Introduction The utilization of knowledge has been seen as a significant factor in giving an enterprise competitive advantage. Organisations that have looked seriously at their use of knowledge have discovered that they possess more knowledge than they realise. As the Danish company Systematic puts it in its recent intellectual capital report:1 If only Systematic knew what Systematic knows - pointing to the great benefits that arise from being able to identify, gather and utilize knowledge in such a way as to derive maximum value from it. This sets new challenges for management. Intellectual capital has to be managed – and Knowledge Management is now on the agenda. A point accentuated by modern management theorists (Drucker, 1993; Peter Holdt Christensen, 2000; Von Krogh et al., 2000). So far, however, there have been few studies of Knowledge Management, and those that exist focus primarily on large enterprises. They provide no basis for cross-border analysis or for linking data with other national or international studies. Moreover, although the concept of Knowledge Management is widespread in use, there is no common terminology to deal with it. The Organisation for Economic Co-operation and Development (OECD) has taken the initiative to conduct an international survey on the Knowledge Management practices used in the private sector – and their perceived effectiveness. This paper builds on results from a Danish pilot study conducted towards this end (consult Annexes 5.1-5.2 for background information on the pilot study). Since the purpose of the Danish pilot study was to test the questionnaire, no attempt was made to make a representative study of the field. Therefore the data reported from the study only provide tendencies (Center for Ledelse, 2002). This paper elaborates on some of these tendencies. The paper consists of three parts. First we look at what survey results tell us on where to place responsibility and which activities seem to be the most effective. Second the most significant results from the Danish study have been comprised into a set of guidelines for top management to adhere to when implementing a Knowledge Management project. Thereby this section also sketches important preliminary considerations to be made. Thirdly we look at what can be expected from the environment when implementing Knowledge Management. Thus the intention of this paper is to report the data from the

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II.5. THE PROMOTION AND IMPLEMENTATION OF KNOWLEDGE MANAGEMENT – A DANISH CONTRIBUTION

Danish survey, however, using it to make a good starting point for managers when considering promoting and implementing Knowledge Management in their organisation.

5.2. Some Overall Results The responsibility for Knowledge Management initiatives lies with top management Preliminary results from the Danish OECD survey suggest that the role of top management is of paramount importance in Knowledge Management activities. A convincing majority of respondents (72.1%) place responsibility for implementing and initiating activities with top management. Not a surprising result when taking the reasons for implementing Knowledge Management into account – the three highest scoring all pertain to the field of strategy and structure,2 thus ultimately involving top level decision making and communication. Although strategy and incentives were seen to affect results most and cited as prime reasons for entering the field of Knowledge Management these two aspects were in fact used the least by the respondents. A result that truly needs reflecting.

Some activities are more effective than others A glance at the list of most commonly used practices indicates a density around more individually founded activities. These activities are primarily perceived to result in improved skills and knowledge of workers, increased customer focus and enhanced sharing across departmental borders.

List of commonly used practices Commonly used Knowledge Management practices (listed in order of rate of use): ●

Encourage experienced workers to transfer their knowledge to less experienced workers.

●

Capture and use knowledge obtained from other private companies (e.g. Competitors, customers or suppliers).

●

Off-site training.

●

Dedication of time to capture and share knowledge.

●

Use of Information Technology.

●

Provide informal training related to knowledge acquisition and sharing.

●

Share knowledge through the physical organisation of the workplace.

●

Share knowledge through written documentation.

●

Create a values system or culture to promote knowledge sharing.


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●

Encourage workers to participate in project teams with external experts.

●

Use partnerships or strategic alliances to acquire knowledge.

●

Has policies or programs intended to improve worker retention.

However, when judging by the explanatory power of results on activity, the most significant results pertain to the organisational (strategy and leadership) and team/group level variables – for further details on perceived results and significance consult Annex 5.2. But few have actually integrated Knowledge Management activities in their strategies although preliminary results indicate this is the best way to gain efficiency from the activities. Only 22.9% has a written Knowledge Management policy or strategy and only half of the respondents have a culture or value system intended to promote knowledg e sharing. Plausible explanations could be that confusion about the overall definition and content of Knowledge Management makes it difficult to incorporate it in the strategy (top-down) or the fact that image is so vital, that the organisation just mimics activities implemented by successful Knowledge Management organisations without making the strategic link. Reversing causality the argument could be that it might be hard to integrate and relate relatively separate activities under a common heading (bottom-up).

Figure 5.1. Suggested Levels of Diffusion in Knowledge Management Strategy founded initiatives, belief Systems, structure, formal incentives Team work, flexible production/ Innovation, horizontal knowledge sharing Organisational Teams/groups

Personal networking Informal mentoring, course activity

Individual Source:

Institut for Ledelse

Organisations need to have a fitting strategy, a corresponding structure, appropriate processes and communication for maximizing the benefits from working with Knowledge Management. An implementation of Knowledge Management practises with the “old” structure will eventually cause information overload at top management level, a point also cited by some respondents in the survey. Although a top management initiative, results point in the direction of most organisations starting out from the bottom of the hierarchy by implementing certain kinds of informal activities on an individual basis, driven by their quest for improving their image. This point is

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supported by the fact that survey results indicate that Know ledg e Management activities are perceived to be most effective in the area of improving workers’ skills and knowledge. Training and mentoring is one of the individual and informal activities that mainly occurs through encouraging experienced workers to share their knowledge with those who are less experienced and encouraging workers to take further training (Figure 5.2). However, no formal procedure exists nor does any formal incentive system. Thus the activity becomes strictly voluntary and not strategically initiated, making it increasingly difficult for top management to follow up on the progress of the process.

Figure 5.2. Practices Used Under the Heading “Training and Mentoring”

72.1% 34.4%

50

47.5%

54.1%

70 60

Source:

Your firm or organisation uses formal mentoring practices

Your firm or organisation encourages experienced workers to transfer their knowledge to less experienced workers

19.7% 6.6%

5.6%

1.9%

3.2%

3.2%

3.2%

13.1%

9.8%

13.1%

Your firm Your firm or organisation or organisation provides formal training provides informal training related to knowledge related to knowledge acquisition and sharing acquisition and sharing

4.9%

10

5.6%

6.6%

20

11.5%

14.8%

30

21.3%

24.6%

40

0

No, not applicable/Don’t know/No answer

67.2%

No, but in the next 24 months

65.6%

Yes, after 1999

70.5%

Yes, before 1999

% 80

Your firm Your firm or organisation or organisation encourages offers off-site training workers to continue education by reimbursing tuition fees


In terms of formalizing and elevating individual activities to group level (Figure 5.3) communication is important. The use of information technology (IT), the physical arrangement of the workplace, and the use of written documentation are the practices employed most frequently in this regard. At the organisational level “policies and strategies” are implemented by approximately half the respondents. But generally this activity is one of the least cited (for details on the practices under the heading of “policies and strategies” consult Table 5.1). However, as evident in the subsequent data presentation, precisely this type of practice yields the most effective results.


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II.5.


Figure 5.3. Practices Used Under the Heading “Communications” Yes, before 1999

Yes, after 1999


No, not applicable/Don t know/No answer

Share, % 70

65.6%

60 50 40

39.3%

37.7%

39.3%

39.3%

39.3%

34.4%

32.8%

31.1%

30 18.0%

20 11.5%

9.8%

10 0 Updating databases of good work practices, etc.

Source:

Written documentation sush as lessons learned etc. (organisational memory)

18.0% 14.8% 11.5% 8.2%

Facilitating collaborative work by projects teams physically separated ("virtual teams")

8.2%

Physical organisation of the workplace (AQ)

19.7% 9.8%

Use of Information Technology (AQ)


Table 5.1. Practices Used Under the Heading "Policies and Strategies" Knowledge Management practices Has a value system or culture intended to promote knowledge sharing

Used by % 54.1

Uses partnerships or strategic alliances to acquire knowledge

52.4

Has policies or program intended to improve worker retention

50.8

Has a written Knowledge Management strategy or policy

22.9

Work with knowledge through preparation of intellectual capital statements Source: Institut for Ledelse

8.2

Different kinds of knowledge are strategically important to different organisations, making it a crucial initial task to identify what and how knowledge is important in a specific organisation. At this point specific activities at the different levels of the hierarchy (Figure 5.1) could help organisations to identify activities at each level, ensuring a holistic and strategically founded approach to working with Knowledge Management.

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5.3. Measuring, Controlling and Documenting Effectiveness3 O nly 22% of th e re spon den ts h ave im plemented performan ce measurement systems for measuring team or organisational effectiveness of the Knowledge Management activities – either through the use of the Balanced Scorecard (Kaplan and Norton, 1996 and 2001), the EFQM Excellence model, budgets or customer satisfaction surveys. But as making the most of i m p l e m e n t i n g K n o w l e d g e M a n a g e m e n t r e q u i r e s a s t r a t e g y, t h e implementation of a strategy requires the establishment and implementation of a Management Control System (Chenhall, 2003; Langfield-Smith, 1997; and Simons, 1995) – harnessing the potential benefits of Knowledge Management thus requires both elements. Recent years have witnessed an increasing interest in Knowledge Management and learning resulting in a growing body of literature in academic journals on the topic4 (Crossan and Guatto, 1996; Prange, 1999). Independent of theoretical standpoints5 researchers and practitioners agree that knowledge is the quintessential resource of this century, without unique knowledge assets no competitive edge. A key management challenge thus becomes measuring, controlling and documenting, how the organisation generates, diffuses and applies its knowledge faster and more effectively than its competitors be it either for product specific knowledge creation (R&D or sales and marketing) or the integration of processes across traditionally separate operation fields aligning them with strategy. Depending on the type of data being processed combinations of increased investments in vertical information systems (a point relating to the communication aspect of IT) and creation of lateral relations (a point relating to the arrangement of the work place) as an overlay to the existing hierarchy are recommended for departments/functions sharing resources. Vertical information systems seek to increase information processing abilities within the existing hierarchy in collecting information at the point of origin and channelling it to the appropriate decision makers either at the point of collection or further up the hierarchy. In order to be effective vehicles for increasing processing capacity, information must be formalised and quantitative, thus the strategy is not applicable in situations where variables and their causal relations are unknown or where major parts of the knowledge is tacit (Galbraith, 1973, p. 46 and 34). As a consequence organisations need to specifically address these causality issues when implementing IT (intranet and systems in general), IT only works when employees know what to do with the information, what decisions to make with what consequences, etc. In situations where more qualitative information is required decisionmaking (discretion, Perrow, 1967) authority is allocated to the points of


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II.5.


information origin through the creation of lateral relations. Depending on the level of organisational search activity and the interdependence between departments' various lateral roles (communication and joint decision-making processes) can be created ranging from the establishment of direct contact between the individuals in departments sharing a problem through to the development of a task force or a specific team and ultimately the development of the matrix organisation (Galbraith, 1973, p.18). Building of lateral relations is a cumulative assignment by nature – higher forms of relations are added on to the existing structure not substituting lower levels. Another way to ensure that the Knowledge Management initiatives are measurable and anchored in strategy are to use practices that reward knowledge sharing through incentives. This is not very common among the Danish respondents where more than 70% state that these practices are not applicable (Figure 5.4).

Figure 5.4. Practices Used Under the Heading “Incentives” Yes, before 1999

Yes, after 1999


No, not applicable/Don’t know/No answer

Share, % 80 72.1%

70.5%

70 60 50 40 30 18.0%

20 13.1% 10

9.8%

9.8% 4.9%

1.6%

0 Specifically rewards knowledge sharing with monetary incentives Source:

Specifically rewards knowledge sharing with non-monetary incentives


Having presented the general and overall results from the survey we now turn to presenting some guidelines that describe the progress and process steps to be adhered to during the implementation of the Knowledge Management project. This guideline is based on the general indications from the survey.

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5.4. Inspiration for Top Managers – Content and Process The decision to implement Knowledge Management – how did it come about The inspiration to work with Knowledge Management typically came from within the Management group. Neither the board nor the owners of the company put any pressure on top management to implement Knowledge Management practices – surprisingly enough. The main sources of inspiration seem to be the competitors6 but also the work of suppliers and customers comprise a solid basis for investigating ways of conducting Knowledge Management. Neither consultants nor universities had any impact in triggering the use of Knowledge Management (Figure 5.5). In either case, the management group seemed to have had thorough discussions on what benefits would result from working with Knowledge Management. Although different in their approaches, general agreement within the group seems to exist that the most obvious reason would be to improve competitiveness, by enhancing the routines of capturing and sharing knowledge in the organisation. However, indications also point in the direction of management perceiving it to help workers meet overall strategic objectives. Also the external aspect of profiling the company and protecting them from loss of knowledge due to workers departure seem to be valid arguments for endeavouring on the Knowledge Management path. Top management hopes to avoid losing key workers by focusing on higher levels of involvement by employees in decision making and improving their skills on a continual basis. Elaborating on the last point agreement seems to exist that loss of competitiveness would trigger many companies to use more comprehensive Knowledge Management practices. Other compelling reasons for implementing Knowledge Management activities were of a more external character for instance the desire to attract workers and to improve corporate image. Discussions in the management group were conducted on the gains from working with Knowledge Management. From their sources of inspiration it seems that many companies learned that Knowledge Management practices are effective in improving workers’ skills and knowledge, in adapting products and services to client requirements, in improving interdisciplinary knowledge sharing throughout the enterprise and in helping to add new products and services.


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II.5.


Figure 5.5. External Sources Triggering the Implementation of Knowledge Management Practices Share, % 45 41.5%

39.6%

40 35 30 25 20.8%

20 15

13.2%

13.2%

15.1%

15.1%

11.3%

10

7.5%

5 0

Source:

0.0% Unions (AQ)

Firm or Competitors organisation with which there is a strategic alliance, joint venture or consortium

Suppliers

Professional, Universities, Consultants trade technical or industrial colleges, associations public or federations laboratories or business schools

Regulatory agencies

Customers or clients

Other


An important point to be made from the researcher’s point of view is that although top managers perceive these links to exist, there is not much evidence to support the hypotheses that these results have been achieved through the use of Knowledge Management (consult Annex 5.2). One exception, however, is the improved communication throughout the workplace. An important learning point here is not just to discard Knowledge Management as having no effect. The organisation needs to consider the levels of diffusion (Figure 5.1) to gain a basic understanding of what Knowledge Management means in the specific organisation and ultimately anchor the separate individual activities at the organisational level (strategic level) and implement corresponding management control systems. Only then does the organisation gain an understanding of the underlying causal relations. Knowledge Management has to be handled by organisations wishing to survive in a more competitive global market, this is an indisputable truth; the question is how to operationalise it and optimize its use within the organisational context.

The choice of practices After making the decision of implementing Knowledge Management the next thing to consider are what kinds of activities to implement. As indicated by the survey presently practices can be grouped under six headings:

128

●

policies and strategies

●

leadership



●

incentives

●

knowledge capture and acquisition

●

training and mentoring

●

communications

O n e of t h e c o n c l us i o n s fro m t h e D an i s h s u r vey wa s t ha t t h e implementation of Knowledge Management practices is most effective if the practices are integrated in the strategy. Nevertheless the practices under the heading “Policies and Strategies” are not very common in Danish companies. For instance only 22.9% of the respondents in the survey have a written policy for Knowledge Management and only 45% have a culture or value system intended to promote knowledge sharing 7 (for further clarification consult Table 5.1 and comments). According to the Danish pilot survey the most widely used practices to capture and share knowledge were ‘knowledge capture and acquisition’ and ‘training and mentoring’. As shown in Table 5.2, knowledge capture and acquisition occurs particularly through acquiring knowledge from other private enterprises and dedicating time to obtaining and communicating knowledge. As shown in Table 5.3 training and mentoring occurs particularly through encouraging experiences workers to transfer knowledge and by tuition and off site training.

Table 5.2. Practices Used Under the Heading “Knowledge Capture and Acquisition” Knowledge Management practices

Used by %

Knowledge obtained from other private companies

82.0

Dedication of time to obtain knowledge

73.8

Dedication of time to communicate knowledge

62.3

Encourage workers to participate in project teams with external experts

54.1

Obtained from public research institutes

49.1

Dedication of budgets to obtain

37.7

Dedication of budgets to communicate Source: Institut for Ledelse

29.5

More than half of the Danish companies stated that they have dedicated budgets for Knowledge Management. Approximately half reported dedicating economic resources to these activities, and half of these expect to dedicate more resources in the next 24 months. Only a quarter of those who do not currently set aside resources for Knowledge Management activities plan to do so within the next 24 months. The management team agreed that it would be necessary for success to dedicate both time and budget to obtain and communicate knowledge.


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II.5.


Table 5.3. Practices Used Under the Heading “Training and Mentoring” Knowledge Management practices

Used by %

Encourage experienced workers to transfer knowledge

83.6

Re-imbursement of tuition fees

73.8

Off site training

73.8

Informal training

59.0

Formal training

31.2

Formal mentoring practices Source: Institut for Ledelse

18.0

Finally methods for following up on the progress are to be discussed. In the Danish survey only 22% of the respondents answered yes to the question on whether the effectiveness of Knowledge Management is measured. These respondents stated that they used the following methods to measure the effectiveness of Knowledge Management: ●

Through guides and instructions;

●

The company’s Balanced Scorecard and satisfaction barometer;

●

Employee satisfaction surveys, customer satisfaction surveys;

●

Weekly follow-up meetings;

●

Through budgets or specific sales results and marketing activities;

●

Target fulfilment – qualitative/quantitative (reported in our Intellectual Capital Report);

●

Through various measures, such as employee satisfaction, customer satisfaction, supplier satisfaction, number of inter-disciplinary improvement groups per year, the development of the employees' competencies, number of days spent on education/supplementary training; and

●

Through systematic use of the EFQM Excellence model.

To conclude this section an interesting point is made. Almost none of the Danish respondents experienced significant resistance to implementing Knowledge Management activities and therefore it would be fair to expect cooperation from the organisation given the clear communication and commitment from top management.

5.5. What Can Top Management Expect from the Environment? Under this heading the more general results from the survey have been gathered – how do employees react to initiatives like this, how size and industry play a role. These are all important pieces of advice for the top manager facing a Knowledge Management implementation.

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The majority of respondents place the responsibility for implementing these practices with the top management, and only a few have measured the impact of these activities. Management is also most frequently cited as the internal source responsible for initiating activities designed to acquire and share knowledge, while the most commonly reported external sources are competitors, customers and clients. Companies experience almost no resistance to implementing Knowledge Management. In the Danish study only 9% of respondents had encountered resistance to implementing these activities. There is a tendency for activities involving communications, as well as policies and strategies, to be used more frequently among large enterprises than among small ones. ‘Small enterprises’ are defined as those having between one and 19 employees, so perhaps what is most surprising is that in other respects they resemble the medium-sized and large enterprises so closely. Finally service enterprises tend to use more Knowledge Management practices than do manufacturing and trading enterprises. There is no doubt that the OECD goal – to put the spotlight on Knowledge Management – is relevant for Denmark. Although no direct correlation can be proved between Knowledge Management activities and business results, those who practice these activities have a clear sense that the acquisition and sharing of knowledge, and especially the utilization of it, have a considerable impact on a firm’s competitiveness. It is therefore important to raise awareness of these activities. A large international survey is an effective means of doing this. The greatest problem in this connection is the lack of clarity in terminology relating to the field, but one of the aims of the survey has been precisely to address this.

5.6. Further Research Apart from the subject of this article, the Danish pilot study has pointed to a number of hypotheses which merit further investigation. First one could hypothesize that activities designed to acquire and share k n ow l e d g e a r e u n d e r t a k e n p r i m a r i l y w i t h a v i e w t o i n c r e a s i n g competitiveness. But how (if at all) do companies make the causal links between improving employee skills and competitiveness and how do they measure their progress in these terms? Second as part of their jobs, it seems that workers immediately accept activities designed to acquire and share knowledge. But is the necessary antecedent clear communication and commitment from top management a sufficient one – or do other factors affect this immediate acceptance? Thirdly the acquisition and sharing of knowledge has a great impact on employees’ level of skills and interpersonal competences. But do companies


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II.5.


only experience higher employee satisfaction or does the increased level of skill really result in increased organisational performance and higher employee retention rates? Fourth intellectual capital reports are not perceived as a relevant tool for knowledge sharing. Does this have to do with the elements or the process of making the reports or the difficulties in translating them to and anchoring them within the organisational context? Fifth fewer than 50% of enterprises make use of public research institutions. Does this apparent inaccessibility of research results matter; does the academic world need acceptance from practice to exist?

Acknowledgements. We would like to thank the following for their useful remarks during the pilot survey: Peter Holdt Christensen, Institut for Ledelse, Politik og Filosofi, Handelshøjskolen i København, København N. (Institute of Management, Politics and Philosophy, Copenhagen Business School) Louise Earl, Science, Innovation and Electronic Information Division of Statistics Canada Jakob Edler, Fraunhofer Institute for Systems and Innovation Research (Karlsruhe) Dominique Foray, Centre for Educational Research and Innovation at the Organisation for Economic Cooperation and Development Marie-Louise Winther Green, Økonomi og Erhvervsministeriet, København K (The Danish Ministry of Economics and Business Affairs, Copenhagen) Lars Kiertzner, Institut for Regnskab, Handelshøjskolen i Århus (Aarhus Business School) H e i n e L a r s e n , E m e n t o r D e n m a r k A / S, K ø b e n h av n N a n d Handelshøjskolen i København, Frederiksberg (Copenhagen Business School) Kurt Larsen, Centre for Educational Research and Innovation at the Organisation for Economic Cooperation and Development Henning Madsen, Handelshøjskolen i Aarhus, Aarhus V (Aarhus Business School) Peter Stendahl Mortensen, Analyseinstitut for forskning, Aarhus (Institute of Analysis and Research) Fle mming Poulfe lt, Institut for Le de lse, Po litik og Filosofi, Handelshøjskolen i København, København N (Institute of Management, Politics and Philosophy, Copenhagen Business School) Bettina Høst Poulsen, Økonomi og Erhvervsministeriet, København K (The Danish Ministry of Economics and Business Affairs, Copenhagen) Benedicte Stakemann, Erhvervsfremme Styrelsen, København Ø (Committee to Promote Industry, Copenhagen) Marianne Stang Våland, Learning Lab Denmark, Copenhagen

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Notes 1. This report can be downloaded on www.systematic.dk 2. 83.3% place greatest weight on improving competitiveness, followed by helping to integrate knowledge (71.1%) and ultimately training workers to meet the organisation’s strategic objectives (60.4%). 3. This part of the paper is based on the theory developed in an ongoing Ph.D project being conducted by Anja Baastrup at The Aarhus School of Business, Department of Accounting. 4. A special issue of Strategic Management Journal (1996, vol 17) was dedicated to covering organisational learning and Knowledge Management. 5. Psychology and organisational development (Grieves, 2000), management science, strategic management (Beeby and Booth, 2000; Grant, 1996; Spender, 1996), production management, sociology and cultural anthropology (Easterby-Smith, 1997). These various vantage points give rise to different ontological perspectives leading to various definitions and contents of organisational learning, not to mention diverse perceptions of the process itself (how does learning take place) and disagreement as to the subject of the learning process. 6. 41.5% was triggered by competitors, 39.6% was triggered by customers or clients, 20.8% from suppliers. 7. More than 30% of the respondents found this practice not applicable!


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Annex 5.1.

Methodology of the Danish Pilot Study Procedures and methods This study came into being as a result of a series of OECD meetings. Two of these meetings were held before the pilot study was undertaken, and are not therefore referred to in this report.1 The pilot study consisted of: ● Two meetings held by the Danish survey group, which consisted of leading

authorities in the fields of both Knowledge Management and survey techniques. ● A series of interviews conducted in 6 Danish organisations. ● A pilot survey carried out among 200 Danish organisations.

These procedures are illustrated in the sequence chart (Figure A5.1.1).

Survey group The group consisted of the following individuals: ● Benedicte Stakemann, Erhvervsfremme Styrelsen (Committee to Promote

Industry), Copenhagen ● Peter Stendahl Mortensen, Analyseinstitut for forskning (Institute of Analysis

and Research), Aarhus ● Marianne Stang Våland, Learning Lab Denmark, Copenhagen ● Henning Madsen, Handelshøjskolen i Aarhus (Aarhus Business School),

Aarhus ● Heine Larsen, Ementor Denmark A/S, Copenhagen and Handelshøjskolen i

København (Copenhagen Business School), Copenhagen ● Peter

Holdt Christensen, Institut for Ledelse, Politik og Filosofi, Handelshøjskolen i København (Institute of Management, Politics and Philosophy, Copenhagen Business School), Copenhagen

● Bettina Høst Poulsen, the former “Erhvervsministeriet” (The Danish Ministry

of Business Affairs), Copenhagen The group held two meetings – the first focussing on the original OECDquestionnaire, the second on the results of the preliminary interviews. Both meetings gave rise to valuable comments as to how to conduct the next stages of the study.

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The interviews Interviews were carried out in six different organisations: one large and one small manufacturing company, one large commercial enterprise, one large and one small service enterprise, and a research centre.2 The interviews were conducted in the following manner: The material was handed to the respondent, as if he/she had received it by regular mail. The respondent was then asked to verbally explain his/her thoughts while filling out the questionnaire. In this way the interviewers were able to get quite a good picture of the questions and formulations that caused difficulty, and in the process a number of modifications were made to the phrasing of individual questions.

The translation process In the first instance the OECD questionnaire was translated directly from Canadian-English into Danish, and the first interviews were carried out on the basis of this questionnaire. The experience of these interviews and the survey group meetings led – after considerable discussion – to a substantial reformulation of most of the questions, so that their meaning and significance were expressed more precisely in terms that made sense to the respondents. Translation is a critical factor in ensuring that a cross-border comparison of the results of the final survey can be made. Those countries that wish to participate in the eventual survey must be prepared to devote significant resources to the translation process, so that appropriate adjustments are made for differences of both language and management procedure.

The pilot survey The pilot survey was carried out in 400 enterprises in Canada, 200 in Germany and 200 in Denmark. In this pilot study Denmark chose not to link up with other databases, since the purpose of the pilot study was to test out and improve the questionnaire, rather than to conduct a representative study of Knowledge Management practices. Instead, the Danish questionnaire for the pilot survey was supplemented with a nu m b e r o f b a ck g ro u n d va r i abl e s . D u r i n g O c to b e r 2 0 0 1 t he D a n is h questionnaires were sent out with the aim of making a pilot survey which could be compared with the other pilot surveys in Canada and Germany. The respondents interviewed expressed the view that the questionnaire was too comprehensive, and several of them would have chosen not to fill it out. It was therefore felt necessary to devote further resources to obtaining as high a percentage of respondents as possible. A very large proportion of the respondents were therefore contacted by telephone before the questionnaire was sent out; similarly, respondents were reminded to return the questionnaire after the deadline had passed. As a result, 61 questionnaires were filled out and returned – representing a 30% response rate. There are strong indications that this response rate could not be obtained with an ordinary survey involving no telephone contact. Obtaining a reasonable rate of response is therefore another


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critical element in the success of the final survey, and methods of gathering data should therefore be discussed. For the pilot survey a random group of private firms and organisations was selected from a total database3 containing all Danish enterprises with more than 50 employees and all corporations and private limited companies with fewer than 50 employees (Tables A5.1.1 and A5.1.2). The number of respondents is too low to make a representative study, nor indeed was this the intention. However, efforts were made to ensure that the distribution of different types of enterprise in the survey – in terms of both size and trade corresponded approximately to that in the database as a whole. The distribution of the different types of enterprise within the survey is shown in the figures below.

Table A5.1.1. Distribution in the pilot survey by number of employees

Employees:

Distribution in the total database:

Distribution in the chosen population of 200 enterprises

Distribution among the questionnaires returned (61 respondents)

1-19

36%

33%

20-49

27%

26%

34% 27%

50-99

20%

16%

17%

100-249

10%

12%

14%

250-499

4%

3%

3%

500-1.999

3%

1%

2%

2000+

0%

9%

3%

Source: Institut for Ledelse

As can be seen, there is a relatively large percentage of enterprises with fewer than 20 employees, a fact that should be borne in mind when the results of the pilot study are analysed. Even though this gives a true picture of the private sector in Denmark, the relevance of including such small enterprises in the final study should be discussed.

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Table A5.1.2. Distribution in the pilot survey by sector

Trade:

Distribution in total database:

Not answered

-

Agriculture, fishing, primary product development Manufacturing

Distribution in test group of 200 respondents -

Distribution among questionnaires returned (61 respondents) 5%

2%

1%

2%

24 %

24 %

20% 2%

Energy and water supply

0%

1%

Building and construction

13 %

10 %

7%

Hotel and restaurant industry

25 %

26 %

21%

Transport, post and telecommunications

9%

8%

5%

Advisory and finance services

12 %

18 %

11%

Public and private service industries

15 %

12 %

3%

0%

0%

24%

Other Source: Institut for Ledelse

Who is included in the survey? The object of the study is the entire private sector. The questionnaire is aimed at the top manager of a given organisation, i.e. the administrative director (chief executive officer), since it is the top manager who may be expected to have overall strategic insight. In the Danish questionnaire, however, no instructions were given as to who was to fill it out. The reason for this was that we hoped to reveal relevant target groups by asking at the end of the questionnaire who in fact had completed it. It has not yet been determined whom the final OECD questionnaire should be aimed at.

Figure A5.1.1. Sequence chart of the Danish pilot study

Reporting: Interim report to the former Ministry Design Translation & Interviews Gathering of data Analysis of of Business Affairs , 17.12.2001 of questionnaire adaptation for pilot survey data from Report to OECD: January 2002 pilot survey Final report to former Ministry of Business Affairs: 2 weeks after fourth OECD meeting June 2001 March 2002

} Third OECD meeting1 First survey group meeting

Second survey group meeting

Fourth OECD meeting Pilot reports from Canada, Germany, Denmark circulated

1. The first OECD meeting was held in February 2001. The Centre for Management has participed in the project since June 2001. Source:



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Annex 5.2.

Which practices has the greatest results? Figure A5.2.1. shows how the Danish respondents evaluated the effect of implementing Knowledge Management practices. It can be seen from this that Knowledge Management activities are seen as having been most effective in the area of improving workers’ skills and knowledge.

Figure A5.2.1. Result achieved from the Knowledge Management activities Result –improved skills and knowledge of workers

2.78

Result –increased our adaptation of products or services to client requirements

2.75

Result –increased our knowledge sharing horizontally

2.75

Result –helped us add new products or services

2.73

Result –improved client or customer relations

2.64

Result –improved worker efficiency

2.63 2.60

Result –increased our knowledge sharing vertically Result –improved our corporate or organisational memory Result –improved involvement of workers in the work place activities

2.58 2.51

Result –new supplier relations (AQ)

2.44 2.39

Result –other new cooperators (AQ) Result –increased flexibility in production and innovation

2.38

Result –increased our ability to capture knowledge from other business enterprises, technical literature, etc. Result –increased our ability to capture knowledge from public research institutions Result –increased our number of markets (more geographic locations)

2.35 2.14 2.07

Result –prevented duplicate research and development

1.00

2.00

1.50

2.00

2.50

3.00

4: Very effective, 3: Effective, 2: Somewhat effective, 1: Not at all effective

Source:


If we compare the answers to this question (results of practices implemented) with the answers to question 1 (types of practices) there is no very clear correlation. Surprisingly (in view of the above) there is nothing to suggest that a higher level of activity4 improves workers’ knowledge and skills. This is an interesting contradiction, which merits further investigation.

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Table A5.2.1 shows the extent to which individual results have an important explanatory effect on the level of activity. The tendency indicated here is that activities under the headings ‘policies and strategies’, ‘training and mentoring’ and ‘leadership’ have the greatest impact on results.

Table A5.2.1. The explanatory effect of results on level of activity5

Results

Average level of activity Knowledge Training and Communicapture and Policies and mentoring cations Leadership acquisition strategies activities activities activities

Incentives


0.662

0.802

0.924

0.115

0.933

0.114

Increased our adaptation of products or services to client requirements

0.819

0.174

0.560

0.401

0.957

0.604

Increased our knowledge-sharing horizontally (across departments/ functions)

0.026

0.017

0.647

0.006

0.103

0.141

Helped us to add new products or services

0.997

0.435

0.381

0.078

0.787

0.077


0.576

0.047

0.171

0.062

0.202

0.071

Improved worker efficiency and/or productivity

0.894

0.034

0.355

0.339

0.018

0.091

Increased our knowledge-sharing vertically (up through the organisational hierarchy)

0.004

0.232

0.477

0.001

0.108

0.108

Improved our corporate memory

0.127

0.000

0.126

0.003

0.009

0.595

Improved involvement of workers in workplace activities

0.457

0.146

0.232

0.017

0.717

0.084

Led to new supplier relations (only in Danish survey)

0.430

0.011

0.028

0.040

0.069

0.112

Led to new partnerships (only in Danish survey)

0.837

0.652

0.436

0.078

0.047

0.685


0.128

0.004

0.143

0.003

0.038

0.025

Improved our ability to capture knowledge from other business enterprises, unions, trade literature etc

0.102

0.260

0.358

0.068

0.799

0.748

Increased our ability to capture knowledge from public research institutions, including universities and other state research institutions

0.144

0.141

0.372

0.009

0.012

0.554

Increased our number of markets (more geographic locations)

0.340

0.778

0.054

0.094

0.527

0.737

Prevented unintended duplication of similar research and development projects

0.107

0.033

0.118

0.016

0.319

0.707

Source: Institut for Ledelse


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II.5.


Notes of the Annexes 1. The Centre of Management participated in the third OECD meeting in July 2001. 2. The latter is not in the target group (private firms). However, at this point in the survey it was thought relevant to test out the questionnaire in an organisation whose existence is based on the ability to gather and process knowledge, since an organisation of this kind could be expected to have given thought to the management questions under consideration. 3. Købmandsstandens CD-direct (The Business World’s CD-Directory) 4. Level of activity’ refers to the length of time that a given Knowledge Management activity has been practiced. Thus the statement ‘Yes, we have done this since before 1999’ is considered indicative of a higher level of activity than the statement ‘Yes, we have done this since 1999.’ 5. The table shows the significance (bold text) by comparing the average level of activity per cluster of sub-questions in Question 1 (dependent variable), with the result variables in Question 4 divided into 2 levels: high effect (very effective and effective) and low effect (somewhat effective and not effective) (independent variable).

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Bibliography Beeby, M. and C. Booth (2000), “Networks and Inter-organisational Learning: A Critical Review”, The Learning Organisation, vol 7, no. 2, pp. 75-88. Burns and Stalker (1961), The Management of Innovation, Tavistock, London. Center for Ledelse (2002), “Danish Pilot-survey for OECD Knowledge Management Survey”. Chenhall, R. (2003 - forthcoming), “Management Control systems design within its organisational context: Findings from contingency based research and directions for the future”, Accounting, Organisations and Society. Ch ris tensen, P. H (2000) (ed.), Vi den o m – ledel se, viden og virksomheden, Samfundslitteratur. Crossan, M. and T. Guatto (1996), "Organisational learning research profile", Journal of Change Management, vol. 9, no 1, pp. 107-112. Drucker (1993), Post-Capitalist Society, HarperBusiness, New York. Easterby-Smith, M. (1997), “Disciplines of Organisational Learning: Contributions and Critiques”, Human Relations, vol 50, no. 19, pp. 1085-1113. Galbraith, J. (1973), Designing Complex Organisations, Addison-Wesley, Mass. Grant, R. M (1996), “Toward A Knowledge-Based Theory of the Firm”, Strategic Management Journal, vol 17, Winter Special Issue, pp. 109-122. Grieves, J. (2000), “Introduction: The Origins of Organisational Development”, Journal of Management Development, vol 19, no. 5, pp. 345-447. Hedberg, B. (1981), “Handbook of Organisational Design”, in Adapting Organisations to their Environments, P.C. Nystrom and Starbuck (eds), vol 1, W.H, Oxford. Kaplan, R. P and D.P. Norton (2001), The Strategy-focused Organisation: how balanced companies thrive in the new business environment, HBSP, Boston, Mass. Kaplan, R.P and D.P. Norton (1996), The Balanced Scorecard – translating strategy into action, HBSP, Boston, Mass. Kim, D.H (1993): “The Link Between Individual and Organisational Learning”, Sloan Management Review, Fall, pp. 37-50. Langfield-Smith, K. (1997), “Management Control Systems and Strategy – A critical Review”, Accounting, Organisations and Society, vol 22, no. 2, pp. 207-232. Nonaka, I. and H. Takeuchi, H (1995), The Knowledge-Creating Company, Oxford University Press. Perrow, C (1967), “A framework for the comparative analysis of organisations”, American Sociological Review, pp. 194-208. Polanyi, M. (1966), The Tacit Dimension, Routledge, London. Prange, C. (1999), “Organisational Learning – Desperately Seeking Theory”, in M. Easterby-Smith, J. Burgoyne and Araujo, Organisational Learning and the Learning Organisation – Developments in theory and practice, Sage, London. Roos, J., G. Roos, G., L. Edvinsson and N.C. Dragonette (1997), Intellectual Capital Navigating in the New Business Landscape, Macmillan Business, London. Simons, R. (1995), Levers of Control, Harvard Business School Press, Boston, Mass.


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Spender, J.C. (1996), “Pluralist Epistemology and the Knowledge-based Theory of the Firm”, Strategic Management Journal, vol 5, no. 2, pp. 233-256. Von Krogh, G., K. Ichijo, K. and I. Nonaka (2000), Enabling Knowledge Creation – How to Unlock the Mystery of Tacit Knowledge and Release the Power of Innovation, Oxford University Press.

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PART II

Chapter 6

Knowledge Management, Innovation and Productivity: A Firm Level Exploration Based on French Manufacturing CIS3 Data1 by Elizabeth Kremp

(SESSI)2

and Jacques Mairesse (CREST-INSEE)3

In modern knowledge driven economies, firms are increasingly aware that individual and collective knowledge is a major factor of economic performance. The larger the firms and the stronger their connection with technology intensive industries, the more are they likely to set up knowledge management (KM) policies, such as promoting a culture of information and knowledge sharing (C), motivating employees and executives to remain with the firm (R), forging alliances and partnerships for knowledge acquisition (A), implementing written knowledge management rules (W). The French 1998-2000 Community Innovation Survey (CIS3) has surveyed the use of these four knowledge management policies for a representative sample of manufacturing firms. The micro-econometric analysis of the survey tends to confirm that knowledge management indeed contributes significantly to firm innovative performance and to its productivity. The impacts of adoption of the four surveyed KM practices on firm innovative and productivity performance are not completely accounted by firm size, industry, research & development (R&D) efforts or other factors, but persist to a sizeable extent after controlling for all these factors. These four practices also appear to be strongly complementary, in the sense that firms tend to adopt them jointly, but also in the sense that their impacts on firm performance tend to be cumulative. The specific impacts of the individual practices are not statistically different on firm innovative performance, measured in terms of propensity and intensity of innovation and patenting. What seems to matter is the number of different KM practices that firms implement, which we can interpret as proxying for “knowledge management intensity” (KMI). For labour productivity, however, adopting an incentive policy to retain employees and executives in the firm comes clearly first, and promoting a culture of knowledge sharing comes second, while the estimated impacts of the other two policies are not statistically significant.


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II.6.

KNOWLEDGE MANAGEMENT, INNOVATION AND PRODUCTIVITY IN FRANCE

6.1. Introduction In the knowledge driven economy, firms are becoming more and more aware of the fact that knowledge is a resource requiring explicit and specific management policies and practices to be acquired, processed and exploited efficiently.4 Among other objectives, the role of knowledge management (KM) policies and practices is to foster all types of firm innovation, whether process or product oriented or mainly organizational, and to improve firm productivity and its medium- and long-term competitive advantage.5 As part of the pilot project initiated by OECD and Statistics Canada to study firm KM behavior, SESSI, the statistical Agency of the French ministry of manufacturing industries, has introduced a set of four new questions, specifically relating to important and relatively well-defined KM policies, in the French Third Community Innovation Survey (CIS3).6 They respectively concern the existence in the firm of a written policy (W) of knowledge management, of a culture (C) of knowledge sharing, of a policy of retention (R) of employees and executives, and of alliances (A) and partnerships for knowledge acquisition (see Box 6.1). In the first section of our exploratory study, we document the diffusion of these four KM policies among French manufacturing firms in 2000, and that of three other related practices (also surveyed in CIS3). In the second section we provide evidence on the complementarity of KM policies, in the sense that firms tend to adopt them jointly, and we introduce an indicator of intensity of knowledge management (KMI). In the next two sections we make an attempt to assess the impacts of implementing KM policies on firm performance, c o n t r o l l i n g f o r a n u m b e r o f o t h e r f a c t o r s , a n d i nv e s t i g a t e t h e i r complementarity also in the sense that their impacts are cumulative. In the fourth section we consider four indicators of firm innovative performance, the propensity and intensity in innovating and in patenting on products, while in the fifth we look similarly at firm productivity. We briefly conclude in the last section.

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II.6.


BOX 6.1 – Knowledge Management in the Third Community Innovation Survey (CIS3) for French manufacturing The Third Community Innovation Survey, which covers the period 1998-2000, was conducted in France jointly by INSEE and the Statistical Departments of the three Ministries respectively in charge of the manufacturing industries, agriculture, and commercial, financial and research and engineering services. It is a mandatory survey. The SESSI (Service des Études et Statistiques Industrielles) was in charge of surveying some 5 500 manufacturing firms with 20 employees or more. Firms have been chosen randomly, using the business register based on legal units and according to the following stratified sampling design: ●

all firms over 500 employees

●

1/2 for firms from 100 to 499 employees

●


●


The rate of response was of 86%, corresponding to an overall coverage of 89% of the total turnover for the manufacturing sector in 2000. See below the paragraph on the weighting of the results presented in this study. The four questions on Knowledge Management… Four questions directly referring to firm policies and strategies of knowledge management have been introduced in the French CIS3 for manufacturing industries. These questions have been chosen as particularly meaningful among the 23 questions on knowledge management considered in the pilot survey by Statistics Canada (L. Earl and F. Gault, 2003) They are the following: ●

By the end of 2000, did your firm have a written knowledge management policy? (W)

●

Did it have a culture to promote knowledge sharing? (C)

●

Did it put into practice an incentive policy to retain employees and executives in firm? (R)

●

Did it forge partnerships or alliances for knowledge acquisition? (A)

…and three other related ones. The French CIS3 for manufacturing industries also includes three other questions which can be related to the KM policies. They concern the adoption of new management practices in general and the use of Internet and ICT to acquire and share information for innovation purposes. They are the following: ●

From 1998 to 2000, did your company implement new managerial methods?

●

Do you use the Internet to acquire information (from the different possible sources, whether internal or external, private or public) for your innovating activities?


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II.6.


BOX 6.1 – Knowledge Management in the Third Community Innovation Survey (CIS3) for French manufacturing (cont.) ●

Do employees use ICT resources (data updates, Intranet, and so on) to share information from external sources?

Note that, since the answers to these last two questions on Internet and ICT are strongly correlated, we pooled them as one binary indicator in our econometric analysis. Note also that these questions were only asked to the innovating firms (that is, in accordance to the definitions of the OECD Oslo Manual, firms which have introduced new or significantly improved products or production processes during the 1998-2000 period). Weighting of results The descriptive statistics shown in Figures 6.1 to 6.4 and Table 6.1, and in Tables A6.1.1 and A6.1.2 in the Annex, are weighted to be representative of the manufacturing sector (i.e., in order to take into account the differences by size and industry in the sampling and response rates). However, the descriptive statistics in Table A6.1.4 and the econometric estimates presented in Figures 6.5 and 6.6 and Tables 6.2 and 6.3, as well as in Tables A6.1.3 to A6.1.5, are not weighted. We have simply introduced size and industry indicators in all the estimated econometric models. We have also checked that the weighted econometric estimates were not meaningfully different from the unweighted ones.

6.2. Diffusion of Knowledge Management An increasing concern… Several reasons explain the increasing concern of firms for knowledge management. Firms have to deal with a more complex world because of rapidly changing technologies. Information and communication technologies (ICT) are ubiquitous, creating new needs and requiring appropriate organizational structures, facilitating the automation of some tasks and the outsourcing of others, supporting technological watch and improving access to external knowledge. Firms have to react faster to keep their competitive edge and to be able to build on all or part of their past experience. They are more and more aware of the fact that their competencies largely rely on individuals and on tacit knowledge special to the company. They are worried about the loss of skills caused by the mobility of their personnel and are striving to motivate their employees and executives to remain within the company, improving their career and remuneration prospects, setting up training courses and encouraging professionalism. Firms are also aware that

146


II.6.


they cannot maintain and develop their knowledge by relying only on internal forces. They have to form alliances and partnerships with other firms, competitors as well as suppliers and clients, to acquire new knowledge and expertise.

…leading to the adoption of knowledge management practices… O ve r t h e p as t ye a r s , f i rm s h ave a d op t ed d i f fe re n t k now l e d g e management practices. In 2000, in manufacturing industries, nearly one out of two have implemented at least one of the four KM policies identified in the Fren ch C IS3 q ues ti onn a ire ( se e Fi gure 6.1 ). M ore p re ci se ly, 2 8% of manufacturing firms with 20 employees or more declared that they have a culture to promote knowledge sharing (C), and almost as many (27%) that they set up an incentive policy to keep employees and executives in the firm (R). Likewise, 23% of them forged alliances or partnerships for knowledge acquisition (A), and significantly less (17%) put into practice a written knowledge management policy (W).

…especially in large firms… The diffusion of KM policies is much more widespread in large than in small firms (see Figure 6.1). Setting up a special organization is much less critical, and more costly, in smaller firms where information circulates more easily and informal procedures can be efficient. In the larger firms, on the other hand, identifying the experts (the knowledge holders) within the company is essential vis-à-vis other employees and working with outside experts is an important asset. In 2000, almost four out of five (80%) of the firms with 2 000 employees or more declared they had a knowledge sharing culture (C) or alliances for knowledge acquisition (A), while only one out of five (20%) of those with 20 to 49 employees said so. Likewise, adopting a written knowledge management policy (W) is much more frequent in the large firms: one out of two (50%) of the firms with 2 000 employees or more had one, and merely one out of ten (10%) among the smaller firms. By contrast to large firms, small firms are likely to be more dependent on the expertise and know how of a few number of their employees, and much more concerned if they leave. That is possibly why the adoption of a policy to retain employees in the firm (R), even if much less common in the smaller firms than in the larger ones, is somewhat more frequent relative to the adoption of the three other policies.


147

II.6.


Figure 6.1. Diffusion of Knowledge Management Practices by Firm Size 20 to 49 employees 500 to 999 employees

50 to 99 employees 1 000 to 1 999 employees

100 to 249 employees 2 000 employees or more

250 to 499 employees Total

% 80 70 60 50 40 30

28

27 23

20

17

10 0

C: Knowledge sharing culture

R: Incentive policy to retain employees

A: Alliances for knowledge acquisition

W: Written KM policy

Scope: Manufacturing firms with 20 employees or more (excluding the food industry), weighted results. Source:

Sessi, CIS3 Survey.

…and in technology intensive industries. KM policies are also particularly widespread in the high and mediumhigh tech industries, such as the pharmaceutical industry, aeronautic and space construction or electronic component manufacturing (see Figure 6.2). In these industries, 40% to 45% of the firms have implemented policies to foster knowledge sharing (C), to retain employees (R), or to establish partnerships to acquire knowledge (A), and about 25% have adopted a knowledge written policy (W). The diffusion of KM policies is about half less prevalent in the low tech industries such as clothing and leather, publishing, printing and reproduction, or home equipment.

Knowledge management policies are more frequent in firms implementing new management methods… From 1998 to 2000, in the manufacturing industries, one firm out of five has implemented new methods of management in the broad sense, that is, with respect to other corporate functions, rather than just knowledge manag ement. A good example is the development of project-based management practices that altered existing work relations within companies, and led t o the pro g re ss of co rpo ra te cro ss-d epart mental c ultu re. Unsurprisingly, knowledge management is more widespread in firms that

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II.6.


have adopted such new management methods (see Table 6.1). Among these firms three out of four (76%) have also implemented at least one of the four KM practices, while among firms that have not adopted new management methods, this is the case of less than two out of five (37%).

Figure 6.2. Diffusion of Knowledge Management Practices by Technology Intensive Industries Low technology

Medium-low

Medium-high

High

Total

% 60

50

40

30

28

27 23

20

17

10

0

C: Knowledge sharing culture

R: Incentive policy to retain employees

A: Alliances for knowledge acquisition

W: Written KM policy

Definition: The classification of industries by technological intensity is mainly based on the average ratio of R&D to output of the industry at the CITI rev2 level. See Table A6.1.1 in the Annex. Scope: Manufacturing firms with 20 employees or more (excluding the food industry), weighted results. Source:

SESSI, CIS3 Survey.

…in firms making R&D investments, innovating and patenting… Knowledge management is also prevalent among firms investing in research and development (R&D), innovating and patenting. In 2000, 30% of French manufacturing firms with 20 employees or more have invested in R&D, and 20% have patents on products protecting part of their output, while from 1998 to 2000 about 35% have generated innovations on products or processes. The diffusion among these firms of all four KM practices is at least double than for the non innovating or non R&D doing firms and at least 60% higher than for the non patenting firms (see Table 6.1).


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Table 6.1. Diffusion of Knowledge Management Practices, according to the Adoption of New Management Methods, to R&D and Innovating Activities, to Internet and ICT Use % of firms having Among

% of firms

All firms R&D doing firms

30%

Knowledge sharing culture

At least Incentive KM Alliances for Written one of the intensity policy to knowledge KM four retain acquisition policy policies employees

28

27

23

17

45

0.9

45

42

39

28

71

1.6 0. 7

NON R&D doing firms

70%

20

20

15

12

34

Innovating firms

34%

41

42

38

26

68

1.5

NON innovating firms

66%

19

19

14

12

34

0.7

Firms with patents

20%

40

39

35

26

62

1.4

Firms with NO patent

80%

25

24

20

15

41

0.8

Firms having adopted new management methods

21%

51

47

42

29

76

1.7

Firms NOT having adopted new management methods

79%

21

21

17

14

37

0.7

–Using the Internet and ICT for acquiring and sharing information

28%

62

56

51

39

82

2.1

–NOT using the Internet and ICT for acquiring and sharing information

68%

37

36

34

21

63

1.3

Innovating firms which are:

Among all firms, 28% of them have implemented a knowledge sharing culture, 45% have adopted at least one of the four KM policies. Among all firms, 30% of them do R&D, 70% do not. Among the R&D doing firms, 45% of them have implemented a knowledge sharing culture; etc… Definitions: The innovating firms are firms earning a turnover from new or significantly changed products on the market from 1998 to 2000 (in %). The firms with patents are firms having patented products in 2000 (in %). Scope: Manufacturing firms with 20 employees or more (excluding the food industry), weighted results. Source: SESSI, CIS3 Survey.

…and in innovating firms that use the Internet and ICT to acquire and share information. As part of their strategy to foster innovation, firms make specific efforts to gain better information on technologies, products and materials, as well as about their customers, suppliers and competitors. They find such information from a wide range of sources: from universities and public or private research laboratories, in technical and economic databases, in professional journals and conferences, trade fairs and exhibitions. Indeed, 40% of innovating firms state that they use the Internet to acquire information for their innovating activities, 35% that they take advantage of ICT resources to share such information between employees, and 25% that they do both. Among this last group of firms, about 60% have a knowledge sharing culture (C) and 40% a written knowledge management policy (W), that is twice as many as for all manufacturing firms (see Table 6.1).

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6.3. Complementarity of Knowledge Management Practices Firms tend to adopt knowledge management practices jointly, … Looking at the occurrence of joint adoption of two among the four KM policies shows that firms view them as complementary and suggests that the basic reasons of their adoption are similar. Firms that implement one KM policy are much more likely to adopt a second one than firms which have not implemented the first one (see Figure 6.3 and Table A6.1.2 in the Annex). For instance, three out of five firms, among the 28% which have a knowledge sharing culture (C), also implement an incentive policy to keep employees (R); one out of two also develops partnerships to acquire knowledge (A), and about one out of two has also a written knowledge management policy (W). On the other hand, among the 72% of firms declaring they did not have a culture of knowledge sharing, only one out of eight sets up partnerships for knowledge acquisition (A) or implements an incentive policy for employees’ retention (R), and fewer than one out of sixteen have a written knowledge policy (W). The complementarity of knowledge management practices is reflected in the high correlations, ranging from 0.30 to 0.50, which we find between the binary indicators of adoption of the four KM policies (see Table A6.1.3 in the Annex). It is also confirmed by the fact that such correlations remain high when we try to control for various factors of adoption. The partial correlations between the four KM policies indicators, conditional on size and industry of the firms, and other control variables (i.e., the ones we also take into account in sections 6.4 and 6.5 when investigating the impacts of KM practices on innovation and productivity) are still in the range of 0.15 to 0.40 (see Table A6.1.3 in the Annex).

…which suggests the definition of a knowledge management intensity indicator. The easiest way to take into account the complementarity of the different KM practices is to define a KM intensity indicator (KMI) as being simply the number of adopted practices. This indicator is thus equal to zero for a firm if the firm implements none of the four KM policies, and respectively to one, two, three or four, if it adopts at least one practice, two, three, or all four. It can be shown that KMI roughly corresponds to the first component in a principal factor analysis (or multiple correspondence analysis) of the correlation matrix (or the contingency table) of the four KM policies binary indicators. As expected from the pattern of adoption of each individual practice, KM intensity increases strongly with the size of the firm as well as with the industry technology intensiveness (see Figure 6.4). It is about 2.7 in firms with 2 000 employees or more as against 0.7 in firms with 20 to 49 employees. Likewise, it averages about 1.6 in high-tech industries and about 0.7 in low-tech intensity industries.


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Figure 6.3. Complementarity of Knowledge Management Practices (R) % of firms with an incentives policy to retain employees (A) % of firms with alliances for knowledge acquisition (W) % of firms with a written KM policy

(C) % of firms with a knowledge sharing culture

% 80 (C)

70 (C)

(R)

(C)

60

(R) (R)

(A)

(A)

50

(A)

(W)

40

(W) (W)

30 20

(C)

(R) (A)

10

(C)

(C) (R) (A)

(R) (A)

(W)

(W)

(W)

0 Firms with Firms a knowledge with no sharing knowledge culture sharing culture

Firms with an incentives policy to retain employees

Firms with no incentives policy to retain employees

Firms with alliances for knowledge acquisition

Firms with no alliances for knowledge acquisition

Firms with a written KM policy

Firms with no written KM policy

Among the 28% of firms having a culture of knowledge sharing, 62% have an incentive policy to retain employees, 49% have alliances for knowledge acquisition, and 45% a written policy of knowledge management. Among the 72% of firms NOT having a culture of knowledge sharing, 13% have an incentive policy to retain employees, 12% have alliances for knowledge acquisition, and 6% have a written policy of knowledge management. Scope: Manufacturing firms with 20 employees or more (excluding the food industry), weighted results. Source:

SESSI, CIS3 Survey.

6.4. Knowledge Management and Innovation Simple descriptive statistics show that the diffusion of KM practices is far from being complete among innovating firms or firms with patents, although much more advanced than among non innovating and non patenting firms (see Table 6.1). It thus makes sense to try to estimate the specific impact of adoption of KM practices on firm innovative performance, controlling for other (observed) factors and firm characteristics. To assess firm innovative performance, we can use four variables from CIS3. The first two are the “propensity to innovate” and the (product) “innovation intensity”, that is the binary indicator of whether the firm “has introduced during the period 1998-2000 any new or significantly improved products”, and if yes “the share of turnover from these new or significantly improved products in

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the overall turnover of the firm in 2000”. The other two variables, defined in an analogous way, are the “propensity to patent” and the “patent intensity”, that is the binary indicator of whether the firm “has any valid product patent at the end of 2000” and if yes “the share of turnover protected by patents in the overall turnover of the firm in 2000”. The average propensities to innovate and to patent are respectively about 35% and 20%, while the average innovation intensity is about 15% for the innovating firms and the average patent intensity about 30% for the firms with patents (see Table A6.1.4 in the Annex).

Figure 6.4. Knowledge Management Intensity by Size and Technology Intensive Industries Knowledge management intensity 3.0

2.5

2.0

1.5

1.0

0.5

0 20-49 50-99 100-249 250-499 500-999 1 000- > = 2 000 1 999 employees employees Firm size

Low tech.

Medium- Medium- High low high

Technology intensive industries

Definitions: The intensity of knowledge management is equal to zero when the firm implements none of the four KM practices; and to 1, 2, 3 or 4 respectively, when the firm implements at least one, two, three, or all four. The classification of industry by technological intensity is mainly based on the average ratio of R&D to output of the industry at the CITI rev2 level. See Table A6.1.1 for some indications about the link between classification of industries by technological intensity and the NES36 classification. Lecture: Firms with more than 2 000 employees have a knowledge management intensity of 2.7; firms belonging to the high-intensive industries have a knowledge management intensity of 1.6. Scope: Manufacturing firms with 20 employees or more (excluding the food industry), weighted results. Source:

SESSI, CIS3 Survey.


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The fact that the innovation and patent intensity variables can only be known for the innovating and patenting firms is a very likely source of selectivity, which would result in biased estimates if we were to estimate the intensity relations separately from the propensity relations. Thus instead of simply considering independent (or seemingly unrelated) regressions to estimate the impact of know ledg e manag em ent on the innovative performance variables, we consider jointly the propensity and intensity relations within the framework of a generalized tobit model. The tobit model allows to correct for selectivity biases in the intensity relation (or outcome equation) by specifying explicitly its linkage with the propensity relation (or selection equation), both through the correlation of the unobserved error terms in the two equations and through the sets of explanatory variables in these equations (i.e., the KM variables and the control variables).7 As control variables in the propensity and intensity equations of our tobit model specification, we use all the available variables in CIS3 which we thought relevant: the firm size (i.e., by means of seven binary indicators, or six in addition to the constant) and industry (i.e., by means of fourteen binary indicators, or thirteen in addition to the constant), R&D intensity for R&D doing firms, and three other binary indicators for belonging to a group, for using new management methods, and for not doing R&D. We can also introduce in the innovation and patent intensity equations another binary indicator to control for the acquisition and sharing of information using the Internet and other ICT tools.8 The mean and standard deviations, and more precise definitions of the control variables, are given in Table A6.1.4 in the Annex. In view of the strong complementarity of KM practices, we consider in fact four different specifications of the tobit model. In the first and simplest specification, or model 1, we use our KM intensity variable (KMI) as the only KM explanatory variable in the propensity and intensity equations, thus assuming that the individual impacts of the four KM practices are both (roughly) equal and linearly cumulative in the two equations. In the next two specifications, or models 2 and 3, we introduce, instead of KMI, four binary indicators in the propensity and intensity equations. In model 2, these indicators respectively correspond to the use of only one, or two, or three, or all four KM practices (i.e., KMI=1, 2, 3 or 4), thus still implying that the impacts of the four practices are equal but allowing them to be more or less (non linearly) cumulative. In model 3, they simply correspond to the separate use of each of the four KM practices (i.e., KMC=1, KMR=1, KMA=1, KMW=1), thus allowing that the impacts of the four practices be different and more or less cumulative. In the last and most general specification, model 4, we introduce, in addition to the four KM practices indicators, all their possible interactions, that is eleven other binary indicators (i.e., six “2 by 2” interactions such as KMC*KMR=1, four “3 by 3” interactions such as KMC*KMR*KMA=1, and the “4 by 4” interaction KMC*KMR*KMA*KMW=1 which is identical to KMI=4). Clearly model 1 is nested in the other three models, while models 2 and 3 are also nested in model 4, thus

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permitting us to test whether these models provide statistically different pictures: that is whether the four KM policies appear interchangeable and more or less cumulative, in terms of their impacts on firm innovative performance.

Table 6.2. Estimated Impacts of Knowledge Management on Firm Innovation and Productivity, Controlling for Other Relevant Factors Impacts in %

Propensity to innovate

Innovation intensity

Number of firms 3 474 1 635 Mean of left hand variable 47.1 15.8 Model 1: regression with the KM intensity variable KM intensity 4.0*** 1.6*** Log likelihood –4226.49 Root MSE 1 19.18 Rho 0.73 Model 2: regression with 4 KM intensity binary indicators KM intensity=1 6.3*** 3.5** KM intensity=2 10.0*** 3.8*** KM intensity=3 11.6*** 4.4*** KM intensity=4 15.7*** 7,5*** Log likelihood –4224.00 Root MSE 1 19.18 Rho 0.73 Model 3: regression with the 4 KM practices indicators (C): Knowledge sharing culture 2.8* –1.6 (R): Incentive policy to retain employees 6.4*** 3.2*** (A): Alliances for knowledge acquisition 4.9*** 1.8* (W): Written KM policy 1.6 1.7* Log likelihood –4222.26 Root MSE 1 19.13 Rho 0.73 Model 4: regression with fully interacted KM practices indicators Log likelihood –4208.16 Root MSE 1 19.02 Rho 0.73

Propensity to patent

Patent intensity

Labour productivity

3 474 32.4

1 125 30.5

3 419 5.64

1.6*** 3.1** –4089.63 1 67.73 0.94

3.0*** –1650.55 39.36

1.3 5.8 2.7 5.4 4.1* 7.6 7.1*** 14.9** –4088.84 1 67.77 0.94

7.1*** 5.6*** 9.0*** 13.3*** –1647.80 39.35

0.5 3.3** 0.5 2.3 –4088.34 1 0.94

67.62

5.0 *** 10.3*** –1.8 –3.5 * –1632.72 39.17

67.11

–1623.96 39.14

–1.2 7.7** 1.0 5.0

–4080.53 1 0.94

The generalized tobit models for innovation (columns 1 and 2) and patents (columns 3 and 4) are estimated by the method of maximum likelihood. The linear regression model for labour productivity (column 5) is estimated by ordinary least squares (which coincides with maximum likelihood for the estimated coefficients and practically for their standard errors). ***, **, and * respectively indicate that the estimated coefficients are statistically significant at the 1%, 5% or 10% confidence level. These coefficients are directly given in the table in terms of the marginal effects computed at the sample means, respectively as a probability in % for the propensity to innovate and to patent equations, and as a share in % for the corresponding intensity equations. These estimated coefficients coincide with the (constant) marginal effects for the productivity equation. Rho is the estimated correlation coefficient between the error terms of the propensity and intensity equations of the generalized tobit models. All equations also include 14 industry indicators and 7 firm size indicators and the other relevant factors as defined in Table A6.1.4 in the Annex. The coefficients (in terms of marginal effects) of all these other relevant factors are given in Table A6.1.5 in the Annex for the Model 1. Scope: Manufacturing companies with 20 employees or more (excluding the food industry), not weighted. Source: SESSI, CIS3 Survey.


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Table 6.3. Tests of the Regression Model with KM Intensity against Models with Four KM Intensity Binary Indicators, and the Four KM Practices Binary Indicators Alone or Fully Interacted Innovation propensity and intensity

Patent propensity and intensity

Productivity

2.49 (6) 87%

0.79 (6) 99%

2.8 (3) 43%

4.23 (6) 65%

1.29 (6) 97%

17.8 (3) 0%

18.33 (28) 92%

9.1 (28) 100%

26.6 (14) 2%

15.84 (22) 82%

8.3 (22) 100%

23.8 (11) 1%

14.1 (22) 90%

7.8 (22) 100%

8.8 (11) 64%

Model 1 against model 2 Chi2(n) P-value in % Model 1 against model 3 Chi2(n) P-value in % Model 1 against model 4 Chi2(n) P-value in % Model 2 against model 4 Chi2(n) P-value in % Model 3 against model 4 Chi2(n) P-value in %

Chi2(n) test statistics are directly computed on the base of the maximum log-likelihood values given for models 1, 2, 3 and 4 in Table 6.2.The number of degrees of freedom n is the difference in the number of KM parameters between the encompassing model and the model tested. Scope: Manufacturing companies with 20 employees or more (excluding the food industry), not weighted. Source: SESSI, CIS3 Survey.

The estimated impacts of the KM indicators (given directly in terms of the marginal effects on the propensity and on the intensity computed at the sample means, respectively as a probability in % and as a share in %) are reported in Table 6.2 for our three first models, and also represented graphically in Figure 6.5. For model 1 these impacts are all statistically very significant; for models 2 and 3 most of them are also very significant in the innovation propensity and intensity equations, while only a few are in the patent propensity and intensity equations. 9 Table 6.2 also reports the (maximum) log-likelihood values for the first three models, as well as for model 4, from which we can simply compute the log-likelihood tests of model 1 against models 2, 3 and 4, and of models 2 and 3 against model 4. These tests are reported in Table 6.3. They show very clearly that the more parsimonious model 1, with the KM intensity variable, cannot be statistically rejected against the other three models, even with a very low critical level of significance. Model 1 can thus be viewed as the (statistically) preferred model. The marginal effects of all variables, not only KM intensity but the R&D doing binary indicator, R&D intensity and the other control variables, are shown for this model in Table A6.1.5 in the Annex.

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Figure 6.5. Estimated Impacts of Knowledge Management Practices on Innovation Performance, “all else equal” KM intensity impact

KM intensity binary indicators

Additional impact of each KM policy

18 16

Propensity to innovate

14 12


10 8 6 4 2 0

KMI = 0

KMI = 1

KMI = 2

KM intensity impact

KMI = 3

KMI = 4

KMI = 0


KMI = 1

KMI = 2

KMI = 3

KMI = 4


17 15

Intensity to innovate

Intensity to patent

13 11 9 7 5 3 1 -1 KMI = 0

KMI = 1

KMI = 2

KMI = 3

KMI = 4

KMI = 0

KMI = 1

KMI = 2

KMI = 3

KMI = 4

-3

The figure illustrates the estimated impacts of the adoption of the KM practices for the four innovation and patent propensity and intensity variables, where: • the continuous straight line corresponds to the tobit model using the KM intensity variable, varying from 0 to 4 (Model 1, Table 6.3); • the small-dotted line corresponds to the tobit model using four KM intensity binary indicators, varying from 0 to 1 sequentially (Model 2, Table 6.3); • the long-dotted line corresponds to the tobit model using the four KM indicators, varying from 0 to 1 in the following order: KM Culture (C), KM Retention policy (R), KM Alliance policy (A), KM Written policy (W) – where this order is in fact irrelevant (Model 3, Table 6.3). Scope: Manufacturing firms with 20 employees or more (excluding the food industry), not weighted. Source:

SESSI, CIS3 Survey.


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Our main results concern the statistical and economic significance of the estimated impacts of KM intensity. Regardless of their size and industry, of their R&D efforts, of whether they belong to a group and have implemented new management methods, firms do tend to innovate and patent more extensively, if they have adopted KM policies. All else equal, when KM intensity increases by one, the propensity to innovate increases by 4% at the sample mean, that is from an average probability of 47.1% to 51.1%, and innovation intensity increases by 1.6% for the innovating firms, from an average share of 15.8% to 17.4%. Similarly the propensity to patent increases by 1.6%, from an average probability of 32.4% to 34.0%, and patenting intensity increases by 3.1% for the patenting firms, from an average share of 30.5% to 33.6%. These estimated impacts on firm performance of KM policies are quite substantial, and all the more since they seem cumulative. They are not so huge, however, that one would have to conclude that they are necessarily wrong (“ils sont trop beaux pour être vrais”), and that they must be largely overestimated and our model badly misspecified. It is true that all the usual reasons of econometric misspecification potentially apply: omitted control variables and unobserved firm characteristics; endogeneity of right hand variables (i.e., of the KM indicators themselves and of the R&D and other control variables). These problems may be particularly serious with crosssectional data as ours. There is not much that we can do to address them very effectively (and convincingly) at this stage, short of being able to gather more and better data (and preferably as panel data over a long enough period, or at least for two cross-sections a few years apart). On the other hand, an extreme degree of disbelief is not warranted. Even if the adoption of knowledge management has become fashionable among firms and for a number of them mainly a shibboleth for good management, one will expect that in average firms will not go through the various costs of implementing KM policies unless they have some real impacts on their performance. Anyhow, whether one views our findings with excessive skepticism or one is willing to give them some causal meaning, even if they are likely to suffer from significant overestimation, in both cases they remain statistically informative. At the minimum, they reflect significant underlying positive correlations, conditional on a fair number of relevant factors. Such descriptive correlations could have been negative or statistically not significant, and they are not. As concerns the orders of magnitude of the estimates we find for the control variables, they look fairly reasonable on the whole, which is comforting (see Table A6.1.5). R&D doing firms innovate and patent much more than non R&D doing firms, and they also tend to innovate and patent more, the higher their R&D intensity. The estimated impacts of R&D intensity, however, may seem to be on the low side, although statistically very

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significant. A doubling of the average of R&D expenditure to sales ratio, which is of 1.7% for the innovating firms and of 2% for the patenting firms, would increase innovation intensity by only 1.2% and patenting intensity by a higher, though still modest, 5.3%. One potential reason for these low estimates could be that instead of a measure of R&D expenditure flow we should use a more appropriate measure of R&D capital stock. The estimated impacts of the implementation of new management methods are statistically very significant, as well as substantial, being in the range of the impacts found for the adoption of KM policies (i.e., corresponding roughly to a KM intensity of 2 or 3). Lastly, there is a clear indication that firms belonging to a group tend to patent more, and a weaker one that they innovate more, while we find not specific impact of the use of Internet and ICT to acquire and share information. As could be expected the impact of firm size and industry is statistically significant and large, particularly so as concerns the impact of size on patent propensity and intensity.10

6.5. Knowledge Management and Productivity Besides focusing on the innovative performances of the firm, it is of interest to investigate whether the adoption of knowledge management practices also appears to have a specific impact, both statistically and economically significant, on labour productivity. To do so, we use basically the same models than the ones just considered for product innovation and patents, although with two differences. The first difference is that we can simply rely on a linear regression specification instead of a generalized tobit. This regression can be viewed as a simple extended production function (in log fo rm ), which is of current use in econ om etri c studies o f R&D productivity.11 The second difference is that we introduce (log) physical capital per employee as an additional control variable, since these studies generally confirm that this is the major variable accounting for productivity differences among firms.12 The results of estimation and tests for productivity are reported in the last column of Tables 6.2, 6.3 and A6.1.5 and in Figure 6.6. The tests of the four models, corresponding to the different ways of entering knowledge management in the productivity equation, tell us a somewhat different story than for innovation and patenting. Model 3, in which the four KM policy indicators are included separately, performs slightly better than the others: it is statistically different from model 1 using our simple measure of KM intensity, but it is not statistically different from the less parsimonious model 4 with fully interacted KM policy indicators (while model 3 differs statistically from model 4, not from model 1). It is clear that the four KM policies do not appear exchangeable anymore and remain only partly cumulative. All else being equal, labour productivity is higher, and very significantly so, by about 10% for


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firms implementing a policy to retain executives and employees (R) than for firms which do not, and by about 5% for firms promoting a culture of knowledge sharing (C) than for firms which do not. At the opposite, all else equal, labour productivity is not statistically different (or barely so) between firms declaring that they have or that they have not a policy to establish alliances to acquire knowledge (A), and a knowledge written policy (W).

Figure 6.6. Impacts of Knowledge Management Practices on Labour Productivity, “all other things being equal” KM intensity impact



18 16 14

Additional impact of each KM

12 10 8 Binary indicator of KM intensity

6 Labour productivity

4 KM intensity impact

2 0 KMI = 0

KMI = 1

KMI = 2

KMI = 3

KMI = 4

The figure illustrates the estimated impacts of the adoption of the KM practices on labour productivity, where: • the continuous straight line corresponds to the regression using the KM intensity variable, varying from 0 to 4 (Model 1, Table 6.3); • the small-dotted line corresponds to the regression using four KM intensity binary indicators, varying from 0 to 1 sequentially (Model 2, Table 6.3); • the long-dotted line corresponds to the regression using the four KM indicators, varying from 0 to 1 in the following order: KM Culture (C), KM Retention policy (R), KM Alliance policy (A), KM Written policy (W) – where this order is in fact irrelevant (Model 3, Table 6.3). Scope: Manufacturing firms with 20 employees or more (excluding the food industry), not weighted. Source:

SESSI, CIS3 Survey.

The estimated elasticities of the physical capital intensity and of R&D intensity, though somewhat on the low side, are consistent with what could be expected from previous productivity studies (see Table A6.1.5 in the Annex). Contrary to what we find for innovation and patenting, the estimated impact of the implementation of new management methods on productivity is barely statistically significant and if anything negative.

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6.6. Conclusion In this exploratory study of the diffusion and impact on firm performance o f f o u r s p e c i fi c k n ow le dg e m a na g e m ent (K M ) p o lic ies fo r a la rg e representative sample of French manufacturing firms, we have found not very surprising results and more surprising ones (at least to us), some of them satisfactory, but others puzzling. Among the expected results, we substantiate the fact that the diffusion of the four KM policies is much more advanced in the larger firms and in the technology intensive industries, and the fact that these practices appear highly complementary, firms tending to adopt them jointly. Among the less obvious but satisfactory findings, we observe that the impacts of KM practices on firm performance are in general statistically and economically significant and more or less cumulative, even controlling for firm size, industry and other important factors such as R&D intensity and physical capital intensity. It is also satisfactory to find that these estimated impacts are on the high side, but still in the range of values that one is a priori ready to accept as not implausible. Less desirable and somewhat puzzling is the observation that our four specific KM practices are not only cumulative, but also apparently interchangeable in the case of innovative performance. In this case the model with KM intensity, simply defined as the number, varying from zero to four, of KM practices implemented by firms, performs statistically as well as the one with the four individual KM indicators. An explanation may be found in the collinearity (or high correlation) of these indicators naturally reflecting the complementarity of KM practices, but also in the intrinsic crudeness and subjective nature of such binary survey reported indicators, which is a likely source of measurement errors (in the form of a misclassification across the yes and no answers). Also rather puzzling is the finding that the estimated impacts of implementing new management methods in the broad sense are about as large as the impacts of KM practices on firm innovative performance, while they are if anything negative on firm productivity, unlike the positive significant impacts of employees retention and knowledge sharing culture policies (R and C). Further studies are of course needed to confirm, better understand and enrich these exploratory results.13 It is clear that our econometric evidence of a significant impact of knowledge management on firm performance does not necessarily mean causality, although such a causal link is not a priori unlikely. It is also clear that our estimates are basically cross-sectional estimates and as such susceptible to various heterogeneity biases. Although they are not economically unreasonable, the orders of magnitude of the estimated impacts we find seem indeed rather high; but, even if they were to be divided by two, or even by three, they still would remain appreciable.


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Notes 1.

We are grateful to Dominique Foray and Fred Gault for encouraging us strongly to perform this study, and we thank SESSI (French “Service des Études et Statistiques Industrielles”) for giving us access to the French CIS3 data. We have also benefited from comments by Rachel Griffith, Bronwyn Hall, Kathryn Shaw and other participants to workshops at the NBER (Summer Institute 2002), IFS (November 2002) and ZEW (March 2003).

2.

SESSI, 20 avenue de Ségur, 75353 Paris 07 SP, E-mail: [email protected]

3.

CREST-INSEE, 15 boulevard Gabriel Péri, 92245 Malakoff Cedex, E-mail: [email protected]

4.

For presentation of the knowledge economy in general and in the French context in particular, see Foray (2003), and Commissariat Général du Plan (2002).

5.

In what follows we will use the words KM policies and practices (or even methods or strategies) interchangeably.

6.

For a summary presentation of the overall results of CIS3 for french manufacturing, see Lhomme (2002).

7.

In tobit models the selection equation is also specified as a probit (or normit) equation, which is more appropriate for a binary dependent variable, and the outcome equation as a linear regression, and it is assumed that the errors in these two equations are normally distributed (with correlation rho). Since the observed innovation and patent intensity variables are share variables, we use in fact as the dependent variable in the outcome equation their logit transformation [i.e., z = log(y/(1-y))], so that the distribution of the “logitshares” be (approximately) consistent with the assumed normal distribution (and limited to the 0 to 1 interval). We estimate the tobit model by the method of maximum likehood, making sure that we reach the absolute maximum (using TSP international version 4.5). For an introduction to tobit models, see for example Greene (1994).

8.

These questions on the Internet and ICT are asked in the French CIS3 only to the innovating firms.

9.

We do not report the estimated impacts for model 4, since they are not significant, with very few exceptions, for the eleven indicators of KM interactions (and practically not different for the four non-interacted KM indicators from the estimated impacts in model 3). We thus do not find evidence of complementarity (or substitutability) between the four KM policies, in the specific sense that if a firm has already adopted one such policy the impact on its performance of adopting another one would be higher (or weaker).

10. For example, the differential impacts between the high tech electric and electronic components industry and the low tech textile industry (in terms of the marginal effects in % computed at sample means) are about 7.5% on both the innovation and patent propensities and about 5.5% on both the innovation and patent intensities, while the differential impacts between the lowest size group of firms of 20 to 49 employees and the largest size group of 2 000 and more employees are respectively about 10% and 30%, on the innovation and patent propensities and about 5.5% and 25% on the corresponding intensities.

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11. We have also experimented using innovation or patent intensity (and an indicator for being innovative or patenting) in the production function, instead of R&D intensity (and an indicator for doing R&D). The results are basically the same, with R&D performing marginally better. For a review of econometric problems encountered in firm level econometric studies on R&D productivity, in particular that of large discrepancies between cross-sectional and timeseries estimates on panel data, see the survey, still useful though now incomplete, by Mairesse and Sassenou (1991). 12. We had to merge CIS3 with the French survey of enterprises in 2000 (“Enquête Annuelle d’Entreprise 2000”) in order to be able to measure physical capital by the gross book value of fixed assets, and also to measure labour productivity in terms of value added per employee (rather than total turnover per employee). This is why the “labour productivity sample” (3 419 firms) is smaller by a few firms than the “full sample” (3 474 firms) of the previous sections. Note that using this sample we could have also included physical capital intensity as an additional control variable in the innovation and patenting equations of Section 6.4. When we do so, however, our results remain basically unchanged; if anything, the estimated impacts of KM intensity on patenting propensity and intensity are slightly less significant and lower. 13. In a recent micro-econometric study based on information from a specific survey on “Firm Competencies to Innovate” for French manufacturing, merged with the innovation data from CIS2 (concerning the period 1994-1996), Gallia and Legros find results which overall seem in accordance with ours.


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Annex 6.1.

Table A6.1.1. Diffusion of Knowledge Management Practices by Industry in Manufacturing % of firms per industry having

Industries by NES36 classification (i.e., in 14 manufacturing industries)

Knowledge sharing culture

Incentive policy to retain employees

Alliances for knowledge acquisition

Written Knowledge knowledge management management intensity policy

Consumer Goods Industry

21

23

19

11

0.73

Clothing and Leather Products (LT)

8

14

8

4

0.34

Publishing, Printing and Reproduction (LT)

23

21

17

9

0.70

Pharmaceuticals, Fragrances and Cleaning Products (MH & HT)

40

39

37

28

1.46

Home Equipment (LT, ML, MH & HT)

21

26

22

12

0.81

Automobile Industry (ML & MH)

33

32

20

24

1.08

Capital Goods Industry

31

32

27

18

1.07

Shipbuilding, Aircraft and Railroad Construction (ML & HT)

46

28

34

28

1.37

Mechanical Engineering Products (ML & MH)

25

29

21

14

0.89

Electric and Electronic Components (MH & HT)

44

40

40

27

1.50

Intermediate Goods Industry

29

26

23

9

0.96

Mineral Products (LT & ML)

27

27

18

13

0.85

Textiles (LT)

25

19

19

12

0.75

Wood and Paper Industry (LT)

27

20

18

15

0.79

Chemicals, Rubber & Plastics (ML & MH)

36

31

30

27

1.23

Metal Processing & Metalworking (LT & ML)

27

24

21

19

0.91

Electric and Electronic Equipment (MH & HT)

32

33

31

22

1.18

Definitions: This table is based on the NES36 classification, corresponding to 14 different manufacturing industries. The classification of industry by technological intensity is mainly based on the average ratio of R&D to output of the industry at the CITI rev2 level. An approximate correspondence to the NES114 is possible but not to the NES36, the NES36 industries containing NES114 sub-industries of different technological intensity. To roughly indicate the degree of technological intensity of the 14 NES36 manufacturing industries, the existence of sub-industry of different technological intensity is noted in parentheses, where HT, MH, ML and LT stand respectively for High-Tech., Medium High tech., Medium Low tech. and Low Tech. Scope: Manufacturing firms with 20 employees or more (excluding the food industry), weighted results. Source: SESSI, CIS3 Survey.

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Table A6.1.2. Complementarity of Knowledge Management Practices In % of firms having Knowledge sharing culture (28%) % of firms having Knowledge sharing culture Incentive policy to retain employees Alliances for knowledge acquisition Written knowledge management policy

100 62 49 45

64 100 49 34

Knowledge sharing culture (72%) % of firms having Knowledge sharing culture Incentive policy to retain employees Alliances for knowledge acquisition Written knowledge management policy

0 13 12 6

Alliances Incentive policy for knowledge to retain acquisition employees (23%) (27%) 60 58 100 37

In % of firms NOT having Alliance Incentive policy for knowledge to retain acquisition employees (77%) (73%) 14 0 13 11

18 17 0 11

Written knowledge management policy (17%) 73 53 48 100

Written knowledge management policy (83%) 18 21 17 0

Among the 28% of firms having a culture of knowledge sharing, 62% have an incentive policy to retain employees, 49% have alliances for knowledge acquisition, and 45% a written policy of knowledge management. Among the 72% of firms NOT having a culture of knowledge sharing, 13% have an incentive policy to retain employees, 12% have alliances for knowledge acquisition, and 6% have a written policy of knowledge management. Scope: Manufacturing firms with 20 employees or more (excluding the food industry), weighted results. Source: SESSI, CIS3 Survey.

Table A6.1.3. Correlations between Knowledge Management Practices

Knowledge sharing culture Incentive policy to retain Employees Alliances for knowledge acquisition Written KM policy KM intensity

Knowledge sharing culture 1 0.47 0.40 0.48 0.81

Incentive Alliances for Written KM policy to retain knowledge KM policy intensity employees acquisition 0.47 0.40 0.48 0.81 1 0.40 0.28 0.74 0.40 1 0.27 0.71 0.28 0.27 1 0.68 0.74 0.71 0.68 1

Partial correlations (after controlling for size, industry and other relevant factors) Knowledge sharing culture Incentive policy to retain employees Alliances for knowledge acquisition Written KM policy KM intensity

Knowledge sharing culture 1 0.36 0.28 0.39 0.76

Incentive Alliances for Written KM KM policy to retain knowledge policy intensity employees acquisition 0.36 0.28 0.39 0.76 1 0.29 0.16 0.68 0.29 1 0.16 0.64 0.16 0.16 1 0.62 0.68 0.64 0.62 1

Raw correlations (before any controls)

The (raw) correlation between the binary indicator of firm adoption of a culture of knowledge sharing (C) and incentive policy to retain employees (R) is of 0.47, while the partial correlation is of 0.36, after (linearly) controlling for size, industry and other factors (included as control factors in the propensity equation, see Table A6.1.5). Scope: Manufacturing firms with 20 employees or more (excluding the food industry), not weighted results. Source: SESSI, CIS3 Survey.


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Table A6.1.4. Descriptive statistics Full sample (3 474 firms)

Innovating firms sample (1 635 firms)

Patenting firms sample (1 125 firms)

Productivity sample (3 419 firms)

–

–

–

–

Performance variables Propensity to innovate Propensity to patent Innovation intensity Patent intensity Labour productivity ( in Ke per person) Explanatory variables KM intensity Group indicator New management methods indicator Internet and ICT for information acquisition and sharing indicator

47.1 (49.9) 32.4 (46.8)

– –

–

–

15.75 (16.7)

–

–

–

30.52 (31.0)

– 50.56 (0.47)

–

–

–

1.25 (1.35) 0.72 (0.45) 0.27 (0.45)

1.77 (1.38) 0.83 (0.37) 0.39 (0.49) 0.37 (0.48) 0.22 (0.42)

1.78 (1.41) 0.88 (0.33) 0.39 (0.49) 0.37 (0.48) 0.25 (0.43)

– 0.55 (0.50)

1.24 (1.35) 0.72 (0.45) 0.27 (0.45)

–

0.55 Non R&D doing indicator (0.50) Physical capital intensity 40.45 (in Ke per person) (1.10) – – – 0.45 0 78 0.75 0 45 Proportion of R&D doing firms (0.50) (0.42) (0.43) (0.50) R&D intensity (in %) 1.58 1.73 1.98 1.57 (for R&D doing firms) (2.32) (2.20) (2.16) (2.33) Standard errors in parenthesis. Labour productivity, physical capital intensity and R&D intensity are introduced in log on the different models. In this table, for these three variables, we give the exponential of the mean of the log. The standard error corresponds to the log variable. Definitions: The propensity to innovate variable is measured by the proportion of firms earning a turnover from new or significantly changed products on the market from 1998 to 2000 (in %). The propensity to patent variable is measured by the proportion of firms having patented products in 2000 (in %). The innovation intensity variable is measured by the logit function of the share ( or “logit-share”), in the firm’s total turnover in 2000, of the turnover from new or significantly changed products introduced on the market from 1998 to 2000 (in %). The patent intensity variable is measured by the logit function of the share ( or “logit-share”), in the firm’s total turnover in 2000, of the patented products sales (in %). The labour productivity variable is measured by the logarithm of the firm’s value added to the total number of employees in 2000 (in Ke per person). The physical capital intensity variable is measured by the logarithm of the firm’s gross book value to the total number employees in 2000 (in Ke per person). The R&D intensity variable is measured by the logarithm of the share of the firm’s R&D expenditure in the firm’s total turnover in 2000. The knowledge management intensity variable is measured by the number (from 0 to four) of knowledge management practices implemented by firms (see definition in Figure 6.3). The group, new management methods, Internet and ICT for external data sharing use, and non R&D doing variables are binary 0-1 indicators (respectively equal to 1 if the firms belong to a group, have adopted new management methods, Internet and ICT for external data sharing use, or are NOT doing R&D). The 14 industry and 7 size binary indicators are defined on the base of the classification of industries shown in Table A6.1.1 and of the groupings by total number of employees used in Figure 6.1. Scope: Manufacturing companies with 20 employees or more (excluding the food industry), not weighted. Source: SESSI, CIS3 Survey.

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II.6.


Table A6.1.5. Estimated Impacts of Knowledge Management Intensity, R&D Intensity and Other Control Variables on Firm Innovation and Productivity Propensity to innovate

Innovation intensity


Patent intensity

Labour productivity

KM intensity

4.0*** (0.5)

1.6*** (0.4)

1.6*** (0.5)

3.1** (1.3)

3.0*** (0.6)

R&D intensity

1.7*** (0.6)

1.2*** (0.4)

2.8*** (0.6)

5.3*** (1.4)

1.6** (0.6)

–13.9***

Impacts in %

–43.4***

–19.8***

–30.3***

–48.3***

Non R&D doing indicator

(3.1)

(2.3)

(3.0)

(7.3)

(3.2)

Group indicator

3.3** (1.6)

2.1 (1.3)

5.2*** (1.8)

13.0** (5.0)

3.8*** (1.7)

New management methods indicator

6.5*** (1.5)

3.9*** (1.0)

2.6* (1.5)

9.3** (3.6)

–3.2*

–

1.5 (0.9)

–

-0.8 (2.9)

–

–

15.4*** (0.7)

Internet and ICT for information acquisition and sharing indicator Physical capital intensity

–

Root MSE

–

–

–4226.49

Log likelihood 1

Rho

–4089.63 19.18

1

0.73

(1.6)

–1650.55 67.73

39.36

0.94

Number of firms

3 474

1 635

3 474

1 125

3 419

Mean of left hand variable

47.1

15.8

32.4

30.5

564.0

This table complements Table 6.2 in the case of Model 1 by giving the estimated impacts (in terms of marginal effects) of all the control variables (except the 6 size and 14 industry indicators). See the footnote to Table 6.2 for details and the footnote to Table A6.1.4 for the precise definitions of the variables. Scope: Manufacturing firms with 20 employees or more (excluding the food industry), not weighted. Source: SESSI, CIS3 Survey.


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II.6.


Bibliography Commissariat Général du Plan (2002), La France dans l’économie du savoir : pour une dynamique collective, La Documentation française, November. Earl, L. and F. Gault (2003), "Knowledge Management: Size Matters", Chapter 7 of this volume. Foray, D. (2003), The Economics of Knowledge, MIT Press, Cambridge, Massachusetts. Gallia, F. and D. Legros (2003), "Knowledge Management and Human Ressource Practices in an Innovation Perspective: Evidence from France", Ermes – Université Panthéon-Assas Paris II, May, mimeo. Greene, W. (1994), Econometric Analysis, 2d edition, MacMillan Publishing Company. Lhomme, Y. (2002), "Technological Innovation in Industry", The Newsletter of Industrial Statistics, SESSI, No. 168, December. Mairesse, J. and M. Sassenou (1991), "R-D and Productivity: a Survey of Econometric Studies at the Firm Level", Science-Technology Industry Review, OECD, Paris, N° 8, pp. 9-43.

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PART II

Chapter 7

Knowledge Management: Size Matters by Louise Earl and Fred Gault

In 2001, for selected industries, Statistics Canada conducted a pilot survey of the use of 23 knowledge management practices. The survey demonstrated that firms could respond to questions about use of knowledge management practices, the reasons for their use, and the results of their use. Size of firm was an important factor in the adoption of knowledge management practices, and the type of practices adopted. This paper presents these findings and suggests direction for future work.


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II.7.

KNOWLEDGE MANAGEMENT: SIZE MATTERS

7.1. Introduction Why is knowledge management important? A series of Organisation for Economic Co-operation and Development (OECD) high-level forums have looked at the use of knowledge management (KM) in sectors of the economy and they report (OECD, 2000) that businesses, especially large businesses, are using KM to do better what they do and to help them adjust to an environment of dynamic economic and social change. This suggests that KM has economic and social consequences, and therefore it becomes a suitable subject for official statisticians to study.

What do we mean by KM? The definition used in the pilot survey is comprehensive: ‘Knowledge management involves any systematic activity related to the capture and sharing of knowledge by the organisation’. To help respondents, the activity of knowledge management was presented as 23 practices grouped under six headings: Policies and Strategies; Leadership; Incentives; Knowledge Capture and Acquisition; Training and Mentoring; Communications. Response to the survey showed that over 90% of the population of firms used at least one of the 23 practices. This showed that the practice of KM is pervasive, but it is also depends upon the size of the firm.

Why does firm size matter? In a very small firm, of fewer than 10 employees, the management of knowledge is not an issue. Most of the knowledge which allows the firm to provide value to clients is tacit, held in the heads of the employees and the manager, or it is available through external sources, such as court records, medical journals, or regulations.1 Knowledge about clients can be captured in commercially available software systems and the sharing of that knowledge can be easily done around a coffeepot in the course of the day. The managers can capture knowledge about changes in their industry by going to conferences, reading the trade press, listening to suppliers, or talking to other practitioners who are not able to compete in their market. There are many examples of independent franchises such as coffee shops, hardware shops, and pharmacists that share common suppliers and other services such as advertising, as well as knowledge gained from market research.

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II.7.


As size increases, it becomes more difficult to manage the firm and the knowledge about suppliers, clients, and the production process. To address this, firms introduce more formal management practices and support services to help the firm to function: human resources, computer networks, legal, financial, and marketing services. These will not all be present in a firm of 10 to 19 employees, but most will be there once the number of employees exceeds 250. Davenport and Prusak (1998, p. 17) suggest that firms will begin to implement knowledge management practices when they attain between 200-300 employees as this is the size at which "people know one another well enough to have a reliable grasp of collective organisational knowledge". The question for the pilot survey of KM was whether it could see these differences in practice.2 The findings reported here would suggest that it could.

The environment of the firm While the practice of KM changes with size, it is also subject to the environment in which the firm operates. A firm that operates in a stable environment, with little staff turn over will function quite differently from a firm that is trading in a volatile market and is subject to frequent staff changes. In the first case, the firm can respond to price and quantity signals and manage its production and suppliers with a basic knowledge of the environment. In the second case, a tactical function has to be present to support different responses to the market, including new products, or new processes requiring different suppliers. At the extreme, the firm has to be prepared to reinvent its vision and what it does to add value, and for that it needs a strategic capacity with a memory of the past and a means of seeing the future (de la Mothe and Foray, 2001, pp. 5-6). The different environments are present for firms of all sizes and should be kept in mind when developing the next generation of KM surveys. Here the focus is on size.

Size According to the Knowledge Management Practices Survey, four-fifths of practitioners, firms that used at least one KM practice, had less than 50 workers (Earl, 2002a, p. 12). This observation reflects the firm size composition of the Canadian economy. Large practitioners had at least 250 workers, mid-sized between 50 and 249, small from 20-49 and micro, 1-19.3 Figure 7.1 displays the firm size composition of KM practitioners discussed in this paper.


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II.7.


Figure 7.1. Firm Size Composition of KM Practitioners in Canada – KMPS 2001 Micro (1-19 workers) 50% Large (250 + workers) 5% Mid-sized (50-249 workers) 14%

Small (20-49 workers) 31%

Source:

Statistics Canada

7.2. Practices The initial results of the survey have been released and they include some analysis of industry differences (Earl, 2002a) and a second paper looks at the affect of period of adoption (Earl, 2002c). This section looks at the KM practices used by practitioners. The principal finding is that large practitioners used more practices on average than smaller practitioners (see Figure 7.2), and a second finding is that the practices are used differently.

Figure 7.2. Average Number of KM Practices in Use by Firm Size – KMPS 2001 All practitioners

Micro (1-19 workers)

Small (20-49 workers)

Mid-sized (50-249 workers)

Large (250+ workers) 0 Source:

172

2

4

6

8

10

12

14

16

Statistics Canada


II.7.


The use of KM practices by micro firms This section concentrates on those practices used by 60% of the micro practitioners. Table 7.1 shows the most popular KM practices for micro practitioners.

Table 7.1. Use of Knowledge Management Practices by Micro Practitioners Knowledge Management Practices (at least 60% of micro firms using)

In Use %

Leadership In the firm knowledge management practices are a responsibility of managers and executives

95 A

Knowledge capture and acquisition The firm regularly captures and uses knowledge obtained from other industry sources such as industrial associations, competitors, clients and suppliers

95 A

Training and Mentoring The firm provides informal training related to knowledge management

88 B

Policies and Strategies The firm uses partnerships or strategic alliances to acquire knowledge

79 C

The firm has policies or programs intended to improve worker retention

66 C

Note: See Annex 7.1 "Methodological Notes" for an explanation of the alphabetic quality indicators. Source: Statistics Canada, Survey of Knowledge Management Practices, 2001.

Leadership by managers and capturing information from other industry sources led the knowledge management practices for micro practitioners. Informal KM training and encouraging workers to share knowledge are two practices that do not require formal structures in order to be put into effect and are therefore not as costly so they are consistent with the running of a micro firm. 4 The fourth most used practice, the use of partnerships, or strategic alliances to acquire knowledge is consistent with the behaviour of small high growth firms (Niosi, 2000) and has been a well established finding for decades (Freeman, 1991). Concern about preventing employee turnover also seems consistent for micro firms as loss of as few as two employees could represent at a minimum one-tenth of workers within the firm.

The use of KM practices by large practitioners Larg e practitioners focussed on human resource development, emphasizing training while still encouraging knowledge sharing. While managers and executives ranked high as those responsible for KM, there was a distribution of that responsibility to other parts of the organisation. Large practitioners also showed that they had a greater capacity for developing strategies and policies, possibly due to size and resource allocation as well as need. As firms grow, part of their strategic development includes enunciating and documenting decisions and policies so that practices and routines are put


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into place consistently over time. These practices of documentation also play an important role in quality assurance that is sought by clients and customers and required of suppliers. Taken together, the practices in place by large firms showed their interest in developing the ability to learn, absorbing knowledge from external sources, communicating knowledge within the firm as well as creating a documented organisational memory (Table 7.2). Why large firms applied the practices and the results of the using these practices compose the other essential component of the absorptive capacity of the firms (Cohen and Levinthal, 2000 reprint).

Table 7.2. Use of Knowledge Management Practices by Large Practitioners Knowledge Management Practices (at least 60% of large firms using)

In Use %

Training and Mentoring Firm encourages workers to continue their education by reimbursing tuition fees for successfully completed work-related courses

96 A

Firm encourages experienced workers to transfer their knowledge to new or less experienced workers

93 A

Firm offers off-site training to workers in order to keep skills current

93 A

Firm provides informal training related to knowledge management

76 A

Knowledge capture and acquisition The firm regularly captures and uses knowledge obtained from other industry sources such as industrial associations, competitors, clients and suppliers

86 A

The firm regularly encourages workers to participate in project team with external experts

69 A

The firm regularly dedicates resources to detecting and obtaining external knowledge and communicating it within the firm

67A

Leadership In the firm knowledge management practices are a responsibility of managers and executives

85 A

Policies and Strategies The firm has a values system or culture intended to promote knowledge sharing

85 A

The firm has policies or programs intended to improve worker retention

78 A

Communications In the firm workers share knowledge or information by regularly preparing written documentation such as lessons learned, training manuals, good work practices, articles for publication, etc. (organisational memory)

77 A

In the firm workers share knowledge or information by regularly updating databases of good work practices, lessons learned or listings of experts

60 A

Source: Statistics Canada, Survey of Knowledge Management Practices, 2001.

7.3. Reasons for Using KM Practices For micro practitioners, improving the competitive advantage at 92% B was the most critical or important reason to use their suites of knowledge management practices, this was followed by improving worker retention at 70% C. These findings are consistent with firm management theory, as the

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II.7.


main strategy behind employing any management practice is to improve competitiveness. Also, it has already been noted that most micro firms had in place policies or programs to improve worker retention suggesting that an important or critical reason to use knowledge management would be worker retention. Large practitioners had a variety of reasons that were critical or important for using their knowledge management practices reflecting in part the more complicated organisational structures generally found in larger firms. In fact, large practitioners found three-quarters of the reasons provided to respondents as critical or important. The one-quarter that the majority of large practitioners (at least 60%) did not find critical or important included external linkages such as improving sharing with partners in strategic alliances, joint ventures or consortia and promoting knowledge sharing with clients or customers. Easing collaborative work of projects that are physically separated also failed to rank as an important reason to employ knowledge management. Table 7.3 shows the reasons why at least 60% large firms employed their suites of knowledge management practices. As can be observed, large practitioners anticipated that their knowledge management practices would assist them in integrating knowledge within their work processes. This suggests that they were interested in assimilating new knowledge, but does not assess whether or not they could recognize vital new knowledge. The findings also indicate that knowledge and its retention were important to large practitioners.

Table 7.3. Reasons why Large Practitioners Used Knowledge Management Practices Reasons why Knowledge Management Practices Were Used by at least 60% of large firms (250 or more workers)

Critical or Important %

To increase efficiency by using knowledge to improve production processes

98 A

To improve the competitive advantage of the firm

89 A

To help integrate knowledge within the firm

89 A

To train workers to meet strategic objectives of the firm

89 A

To protect the firm from loss of knowledge due to workers' departures

86 A

To improve worker retention

83 A

To identify and / or protect strategic knowledge present in the firm

83 A

To increase worker acceptance of innovations

73 A

To improve the capture and use of knowledge from sources outside of the firm

68 A



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7.4. Results of Using KM Practices Large practitioners found more results to be very effective or effective than the micro practitioners, perhaps indicating that the list of results may not have matched the needs of micro practitioners as closely as it did the needs of large practitioners. For instance, the most very effective or effective result for large practitioners was improved horizontal knowledge sharing. Communications issues across departments, functions or business units are o ften c ited as pro blem area s in large administratio ns. Im proving communications therefore is much sought after as a result for these firms. Micro firms, on the other hand, probably do not suffer from as many horizontal communications issues due to their size. Providing that all the firm's workers are located at the same location, it is more convenient to bring them together regularly to meet. And in micro firms it is also easier for workers to discuss their projects as they are probably highly related. This may not be the case for larger firms that often offer a diverse range of goods and services and in which workers may have more opportunities to specialize in one aspect of the firm's operations. The same may hold true for improved vertical communications, again a highly rated positive result for employing knowledge management for large firms. When the two communications-related results, improved horizontal and vertical communications, are removed from the top results for large practitioners, the patterns for large and micro practitioners are similar. Each size of practitioner found that knowledge management practices were very effective or effective at improving workers' skills and knowledge and improving worker efficiency and / or productivity (Tables 7.4 and 7.5). Similarly, knowledge management had positive effects on client relations as micro and large practitioners found that they increased their adaptation of products of services to client requirements and that they had improved their customer relations. Finally, knowledge management practices allowed these practitioners to add new products and services suggesting that knowledge management practices may have assisted in the innovation process and absorptive capacity of firms. Large practitioners went on to note that knowledge management practices helped to improve corporate memory and increase flexibility in production and innovation. Also, of importance to complex firms, knowledge management helped to prevent duplicate research and development while at the same time increasing worker involvement in the workplace activities. For large practitioners, therefore, knowledge management practices were seen to engage workers, facilitate internal and external communications, assist product or service innovation and prevent expensive repetition of research at the present time or in the future by developing a corporate memory.

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Table 7.4. Results of Using Knowledge Management Practices, Micro Practitioners Effectiveness of Results of using Knowledge Management Practices for at least 60% of micro firms (1-19 workers)

Critical or Important Sub-total

Using Knowledge Management Practices

%


92 B


88 B

Increased the firm's adaptation of products or services to client requirements

88 B


83 C

Helped the firm to add new products or services

77 C


Table 7.5. Results of Using Knowledge Management Practices, Large Practitioners Effectiveness of Results of using Knowledge Management Practices for at least 60% of large firms (250 or more workers) Using Knowledge Management Practices

Critical or Important Sub-total %

Increased knowledge sharing horizontally (across departments, functions or business units)

80 A


78 A

Increased knowledge sharing vertically (up the organisational hierarchy)

74 A


74 A

Increased the firm's adaptation of products or services to client requirements

74 A


68 A


67 A

Improved the firm's corporate or organisational memory

67 A

Helped the firm to add new products or services

64 A

Improved involvement of workers in the workplace activities

63 A

Prevented duplicate research and development

61 A


7.5. Incentives to Use KM For over half of micro practitioners there were only two drivers to implement or to increase their use of knowledge management: loss of key personnel (82% B) and loss of market share (75% C). The high rating of concern about turnover by micro firms could be a factor of size. As already mentioned, losing one or two key workers in a firm of less than 20 corresponds to a high turnover rate. And knowledge management practices were seen as a means of mitigating worker turnover. Micro practitioners, as all firms, also exhibited their concern about the bottom line. Maintaining or growing market share is a standard objective of all firms in the marketplace. Finding management tools and practices that assist this objective is therefore paramount to firms of all sizes.


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The majority of large practitioners selected three triggers to increase their use of knowledge management practices or to implement new practices. For large practitioners loss of key personnel (63% A) and capturing workers' undocumented knowledge (know-how) (62% A) were the hot triggers. These two triggers go hand in hand. If a firm has problems documenting its corporate memory then losing key personnel can be even more catastrophic as knowledge walks out of the door. As already seen, large practitioners were positive about their efforts through knowledge management to improve their corporate memories. Finally just over half (51% A) of large firms also noted that loss of market share would encourage them to increase their repertoire of knowledge management practices.

7.6. Moving from Micro to Large This paper has highlighted some of the differences between extremes in firm size - micro (1-19 workers) and large (250 or more workers). However, it has been suggested that firms need to attain a critical mass before they begin to adopt a series of management practices for differing reasons. Firms' perceptions of the effectiveness of their results also could depend upon size. The following sections look at selected practices, reasons to use knowledge management, effectiveness of results and incentives to use more knowledge management to ascertain if there are natural breakpoints in use that is size related.

7.7. Intensity of KM Use As we have already seen, the average number of knowledge management practices in use increases across firm size from 10 for micro firms to 15 for large firms. Therefore the rate of use of practices across firm size also varies.

7.8. Specific KM Applications A breakpoint occurs for using knowledge management as explicit criterion for assessing worker performance. Practitioners need at least 20 workers in order that one out of two uses this practice. The rate is one out of five for micro practitioners to use knowledge management as explicit criterion of assessing worker performance. Micro firms may operate with less structured worker performance instruments than larger firms that, due to labour laws as well as the potential presence of unions, may have more formal structures in place. As already mentioned in the results section, large practitioners have seen m o r e p o s i t i v e r e s u l t s b a s e d o n i n t e r n a l h o r i z o n t a l a n d ve r t i c a l communications patterns. Micro practitioners lagged firms of at least 20 workers in a communications-related knowledge management practice. At

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least three out of five small (20-49 workers), mid (50-249 workers) and large firms regularly updated databases of good work practices, lessons learned or listings of experts and prepared written documentation such as lessons learned, training manuals, good work practices, articles for publication, etc. (organisational memory). For micro practitioners, the proportions were 24% (C) and 22% (C) respectively. Micro practitioners therefore showed a lower interest in developing corporate memory or documenting their experiences than their larger practitioner counterparts. This could reflect, in part, the age of the organisation, as many smaller firms may be start-ups or spin-offs. How the firms are operated may also be a factor as the smaller firms could be family-run businesses thus providing privileged access to both previous and potentially future employees. It may also indicate a reliance on other forms of corporate memory including filing cabinets and the workers' memories. Finally, micro practitioners failed to share the larger practitioners' enthusiasm for using monetary incentives to encourage workers to share their knowledge. Again, this may be a reflection of size with micro firms not perceiving that the benefits of such incentive programs outweighing their costs.

Micro and small practitioners shared some characteristics in using KM practices In some cases micro and small practitioners showed similar usage rates with a gap to mid and large practitioners. Devolving responsibility for knowledge management practices to non-management workers is such an example of where the difference in the usage rate occurred at firms with at least 50 workers. Knowledge management practices were a responsibility of non-management workers for just less than 30% of micro and small firms. Whereas almost three out of five mid sized firms and one-half of large firms gave their non-management workers this responsibility. Again, this could be a function of size with smaller firms having fewer levels within their organisational hierarchy and hence not needing to devolve responsibility to non-management workers. Finally, it must be noted that a larger sample size would assist in better determining these breakpoints, although the pilot survey has shown that how firms manage their knowledge is somewhat size dependent.

Breakpoints by firm size also occur for reasons to use KM Not only do there appear to be thresholds for what practices firms employ based on firm size, but also for the reasons the practitioners put the practices into use. For example, there is a radical jump from the proportion of micro practitioners finding some reasons critical or important to the almost consistent levels for small, mid and large practitioners. For just a quarter of micro practitioners protecting the firm from loss of knowledge due to workers'


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departures was critical or important. On the other hand, a strong majority of the practitioners in the other size groups found this reason to be critical or important. This pattern repeats for increasing efficiency by using knowledge to improve production processes.

Micro practitioners expected different results from KM to larger practitioners The pattern of discerning differences in how micro practitioners responded to questions on knowledge management practices and reasons for employing knowledge management continued in their perceptions of results ascribed to their KM practices. Perhaps in part due to their size and therefore a better understanding of direct response to client requirements, a higher proportion of micro practitioners found that their suites of knowledge management practices were very effective or effective at adding new products or services than did the three larger firm sizes. However, the reverse pattern also occurred with a much lower proportion of micro practitioners finding that their knowledge management practices were very effective or effective at increasing knowledge sharing vertically than did larger practitioners. Again, these results could point to the effect that firm size has on internal firm communications. Larger firms with more complex hierarchies could experience more challenges concerning flows of knowledge and information between managers and workers as well as across departments or functions. The threshold of distinguishing between firms of less than or more than 20 workers also characterizes to some extent how firms perceived triggers to use or use more knowledge management practices. For instance, difficulties in incorporating external knowledge undocumented knowledge was a strong trigger for practitioners of at least 20 workers, and much weaker trigger to micro practitioners. This section has shown that micro practitioners behave differently not only to large practitioners, but also in many instances to small and mid-size practitioners. The current structure of the Knowledge Management Practices questionnaire while making it apparent that these differences occur may be masking important details in how firms of different sizes, particularly extreme sizes, behave. The questionnaire deliberately included many informal management practices in order to accommodate how micro and small firms are managed. While successfully ensuring smaller firms could identify knowledge management practices that they used, these inclusions of informal practices caused the elimination of some practices related to information communication technologies and created more generalized wording of other practices. Therefore, it is more difficult to determine how firms of different sizes employ their practices and the strategies behind the behaviours of using selected practices.

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7.9. What was Learned? The pilot Knowledge Management Practices Survey has taught much. First, for statistical agencies, it has shown that the measurement process can begin. Firm behaviour is important to understanding innovation in the economy. This paper has shown us that firms manage their knowledge resources differently depending upon their size with little regard for industrial classification. Earlier work showed that micro firms comprised the largest proportion of non-users of knowledge management practices (Earl 2002a). Another study (Earl, 2002c) will show that firms that recently adopted at least one knowledge management practice of eighteen potential practices behave more like large firms although these recent adopters are comprised mostly of small and micro firms. The findings of this paper provide a strong profile of firms using knowledge management by size that could be used in support of policies to promote alliances, or knowledge sharing or human resource development. Targeting policies by firm size is possible, in particular as we improve our understanding of how firms manage their knowledge resources and how they seek new knowledge. The survey indicated that only selected and mainly larger firms are looking towards public research institutions for new knowledge; and that firms are selective with whom they share their knowledge. Micro firms may be more willing to embark upon strategic alliances, partnerships and joint ventures than are larger firms perhaps indicating that micro firms might require assistance in order to be more successful in their markets. The survey also showed that knowledge management is more than developing an information communications technology infrastructure. Knowledge management spans and exceeds human resources, information technology and financial operations. By using a variety of management practices, the firms involved in knowledge management are using all of the support roles and functions available to them to add value to their products and services.

7.10. Where Next? It appears that knowledge management is more of a large administration phenomenon, with small and mid firms imitating the application of practices, reasons and results of the large firms. Future surveys should perhaps exclude micro firms and focus on larger firms with emphasis on the economic and social environment in which they operate. This presents problems for international comparisons, as the size of firm varies by country. As the pilot survey shows, in Canada, four-fifths of firms have fewer than 50 employees and only 5% have 250 or more. Large firms also present other measurement


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challenges as they can be multi-national, 54% had workers outside Canada, and they can operate across more than one industry.

Notes 1. Schuetze (2001) presents a strong argument that small firms (10-99) manage their knowledge differently to large firms for a number of reasons that are a function of their size and include reporting relationships and hierarchy for decision-making, availability of resources for training and implementation of new techniques and the social interactions of workers within small firms. He suggests that finding "relevant information and know-how from outside the firm, and absorbing and applying it to the firm's business" is a problem for small firms (p. 98). He comments that "for the purpose of a study on knowledge management, one would probably have to eliminate the very small category" (1-9 employees) of firms (p. 97). 2. François (1997) obtained similar findings on thresholds for the French survey on competencies. 3. The pilot Knowledge Management Practices Survey, 2001, was stratified to exclude firms with less than 10 workers. However, the size categories provided for respondents went from 1-19 workers and some firms with less 10 workers may have been sampled. See Annex 7.1. "Methodological Notes" for more information. 4. Leckie et al (2001, pp. 21-23) discuss differences in terms of training offered by firm sizes and indicate that smaller firms are more inclined to use informal and therefore less costly methods of training. Earl (2002b, pp. 12-13) showed that firm size also impacts the level of introduction of organisational change with micro firms (at just 37%) less likely to introduce significantly improved organisational structures or implement improved management techniques than larger firms.

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Annex 7.1

Methodological Notes Questionnaire development Statistics Canada conducted the pilot survey on Knowledge Management Practices between September and December 2001 as part of an international initiative headed by the Organisation for Economic Co-operation and Development.

Survey content The survey is based on in-use / planned-use identification of a series of knowledge management practices. Respondents that indicated that any practice listed in the first question was “In Use” (In Use Before 1999 or Used Since 1999) continued to the next section. Respondents not using any of the practices skipped to question 10 – “Incentives to Use”. Questions 3-9 captured the reasons, results, effectiveness and responsibility for using knowledge management practices. All respondents answered questions 10-14. Question 10 related to incentives to use knowledge management practices. Question 11 provided employment structure information for the firm. Questions 12-14 were administrative questions.

Data reliability Code

Rating

Standard Error

A

Very good

B

Good

< 2.5% > 2.5% and < 7.5%

C

Good to poor –use with caution

> 7.5% and < 15.0%

D

Very poor –may not be acceptable

> 15.0%

Collection methodology and survey frame In order to reduce response burden, the KMPS used samples from the Annual Survey of Manufacturers (ASM) and the Unified Enterprise Survey (UES).

Enterprise coverage is limited to these sub-sectors: ● Forestry and Logging (113) (ASM - 1999) ● Chemical Manufacturing (325) (ASM - 1999)


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● Transportation Equipment Manufacturing (336) (ASM - 1999) ● Machinery, Equipment and Supplies Wholesaler-Distributors (417) (UES - 1999) ● Management, Scientific and Technical Consulting Services (5416) (UES - 1999)

Sampling A two-stage survey was developed. For the first stage, refer to the documentation in the ASM and UES to understand the sample stratification, allocation and selection process. The statistical unit of these surveys is the establishment. At the second stage, the statistical units were responding enterprises from the ASM and UES with at least 10 employees and revenue of $250,000 or more. The establishments in these two surveys were grouped at the enterprise level. The activity sectors (5) and the size of the enterprises (10-49, 50-199, 200 and more employees) were used for stratification purposes. 510 enterprises were distributed in such a way that the Coefficients of Variation (CVs) are similar for all strata. Simple random sampling was carried out for each stratum.

Verification and imputation All questionnaires confirmed as completed passed through a verification and imputation system. As one of the objectives was to evaluate the questionnaire, minimal imputation took place. Verification was limited to ensuring that the responding values were valid and that the question skips were respected. In cases identified as incorrect, the following occurred: ● imputation of a value from a donor for questions identified as mandatory, ● imputation

of a non-response code for questions identified as non-

mandatory. ● Donors were selected randomly according to certain characteristics (hot deck)

and independently for each of the questions. Groups of donors were assembled based on their characteristics: ● Group I: same province, same activity sector and same category - number of

workers (question 11), ● Group II: same activity sector and same category - number of workers

(question 11), ● Group III: same activity sector and category grouping - number of workers

(question 11). For each imputed value, the first attempt was made to find a donor in the Group I’s, then Group II's and finally Group III.

Response rate The distribution of the response for the 510 enterprises was: ● 407 enterprises suitable to receive a questionnaire, ● 48 non-respondent enterprises (refusal, no contact, ...), ● 51 out-of-scope enterprises, ● 4 inactive enterprises.

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Of the 407 questionnaires mailed, the distribution of the response is: ● 348 enterprises with complete questionnaires, ● 58 enterprises with incomplete questionnaires or non-respondents, ● 1 out-of-scope enterprise.

The response rate for the survey is 76.5% (348/455).

Estimation The statistical units of the first stage are enterprises whereas the second stage they are establishments. To produce estimates at the enterprise level, the weight share method was used. All the estimates were produced using Statistics Canada’s Generalized Estimation System (GES). For the formulas used in variance calculations, please refer to the GES documentation.


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Bibliography Cohen, W.M and D.A. Levinthal (2000), “Absorptive Capacity: A New Perspective on Learning and Innovation” (reprint of 1990 article) in Cross, R. and S. Israelit (eds), Strategic Learning in a Knowledge Economy: Individual, Collective and Organisational Learning Process, Resources for the Knowledge-Based Economy Series, Butterworth-Heinemann, Woburn, pp. 39-67. Davenport, T.H. and L. Prusak (1998), Working Knowledge, How Organisations Manage What They Know, Harvard Business School Press, Cambridge, MA. de la Mothe, J. and D. Foray (2001), “Introduction”, Knowledge Management in the Innovation Process, Kluwer Academic Press, Boston, pp. 3-6. Earl, L. (2002a), “Are We Managing Our Knowledge? Results from the Pilot Knowledge Management Practices Survey, 2001”, Ottawa: Statistics Canada, Cat. No. 88F0006XIE2002006, Working Papers Series No. 6, Science, Innovation and Electronic Information Division. Earl, L. (2002b), “Innovation and Change in the Public Sector: A Seeming Oxymoron”, Ottawa: Statistics Canada, Cat. No. 88F0006XIE2002001, Working Papers Series No. 1, Science, Innovation and Electronic Information Division. Earl, L. (2002c), “Knowledge Management in Practice in Canada”, (forthcoming) Ottawa: Statistics Canada, Cat. No. 88F0006XIE20020XX, Working Papers Series No. X, Science, Innovation and Electronic Information Division. François, J.P. (1997), “Les compétences pour innover dans l'industrie française : premiers resultats” presented at “The Development of Practical Tools for Improving the Innovation Performance of Firms”, OECD June 30-July 1, Paris. Freeman, C. (1991), “Networks of Innovators: A Synthesis of Research Ideas”, Research Policy, Vol. 20. pp. 499-514. Leckie, N., A. Léonard, J. Turcotte and D. Wallace (2001), Employer and Employee Perspectives on Human Resource Practices, Catalogue No. 71-584-MPE No. 1, Ottawa: Statistics Canada. Niosi, J. (2000), “Explaining Rapid Growth in Canadian Biotechnology Firms”, Ottawa: Statistics Canada, Cat. No. 88F0017MIE2000008, Research Papers Series, Science, Innovation and Electronic Information Division. OECD (2000), Knowledge Management in the Learning Society: Education and Skills, Paris. Schuetze, H.G. (2001), “Knowledge Management in Small Firms: Theoretical Perspectives and Evidence” in de la J. Mothe and D. Foray (eds), Knowledge Management in the Innovation Process, Kluwer Academic Press, Boston, pp. 97-122.

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PART III

Methodological Aspects



PART III

Chapter 8

A Word to the Wise – Advice for Conducting the OECD Knowledge Management Survey by Louise Earl and Michael Bordt

This chapter provides some “best practices” insights for those considering conducting the OECD core Survey of Knowledge Management. That this background is seen as necessary by those involved in the development of the survey is testimony to the fact that measuring KM is not a straightforward undertaking. Our understanding of the ways in which KM practices are perceived and applied is still very rudimentary. Rather than providing a manual that specifies the exact processes required to conduct, analyse and report the survey, we hope to gently advise the prospective KM survey manager and, perhaps, to enlist him or her in contributing to our collective understanding of what we are all attempting to measure.


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A WORD TO THE WISE – ADVICE FOR CONDUCTING THE OECD KNOWLEDGE MANAGEMENT SURVEY

8.1. Introduction The purpose of this document is to provide some “best practices” insights to those considering conducting the OECD core Survey of Knowledge Management. These insights are based largely on the Canadian experience in testing, conducting, analysing and explaining the results of the pilot Knowledge Management Practices Survey 2001 (Earl, 2002). In addition, discussions with other interested countries have greatly contributed to this “knowledge base”. The core questionnaire presented in this publication is the result of considerable international consensus building and at least four national pilot surveys. Throughout the process, several experts in the field have played an active role in the design and analysis of the surveys. That this background document is seen as necessary by most of those involved in the development of the survey is testimony to the fact that m e a s u ri n g k now l e d g e m a n ag e m e n t ( K M ) i s n ot a s t ra i g ht f o rwa rd undertaking. Our understanding of the ways in which KM practices are perceived and applied is still very rudimentary. Rather than providing a manual that specifies the exact processes required to conduct, analyse and report the survey, we hope to gently advise the prospective KM survey manager and, perhaps, to enlist him or her in contributing to our collective understanding of what we are all attempting to measure. Further detail into some of the topics covered here was published in Statistics Canada’s Innovation Analysis Bulletin (2002, Vol. 3, No. 3; Vol. 4. No. 1 and Vol. 4 No 2).

8.2. Questionnaire Content The definition It is not evident that respondents always read the definitions provided. However, for those who do, the definitions need to be sufficiently inclusive to cover the topic. They also need to be sufficiently detailed so that the respondent can relate to the topic. Unfortunately, detail that makes sense to one respondent may alienate another. The definition of KM used in the pilot survey is a compromise. The body of the definition covers the important processes but leaves the definition of

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knowledge to the imagination of the respondent. The term capture may have varying interpretations as well: Knowledge management involves any activity related to the capture, use and sharing of knowledge by the organisation. Some respondents may interpret knowledge as information: the contents of filing systems, databases or books. This narrow interpretation may detract from the respondent’s understanding of the remainder of the questionnaire. For this reason, the second part of the definition adds some specific examples: e.g., circulation of information across divisions of the organisation, dedication of resources to obtain external knowledge, encouragement of experienced workers to transfer their knowledge to new or less experienced workers, preparation of written documentation such as lessons learned, training manuals, good work practices, articles for publication, etc. While being specific, this second part of the definition gently introduces the respondent to some of the processes that we consider part of knowledge management. It would be pointless to give comprehensive examples that would be useful for all respondents – the list would extend to several pages. In practice, the definition is a good introduction to the questions being asked in the questionnaire. However, it may not assist in interpreting questions, if the respondent cannot relate to a given practice. For example, Question 1 asks about formal mentoring practices. There are several approaches to mentoring and apprenticeship that would be valid in response to this question (for example, co-working, recruitment programs, “buddy” programs, etc.). Specific implementations of the questionnaire might consider including a guide to the interpretation of individual questions with more extensive examples. In pre-testing the questionnaire without the benefit of this definition, the respondents were all generally aware of KM as a management concept. Most of them, though, did not think of KM in terms as broad as implied by the above definition. The definition probably did assist in assuring that respondents did not apply the narrower conceptualizations of KM (i.e., related to filing systems, better information management, something that software can do for you) in responding to the survey.

8.3. The Questions A note on scales (Likert or not) A general problem occurs in the application of opinion scales (Likert Scales) in questionnaires: responses tend towards the neutral position. That is, when asked to strongly agree or strongly disagree on a 5-point or 7-point


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scale, many respondents would prefer to choose neither agree nor disagree. This is so pervasive that many analysts exclude neutral responses from their analysis. The disadvantage for small surveys is that this approach reduces the quantity of data. It also reduces the quality of the remaining data. 1 Another similar problem is one of differentiation. This tendency for respondents to avoid extreme responses results in many responses in the middle range. For example, on a 5-point scale, many respondents will choose the neutral, some will choose “somewhat agree” or “somewhat disagree” but very few will choose “strongly agree” or “strongly disagree”. This has compelled some questionnaire designers to extend the scales to 7 or 9 points. This gives respondents a wider choice of non-extreme responses. Furthermore, the neutral response is often used to express the respondent’s lack of opinion. The respondent may not have understood the question, the question may not be applicable or the respondent may not know the answer. In these cases, respondents will sometimes choose a safe neutral response rather than leaving the question unanswered. For this reason, Questions 3 and 4 provide essentially a 6-point scale of importance ranging from “Critical” to “Not at all important”. Since the neutral option is eliminated, the respondent is compelled to choose at least “somewhat important” (“+”) or “somewhat unimportant (“-“). Since there are three options for important (“critical” or “+++”, “very important” or “++”, and “somewhat important” or “+”), it is expected that the respondent will have sufficient variety to express his or her opinion. In testing this approach with the Canadian pilot survey, one respondent mused “Now I have to think about my answer!” All questions provide a response option of “don’t know” or “not applicable”. This approach will help to reduce the number of unanswered questions.

Question 1: Knowledge management practices For this question, the respondent is asked whether a series of knowledge management practices are in regular use or if the firm plans to use them regularly. They are asked to choose one of the following use categories: “In Use Before 1999”, “Used Since 1999”, “Plan to Use in the Next 24 Months”, “Not in use/Not applicable” and “Don’t Know”. The wording of these practices has undergone substantial refinement since the Canadian pilot was conducted. In earlier versions, some of the wording was considered too specific for one type of business (for example, large manufacturers with R&D departments). This has been generalized to be applicable to most types of businesses. Nonetheless, there may still be some instances in which the respondent does not immediately identify with the

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practice. For example, Question 1.3c (“uses partnerships or strategic alliances…”) is intended to capture any regular collaboration with other companies. There are many other words for the concept: association, group, cooperation, etc. Respondents may be engaged in a cooperative research effort and not identify with the words “partnership” or “strategic alliance”. Again, specific implementations may benefit from more extensive examples of these forms of collaboration in an annex. Question 1.3b (“has a values system or culture promoting knowledge sharing”) was intended to identify those companies that had less need for formal KM practices. If a company has a long history of apprenticeship and knowledge sharing, practices mentioned here may have been internalized and not recognized as explicit management practices. In some respects, this company is still engaging in knowledge management. While conducting the testing of the Canadian pilot, we were asked to add a response option for “tried but didn’t adopt”. This would have been a rare occurrence but should be kept in mind for future surveys.

Question 2: Are there any knowledge management practices that your firm or organisation uses that we have not included in this survey? This question is intended to capture practices that haven’t been included in the questionnaire (or in the definitions). In the Canadian pilot, there were few write-in responses. Either the examples were sufficiently comprehensive and covered all the possibilities or respondents restricted themselves to the examples given. During questionnaire testing, this question is a good opportunity to explore the informal KM practices in which the firm engages. One could ask about how new employees are trained, how processes are documented, what they do when a knowledgeable employee leaves the company, etc.

Question 3: Reasons for using knowledge management practices For this question the respondents are asked to rate a series of reasons that their firm uses knowledge management practices. Respondents indicate the level of importance they attribute to each reason for using the knowledge practices currently in use in their firm or organisation on a scale between “Critical”, and “Not at all important”. There is also an option for “Not applicable/Don’t know”. We understand that each practice may have a different reason for using it. A question requiring a respondent to assess each reason in relationship to each practice, however would not only be difficult to answer, it would be challenging to analyse. Instead, the questionnaire asks the respondent to relate the reasons to the group of KM practices currently being used.


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The “reasons” provided might not be applicable for certain respondents. Those with a history of informal KM (such as “a culture for knowledge sharing” or “apprenticeship”) but no formal KM practices might find it impossible to single out specific reasons for implementing their informal practices. Smaller companies would also be less likely to have introduced formal KM practices and would find most of the reasons not applicable. Companies that adopted the practices far in the past might not remember the reasons. This could also occur if the management team has changed since the practices were introduced. While testing the Canadian questionnaire, we found that respondents sometimes had difficulty in differentiating KM practices from overall management practices. This was true especially in the assessment of reasons these practices were used. The reasons are sometimes seen as overall management priorities rather than specific reasons for implementing the practices listed in the preceding question. For example, a company that has not implemented a formal KM strategy has answered “in use before 1999” for several of the informal KM practices. The company has been using these practices (such as apprenticeship or partnership) for generations. When answering the question on reasons, the interpretation may relate more to their priorities for management than to the KM practices. For example, “to protect your firm from loss of knowledge due to workers’ departures” may be “critical” for management but it may not be a “critical” reason for using any of the KM practices. This possible difference in interpretation should be taken into account in analysing the question. Companies engaging in formal practices might be analyzed separately from those not engaging in formal practices. Similarly, recent adopters might be analyzed separately from early adopters. Future questionnaire development might consider linking the reasons with individual practices. The respondent would then be asked to assign one or more reasons to individual practices. This would certainly reduce the ambiguity of the relationships.

Question 4: Results of using knowledge management practices For this question the respondents are asked to rate the effectiveness of using knowledge management practices. Respondents indicate the level of importance they attribute to each result of using the knowledge practices currently in use in their firm or organisation on a scale between “Critical”, and “Not at all important”. There is also an option for “Not applicable/Don’t know”.

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Most of the issues for Question 3 also apply to this question: smaller companies or companies engaging only in informal KM practices may not be able to link these specific outcomes with their reported KM practices.

Question 5: Responsibility for knowledge management practices For this question respondents are asked to specify whether or not there is an explicit KM function in the organisation. This question has not been extensively tested in its current incarnation. The version used on the Canadian pilot attempted to identify which organisational unit was responsible for KM. The question was largely unsuccessful since most respondents indicated that the “executive management team” was responsible. Ultimately, the executive management team is responsible for all management decisions. The German pilot study (see the article by Jakob Edler, 2002, on this topic) obtained useful results from asking a similar question. In this case, the respondent was asked if KM was the responsibility of the organisation’s top management and whether or not a dedicated KM unit existed. This would serve to differentiate whether KM was viewed as an issue for the entire organisation or as an issue that could be localized (e.g., in the IT or R&D department).

Question 6: Spending on knowledge management practices The respondent is asked whether the organisation has a dedicated KM budget. This question has also not been tested in detail. The Canadian pilot included a more complex set of questions that was difficult for some respondents to answer. Together with the previous question on responsibility, this question provides a simple indication of the degree of formality of the firm’s KM practices and policies. The German pilot did achieve success with a similar question.

Question 7: Employment structure This question asks the respondent to specify whether the organisation has multiple work sites and the number of employees in the country and outside of the country. One reason for this question is to classify respondents by size for imputation and for analysis. The question asks for the number of regular workers (employees) as well as managers, executives, partners, directors, and persons employed under contract. In Canada, these are not all included in measures of productivity (output per employee). If the objective is to calculate


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productivity measures, another measure of the number of employees should be obtained in a different manner. The question also provides another piece of information about the corporate structure: the existence of multiple sites. It is expected that companies with multiple work sites would apply KM differently from those with a single site. For example, the use of virtual teams would not be expected for companies with a single site.

8.4. Conducting the Survey The importance of questionnaire testing Statistics Canada conducted extensive “cognitive testing” of an earlier version of the OECD pilot survey. About 30 test respondents were asked to “think aloud” as they answered the question. Tests were conducted on both English and French versions of the questionnaire. Contrary to standard questionnaire testing procedures, the analysts attended all the interviews and, in some cases, engaged in a dialogue with the respondents. The testing served to sensitize the analysts as to the variety of ways in which KM is perceived. Firms that engaged in informal activities such as sharing best practices or co-work programs would often not think of them as KM until prompted by the interviewer. The original Canadian questionnaire was modified to include some more of these informal practices resulting from questionnaire testing. This insight into how KM is perceived was very helpful in interpreting responses and conducting the analysis. The current KM defi nition was adde d after in iti al tes tin g and l ikely im prove d th e interpretation of the questions. Because of the newness of the topic of KM, its varying interpretations between businesses and between countries, it is highly recommended that each implementation of the OECD pilot questionnaire be preceded by a testing phase. This will allow the analysts to supplement the core questionnaire with additional questions. Testing the questionnaire in both Canadian official languages (English and French) was essential to ensuring that questions were understood in the same way in both languages. Since the OECD questionnaire will be translated into many languages, each translation should be tested and compared to our understanding of the original English version.

Choosing the appropriate sample frame The sample frame for the Canadian pilot was complex since it was intended to obtain productivity measures and other statistics such as technology use by linking the survey with existing databases. Linkage is a

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complex process that requires substantial effort. Linking the Canadian pilot to other data sources did not produce useable results. While the linking worked well, data gaps occurred resulting in poor quality productivity data. For future implementations of the questionnaire, it is recommended that any productivity or technology use data be obtained directly. This could be done in several ways. A few simple questions on employment and revenues could be added to the questionnaire. Alternatively, these questions could be asked during pre-contact or obtained from annual reports. As mentioned earlier, the definition of employment would have to coincide with that used in the standard calculation of productivity. Regardless of how the frame is chosen, it is essential that it is consistent and well understood. Statistics Canada, for its business surveys, targets enterprises, establishments or locations depending on the nature of the questions. We take care not to ask questions at one level of management that can only be answered by another. Since most of the KM questions relate to the behaviour of the enterprise, the most appropriate respondent is the CEO or designate (see the next section on Targeting the appropriate respondent). We have found that large corporations approach KM differently from small ones and that companies in manufacturing will have different practices than ones in services. Since it is important to be able to differentiate the industry and size class of respondents in the analysis, these factors need to be considered in the selection of the sample. The number of categories in these classes will depend on the size of the overall sample. For example, if the budget for the survey allows for 300 respondents, it might be possible to stratify by 5 industry classes and 3 size classes. Attempting more industry or size classes could jeopardise the analysis.

Targeting the appropriate respondent Selecting the appropriate respondent and persuading the person to respond are two of the most challenging aspects of running a KM survey. As already mentioned, the target for the Canadian KM survey was the Chief Executive Officer or his or her designate. It is essential that the CEO chooses the appropriate respondent since the choice will reflect the CEO’s definition of knowledge management. It is suggested that the name and function of the actual respondent be included in the contact section of the questionnaire. To improve the response rate, the Canadian pilot systematically contacted all potential respondents initially by telephone. This allowed trained interviewers to verify survey frame information such as industrial classification and number of workers and to determine the correct name and address of the target respondent. As often as possible when speaking with the


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potential respondent, the interviewers also used the opportunity to introduce the survey, its topic, why the respondent's participation was essential and how the data would be used. This "sales pitch" helped to convert wary candidates into enthusiastic participants who could and did quiz the interviewers about the survey. Many of them requested the questionnaire by fax for immediate com pletion and to ok the time to complete th e questionnaire with the interviewer or arranged for follow-up telephone calls. The awareness that was aroused by using pre-contact positively influenced the response to the survey. Respondents knew what to expect and how much time was required of them to complete the questionnaire. They were also assured that, unlike for traditional quantitative questionnaires, they were the correct respondents for their firms, as only they would know all of the intricacies of managing their firms. Finally, a formal letter that reinforced the interviewers' sales talk on the survey accompanied every questionnaire. The number of times a given respondent is contacted may raise questions of response burden and cost of conducting the survey. In the Canadian case, respondents were generally very cooperative. Pre-contact and selective follow-up make the questionnaire easier to answer. They also may reduce the cost of obtaining a viable sample. Without pre-contact and followup, the initial sample size would need to be doubled to obtain the same quality of information.

Follow-up To ensure a high response rate, interviewers conducted extensive followup without becoming intrusive or irritating to respondents. The first follow-up telephone calls occurred about 15 days after the questionnaires were mailed. Ostensibly, this telephone call was to verify that the respondent had indeed received the questionnaire. It also afforded the interviewer the chance to remind the respondent to complete the questionnaire and return it by mail or fax or to do the questionnaire over the telephone. Interviewers also mailed second copies of questionnaires to respondents that requested them. In total, respondents could receive up to four follow-up telephone calls to encourage them to participate in the survey.

Edit and imputation Since one of the main purposes of the Canadian pilot survey was to evaluate the questionnaire, edit and imputation were kept to a minimum. Happily, the data received were of a high quality, which facilitated low imputation rates. Editing was restricted to ensuring that respondents had respected skip patterns. Hot deck imputation from groups of donor records was used to impute "mandatory" cells. Donors were selected based on their firm size as recorded on the questionnaire, industrial classification and

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location. One of the reasons that data quality was high was due to the use of interviewers to follow-up on questionnaires after they had been returned. If respondents had skipped mandatory cells, the interviewers called and obtained the required information over the phone. When two responses were received for non-mandatory cells interviewers resolved the situation by applying a set of rules outlined in an Interviewer Guide. The Interviewer Guide was developed to assist interviewers in answering respondents' questions about the survey and in cleaning questionnaires for data capture.

8.5. Analysing and Reporting the Results Initial tabulations The first reporting of results from the Canadian pilot was “Are we managing our knowledge?” (Earl, 2002), a working paper released in April 2002. The paper provides a thorough review of the methodology as well as initial results. Initial charts and tables included: ●

average number of KM practices by employment size;

●

use of each KM practice by vintage of adoption (early and recent adopters);

●

firms obtaining knowledge from other industry sectors (by sector);

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firms encouraging transfer of knowledge from experienced to less experienced;

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reasons for using KM practices;

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effectiveness of KM practices;

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firms with dedicated KM budgets by firm size;

●

incentives to implement KM practices;

●

reasons to use more KM practices by firm size; and

●

reasons to use more KM practices by industry sector.

An important criteria for selecting these tables was data quality. The sample size did not support some other possibly important tabulations. Further studies include analysing the differences between early and recent adopters of KM practices. For example, those that adopted the key practices within the past 3 years were considered recent adopters.

Research questions KM is one of many management practices and, as such, it may overlap or be complementary to other practices that a firm may adopt. For example, a firm may have a strategy for implementing advanced manufacturing technologies ostensibly to improve its productivity. However, adopting new technologies implies a cycle of learning and knowledge retention. Managing


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this new knowledge effectively could influence the success of the technology strategy. KM and advanced technologies, together with partnerships, intellectual property rights, and mergers and acquisitions constitute a smorgasbord of management tools from which managers have chosen to create their current management style. Out of necessity, we are treating KM as a singular entity when it is in reality a morsel in a management salad. One question, therefore, is: “How does KM relate to the other techniques available to the manager?” Initial investigations into these questions have been of the form: ●

“What is the extent to which KM practices are employed?” – The pilots to date provide some evidence as to the pervasiveness of KM practices in the respective countries. Full surveys would be required to provide reliable national estimates, indicators that could be used to benchmark trends, and sufficient detail to conduct rigoro us an alysis and internatio nal comparisons.

●

“Who engages in KM and why?” – This was the rationale behind sampling and analysing the Canadian pilot by industry and company size.

●

“Is KM perceived as a set of useful tools or as a fleeting fad?” This can only be answered with more detailed data. One approach to answering this question is described in the section on Modelling and more detailed analysis.

In our attempt to separate KM from other techniques, we have focussed on several practices that are related to KM. It is evident that large companies use different strategies from smaller ones and that many SMEs engage in informal KM practices without thinking of them as KM. This brings up the question “What is the normative?” What is the appropriate level and mix of KM practices for a company? Small companies may not benefit from a CKO and medium-sized informal companies may not benefit from formalizing their culture. Rather than recommending one standard KM package for all companies, a program to enhance KM could take into account the current style and assess the potential for implementing additional KM practices.

Modelling and more detailed analysis Two forms of detailed analysis have already been attempted with data from the pilot surveys. Both have led to inconclusive results for varying reasons. One approach was intended to analyse the correlation of the practices with each other. The first hypothesis was that certain practices are likely to occur together (for example, “uses knowledge obtained from other industry sources” and “dedicates resources to obtaining external knowledge”). If there is strong evidence that these practices occur together, then this would be

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sufficient rationale to drop one question or the other from subsequent analysis (and, eventually from the questionnaire). The second hypothesis was that organisations tend to adopt certain practices before they adopt others. That is, certain strategies are either easier to implement or they are necessary precursors to other, perhaps more complex or expensive practices. Attributing an order to practices would allow two sorts of further analysis: (a) one could establish a “normal path” of implementation and assess where companies are on that path, and (b) one could determine if companies have “skipped” stages of the “normal path”. The first analysis would provide a background for establishing the normative. If we have a “normal” or “ideal” path, it would be possible to assess where a given company is on that path. The second analysis would be useful for determining if companies have jumped into formal KM practices, such as KM strategies, before having established the informal practices, such as encouraging experienced workers to transfer their knowledge to new or less experienced ones. Traditional analytical methods such as principal component analysis do not work well on the categorical data obtained on the practices (In Use Before 1999, Used Since 1999, Plan to Use in the Next 24 Months, Not in use / Not applicable, Don’t Know). Other approaches such as correspondence analysis are being investigated.

Non-response The German pilot benefited from the analysis of non-respondents. Nonrespondents were asked some simple questions about why they chose not to respond. The article by Jakob Edler (2002) provides substantial detail on the non-response categories and their interpretation.

8.6. Conclusions This report has endeavoured to provide a starting point for the knowledge base on how to conduct a KM survey. It certainly leaves many questions unanswered – even unasked. With some luck, not too many years from now, the resulting knowledge base will be sufficient to provide a more detailed manual on the topic.


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Acknowledgements. This article, as well as the OECD core Survey of Knowledge Management, is a tribute to the selfless contributions of countless individuals around the world. The process of attempting to understand knowledge management in a statistical sense began in September 2000 with a small group of like-minded individuals representing academia, private industry, statistical agencies and policy departments from a number of countries tossed around the idea of developing a survey on knowledge management. Within a year of the initial meetings, four countries had in place or were preparing pilot surveys based on a set of core questions: Canada, France, Germany and Denmark. This article is a result of discussions reflecting on the experience of these pilot surveys. In March 2002, the OECD called together those who had completed pilot surveys and those who were considering them in Karlsruhe, Germany. The workshop raised many questions about why certain procedures were followed and why questions were worded in a specific way. In providing advice to the countries that were considering conducting a KM survey, it quickly became clear that some of the rationale needed to be documented. The authors would like to especially extend their gratitude to the participants of that workshop in Karlsruhe for their questions, advice and support: Jakob Edler, Fraunhofer Institute (Germany); Dominique Foray, OECD (France); Frieder Meyer-Krahmer, Fraunhofer Institute (Germany); Elisabeth Kremp (France); Stéphane Lhuillery, Université Paris Nord (France); Marian Murphy, OECD; Camilla Noonan, University College Dublin (Ireland); Susu Nousala, Econ-KM/RMIT (Australia); Giulio Perani, ISTAT – Italian National Statistics Institute (Italy); Sjaak Pronk Statistics Netherlands (the Netherlands); Maria Säfström, Statistics Sweden (Sweden); Wenche Strømsnes, Center for Ledelse (Denmark); Stéphan Vincent-Lancrin, OECD.

Note 1. A coefficient of variation calculated on data in which the neutral response has been eliminated would be much higher than a coefficient of variation based on data that included the neutral responses.

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Bibliography Earl, Louise (2002), “Are we managing our knowledge?” SIEID working paper, Statistics Canada Cat. No. 88F0006XIE2002006, Ottawa, Canada. Edler, Jakob (2003), The Management of Knowledge in German Industry [this book] OECD, Paris, France. Statistics Canada (2001), Innovation Analysis Bulletin, Cat. No. 88-003-XIE, Ottawa, Canada.


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PART III

Chapter 9

Knowledge Management Practices Questionnaire by OECD

This questionnaire is the revised version of a questionnaire initially drafted by the Science, Innovation and Electronic Information Division of Statistics Canada in collaboration with OECD, some statistical offices and research bodies. This version has been developed on the basis of the results and feedback from cognitive testing and pilot surveys carried out in Canada, Denmark and Germany. M. Bordt, L. Earl and F. Gault (Statistics Canada), D. Foray, K. Larsen and S. Vincent-Lancrin (OECD), J. Edler (ISI), W. Strømsnes (Centre for Management), E. Kremp (SESSI), S. Lhuillery (University of Paris), G. Perani (ISTAT), C. Noonan (University of Dublin) and S. Pronk (Statistics Netherlands) have contributed to this new version.


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KNOWLEDGE MANAGEMENT PRACTICES QUESTIONNAIRE

Definition Knowledge Management Knowledge management involves any activity related to the capture, use and sharing of knowledge by the organisation. E.g. circulation of information across divisions of the organisation, dedication of resources to obtain external knowledge, encouragement of experienced workers to transfer their knowledge to new or less experienced workers, preparation of written documentation such as lessons learned, training manuals, good work practices, articles for publication, etc.

PLEASE COMPLETE AND RETURN THIS QUESTIONNAIRE WITHIN 10 DAYS OF RECEIPT USING THE ENVELOPE PROVIDED KNOWLEDGE MANAGEMENT PRACTICES This section measures the use of formal, informal and everyday knowledge management practices 1. Using the tables below, please indicate the use your firm or organisation makes of each of the knowledge management practices listed. Use the following response categories in your answers: ● In Use Before 1999 ● Used Since 1999 ● Plan to Use in the

Next 24 months ● Not in use /

Not Applicable

A Firm or organisation began regularly using this practice before 1999 A Firm or organisation has regularly used this practice since 1999 A Firm or organisation intends to regularly use this practice in the next 24 months A Firm or organisation do not use and do not intend to regularly use this practice in the next 24 months

● Don’t know

For the purposes of this survey, the term workers includes your regular workers (employees) as well as managers, executives, partners, directors, and persons employed under contract.

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; Check ONE response for each item. Knowledge Management Practices Within your Firm or Organisation 1.1

C facilitating collaborative work by projects teams that are physically separated (“virtual teams”)

1U

2U

3U

4U

5U

1U

2U

3U

4U

5U

1U

2U

3U

4U

5U

1U

2U

3U

4U

5U

1U

2U

3U

4U

5U

1U

2U

3U

4U

5U

1U

2U

3U

4U

5U

1U

2U

3U

4U

5U

1U

2U

3U

4U

5U

1U

2U

3U

4U

5U

1U

2U

3U

4U

5U

1U

2U

3U

4U

5U

Training and Mentoring Your firm or organisation: A provides formal training related to knowledge management practices B provides informal training related to knowledge management C uses formal mentoring practices, including apprenticeships D encourages experienced workers to transfer their knowledge to new or less experienced workers E encourages workers to continue their education by reimbursing tuition fees for successfully completed workrelated courses F offers off-site training to workers in order to keep skills current

1.3

Used Plan to Use Not in use / Don’t Since in the Next Not Know 1999 24 Months applicable

Communications In your firm or organisation workers share knowledge or information by: A regularly updating databases of good work practices, lessons learned or listings of experts B preparing written documentation such as lessons learned, training manuals, good work practices, articles for publication, etc. (organisational memory… )

1.2

In Use Before 1999

Policies and Strategies Your firm or organisation: A has a written knowledge management policy or strategy B has a values system or culture promoting knowledge sharing C uses partnerships or strategic alliances to acquire knowledge

1.4

Knowledge capture and acquisition Your firm or organisation regularly: A uses knowledge obtained from other industry sources


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Knowledge Management Practices Within your Firm or Organisation B uses knowledge obtained from public research institutions C dedicates resources to obtaining external knowledge D uses the Internet to obtain external knowledge E encourages workers to participate in project teams with external experts 1.5

In Use Before 1999

Used Plan to Use Not in use / Don’t Since in the Next Not Know 1999 24 Months applicable

1U

2U

3U

4U

5U

1U

2U

3U

4U

5U

1U

2U

3U

4U

5U

1U

2U

3U

4U

5U

Are there any knowledge management practices that your firm or organisation uses that we have not included in this survey? 2 U No

3 U Yes, please specify

1102 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

REASONS FOR USING KNOWLEDGE MANAGEMENT PRACTICES. This section measures the reasons for using knowledge management practices. 2. Please indicate the level of importance you attribute to each reason for using the knowledge management practices currently in use in your firm or organisation.

; Check ONE response for each item. Reasons knowledge management practices are used in your firm or organisation 2.1

+++

++

+

–

Not at all Not important applicable / –– – – – Don’t know

Knowledge Integration / Sharing 1U 2U 3U 4U 5U

6U

7U

B To accelerate and improve the 1U 2U 3U 4U 5U transfer of knowledge to new workers

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

A To help integrate knowledge within your firm or organisation

C Following merger or acquisition to help integrate knowledge within your new firm or organisation D To ensure that knowledge resident in all international work sites is accessible to the entire firm or organisation

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III.9.

Reasons knowledge management practices are used in your firm or organisation

+

–

F To improve sharing or transferring of 1 U 2 U 3 U 4 U 5 U knowledge with partners in strategic alliances, joint ventures or consortia

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

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1U 2U 3U 4U 5U

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7U

Knowledge capture and control

J To protect your firm or organisation from loss of knowledge due to workers’ departure K To identify and/or protect strategic knowledge present in your firm or organisation L To capture workers’ undocumented knowledge (know-how) Information Management M To avoid information overload problems within your organisation N To help managers to focus their attention to key information Human Resource Management O To train workers to meet strategic objectives of your firm or organisation P To train workers to develop their human resources Q To encourage managers to share knowledge as a tool for professional promotion of their subordinates R To increase worker acceptance of innovations 2.5

++

7U

I To improve the capture and use of knowledge from sources outside your firm or organisation

2.4

+++

Not at all Not important applicable / –– – – – Don’t know 6U

H To promote sharing and transfer of knowledge with customers

2.3

Critical

E To ease collaborative work of projects 1 U 2 U 3 U 4 U 5 U or teams that are physically separated (i.e. different work sites)

G To promote sharing and transfer of knowledge with suppliers

2.2


External reasons S To update your firm or organisation on knowledge management tools or practices used by competitors


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RESULTS OF USING KNOWLEDGE MANAGEMENT PRACTICES This section measures the results of using knowledge management practices. 3. In the table below, please indicate the level of effectiveness you attribute to knowledge management practices currently in use in your firm or organisation as regards the following objectives.

; Check ONE response for each item. Results of using knowledge management practices

Critical +++

++

+

–

Not at all Not important applicable / –– ––– Don’t know

Using knowledge management practices A Increased our ability to capture knowledge from public research institutions B Increased our ability to capture knowledge from other businesses C Improved skills and knowledge of workers D Improved worker efficiency and productivity E Increased our adaptation of products or services to client requirements F Helped us add new products and services G Alleviated the impacts of workers departures

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

1U 2U 3U 4U 5U

6U

7U

RESPONSIBILITY FOR KNOWLEDGE MANAGEMENT PRACTICES 4. Your firm or organisation 1 U Does not have explicit KM function(s) but knowledge sharing is an important part of the culture. 2 UHas a chief knowledge officer or a unit or function mainly responsible for knowledge management Please specify ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

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SPENDING ON KNOWLEDGE MANAGEMENT PRACTICES 5. Does your firm or organisation have a dedicated knowledge management budget? 1 U Yes

2 U No

EMPLOYMENT STRUCTURE 6.

Your firm or organisation

; Check all that apply

1 U has multiple work sites

2 U is part of an international company

3 U has been involved in a major acquisition or merger in the last three years

For each category listed below, please indicate the range that best represents the current number of workers in your firm or organisation. Please include your regular workers (employees) as well as managers, executives, partners, directors, and persons employed under contract. Employment in country

Employment outside of country

Number of full-time equivalent workers in COUNTRY (“Full-time equivalents” represents the number of person-years.)

Number of full-time equivalent workers outside of COUNTRY (exclude COUNTRY -based workers).

01 U 0

01 U 0

03 U 20-49

03 U 20-49

; Check ONE response only. 02 U 1-19

04 U 50-99

; Check ONE response only. 02 U 1-19

04 U 50-99

05 U 100-249

05 U 100-249

07 U 500-1,999

07 U 500-1,999

06 U 250-499 08 U 2,000 +

06 U 250-499 08 U 2,000 +


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Conclusion by D. Foray and F. Gault

This chapter draws conclusions of the previous chapters. It stresses the importance on how to publish diligently these initial results as well as methodological advices and the tools which have been developed in order to stimulate, encourage and help new countries to proceed with further tests and experiments, while using the available statistical framework. It also opens broader perspectives about the importance of knowledge management and its measurement in the context of the knowledge-based economies.


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CONCLUSION

This book is not a Manual in the sense of the famous Frascati and Oslo Manuals, although it does contain a questionnaire and a methodological guide for a statistical survey. The production of a Manual marks the end of the trial, test and pilot study period and moves the survey forward into the stability – at least temporary – of the concepts and categories, questions and methodology of the statistical survey. It is also the phase when certain basic assumptions concerning the link between the purpose of the survey (R&D, for example) and performance have become certainties shared by the community of experts. The survey on knowledge management has obviously not yet reached that phase. Tests and further studies will be needed in many countries, and the findings will have to be assessed and evaluated by broader expert groups in the international organisations concerned. Also, only the passage of time, i.e. the slow process whereby practitioners, researchers and decision-makers b e co me fam ilia r w ith the p ra ctices a n d ch alleng es o f kn ow ledg e management, will in the end result in a certain standardisation of the terms and categories.

“Early tools and first figures” Yet it would have been unwise to wait for the standardisation phase before publishing and circulating the tools used in the survey and the initial findings, one of the most significant results of the project being the demonstration that knowledge management is measurable and that aggregate statistics can be produced. This was by no means clear at the beginning when the first versions of the survey questions were being tested in interviews, so it was vitally important to demonstrate that statistical measurement was feasible in order to encourage other countries and other experts to take part. What is more, waiting for the age of maturity for too long could very well be like waiting for Godot… who never comes! The slow maturing of the subject matter and the gradual standardisation of the tools and concepts cannot happen spontaneously, being processes that are very much driven by the first trial surveys, the initial definitions proposed, the preliminary results obtained and all the reactions and discussions that this pioneering work may generate. This is why it is essential not to wait to be in a position to produce a Manual before publishing and circulating the “early tools and first figures” arrived at

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CONCLUSION

with the help of these tools. The results should provide the encouragement to take the experiment further, in other countries, and to reconsider the questionnaire in the light of new, developing sets of problems – in short, to continue to diversify the experiments and trials. The discovery process, which is set to go on for a little while yet, will however have to be counterbalanced by initial efforts to achieve a degree of stabilisation, for example by inviting certain countries that are already involved to repeat the experiment with the same questionnaire in one or two years’ time, or else by underlining the questions and terminology that seem to be gathering momentum as essential components of the future standard survey. It is this conflict between diversity and standardisation that the experts in charge of the survey in future years will have to manage to the best of their ability. It is also important not to wait too long before publishing this research in that some of the findings are clear and very meaningful for political and economic decision-makers. How could it be thought that the correlations established between the intensity of knowledge management, innovation and productivity would not trigger considerable debate in innovation policy circles? For nearly 5 years now, discussion has been focusing on the famous “R&D gap” (between the United States and Japan on the one hand and Europe on the other) which has prompted the European Community to set a target for domestic R&D expenditure of 3 per cent of GDP by 2010. Without challenging the relevance of the target (which is confirmed by all the empirical and theoretical studies showing the great magnitude of the positive externalities generated by R&D), it is nevertheless reasonable to think that the initial findings concerning the correlation between knowledge management, innovation and productivity ought to fuel the debate and prompt people to think about the possible existence of a “KM gap” as an explanation for some of the differences in innovation and productivity performances between the major OECD regions. What is more, this fundamental finding is itself informed by a whole series of other findings on the conditions surrounding the setting up of knowledge management policies, size and sector effects, and the necessary compatibility between the different practices themselves which provide a solid and detailed basis for implementing coherent knowledge management support policies. It was also important to bring to the attention of the public sector an initial overview of the rapid changes that the private sector is bringing about in the area of knowledge management. It is now quite clear that the renewal and regeneration of the various components of the public sector essentially involve adopting and introducing new methods of knowledge management, combined with efficient use of ICTs (OECD, 2003; Foray and Hargreaves, 2003). Yet, as an education sector specialist has written: “business has accumulated considerable know-how in applying new technologies to a wide range of


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CONCLUSION

situations, supporting change to both process and management systems. This knowledge can be adapted to the particular needs of the education and training system.” (Van Burskirk and Lee, 2001). Circulating our findings to public sector experts and practitioners is thus of particular importance. With these various points in mind, it may be time for the indicators related to the management of knowledge to be adopted by one or more of the OECD expert committees dealing with productivity, technological and organizational change as well as public management.

A key competence: knowing how to manage knowledge The fact that this survey originated, in collaboration with Statistics Canada, in the OECD Education Directorate’s CERI is not without significance. Researchers and decision-makers in education have to contend with an enigma: is it possible to identify any real discontinuities in terms of competences and skills that people need to have in order to live and prosper in the knowledge-based economy? There is something of a paradox here. Despite all the fractures and shocks that can be observed at the economic and technological level, competences remain remarkably stable. “How old are new skills? asked B. Pont and P. Werquin of the Education Directorate recently. “The competences required in the knowledge economy are not necessarily new. With the exception of ICT skills, they are hardly cutting edge”. (Pont and Werquin, 2001). So the famous “soft skills” – of leadership, the ability to work in a team, learning to learn, the ability to communicate and analytical skills – are not new and the craftsman in the Middle Ages must have possessed much the same skills (Berthold and Fehn, 2002). It is our hypothesis that knowing how to manage knowledge is a generic form of the new competences required and that taking it as such makes it possible to deduce a considerable number of skills that everyone needs to develop (Romer, 1995): sharing, sorting and memorising, communicating, codifying, retrieving documents, etc. This general concept – knowing how to manage knowledge – is a heuristic procedure for identifying and classifying the new skills required and establishing what education programmes are best suited to the knowledge economy.

The new challenges for knowledge management: using ICTs mastering complexity and reinventing the company The said heuristic procedure points to a hiatus by comparison with the knowledge and competences required during previous periods, the hiatus being represented by information and communication technologies (ICTs) combined with a systemic approach to management. In the 21st century, ICTs are making it easier for big firms to acquire information and share it between knowledge workers. Never before have there been such opportunities to collect information on a large number of operational areas (inputs, staff, energy, raw materials and

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CONCLUSION

information, processing and manufacturing techniques, background information on customer purchases and preferences). That information, combined with the experience of managers throughout the firm – from the mail sorting department to the boardroom – is a source of information that has to be managed if it is to generate value. This means adopting a strategic approach to knowledge management, based on an efficient infrastructure. In small businesses, on the other hand, all the staff can share the firm’s knowledge more easily, without any need for a complex technological infrastructure. It is no surprise that the studies presented should have found correlations between knowledge management, productivity and innovation. Nor is it any surprise to find that knowledge management is linked to the size of the firm, whereas the sectoral variable is of less significance. As firms grow in size, management becomes more complex and the need for efficient knowledge management also increases. Without that capacity, the ability to bring new products to the market and develop new processes for producing and delivering the said new products is reduced, and any such reduction in opportunities to innovate has far-reaching economic and social implications – in particular because it is big firms that are responsible for mass production in the industrialised economies. Knowledge management covers not only such areas as inputs, processing, outputs and customers, but extends to the commercial environment in which the firm exists. This environment nowadays includes tax laws, consumer protection in the countries in which the firm operates or exports, environmental regulations, energy costs, the supply of skilled employees, labour market legislation and changes in consumer preferences – linked in part to population changes. As a result of the collapse of Enron, there are now also issues such as risk management and confidence vis-à-vis employees, customers, shareholders and governments. Internal and external factors such as these are prompting firms to evolve all the time and adjust, or even fundamentally change their views (Dierkes, 2002). If policy-makers want to learn more on the basis of identifying “best practices”, then work on measuring and understanding knowledge management has to continue.

By way of conclusion The research presented in this publication can influence certain private or public policy-making circles. However, it will take more than one exposure to ensure that the activities of knowledge management are measured as part of official statistics, and for those statistics to be used to effect change. Like production teams or football teams, management teams learn by doing, by collecting knowledge from a variety of sources and applying it in an experimental manner. Group learning is interactive and takes time. This book is just the first interaction.


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CONCLUSION

Bibliography Berthold, N. and R. Fehn (2002), “Labor Market Policy in the New Economy”, in H. Siebert, Economic Policy Issues of the New Economy, Springer. Dierkes, M. (2002), “Visions, technology and organizational knowledge: an analysis of the interplay between enabling factors and triggers of knowledge generation”, in J. de la Mothe and D. Foray (eds.), Knowledge Management in the Innovation Process, Kluwer Academic Publishers, Boston. Foray, D. and D. Hargreaves (2003), “The production of knowledge in different sectors : a model and some hypotheses”, London Review of Education, vol.1, n°1. OECD (2003), Conference on the “Learning Governement”, PUMA/OECD, Paris, 34 February 2003. Pont, B. and P. Werquin (2001), “How old are new skills ?”, Observer, n°225. Romer, P. (1995), “Beyond the knowledge worker”, Worldlink, January/February. Van Burskirk, E. M. and D. Lee (2001), “Knowledge management and employees”, Lline, 3.

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List of Authors A. Baastrup, Centre of Management, Denmark. M. Bordt, Statistics Canada, Science, Innovation and Electronic Information Division, Ottawa, Canada. L. Earl, Statistics Canada, Science, Innovation and Electronic Information Division, Ottawa, Canada. J. Edler, Fraunhofer Institute for Systems and Innovation Research, Karlsruhe, Germany. D. Foray, OECD, Paris, France. F. Gault, Statistics C anada, Science, Innovation and Electronic Information Division, Ottawa, Canada. E. Kremp, SESSI, Ministère de l’Économie, des Finances et de l’Industrie, Paris, France. J. Mairesse, CREST/INSEE, Paris, France. P. Quintas, Open University Business School, London, United Kingdom. W. Strømsnes, Centre of Management, Denmark.


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OECD PUBLICATIONS, 2, rue André-Pascal, 75775 PARIS CEDEX 16 PRINTED IN FRANCE (96 2003 02 1 P) ISBN 92-64-10026-1 – No. 53127 2003

Knowledge management Measuring Knowledge Management in the Business Sector: First Steps (Knowledge Management)

Knowledge Management

Knowledge Management