139,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
- Veröffentlichungsjahr: 2018
Knowledge Management is a strategic issue for companies, and international standards such as ISO recently integrate it into its requirements. However, it is still an ill-defined concept, and methodologies to implement it are not very well known. This book is the result of over twenty years of research in different labs and application in a wide range of public or private companies around the world. It gives a global and coherent view both from the theoretical and practical point of views.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 303
Ähnliche
Table of Contents
Cover
Title
Copyright
Preface
PART 1: Theoretical Elements
1 A Knowledge Value Chain
1.1. Introduction
1.2. Different KVCs
1.3. The DIKW model
1.4. KVC and management
1.5. Transformation processes in the KVC
1.6. Practical application
1.7. Conclusion
2 The Knowledge Capital of a Company
2.1. Introduction
2.2. Modeling a company as a knowledge producer
2.3. The operators of the AIK model
2.4. Tacit/explicit knowledge and knowledge communities
2.5. Mapping as a modeling tool to steer the AIK system
2.6. Practical application
2.7. Conclusion
3 The Structure of Knowledge
3.1. Introduction
3.2. The semiotic triangle of knowledge
3.3. The systemic triangle of knowledge
3.4. The knowledge macroscope
3.5. Practical application
3.6. Conclusion
4 Shannon’s Theory of Knowledge
4.1. Introduction
4.2. Some definitions and notations
4.3. Measurement of the quantity of information in a corpus
4.4. Measurement of the quantity of meaning in a corpus
4.5. Measurement of usage context in a corpus
4.6. Practical application
4.7. Conclusion
PART 2: Practical Elements
5 A New Approach to KM
5.1. Introduction
5.2. Two examples of KM standardization
5.3. The French Knowledge Management Club
5.4. Conclusion
6 A Framework for Knowledge-based KM
6.1. Introduction
6.2. The Daisy Model
6.3. Building a KM process framework
6.4. Conclusion
7 KM: From Strategy to Implementation
7.1. Introduction
7.2. Framing a KM project
7.3. Implementing the KM project
7.4. Monitoring the KM system
7.5. Conclusion
8 Analyzing Knowledge Capital and Elaborating a KM Plan
8.1. Introduction
8.2. Tools for analyzing knowledge capital
8.3. The knowledge capital analysis process
8.4. Conclusion
9 Implementing the KM Plan
9.1. Introduction
9.2. Knowledge organization
9.3. Knowledge codification
9.4. Knowledge sharing
9.5. Knowledge search
9.6. Knowledge creation
9.7. Conclusion
Bibliography
Index
End User License Agreement
List of Tables
1 A Knowledge Value Chain
Table 1.1. Analytical framework of a knowledge value chain
8 Analyzing Knowledge Capital and Elaborating a KM Plan
Table 8.1. Critical knowledge factors (CKF)
9 Implementing the KM Plan
Table 9.1. Grid of CoMM criteria
Table 9.2. Grid of IMM criteria
List of Illustrations
1 A Knowledge Value Chain
Figure 1.1. An example of a KVC based on KM processes
Figure 1.2. An example of a KVC based on cognitive tasks
Figure 1.3. The DIKW Pyramid
Figure 1.4. The DIKW value chain
Figure 1.5. KVC management chain
Figure 1.6. Transformation processes in the KVC
Figure 1.7. The knowledge pyramid, a support for the KMAV tool
2 The Knowledge Capital of a Company
Figure 2.1. The company seen as a black box
Figure 2.2. A Taylorian model of a company
Figure 2.3. The model of a company integrating the information flow (OID model)
Figure 2.4. The model of a company that integrates knowledge flows
Figure 2.5. A company model including knowledge capital and knowledge actors (AIK model)
Figure 2.6. The model of a company and its environment
Figure 2.7. The process of knowledge circulation in a company according to Nonaka
Figure 2.8. The complete AIK model
Figure 2.9. The knowledge transfer process in knowledge communities
Figure 2.10. Knowledge capital in an electronic domain
3 The Structure of Knowledge
Figure 3.1. The semiotic triangle
Figure 3.2. The semiotic triangle of knowledge
Figure 3.3. Two different systems of coding time
Figure 3.4. The systemic triangle
Figure 3.5. The three systemic points of view for mechanical watches
Figure 3.6. The knowledge macroscope
4 Shannon’s Theory of Knowledge
Figure 4.1. The kit (knowledge unit), elementary unit of knowledge (“cogniton”)
Figure 4.2. Abstract representation of information from a traffic light
Figure 4.3. Set of possible messages from a traffic light
Figure 4.4. Breakdown of the choices
Figure 4.5. Semantic graph of a phone book
Figure 4.6. Semantic graph of an inverted phone book
Figure 4.7. Markov graph
Figure 4.8. Google’s algorithm and the semiotic triangle of knowledge
6 A Framework for Knowledge-based KM
Figure 6.1. The Daisy Model: The key processes of Knowledge Management
7 KM: From Strategy to Implementation
Figure 7.1. The roles in a KM organization
Figure 7.2. The virtuous cycle of KM
8 Analyzing Knowledge Capital and Elaborating a KM Plan
Figure 8.1. Elaborating a KM plan
Figure 8.2. An example of critical knowledge factors and their rating scale
Figure 8.3. Objectives map for an air cargo company
Figure 8.4. Map of critical capacities for an air cargo company. For a color version of this figure, see www.iste.co.uk/ermine/knowledge.zip
Figure 8.5. Map of critical knowledge (with referents) for an air cargo company. For a color version of this figure, see www.iste.co.uk/ermine/knowledge.zip
Figure 8.6. Alignment of capacities and knowledge for an air cargo company
9 Implementing the KM Plan
Figure 9.1. A general architecture for a knowledge repository (example)
Figure 9.2. Editorial process for a knowledge-based document
Figure 9.3. Process of creating a knowledge book
Figure 9.4. Example of a phenomenon model. For a color version of this figure, see www.iste.co.uk/ermine/knowledge.zip
Figure 9.5. Example of the activity model
Figure 9.6. Example of a history model. For a color version of this figure, see www.iste.co.uk/ermine/knowledge.zip
Figure 9.7. Example of the concept model. For a color version of this figure, see www.iste.co.uk/ermine/knowledge.zip
Figure 9.8. Example of the task model. For a color version of this figure, see www.iste.co.uk/ermine/knowledge.zip
Figure 9.9. Example of a lineage model
Figure 9.10. Example of CoMM criteria
Figure 9.11. “Signature radar” of a community following an evaluation with the CoMM grid
Figure 9.12. The knowledge transfer process
Figure 9.13. The knowledge search process
Figure 9.14. The innovation process
Figure 9.15. The knowledge-based innovation process
Figure 9.16. The stimulated creativity process for each expert in the domain
Figure 9.17. The creativity process
Figure 9.18. Example of IMM criteria
Guide
Cover
Table of Contents
Begin Reading
Pages
C1
iii
iv
v
ix
x
xi
xii
xiii
1
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
97
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
115
116
117
118
119
120
121
122
123
124
125
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
219
220
G1
G2
G3
G4
G5
G6
G7
e1
Innovation and Technology Set
coordinated by
Chantal Ammi
Volume 5
Knowledge Management
The Creative Loop
Jean-Louis Ermine
First published 2018 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd
27-37 St George’s Road
London SW19 4EU
UK
www.iste.co.uk
John Wiley & Sons, Inc.
111 River Street
Hoboken, NJ 07030
USA
www.wiley.com
© ISTE Ltd 2018
The rights of Jean-Louis Ermine to be identified as the author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2017962951
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-170-3
Preface
Knowledge management (KM) has become a necessity in companies and all other types of public or private organizations.
More than 20 years ago, the business community clearly entered into what is known as the “knowledge economy”. Up until that point, the forces that supported the economy were production and workforce. Now, knowledge is the primary engine for growth and competitiveness. Knowledge has become economic capital, a strategic resource, a stabilizing factor, a competitive advantage and so on. It is now a matter for an organization to capitalize on its knowledge (“Know where we come from, where we are, to better know where we are going”), to share it (“Move from individual intelligence to collective intelligence”) and to constantly create new knowledge (“Create, innovate to survive”).
Today, the issue even extends beyond the economic context, because we talk about a Knowledge Society, a Knowledge City or Smart City, etc. This falls under another point of view that depends on a new development relationship between people (citizens, workers, etc.) and Information and Communications Technologies (ICTs). The spread of ICTs will have major consequences on education, social expression, the nature of labor and the economy. Every society can establish institutions and organizations to allow people and information to flourish without restrictions. This fundamental and inevitable connection between knowledge and ICTs is now part of the dominant thought regarding knowledge societies, often to the point of inverting the predominance between ICTs and knowledge. International organizations (notably the United Nations), governments and local actors are now mobilized on these subjects.
For these reasons, KM is currently a rapidly growing field. It has returned in full force in companies, because it responds to real underlying issues that are only increasing with the phenomena of globalization, aging populations, knowledge societies, etc. There is an abundance of literature on the subject, and even providing an overview has become impossible. Identifying a clear issue in this movement, which includes the economic, social, and cultural spheres, is occurring relatively slowly, because the creation of such a field is fairly complex. It borrows from economics, management, social sciences, information systems, computer sciences, etc. Discerning what KM really is in an organization is not an easy thing, because it includes almost all of its components.
KM concerns strategy, because it is really a new type of management responding to a new socioeconomic environment and a new vision of the organization. It concerns the structure of the organization, because knowledge is made and unmade through complex networks connected to the environment that can challenge traditional systems. It concerns many processes that are already implemented in organizations (fortunately, human beings have always managed their knowledge!), but that need to be revised from new perspectives, optimized or developed. It concerns the personnel of the organization, who is at the heart of the issue, because it is true that knowledge is only created, shared or developed through people, who must mobilize personally and collectively for this purpose. It concerns information and communications technologies, which are powerful vectors for KM if they are used effectively.
It is important to have a well-founded and practical approach that can help companies implement their KM system. This is all the more necessary because the international standardization of KM is in progress through the International Organization for Standardization (ISO) and other organizations.
That is the objective of this book.
This book is the result of more than 20 years of research and experience in the field of KM, begun even before the subject arrived on the scene. It is composed of two parts that can be read independently, although they are inextricably tied.
The first part of this book consists of the theoretical part. Based on literature that reflects the diversity and depth of the research on this subject, it sets out the main concepts on which KM must be based.
The first important concept is the knowledge value chain, which relates knowledge to other connected concepts that are often more or less confused with the notion of knowledge, such as data, information, skill and capacity.
The second concept, often poorly understood and poorly defined, is that of knowledge capital, which is intangible but precious capital that all organizations have, and that is the central element of all KM policies. In fact, we can define KM in a company as the management of this company’s knowledge capital. Although this definition may seem tautological, in actual fact, it is far from being put into practice.
Last but certainly not least, a third concept defined is knowledge itself. Most of the organizations that consider this problem propose their own definition of knowledge. There are hundreds of definitions that can be found in our information system that are all both similar and distinct, and they can generate interminable debates. However, the nature of knowledge is a subject that humanity has discussed almost since its origins, and many things have been thought and written on this topic, often in a very in-depth way. In this book, we propose a definition of knowledge based on a large corpus of reflections, an approach that is certainly not exhaustive, even reductive, but which is well founded and has led to the development of methods and operational tools for KM. We even sketch out a mathematical theory of knowledge.
The second part of this book consists of the practical part. It is based on 20 years of feedback and experience of a group of professionals from all types of companies (the French KM club), who implemented KM in their organizations and developed this experience into a KM framework, which is now nearly completed and freely available. This framework is compatible with the existing and future standards (in which it participated) and provides a practical and useful guide for companies.
This section contains an organizational part and an operational part. The organizational part concerns the implementation of a company strategy for KM and the design of a global action plan based on an analysis of the company’s knowledge capital. The operational part concerns the implementation of these processes in the goal of reaching the objectives of the action plan. These processes are divided into five categories: organizing knowledge, codifying knowledge, sharing knowledge, researching knowledge and creating knowledge. This covers the existing processes to be reinforced or created that are necessary and sufficient to manage a company’s knowledge capital.
We hope that this book will be useful for researchers who want to work on this topic and for professionals who want to implement all or part of a KM system in their organization.
This book is far from being an individual effort, and it benefitted from the results and collaboration of a large number of people with whom I worked.
In terms of theory, I had the support of numerous colleagues in different research teams where I have worked, at the Université de Bordeaux, the Commissariat à l’Énergie Atomique (French Alternative Energies and Atomic Energy Commission), the Université de Technologie de Troyes and the Institut Mines-Télécom (School of Management). I also shared a great deal within AGeCSO (Association pour la Gestion des Connaissances dans la Société et les Organisations, or the Association for Knowledge Management in Society and Organizations), which I have had the honor and the pleasure to create and preside over since 2008 and which organizes an annual conference on the subject. Thank you to everyone who shared in my journey.
At the practical level, I had the support of all the enthusiastic participants in the Club gestion des connaissances (French Knowledge Management Club) which I have had the honor and the pleasure of creating and presiding over since 1999. Thank you to this entire community, with whom we were able to build invaluable and useful capital based on KM.
Aware from the start that this new subject would require continuous experimentation in the field, I was the project manager or advisor for many research projects and industrial projects concerning KM in private and public organizations in France (industry, energy, transportation, defense, banks, Small to Medium-sized Enterprise (SMEs), etc.) and abroad (Algeria, Canada, United States, Brazil, Asia, United Nations, etc.). I would like to thank all of the organizations who put their trust in me and with whom I learned a great deal.
The adventure is only just beginning. I hope that this book will provide a background for everyone who wants to invest in this forward-looking field and that it will contribute to developing this domain.
Jean-Louis ERMINEJanuary 2018
PART 1Theoretical Elements
1A Knowledge Value Chain
1.1. Introduction
This chapter introduces the notion of knowledge through the concept of a value chain.
Its purpose is to clarify the relationships between the concepts of data, information, knowledge and skill, by relying on the abundant literature that has been written on these subjects. All of these concepts, which are rarely formalized and often conflated, are related and dependent, and they need to be better defined. In this chapter, this clarification results in a guidance tool to help managers understand the added value produced by knowledge and act to develop this resource.
In the “knowledge economy” [FOR 09], knowledge is viewed as a resource that is a key factor in success and the basis for a company’s competitive advantage. The objective of knowledge management (KM) is to optimize this new resource. It is therefore important to analyze the added value that KM can bring to a company. This is a difficult problem to address. For example, cost/benefit analyses for KM have never really been completed successfully. The approach proposed here is not based on the unpromising cost analysis, but on the value analysis. It is based on the nature of knowledge and its use in a company. We will see that knowledge is the result of closed-loop, continuous and simultaneous transformations within a company. We can, however, distinguish several formal transformation steps that are known as the knowledge value chain (KVC) [ERM 12]. This value chain is conceptual and does not presume any complexity in its implementation within a company. It is very useful for managers to locate potential sources of value of KM. The objective of the KVC is to provide an analysis and action framework that will make it possible to act on this value chain and thereby improve the company’s performance.
1.2. Different KVCs
The value chain is a management concept that was developed and popularized by Michael Porter [POR 85]. A value chain is a chain of production activities in a company, from the input to the end user. The products or services pass successively through all of the activities in the chain and, with each step, the products and services acquire value. A value chain is a breakdown of a company’s approach into activities that produce value. These components are the basic elements on which a company relies to create a product or provide a valuable service for their customers. The activity chain confers more added value to the products or services than the sum of the values added by each activity.
Identifying the value generated through this chain is the approach chosen by top management. The differences between the value chains of competitors are the key factors of competitiveness. In terms of competitiveness, the value is what customers are willing to pay for what the company provides them. A company is profitable if the value that it generates is greater than the costs to create the product or the service. Creating such a value is the goal of all competitive strategy. The value, instead of the cost, must be used to analyze competitive standing. The value chain characterizes the generic activities that add value to a company: the “primary activities” including logistics, production, marketing and sales and services; and the “secondary activities” including infrastructure, human resources management, R&D and supply. The vectors of cost and value are identified for each activity.
Classic value chains do not include knowledge, although it is now seen as a company’s most important strategic resource [DAV 98, DRU 93, HAL 93, STA 92]. The value incorporated in products or services is essentially due to the development of resources derived from organizational knowledge [QUI 92]. In fact, a company’s ability to produce can be considered to be the integration and application of specialized knowledge collectively generated by the individuals in the company [GRA 91].
Consequently, the notion of value is not directed by the customer, as in Porter’s chain, but by the incorporation of knowledge in products or services in the company’s production process. This raises the question of defining more precisely what this “cognitive resource” is and how it is incorporated into the activity of a company. The goal of KM is to manage this resource integration in the company’s process. KM is a fairly new perspective on companies. Its philosophy, which must still be strengthened of course, is that a company produces value for its customers when it best manages the incorporation of its cognitive resources in its products and services. Thus, very simply, KM supposes that the production of knowledge implies the production of value. KM is interested in knowledge as a strategic resource that optimizes the production processes of a company.
To support the success of KM, it is useful to analyze the chain of knowledge integration in a company in order to identify and manage the different fundamental stages of enrichment for this cognitive resource and its incorporation into company activities. This is the KVC, viewed from a global point of view in a company.
The definition of a KVC based on a financial analysis of performance is problematic [CHO 00, MPH 94]. The competence-based view business theory offers an alternative approach. This theory considers the company as a portfolio of competences. Its competitiveness is based on the creation and development of competences and on its realization of a strategy capable of creating a link between goals, resources and objectives [PRA 90]. These competences have a cognitive nature, and this allows managers to identify the basic processes, like knowledge creation and organizational learning [LEO 95, NEL 91, PRA 90]. Carlucci et al. [CAR 04, p. 579] assert that the cognitive perspective of competence can be summarized by the interpretation that defines the competence of a company as a combination of knowledge assets, which make up what is called the company’s knowledge capital, and knowledge processes, which allow a company to successfully complete its operational processes. This provides a foundation for the definition of a KVC.
Following the considerable development of KM in the past few years, the concept of the KVC appeared and was recently debated. The authors [CAR 04, EUS 03, HOL 01, LEE 00, WAN 05] define a KVC as a set of KM processes. A KVC is therefore a KM framework organizing the basic KM processes, such as the knowledge process wheel described in Carlucci et al. [CAR 04]. The main processes in these different KVCs are as follows:
– knowledge creation: this is definitely the most important process, because it creates knowledge capital, the purpose of all knowledge-based companies;
– knowledge codification: this process concerns the appropriation of tacit knowledge, which is a very complex problem;
– knowledge sharing: once a knowledge corpus is identified and a knowledge repository is elaborated, sharing this knowledge in a community is not really a standard task. This requires a lot of effort starting from the construction of the appropriate community to the implementation of access infrastructure;
– knowledge dissemination: access to knowledge for most people concerned (“the right information, the right person, the right time”) is the famous problem of the “last kilometer”, it involves information and communication infrastructure, and specialized designs of dedicated systems;
– knowledge portfolio analysis: the company, to implement a KM strategy, must implement a continuous process of analyzing and characterizing its knowledge portfolio: what is its strategic knowledge? What is its available knowledge? What are the risks associated with its knowledge? etc.;
– knowledge assessment: to carry out effective KM processes, it is necessary to have an evaluation matrix for their performance.
The KVC provides a KM framework to analyze the value added by each KM process. Figure 1.1 shows an example of a KVC (from [WAN 05]), with a series of KM processes in the form of a Porter-like model.
Figure 1.1.An example of a KVC based on KM processes
Figure 1.2.An example of a KVC based on cognitive tasks
Figure 1.2, from Powell [POW 01], proposes another type of KVC, which is a sequence of tasks whereby knowledge workers transform data into decisions and actions to construct the unique competitive advantage of their employer and/or social and environmental benefits. These tasks are intellectual tasks, which we call “cognitive tasks”, that successively enrich available information to act in line with the company’s objectives. Here, the value chain is not a sequence of KM processes that act on the knowledge capital of the company, but a sequence of cognitive tasks, realized by Knowledge Workers, that initially rely on the available information capital in the company to gradually give it a strategic value resulting in decision and action.
In this chapter, we will develop a KVC based on cognitive tasks. The objective is to use a chain of information transformations, to identify the cognitive tasks associated with each step and to define a transformation sequence whose management makes it possible to add value to the knowledge capital in a manner aligned with the company’s strategy.
A well-known transformation chain, partially taken up in [POW 01], exists in the domain of information management. It is the chain: data → information → knowledge → wisdom. We will examine it in the following sections and adapt it to our problem.
1.3. The DIKW model
The DIKW (Data, Information, Knowledge, Wisdom) model is one of the most famous models in the literature about information and knowledge and it is considered to be a self-evident truth. It is mostly used in information and KM, but this model remains somewhat vague and has not been discussed or verified in an in-depth way. For a history of this model and a critical study, see [ROW 07].
The most popular visual representation of DIKW is a pyramid, like the famous Maslow pyramid, with data at the base and wisdom at the peak (Figure 1.3). This representation implicitly supposes that the higher elements in the pyramid require the lower elements to be defined, and that they can be attained through the transformation of the lower elements. The DIKW model is therefore a chain where information is the result of data processing, knowledge is the result of information processing and wisdom is the result of knowledge processing.
Another visual representation of the DIKW model is a flow chart where the relationship between the components are less hierarchical, with return loops and controls, which show the complex interconnection of the transformations in the chain (Figure 1.4).
Figure 1.3.The DIKW Pyramid
Figure 1.4.The DIKW value chain
There seems to be little consensus in the abundant literature (notably studied in [ROW 07]) about the DIKW model. Below, we will set out our own definitions for the different levels in order to provide a refutable framework for DIKW. In general, they reflect the usual definitions, elaborated in the references cited. This voluntary choice, which is based on classic works, is deliberate. It is reductive but necessary to avoid ambiguity and to make it possible to study the different possible transformations.
–
Data
The data are defined as raw facts, and learning from the data is defined as a fact accumulation process [BIE 00]. The data are raw materials that have been gathered by people or machines through observation. According to Rowley [ROW 07], some authors ([JAS 05, CHO 05]) introduce a new element in the DIKW chain, the “signal,”, which represents the reality that is perceived, selected and processed by our senses to acquire data. In fact, in semiotic theory [ECO 76], founded by Pierce [PIE 34], it is assumed that reality is always perceived as a “sign system”. We define data as the perception of reality by the senses (which can be extended by observations made by machines with artificial sensors). The data are therefore the result of a perception process through a sign system.
–
Information
The only unambiguous definition of information is the mathematical definition proposed by Shannon and Weaver [SHA 49]. This theory of information is a probabilistic perspective of information produced by a system. During the communication process, the receiver expects a certain message. Consider the case of a traffic light. When a person looks at a given light (the observed sign system), they already have an idea of the set of messages transmitted by this light. A priori, they do not know what message specifically will be transmitted to them. However, because of their experience, they expect to receive certain messages with different probabilities (red, green and yellow lights, or combinations of these colors). The quantity of information received through a set of messages (the observed sign system) is calculated as the average probability of occurrence for this set of messages, called entropy. In information theory, the introduction of the notion of entropy was a significant innovation that has been incredibly productive, even as a metaphorical tool to understand what information is.
When information is considered as a concept, this theory of information is not often mentioned. According to Nonaka-Takeuchi [NON 95], information can be viewed from two perspectives: syntactic (volume of information) and semantic (meaning of information). The syntactic perspective is based on Shannon’s theory, but the semantic perspective is more important for knowledge creation because it focuses on the transfer of meaning. According to Floridi’s analysis [FLO 10], during the past 10 years, a General Definition of Information (GDI) has emerged as data + meaning. A simple way to formulate a GDI, that we will use here, is a tripartite definition: information is made of data, the data are well-formed (remember that “information” comes from the Latin “in-formare”, or “to give form to”) and well-formed data have meaning (e.g., the data must be compatible with the meanings – the semantics – of the system in question).
–
Knowledge
The most common definition of knowledge is a Justified True Belief (JTB) [CHI 82]. This means: “I know something if I believe it, if I have a proof that it is true, and if it is true”. But in the perspective of KM, the definitions of knowledge are much more diverse and complex than the definitions of data or information. By summarizing all of the definitions given in the literature about the DIKW chain, Rowley [ROW 07] established that knowledge can be seen as a mix of information, comprehension, capability, experience, skills and values. Knowledge is a resource for an entity’s capacity to act effectively. For example, Spender [SPE 96] considers knowledge to be data, meaning and practice. In the content of KM, there is a well-known distinction between explicit and tacit knowledge: generally, tacit knowledge is defined as internal to an individual and explicit knowledge is defined as residing in documents, databases and other recorded formats.
In [ERM 07], the authors outline an attempt at a formal theory of knowledge that is an extension of Shannon’s theory of information. In this theory, knowledge has three interconnected components: information, meaning and context. Information is governed by Shannon’s theory, meaning is governed by semiotic theory and context is governed by the connected graph theory. It is possible to define formal entropy that represents knowledge based on these three components. Meaning is strongly dependent on context, which can be social, professional or operational. This theory was fully developed in [ERM 00]. We will define knowledge as information (a set of messages produced by a system) that has a specific meaning in a specific context. This is detailed in Chapters 2 and 4 of this book.
–
Wisdom
If the definition of knowledge is complex and contested, then the definition of wisdom is almost non-existent. Rowley [ROW 07] shows that there are very limited discussions about it in the literature related to the DIKW model. We have therefore decided to provide a definition that suits our own purposes. Wisdom is defined, in the common sense, as a “deep understanding of people, things, events and situations that confers the capacity to choose or act in order to produce optimal results with a minimum amount of time and energy”. Thus, wisdom is the capacity to use knowledge optimally to establish and achieve the desired objectives. We will retain this definition while making a distinction between the individual level and the collective level.
–
Individual wisdom (competence)
According to this definition, for an individual, wisdom is similar to the common notion of competence or expertise. Competence is what allows an individual to correctly complete a specific job. It includes a combination of knowledge, abilities and behaviors used to improve performance. In terms of human resources, it traditionally includes knowledge, know-how and social skills. Expertise, for its part, is a characteristic of individuals and is a consequence of the human capacity to adapt to physical and social environments. Thus, competence (or expertise) can be defined as the individual integration and transfer of knowledge and capacities in order to obtain the expected results. It is in this sense that we will define and integrate the notion of competence as “individual wisdom” in the KVC.
–
Organizational wisdom (capacity)
Capacity is the ability to complete actions. According to [GRA 96], organizational capacity is the result of the integration of knowledge and complex productive team activities as well as being dependent on a company’s potential to develop and integrate the knowledge of several individual specialists. It is a capacity that is specific to each company, which corresponds to the definition of “wisdom” at the collective level. This notion of organizational capacity appears in the literature in many ways and under a variety of terms: “absorptive” capacity [COH 90] (the organizational capacity to assimilate new exterior knowledge), “combinative” capability [KOG 92] (the organizational capacity to combine existing internal knowledge), “dynamic” capability [TEE 97], core competence [PRA 90], organizational learning [HUB 91], agility [ROT 96], etc. It is in this sense that we will define and integrate the notion of capacity as “organizational wisdom” into the KVC.
