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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.
Visit www.statcan.ca for more information about Statistics Canada.
OECD’s books, periodicals and statistical databases are now available via www.SourceOECD.org,
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Measuring Knowledge Management in the Business Sector
Co-published with Statistics Canada.
Knowledge Management
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.
FIRST STEPS
w w w. o e c d . o rg
-:HSTCQE=VUUW[]:
ISBN 92-64-10026-1
96 2003 02 1 P
Knowledge Management
Measuring Knowledge
Management
in the Business Sector
FIRST STEPS
© OECD, 2003.
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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
MEASURING KNOWLEDGE MANAGEMENT IN THE BUSINESS SECTOR – ISBN 92-64-10026-1 – © OECD/MINISTER OF INDUSTRY, CANADA, 2003
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.
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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
MEASURING KNOWLEDGE MANAGEMENT IN THE BUSINESS SECTOR – ISBN 92-64-10026-1 – © OECD/MINISTER OF INDUSTRY, CANADA, 2003
TABLE OF CONTENTS
Chapter 5.
The Promotion and Implementation of Knowledge
Management – A Danish Contribution
Anja Baastrup and Wenche Strømsnes ...........................................
119
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 ..............................................................................................
120
121
125
127
130
131
134
141
Chapter 6.
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 ..............................................................................................
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.
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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
MEASURING KNOWLEDGE MANAGEMENT IN THE BUSINESS SECTOR – ISBN 92-64-10026-1 – © OECD/MINISTER OF INDUSTRY, CANADA, 2003
PART I
Frameworks
MEASURING KNOWLEDGE MANAGEMENT IN THE BUSINESS SECTOR – ISBN 92-64-10026-1 – © OECD/MINISTER OF INDUSTRY, CANADA, 2003
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.
MEASURING KNOWLEDGE MANAGEMENT IN THE BUSINESS SECTOR – ISBN 92-64-10026-1 – © OECD/MINISTER OF INDUSTRY, CANADA, 2003
11
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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I.1. MEASUREMENT OF KNOWLEDGE MANAGEMENT PRACTICES
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
MEASURING KNOWLEDGE MANAGEMENT IN THE BUSINESS SECTOR – ISBN 92-64-10026-1 – © OECD/MINISTER OF INDUSTRY, CANADA, 2003
13
I.1. MEASUREMENT OF KNOWLEDGE MANAGEMENT PRACTICES
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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I.1. MEASUREMENT OF KNOWLEDGE MANAGEMENT PRACTICES
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.
MEASURING KNOWLEDGE MANAGEMENT IN THE BUSINESS SECTOR – ISBN 92-64-10026-1 – © OECD/MINISTER OF INDUSTRY, CANADA, 2003
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I.1. MEASUREMENT OF KNOWLEDGE MANAGEMENT PRACTICES
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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I.1. MEASUREMENT OF KNOWLEDGE MANAGEMENT PRACTICES
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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I.1. MEASUREMENT OF KNOWLEDGE MANAGEMENT PRACTICES
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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I.1. MEASUREMENT OF KNOWLEDGE MANAGEMENT PRACTICES
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.
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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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ISBN 92-64-10026-1
Measuring Knowledge Management in the Business Sector
© OECD/MINISTER OF INDUSTRY, CANADA, 2003
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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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
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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:
●
34
knowledge sharing (targeted on communicating, learning, reviewing,
capturing and sharing knowledge);
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●
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 <capture–deploy–
use> 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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I.2. MANAGING KNOWLEDGE IN PRACTICE
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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PART II
Country Reports
MEASURING KNOWLEDGE MANAGEMENT IN THE BUSINESS SECTOR – ISBN 92-64-10026-1 – © OECD/MINISTER OF INDUSTRY, CANADA, 2003
ISBN 92-64-10026-1
Measuring Knowledge Management in the Business Sector
© OECD/MINISTER OF INDUSTRY, CANADA, 2003
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.
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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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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)
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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.
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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
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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 %
Management, Scientific and Technical Consulting Services
100% A
Machinery, Equipment and Supplies Wholesaler-Distributors
96% A
Chemical Manufacturing
89% A
Forestry and Logging
81% A
Transportation Equipment Manufacturing
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
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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 %
Forestry and Logging
98% A
Management, Scientific and Technical Consulting Services
96% B
Transportation Equipment Manufacturing
92% A
Chemical Manufacturing
88% A
Machinery, Equipment and Supplies Wholesaler-Distributors
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
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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
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(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).
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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
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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
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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)
Machinery, Equipment and Supplies
Wholesaler-Distributors
88% C
77% C
29% C
Forestry and Logging
69% A
29% A
44% A
Transportation Equipment
Manufacturing
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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II.3. ARE WE MANAGING OUR KNOWLEDGE? THE CANADIAN EXPERIENCE
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ISBN 92-64-10026-1
Measuring Knowledge Management in the Business Sector
© OECD/MINISTER OF INDUSTRY, CANADA, 2003
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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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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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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II.4. THE MANAGEMENT OF KNOWLEDGE IN GERMAN INDUSTRY
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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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
Fraunhofer ISI Survey 2002
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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II.4. THE MANAGEMENT OF KNOWLEDGE IN GERMAN INDUSTRY
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
Fraunhofer ISI Survey 2002
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
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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”)
33
12
29
75
Rankb
plan not in use
Communication
85
59
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
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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
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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
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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
To encourage managers to share knowledge as a tool for professional promotion
of their subordinates
80
1,91
5
To train workers to develop their human resources
77
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
8
To capture workers’ undocumented knowledge (know-how)
75
2,06
9
To ensure that knowledge resident in all international work sites is accessible
to the entire firm or organisation
69
2,29
10
To train workers to meet strategic objectives of your firm or organisation
68
2,21
11
To help managers to focus their attention to key information
67
2,28
12
To improve the capture and use of knowledge from sources outside your firm
or organisation
67
2,22
13
To increase worker acceptance of innovations
65
2,30
14
To avoid information overload problems within your organisation
59
2,45
15
Following merger or acquisition to help integrate knowledge within your new firm
or organ
47
2,92
16
To promote sharing and transfer of knowledge with suppliers
47
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
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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
16
To promote sharing and transfer of knowledge with suppliers
0,785
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
1 = extremely important, 6 = not important at all
Source:
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.
Improved skills and knowledge of workers
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
1 = extremely important, 6 = not important at all
Source:
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
1 = extremely important, 6 = not important at all
Source:
Fraunhofer ISI Survey 2002
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:
Fraunhofer ISI Survey 2002
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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II.4. THE MANAGEMENT OF KNOWLEDGE IN GERMAN INDUSTRY
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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II.4. THE MANAGEMENT OF KNOWLEDGE IN GERMAN INDUSTRY
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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Calvo, L. and M. Sanchez Munoz (2002), “Guidelines for Managing and Reporting on
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Den Hertog, F. and E. Huizenga (2000), The Knowledge Enterprise. Implementation of
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Edler, J. and U. Schmoch (2001), “Wissens- und Technologietransfer in öffentlichen
Einrichtungen”, in ifo-Schnelldienst, 4/54, pp. 18-27.
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(2001), Knowledge Management. Best Practices in Europe, Springer-Verlag,
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Janz, N. et al. (2001), Innovationsverhalten in der Deutschen Wirtschaft. Indikatorenbericht
zur Innovationserhebung 2001, Mannheim.
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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.
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ISBN 92-64-10026-1
Measuring Knowledge Management in the Business Sector
© OECD/MINISTER OF INDUSTRY, CANADA, 2003
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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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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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
Institut for Ledelse
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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Figure 5.3. Practices Used Under the Heading “Communications”
Yes, before 1999
Yes, after 1999
No, but in the next 24 months
No, not applicable/Don t know/No answer
Share, %
70
65.6%
60
50
39.3%
40
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)
Institut for Ledelse
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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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, but in the next 24 months
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
Institut for Ledelse
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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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,
public
associations
or federations laboratories
or business
schools
Regulatory
agencies
Customers
or clients
Other
Institut for Ledelse
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
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●
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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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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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%
14%
100-249
10%
12%
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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II.5. THE PROMOTION AND IMPLEMENTATION OF KNOWLEDGE MANAGEMENT – A DANISH CONTRIBUTION
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
Translation & Interviews Gathering of data Analysis of of Business Affairs , 17.12.2001
Design
for pilot survey data from
of questionnaire adaptation
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:
Institut for Ledelse
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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:
Institut for Ledelse
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
Improved skills and knowledge
of workers
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
Improved client or customer
relations
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
Increased flexibility in production
and innovation
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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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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ISBN 92-64-10026-1
Measuring Knowledge Management in the Business Sector
© OECD/MINISTER OF INDUSTRY, CANADA, 2003
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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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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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
●
1/4 for firms from 50 to 99 employees
●
1/8 for firms from 20 to 49 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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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
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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.
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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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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
KM
Incentive
At least
Alliances for Written
policy to
one of the intensity
knowledge KM
retain
four
acquisition policy
employees
policies
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
Propensity to patent
10
8
6
4
2
0
KMI = 0
KMI = 1
KMI = 2
KM intensity impact
KMI = 3
KMI = 4
KMI = 0
KM intensity binary indicators
KMI = 1
KMI = 2
KMI = 3
KMI = 4
Additional impact of each KM policy
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
KM intensity binary indicators
Additional impact of each KM policy
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 d g e m a n a g e m e nt ( K M ) p o l ic i es f o r a l a 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:
elisabeth.kremp@industrie.gouv.fr
3.
CREST-INSEE, 15 boulevard Gabriel Péri, 92245 Malakoff Cedex, E-mail:
mairesse@ensae.fr
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)
0.89
25
29
21
14
Electric and Electronic Components
(MH & HT)
44
40
40
27
1.50
Intermediate Goods Industry
29
26
23
9
0.96
0.85
Mineral Products (LT & ML)
27
27
18
13
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
Incentive policy
Alliances
to retain
for knowledge
employees
acquisition
(27%)
(23%)
64
100
49
34
60
58
100
37
Written
knowledge
management
policy (17%)
73
53
48
100
In % of firms NOT having
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
Incentive policy
Alliance
to retain
for knowledge
employees
acquisition
(73%)
(77%)
14
0
13
11
18
17
0
11
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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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
Propensity to
patent
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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Measuring Knowledge Management in the Business Sector
© OECD/MINISTER OF INDUSTRY, CANADA, 2003
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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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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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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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
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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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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
Source: Statistics Canada, Survey of Knowledge Management Practices, 2001.
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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
%
Improved skills and knowledge of workers
92 B
Improved worker efficiency and / or productivity
88 B
Increased the firm's adaptation of products or services to client requirements
88 B
Improved client or customer relations
83 C
Helped the firm to add new products or services
77 C
Source: Statistics Canada, Survey of Knowledge Management Practices, 2001.
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
Improved skills and knowledge of workers
78 A
Increased knowledge sharing vertically (up the organisational hierarchy)
74 A
Improved worker efficiency and / or productivity
74 A
Increased the firm's adaptation of products or services to client requirements
74 A
Improved client or customer relations
68 A
Increased flexibility in production and innovation
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
Source: Statistics Canada, Survey of Knowledge Management Practices, 2001.
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
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Learning and Innovation” (reprint of 1990 article) in Cross, R. and S. Israelit
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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,
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and Electronic Information Division.
Earl, L. (2002b), “Innovation and Change in the Public Sector: A Seeming
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Earl, L. (2002c), “Knowledge Management in Practice in Canada”, (forthcoming)
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OECD (2000), Knowledge Management in the Learning Society: Education and Skills, Paris.
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PART III
Methodological Aspects
MEASURING KNOWLEDGE MANAGEMENT IN THE BUSINESS SECTOR – ISBN 92-64-10026-1 – © OECD/MINISTER OF INDUSTRY, CANADA, 2003
ISBN 92-64-10026-1
Measuring Knowledge Management in the Business Sector
© OECD/MINISTER OF INDUSTRY, CANADA, 2003
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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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);
●
firms encouraging transfer of knowledge from experienced to less
experienced;
●
reasons for using KM practices;
●
effectiveness of KM practices;
●
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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ISBN 92-64-10026-1
Measuring Knowledge Management in the Business Sector
© OECD/MINISTER OF INDUSTRY, CANADA, 2003
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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KNOWLEDGE MANAGEMENT PRACTICES QUESTIONNAIRE
; 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 QUESTIONNAIRE
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
208
Critical
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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
7U
1U 2U 3U 4U 5U
6U
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
KNOWLEDGE MANAGEMENT PRACTICES QUESTIONNAIRE
External reasons
S To update your firm or organisation
on knowledge management tools
or practices used by competitors
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KNOWLEDGE MANAGEMENT PRACTICES QUESTIONNAIRE
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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III.9.
KNOWLEDGE MANAGEMENT PRACTICES QUESTIONNAIRE
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).
; Check ONE response only.
; Check ONE response only.
01 U 0
01 U 0
03 U 20-49
03 U 20-49
02 U 1-19
04 U 50-99
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
216
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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.
MEASURING KNOWLEDGE MANAGEMENT IN THE BUSINESS SECTOR – ISBN 92-64-10026-1 – © OECD/MINISTER OF INDUSTRY, CANADA, 2003
217
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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MEASURING KNOWLEDGE MANAGEMENT IN THE BUSINESS SECTOR – ISBN 92-64-10026-1 – © OECD/MINISTER OF INDUSTRY, CANADA, 2003
ISBN 92-64-10026-1
Measuring Knowledge Management in the Business Sector
© OECD/MINISTER OF INDUSTRY, CANADA, 2003
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.
MEASURING KNOWLEDGE MANAGEMENT IN THE BUSINESS SECTOR – ISBN 92-64-10026-1 – © OECD/MINISTER OF INDUSTRY, CANADA, 2003
219
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