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- Herausgeber: John Wiley & Sons
- Kategorie: Geisteswissenschaft
- Sprache: Englisch
Our world overwhelms us with more and more data everyday. Yet we need to face many challenges in order to deal with its complexity - notably to discern the essential from the accessory, to exploit quality and not quantity, to explore the depth of our knowledge and to produce from it, in a reasoned way, effective ideas to be put into action. A synthesis of a triple experience in industry, pedagogy and academia, Knowledge and Ideation presents numerous concepts, such as the dematerialized knowledge object, inventive intellectual heritage, inventive potential, and knowledge-based ideation. This book develops and describes applications in the form of case studies while proposing prospects.
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Seitenzahl: 466
Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Title Page
Copyright Page
Foreword
Preface
PART 1: Inventive Knowledge and Inventive Intellectual Corpus
1 Nature of Inventive Knowledge
1.1. Knowledge levels
1.2. The limits of knowledge
1.3. Value chain and knowledge evolution chain
1.4. Inventive knowledge concepts
1.5. Cognitive and social dimensions of the knowledge actor
1.6. Conclusion
2 Representation and Analysis of Inventive Knowledge
2.1. The concept of dematerialized knowledge object
2.2. Cartography or mapping
2.3. The map
2.4. Cognitive mapping
2.5. The cognitive map
2.6. A reasoned procedure for analyzing inventive knowledge
2.7. Conclusion
3 Knowledge: Bridge between Innovation, Invention and Intellectual Property
3.1. Innovation
3.2. Invention and the ability to invent
3.3. Intellectual property rights
3.4. Analysis of the links between invention, innovation and inventive intellectual corpus
3.5. The nature of the bridges between knowledge domains
3.6. Conclusion
4 Knowledge Capital and Inventive Intellectual Corpus
4.1. Knowledge capital and intellectual corpus
4.2. Inventive intellectual corpus
4.3. Projection of the inventive intellectual corpus on the inventive knowledge map
®
4.4. Conclusion
PART 2: Knowledge-Based Innovation
5 Innovation Dynamics and Innovation as a Mode of Innovative Problem Solving
5.1. Innovation dynamics
5.2. Using knowledge to find innovative solutions
5.3. Overview of some common methods and techniques
5.4. Innovation and knowledge evolution by the principle of divergence-convergence
5.5. Innovation and knowledge evolution by the principle of analogy
5.6. Innovation and knowledge evolution by the principle of expansion
5.7. Generalization: global problem-solving process
5.8. Conclusion
6 Innovation in Ideation Mode
6.1. The concept of ideation
6.2. Knowledge-based innovation (KBI) field
6.3. Principle of emergence
6.4. Theoretical model of knowledge evolution (the “chaotically” inspired model of knowledge evolution by emergence)
6.5. Theoretical model of inventive knowledge creation (step 5)
6.6. Instantiation of the “chaotically” inspired model of knowledge evolution by the ICAROS
®
method (step 6)
6.7. The purpose of ideation for innovation
6.8. Conclusion
7 Implementation of the ICAROS
®
Method: Case Study
7.1. Introduction to the case study
7.2. Funnel model
7.3. Presentation of the experiment context
7.4. Preliminary step: constitution of cognitive stimulus
7.5. Course
7.6. Conclusion in the form of lessons learned
PART 3: Inventive Activity and Visibility of Inventive Potential
8 The Inventive Potential of a Company
8.1. Reminder on inventive activity
8.2. Notion of inventive potential
8.3. Annual innovation and invention activity file
8.4. Concept of making the inventive potential visible
8.5. Inventive data knowledge base
8.6. Introduction to the activation of inventive knowledge extracted from inventive intellectual corpus
8.7. Conclusion
9 Managerial Applications
9.1. Reasoned contribution to technical strategic decision-making support
9.2. Strategic surveillance
9.3. Information system on patent portfolio management
9.4. Valorization of inventive activity associated with intangible assets
9.5. Publication policy
9.6. Determination of the inventive activity for the research tax credit
9.7. Reasoned contribution to innovation management
9.8. The knowledge worker
9.9. A new profession: the inventive activity expert
9.10. The cognitive scientist and inventive activity expert pair
9.11. Need for a change in culture
9.12. Conclusion
PART 4: Perspectives
10 Knowledge Assessment Based on Knowledge
10.1. Introduction
10.2. Fundamental principles of knowledge management
10.3. Reminder on the social mechanism for stimulating creativity and reflexivity
10.4. Transposition to the knowledge assessment field
10.5. Case study (2019–2020 academic year)
10.6. Conclusion
11 Towards an IKM
®
: Inventive Knowledge Management
11.1. Introduction to the second level of the ICAROS
®
method
11.2. Knowledge-based ideation
11.3. Inventive profile engineering
11.4. Perspectives from the academic point of view
11.5. Conclusion
Glossary
References
Index
Other titles from ISTE in Innovation, Entrepreneurship and Management
End User License Agreement
List of Tables
Chapter 2
Table 2.1.
Example of an invention description outline
Chapter 3
Table 3.1.
Divergent thinking
Table 3.2.
Convergent thinking
Chapter 5
Table 5.1.
Organizational applications of common creativity techniques
Chapter 9
Table 9.1.
Surveillance queries
List of Illustrations
Chapter 1
Figure 1.1.
Porter’s general-purpose value chain
Figure 1.2.
Example of an airline value chain
Figure 1.3.
The knowledge value chain
Figure 1.4.
The knowledge value chain as an advancement of the company’s “cog
...
Figure 1.5.
DIKW chain
Chapter 2
Figure 2.1.
Cassini map (source: Des villages de Cassini aux communes d’aujou
...
Figure 2.2.
Example of a cognitive map for the algorithm domain
Figure 2.3.
Analysis of a knowledge structure
Chapter 3
Figure 3.1.
The innovation procedure
Figure 3.2.
The “front end” of innovation
Figure 3.3.
Diagram of the production of inventive knowledge by ideation
Figure 3.4.
Analysis of the inventive activity
Figure 3.5.
Applications of the analysis of inventive activity
Chapter 4
Figure 4.1.
The AIL systemic model of intellectual corpus
Figure 4.2.
Epistemic diagram of the inventive intellectual corpus
Figure 4.3.
Creation of knowledge capital and inventive intellectual corpus
Figure 4.4.
Example of an inventive knowledge map
®
.
Chapter 5
Figure 5.1.
From problem to solution
Figure 5.2.
From ideation to innovation
Figure 5.3.
The spiral of transformation via ICT
Figure 5.4.
The C-K process
Figure 5.5.
Global creativity techniques incorporating process
Chapter 6
Figure 6.1.
Principle of knowledge-based innovation
Figure 6.2.
The principle of variation-stabilization
Figure 6.3.
Natural selection in the principle of variation-stabilization
Figure 6.4.
The model of “chaotic” evolution by emergence of systems
...
Figure 6.5.
Knowledge evolution model
Figure 6.6.
“Chaotically” inspired model of inventive knowledge evolution by
...
Figure 6.7.
Concept of individual inventive knowledge creation
Chapter 7
Figure 7.1.
The funnel model for innovation (source: Benoît-Cervantes 2008)
...
Figure 7.2.
Knowledge and Technology Areas Portfolio
Figure 7.3.
Prior inventory by domain
Figure 7.4.
Cognitive map of the radar knowledge object
Figure 7.5.
Cognitive map of the algorithms domain
Figure 7.6.
Knowledge-based creativity process
Chapter 8
Figure 8.1.
Concept of visibility of making the inventive potential visible
Figure 8.2.
Activation of the concept of making the inventive potential visib
...
Figure 8.3.
Strategy and technical communication applications
Figure 8.4.
Applications and actors
Chapter 9
Figure 9.1.
Representation of the steering procedure [DEM 09]
Figure 9.2.
Process of interaction between knowledge and the environment
Figure 9.3.
Parasitic effect of hydrogen consumption by oxygen
Figure 9.4.
Map of strategic axes of information search
Figure 9.5.
Committed actors
Figure 9.6.
Structuring of the patent information system
Figure 9.7.
Integration of industrial property in the strategy
Figure 9.8.
Industrial property challenges
Figure 9.9.
Nature of indicators
Figure 9.10.
Publication–patent comparison
Figure 9.11.
Inventive knowledge put into action
Chapter 10
Figure 10.1.
The virtuous cycle of knowledge management
Figure 10.2.
DIKW chain
Figure 10.3.
Reminder on the model of “chaotic” evolution by emergence of sys
...
Figure 10.4.
Histogram of scores obtained over four years in a) probability a
...
Figure 10.5.
Overall probability and statistics scores.
Figure 10.6.
Probability (partial and final).
Figure 10.7.
Ranking of scores by type (Tstd or Tcap).
Figure 10.8.
Variation of score according to scoring type.
Figure 10.9.
Variation of rank according to scoring type.
Figure 10.10.
Variance of differences between fixed and floating ranks.
Chapter 11
Figure 11.1.
First approach: knowledge-based innovation
Figure 11.2.
Means-ends ascent: spiral of improvement of knowledge
Figure 11.3.
Second approach: knowledge-based ideation
Figure 11.4.
Phenomenal state and action
Figure 11.5.
Orderly organization of the architecture of
In Search of Lost Ti...
Figure 11.6.
Theoretical pivot of
In Search of Lost Time
Figure 11.7.
“Chaotically” inspired reasoning
Figure 11.8.
Paradoxes of creativity
Figure 11.9.
Perspectives on deepening of creativity
Figure 11.10.
Creativity in the field of knowledge
Figure 11.11.
Inventiveness in the field of knowledge
Figure 11.12.
Distinction between content and container
Figure 11.13.
Dialectic ascension
Figure 11.14.
Ideation
Figure 11.15.
Innovation
Figure 11.16.
Classical production diagram
Figure 11.17.
Diagram of the production of inventive knowledge by ideation
Figure 11.18.
Transition from the archaic root ει − δ/ιδ to the structured id
...
Figure 11.19.
From the essence to the mental idea
Figure 11.20. Gestalt
model
Figure 11.21.
Epistemic connection between the analysis of the structure of k
...
Figure 11.22.
Inventive profile engineering
Figure 11.23.
Reminder on the “chaotically” inspired model of inventive knowl
...
Guide
Cover Page
Title Page
Copyright Page
Foreword
Preface
Table of Contents
Begin Reading
Glossary
References
Index
Other titles from ISTE in Innovation, Entrepreneurship and Management
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Innovation and Technology Setcoordinated byChantal Ammi
Volume 17
Knowledge and Ideation
Inventive Knowledge Analysis for Ideation Stimulation
Pierre Saulais
First published 2023 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
John Wiley & Sons, Inc.
27-37 St George’s Road
111 River Street
London SW19 4EU
Hoboken, NJ 07030
UK
USA
www.iste.co.uk
www.wiley.com
© ISTE Ltd 2023The rights of Pierre Saulais to be identified as the author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s), contributor(s) or editor(s) and do not necessarily reflect the views of ISTE Group.
Library of Congress Control Number: 2022945795
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-323-3
Foreword
This book is one of those that give you the feeling of an extraordinary journey into the world of ideas, which leave a lasting mark on a field of thought and that become essential references once they have been read.
It is an absolutely fascinating work on the nature, genesis, management and stimulation of inventive knowledge, which places emphasis on philosophical reflection and technological aspects, but which is also interspersed with numerous topics in the field of human and social sciences. At the same time, it is a book that offers new ideas and a very original vision on this subject, in other words a book that brings the message it conveys to life and which is in itself an example of the creation of inventive knowledge.
The author is an eminent engineer, who acquired a perfect mastery and profound knowledge of technological innovation management, at the end of an absolutely remarkable professional career within a large industrial group. After successfully defending a doctoral thesis in the field of knowledge management sciences, he became a tireless researcher with considerable scientific perspective and maturity. These rare qualities make him handle complex issues with impressive ease, as well as develop conceptual architectures with exceptional elegance.
Over the pages of this book, the reader will discover an epistemological theory of scientific knowledge, both of great rigor and undeniable societal relevance, supplemented by original methodological approaches, whose effectiveness has been clearly demonstrated, in the field of scientific knowledge management. Yet, despite the complexity and the almost elusive nature of this broad issue, reading is very pleasant and amazingly easy, and there is no falling into the traps of the pretentious and hollow discourse of pointless academism.
Demonstrating great erudition, which finds its roots in Western (Aristotle, Plato, Descartes, Kant, Locke, Leibnitz, etc.) and Japanese (Nonaka, Nitobe, etc.) philosophical traditions, the author takes us through an extremely broad conceptual field, by revealing the nature of inventive knowledge, examining the relationship and subtle dynamics between the subject and object of knowledge, showing us how to avoid “knowledge blindness”, which is error and illusion or how to identify, structure and use inventive knowledge within a human organization, in this case the company. He also draws from the works of a great number of philosophers or renowned experts, who have devoted reference works to the study of knowledge and its role in structuring the information society (Stuart Mill, Morgan, Lewes, Lhuilier, Morin, Drucker, Toffler, Moradi, Brunel, Vallespir, Littré, Tarondeau, Boterf, Bierly, Kessler, Dauzat, Ermine, etc.), to guide us in understanding Porter’s productive value chain, the DIKW (Data, Information, Knowledge, Wisdom) chain, and the subtle differences between creativity and inventiveness, ideation and idea development, dematerialized work and rights likely to be appropriated, or between knowledge and knowledge creation.
The author discusses the representation and analysis of inventive knowledge, by illustrating in an original way the concept of dematerialized knowledge object through a radar object, before establishing the mapping of information and knowledge and introducing the concept of a cognitive map. He then proposes a reasoned procedure for analyzing inventive knowledge and demonstrates its application in the form of an invention file. This inventive knowledge is considered as a bridge between innovation, invention and intellectual property, a relevant and unifying vision to exploit the wealth and analyze the links between knowledge corpus and inventive intellectual corpus.
An important part is devoted to knowledge-based innovation, a very state-of-the-art topic that is the subject of many studies. The author first looks at innovation in inventive problem-solving mode, which rests on the brainstorming approach, and proposes a generalization of this approach, based on the principle of expansion and a chaotically inspired model of knowledge evolution.
He then discusses innovation in ideation mode and introduces the principle of “emergence–convergence”, in line with the classical principle of “divergence– convergence”, linked to this model. From this solid theoretical basis, he proposes a creativity method, which calls upon the deep knowledge of the actors and the knowledge capital of the company and which is presented in the form of a chaotically inspired model of knowledge development by emergence and instantiated by applying the ICAROS® (Intellectual Corpus Analysis for Reasoned Openmindness Stimulation) method. The implementation of this method is the subject of a very interesting and convincing case study, carried out under real conditions, in a large international company in the field of defense, specialized in electromagnetic detection systems.
The last part deals with the inventive potential of an organization and the implementation of the inventive knowledge resulting from it, before addressing the managerial applications of the analysis of inventive activity. Decision-makers and technical managers will find considerable support and valuable methodological resources on the various aspects of innovation management, ranging from technical strategic decision support and strategic intelligence, to the administration of a patents portfolio, the valuation of inventive activity associated with intangible assets, publication policy or research tax credit. Equally remarkable and meaningful is the author’s proposed projection into the future towards a new organization based on knowledge, a plea for the need for corporate culture change, illustrated among other things by the original idea of the creation of the cognitics binomial – inventive knowledge expert − as well as by original approaches for knowledge assessment based on knowledge and inventive knowledge management.
At the beginning of this foreword, I said that this work is one of those that give you the impression of an extraordinary journey into the world of ideas. I would say that it is also one of those that make you feel, once you have finished reading, an ounce of frustration mixed with the great satisfaction of its discovery; the associated intellectual journey is so passionate and fascinating. It remains for us, as readers, to be patient and wait for the release of the next book on this subject of inexhaustible wealth which the author will undoubtedly offer to us.
Emanuel RADOI
University Professor
University of Western Brittany
Lab-STICC, CNRS
Preface
The world around us overwhelms us with more data every day. The mass media fill us with disparate new items, with the new item displacing the previous one, and they are pompously called information. Of all this flow, what can we say, except that its excessive quantity masks its poor quality and that meaning is increasingly absent?
The result is a great confusion where data, information and knowledge are mixed into a great whole which, in daily life, is ultimately very little and very badly used to understand and master the growing complexity of our globalized universe.
Yet, there are many challenges to be met in order to deal with the complexity, in particular to discern, in this mass, the essential from the accessory, the useful from the futile, to exploit quality and not quantity, to explore the depth of our knowledge and to bring out, in a reasoned way, effective ideas to put into action.
Such is the ambition of this work, Knowledge and Ideation, whose subtitle Inventive Knowledge Analysis for Ideation Stimulation clearly indicates its purpose. Nourished by the triple industrial, pedagogical and academic experience of its author, the main theme that runs through this book is exploration through knowledge by introducing many concepts such as dematerialized knowledge object, inventive intellectual corpus, inventive potential, knowledge-based ideation, etc. This conceptual exploration constitutes the substance of a number of applications developed and described by the author in the form of case studies, then expatiated by numerous perspectives. The book thus summarizes the author’s work to date on the themes of inventive knowledge and ideation.
This book is divided into four parts. The first part fully introduces the concepts of inventive knowledge and inventive intellectual corpus in four chapters. Chapter 1introduces the need to analyze the structure of inventive knowledge. Chapter 2 deals with the representation and extraction of inventive knowledge. Chapter 3 shows how inventive knowledge constitutes a bridge between innovation, invention and intellectual property. Chapter 4 describes the dematerialized base of inventive knowledge (inventive intellectual corpus) and the map that represents it.
The second part deals with KBI (Knowledge-Based Innovation), that is, the application of knowledge to innovation, in three chapters. Chapter 5 is dedicated to the principles of analogy (TRIZ) and expansion (C-K theory), used in problem-solving mode. Chapter 6 explores the principle of emergence (the core aspect of the ICAROS® method), allowing ideation ex nihilo. Chapter 7 provides the analysis of a case study of the application of the ICAROS® method in a large industrial group.
The third part develops a promising managerial approach that consists of intellectually valuing its intangible assets, whether recognized or not. It is the logical continuation of the analysis of the inventive intellectual corpus, after applying it to innovation: this involves defining and instrumenting an application to the technical strategy in a reasoned contribution to the technical strategic decision support. This part has two chapters. Chapter 8 describes the mechanisms by which inventive knowledge is made visible and put into action. Chapter 9 deals with the numerous managerial applications of the reasoned contribution of inventive knowledge analysis (technical strategic decision support, strategic intelligence, reasoned administration of a patents portfolio, valuation of inventive activity, scientific publication policy, supporting document of inventive activity for research tax credit, reasoned contribution to innovation management, etc.). This chapter also introduces a new profession: inventive knowledge expert.
The fourth part is devoted to perspectives related to the concept of inventive knowledge, their extraction and valuation. Chapter 10 examines how the possible stimulation of creativity through knowledge could be a contribution to imparting a desire for knowledge creation, innovation and entrepreneurship. Can the teacher– researcher become a knowledge assessment expert, in the deep sense of the possibility of their students activating this knowledge in their future professional context? First, the teacher must determine the criticality of the knowledge transmitted with regard to the future use that the professional environment will require of future engineers, in the business field considered. Chapter 11 discusses the appropriateness of extending the current field of knowledge management into an inventive complement dedicated to IKM® (or inventive knowledge management, which can also be interpreted as inventive management of knowledge). This involves exploring the second level of the ICAROS® method in its most abstract sense. A much more concrete application perspective is examined with the exploration of the inventive profiles engineering in a company.
There is a glossary that contains the most relevant definitions.
We hope that this book will be useful to researchers wishing to work on this theme based on understanding inventive knowledge and to professionals who want to improve their expertise management practices.
October 2022
PART 1Inventive Knowledge and Inventive Intellectual Corpus
1Nature of Inventive Knowledge
All men by nature desire to know (in ancient Greek, τὸ εἰδέναι); an indication of this is the delight we take in our senses; for even apart from their usefulness they are loved for themselves; and above all others the sense of sight. For not only with a view to action, but even when we are not going to do anything, we prefer seeing (one might say) to everything else. The reason is that this, most of all the senses, makes us know and brings to light many differences between things.
Aristotle, Metaphysics, Book A, 1, 980 a 21–27
It is with these words that Aristotle begins his major work, Metaphysics. This observation expresses a fundamental need in humans: knowledge is the expression of the metaphysical condition of humans, and metaphysics is expressed by the desire to know [VER 06].
We will see that the term “knowledge“ has two equivalents with different meanings: knowledge (singular) and knowledge (plural).
Our reflection will begin by questioning the nature of knowledge and its limits.
1.1. Knowledge levels
We start with knowledge in the plural form.
The approach to inventive knowledge begins with understanding the nature of knowledge, of which it is usual, first, to distinguish three levels: knowledge in everyday life, scientific knowledge and knowledge according to cognitive science, then to draw the limits.
1.1.1. Knowledge in everyday life
Knowledge of a thing means nothing more in everyday life than giving it its true name [SCH 25]. Thus, for Moritz Schlick, knowledge in everyday life (an object, for example) is constituted in three steps:
– an object is recognized;
– something old is rediscovered in something new (the object can now be designated by a familiar name);
– the name is found that belongs to the object and no other.
1.1.2. Scientific knowledge
Scientific knowledge consists of reducing one thing to another [SCH 25]. All understanding (in the sense of the search for an explanation) progresses by steps, by finding one thing in another, then another thing again in the first, etc. The ultimate degree is reached when there remains only a minimum of explanatory principles that cannot in their turn be explained. Making this minimum as small as possible is therefore the ultimate task of knowledge, while integrally determining each of the individual phenomena of the universe by means of this small number of explanatory principles [SCH 25].
1.1.3. Knowledge in the Japanese intellectual tradition
According to Nonaka, in Western philosophy, there has long been a tradition separating the subject who knows from the object that is known [NON 97]. For the Japanese intellectual tradition, the separation between subject and object is not so deeply rooted. This author’s theory is based on the idea that these two approaches are complementary and that an adequate theory of knowledge creation must borrow elements from both approaches.
In Western philosophy, the foundations of the history of philosophy encompass two opposing, but complementary traditions: rationalism in essence says that knowledge can be acquired mainly in a deductive way through reason (mathematics, valuing precise and conceptual reasoning), while empiricism holds that knowledge can be gained inductively from sensory experiences (experimental science, personal experience in the field). In antiquity, rationalism was represented by Plato and empiricism by Aristotle. In the 17th century, rationalism was represented by René Descartes and empiricism by John Locke. Rationalism and empiricism theories were brought together by Immanuel Kant in the 18th century. Kant held that the basis of knowledge is experience, and asserted that knowledge arises only when the logical reflection of rationalism and the sensory experience of empiricism work together. For Immanuel Kant, the human mind is not a passive tabula rasa, but active in ordering sensory experiences in time and space and supplying concepts as tools for understanding them [KAN 07].
For the Japanese, knowledge represents wisdom that is gained from the perspective of the whole personality (body plus mind). This approach provided a basis for emphasizing personal and physical experience rather than indirect intellectual abstraction. Inazo Nitobe pointed out that, in traditional samurai education, knowledge is acquired when it is integrated into our “personal character” [NIT 99]. While a Westerner “conceptualizes” things from an objective point of view, the Japanese emphasizes subjective knowledge and intuitive intelligence to “conceptualize” things by connecting to other things or people from a “tactile” and interpersonal perspective. They see reality in physical interaction with nature and other human beings [NON 97].
1.1.4. Knowledge according to cognitive science
For a very long time, the main method of studying thought was introspection, the reflection of the philosopher on their own thought [LHU 05]. But many philosophers of past centuries were also scientists, theoreticians and experimenters. The reflections of some non-conformists and the astonishing advancement of mathematical and physical sciences (mechanics and astronomy, in particular) made a good number of scholars see the world as a machine that one day could be explained by laws and mechanisms. The world of the mind should not escape it, whether its laws and mechanisms pre-exist and are innate or have also come to us from our perceptions. Gottfried Leibnitz said: “Thinking is calculating.” Knowledge would then be the objective trace that information leaves in us, who are complex mechanics [LHU 05]. In modern times, these functionalists and materialists rely on the work of neurologists or neurophysiologists. This reduction of the spiritual to known precise physiological phenomena is not something for tomorrow, but the patterns that emerge inform us about cognition and they can, for example, in the near future, guide us in the development of more efficient learning methods. The science of cognition (or cognitive science) gives us a multidisciplinary vision of the mental representation of the world, of memorization, of communication. It attempts to reduce mental mechanisms to a limited number of types of mental actions [LHU 05]. In the simplest, so-called standard, cognitive model [DOR 03], these are: filtering information, formatting it (decoding) and computing (combining, processing). Cognitive science categorizes relations between objects of thought (included in, instance of, analogous to, etc.). Memory is made up of stable representations of the world, verbalized (predicates, for example) or not (mental images). There are numerous works and schools of cognitive science: symbolist or connectionist. Is knowledge ultimately irreducible to objective information? Just as emergence theorists (John Stuart Mill, C.L. Morgan, C.H. Lewes, etc.) see complex systems emerging from the interactive gathering of simpler components or systems, can we say that knowledge emerges from objective information? Connectionists apply this vision to the mechanisms of thought. We therefore inherit all these partly contradictory works. In practice, most authors agree that the mechanisms of knowledge are too subtle to be well modeled and they advocate the use of human and social sciences (psychology, pedagogy, sociology) to transmit knowledge. This approach is one of the current foundations of knowledge management, which increasingly calls upon the human and social sciences [LHU 05]. This will also be our posture, but with an attempt to analyze to what extent knowledge can or cannot be objective and codified.
1.2. The limits of knowledge
We are dealing here with knowledge in the singular form, which consists of a cognitive capacity (to sort and exploit information) and a learning capacity (to ensure interpretation that will give meaning). Edgar Morin points out “the blindness of knowledge” that are error and illusion, which come from the use of knowledge without having first examined its nature, with its cerebral, mental and cultural characteristics [MOR 00]: “Knowledge of knowledge should be considered as a primary necessity” [MOR 08a]. According to the same author,
there is a central problem, still misunderstood, that of the necessity to promote a knowledge capable of understanding global and fundamental problems in order to include partial and local knowledge in it […] The supremacy of a knowledge fragmented according to disciplines often makes it impossible to establish the link between parts and wholes and must therefore make way for a mode of knowledge capable of understanding its objects in their contexts, their complexes, their wholes […] It is necessary to develop the natural ability of the human mind to place all its information in a context and a whole and to teach the methods for understanding the mutual and reciprocal relationships between parts and wholes in a complex world [MOR 00].
This questioning of the need for a prior knowledge of knowledge was already one of the essential points of Immanuel Kant’s philosophy of knowledge, for whom it is the ability to know that organizes knowledge, and not the objects that determine it: he indeed showed that human knowledge has definite limits. Rather than asking, as was traditionally the case, whether our knowledge reflects reality, Kant asked how our knowledge reflects our cognition. According to him, knowledge is derived from experience, but it needs to be ordered by the mind and it is possible, thanks to reason, to describe the structure that experience must have in order to discover universal truths about our world [KAN 07].
Just as Nicolas Copernic showed that the Earth revolves around the sun and not the other way around, Kant asserted that the “center” of knowledge is the knowing subject (human or rational being) and not an external reality to which we are simply passive. It is no longer the object which obliges the subject to conform to its rules, but the subject which gives its rules to the object in order to know it [KAN 07]. The immediate consequence of this is that we cannot know reality in itself, but only reality as it appears to us in the form of a phenomenon.
From these limits of knowledge, we will retain above all the need to structure our ability to know, which will be expressed by the exploration of its mechanisms and its results. This prompts us to consider an object of interest as a dematerialized entity carrying knowledge.
1.3. Value chain and knowledge evolution chain
The question that follows concerns the identification, structuring and use of knowledge in an organization: how can the knowledge already produced by an organization be characterized so that such knowledge is put into action for the benefit of this organization’s technical strategy and perspective?
The aim is to develop a management tool that can help company managers understand where the added value produced by knowledge is and to act to enhance this resource [ERM 18a]. Thus, there are two ways to build a knowledge evolution chain [ERM 12]:
– the first is directly inspired by Porter’s famous productive value chain [POR 85];
– the second is a sequence of cognitive activities that acts on knowledge processing procedures in the company, proposed by [BRU 08] using Porter’s approach to the value chain: this chain is called the DIKW chain.
1.3.1. The knowledge value chain inspired by Porter
1.3.1.1. Porter’s knowledge value chain
The value chain is a management concept that was designed, developed and popularized by Michael Porter [POR 85]. This concept makes it possible to systematically analyze the company’s sources of competitive advantage and its activities. Starting from the principle that a company can be divided into a series of basic functions or activities (design, manufacturing, marketing, etc.), the analysis focuses on the latter, articulated along a value chain. The value chain here represents all the basic tasks of the company: from the generation of ideas to the sale of products and the services associated with them. The value chain analysis describes the different steps for a company to obtain a competitive advantage over its competitors by proposing an offer valued by customers. The sources of competitive advantage of each activity of the company are in its cost/value ratio. It is therefore necessary to define a chain of activities that creates value beyond the costs generated.
Figure 1.1 illustrates a classical value chain with all its related basic activities. These basic activities or tasks are broken down into two broad categories: primary activities and support activities. Primary activities are those directly involved in the manufacture and sale of products. They are specific to the product or at the center of strategic activities analyzed. Support activities, as their name suggests, are indirectly involved in manufacturing and sales. They are generally common to all of the company’s strategic products or center of strategic activities and facilitate the proper performance of primary activities.
Figure 1.1.Porter’s general-purpose value chain
Primary activities are directly involved in value creation. They include inbound and outbound logistics, production, distribution and sales, and service activities:
– inbound logistics includes the activities of reception, storage and allocation of the means of production necessary for the product (handling, inventory control, return to suppliers, etc.);
– production uses these means of production to transform them into finished products or services (processing, packaging, maintenance and upkeep of the generation facilities, quality control, etc.);
– outbound logistics collects, stores and delivers the product to the end customer or distribution networks;
– distribution, marketing and sales include all activities associated with providing the means by which customers can buy the product and are encouraged to do so, such as advertising, promotion, sales force, selection of distribution channels, relations with distributors, pricing, loyalty, etc.;
– services include all activities that maintain or increase the value of a good, such as installation, repair, training, supply of spare parts and product adaptation.
Support functions support the primary functions and help to improve their efficiency. They include infrastructure, human resource management, technology development, and procurement and supplies:
– infrastructure covers the administrative activities that are essential to the smooth running of the company: these activities include general management, planning, finance, accounting, legal activities, external relations and quality management. Infrastructure also includes the procedures that cut across the various steps of the value chain and the activities they encompass. Finally, infrastructure develops and manages the company’s information systems;
– human resource management is concerned with all activities relating to the recruitment, hiring, training, development and remuneration of personnel, etc.;
– technological development is concerned exclusively with technologies directly related to products and associated services or to the production process (process, improvement of a raw material, etc.). The development or acquisition of other technologies is the responsibility of the relevant functions or activities;
– procurement and supplies concern the procedures for acquiring the resources necessary for the primary and support functions. They are involved in the entire value chain.
The value chain constituted by primary and support functions is a general framework for analysis that must be adapted to the specificities of the company or its industrial sector. For example (Figure 1.2), the value chain (excluding support functions) of an airline has few similarities with the general-purpose model developed by Porter. However, the logic remains the same and in all cases consists of representing the sequence of activities that create value for both the customer and the company.
Figure 1.2.Example of an airline value chain
The analysis of a company’s value chain makes it possible to assign a set of specific costs to each activity and to determine which activities contribute the most to the creation of value for the customer or for the company. For example, the three activities that create the most value for an airline are the program, marketing/revenue management and the business loyalty activities (mainly through the negotiation of framework agreements) carried out by the sales departments. The other activities, although necessary for the proper operation of an airline, create little value compared to the three activities mentioned above. At the end of this analysis, the company can identify the activities or parts of activities that are the source of competitive advantage and those that can be outsourced or that do not need to be strengthened beyond industry standards. Optimizing the entire value chain is generally not possible due to the corresponding limited resources of any organization. Moreover, it is not necessarily desirable, as a number of activities, although required for the smooth running of the organization, contribute very little to the overall value creation and are not a source of competitive advantage. Analysis of the value chain helps us to identify critical activities. Concentrating investments on the key success factors of these decisive activities is generally sufficient to ensure, if not the success, at least the sustainability of a company.
1.3.1.2. Porter’s value chain applied to knowledge
Following Peter Drucker [DRU 92] and Alvin Toffler [TOF 90], Mahmoud Moradi, Stéphane Brunel and Bruno Vallespir observe that society has gradually become a knowledge society [MOR 08b]. According to these authors, the competitiveness of companies increasingly depends on the ability to produce, transfer, use and protect the knowledge produced internally [TEE 00]. “In a world of rapidly changing markets, products, technologies, competitors, laws, and often social constructs themselves, constant knowledge-based innovation becomes a very important source of major competitive advantage. Therefore, in an increasingly unstable economy, the only palpable certainty within companies is knowledge” [MOR 08b].
For these authors, this view that companies have about sustainable competitiveness acts as a proposal for intellectual resources to become active structural elements of the company’s global strategy. Companies capable of managing knowledge as efficiently as possible will offer their customers increasingly efficient services and products, thus reducing personnel and infrastructure costs, improving decision-making and innovation, improving the reaction time of structures, allowing the rapid development of new product lines, responding more quickly and efficiently to problem solving and ensuring the transfer of best practices in the most efficient way possible [MOR 08b]. The literature illustrates many records of success based on effective knowledge management seeking to embark on such efforts in the face of these challenges [ALA 05]. The multiple challenges that organizations have to face lead them to develop different frameworks, methods, models, perspectives, strategies and procedures. Based on the analysis and adaptation of formalization frameworks, other authors have adopted a knowledge management system consisting of six elements: context, goals, strategy, culture, knowledge management procedures, and technological and organizational structures [APO 99]. This system can be used to carry out a comparative analysis of the efforts to be made for better knowledge management [MOR 08b].
According to Ermine and Moradi, the knowledge value chain is, relative to a given organizational framework, necessarily determined by a field of activity, characterized by products or services [ERM 12]. Within the organization, this chain begins with data, transformed into information, then into knowledge, then into skills and finally into abilities.
It is a sequence of knowledge-related activities (creation, codification, sharing, transfer, identification, evaluation, etc.) that acts on the company’s knowledge capital. According to Porter, a company is profitable when its sales exceed its expenses [POR 85]. Producing added value that exceeds the cost of manufacturing is the goal of any competitive strategy.
Integrating knowledge management into the framework of the innovative company means moving from the company’s ability to manage its knowledge capital to the ability to use this knowledge to help its innovative employees to develop inventive skills and transform these individual skills into a portfolio of product/service innovations aligned with the organization’s strategy. These transformations are included in a value chain: the knowledge value chain.
According to [BRU 08], the evolution mechanisms highlighted in the knowledge value chain fall into two main categories:
– The first category is real and objective. It can be performed by humans and the reasoning can be automated:
- transforming reality into data means receiving signs through perceptual filters in an observational activity, so as to constitute data banks;
- transforming data into information means coding data through conceptual filters in a structuring activity. Data processing gives form and function to data. The conceptual filters, the context in which the meaning is made, the relevance and the purpose are the main transformations of the data that will end up producing information.
– The second category goes from information to metacognition. In this category, human participation is paramount and, by its nature, it is intangible and subjective, so the role of information technology becomes the main element:
- transforming information into knowledge means building models through theories, in a learning activity: the clear understanding of information is the objective that naturally leads us to knowledge. Understanding, realization, modeling, insight, authentication, application, control and refinement and use constitute the basis of transformation activities towards knowledge generation. Processed information, experiences and theories in an identified semantic context are the highest level of knowledge;
- transforming knowledge into competences-capacities means developing a set of practices (know-how) in action, in a context of experience: producing knowledge through practice, through action, is a process of reflection that leads actors towards greater skills. If competence is defined as quick, accurate and precise advice, allowing an explanation and justification of results through reasoning, it also allows decision-making. Thus, the transformation activity allows adaptation to environments through intuition and experience, learning, and memorization. According to Bierly and Kessler, three paths have been established that lead to individual metacognition: experience, spirituality and passion [BIE 00];
- transforming competences into capacities means building a knowledge strategy, through filters that achieve strategic alignment, within a global vision of the organization: the conceptualization, integration, management and distribution of people’s competences in the organization lead to organizational capacity. According to Bierly and Kessler, the transformation of people’s potential into organizational capacity requires having an idea of the transformational direction, a coherent organizational culture and structure, and being able to be one of the actors of knowledge transfer [BIE 00].
This analysis, summarized in Figure 1.3, gives us the tools (signs, codes, models, practices, strategy), cognitive activities (perception, conceptualization, theorization, action, strategic alignment) and postures (observation, structuring, learning, experience, vision) to implement [ERM 18a].
Figure 1.3.The knowledge value chain
At each transformation step in the chain, if the organization implements the appropriate management tools, it increases its cognitive capacities, as described in Figure 1.4.
The first phase is the construction of its organizational memory by storing and processing data. Then, the implementation of procedures and information processing tools gives meaning to this data accumulation by allowing them to be used for operational or decision-making purposes. Knowledge management enables the company to become a “learning company”, that is, it optimizes the use of available information (“the right information, to the right person, at the right time”) to create, acquire and transfer knowledge and modify its behavior. This knowledge is embodied in individuals through action: they acquire experience and are able to behave intelligently, that is, to adapt to new situations and to think of inventive solutions to the problems they experience in their daily activity. If this individual inventiveness is recognized and integrated into an overall strategic plan, then the company develops a portfolio of new products and services that can satisfy its customers or new markets and it becomes a mature and innovative company in the full sense of the term.
Figure 1.4.The knowledge value chain as an advancement of the company’s “cognitive capacities”
1.3.1.3. Competence and capacities
1.3.1.3.1. Competence
Competence is a standardized requirement for a person to perform a particular function correctly. It encompasses a combination of knowledge, skills and behaviors, all used to enhance performance. More generally, competence is the condition or quality of becoming suitable or competent by having the ability to perform a particular role. For example, management competence highlights capacities to design systems, as well as negotiation skills. A person has competence as long as the skills, abilities and knowledge that constitute that competence are part of the person, enabling the person to perform the effective action required in a certain work environment. Therefore, a knowledge, technique or ability cannot be eliminated, but a competence can be eliminated if what is needed to carry it out is not available. Expertise is an individual characteristic and a result of the individual’s capacity to adapt to different physical and social environments. Prahalad and Hamel define competence as the foundation of competitiveness [PRA 90]. Competence can then be defined as the individual mobility, integration and transfer of knowledge and capacities in order to achieve desired outcomes.
For Guy Le Boterf, acting competently involves three dimensions including: “the activity as a contextualized action aspect, the available resources (including knowledge) aspect, either personal or accessible resources in one’s environment and the reflexivity (or distancing) aspect, that is, the conceptualization of action” [LEB 06].
1.3.1.3.2. Competence–capacity
Competence–capacity is a dynamic concept moving from a concept in the process of being made in an intellectual dynamic towards an operative dimension in a dimension of effective practicality [LEB 06]: thus, competence–capacity is seen in an act, in a situation, in a construction in finalized actions. It would be, so to speak, an intermediary between competence and capacity, but only from an individual perspective.
Still, according to Guy Le Boterf, competence–capacity involves the following resources:
– theoretical knowledge: understanding a phenomenon, concepts, assimilating patterns, schemes;
– procedural knowledge: knowing how to do it, methods, operating modes;
– procedural know-how: knowing how to carry it out;
– know-how from experience: lessons from practical experience;
– social know-how: “know-how-to-be”, habitus, professional socialization.
Competences are “particular capacities to implement assets in an organized way in order to achieve goals; they are used in intentional and finalized actions where they are built and enriched by learning” [TAR 03]. Competences express an intention to achieve goals through action. The accumulation of individual and collective knowledge and the learning achieved in their implementation generate skills, capacities and competences [TAR 03]. Some authors translate competence– capacity by the term “capability”.
1.3.1.3.3. Capacity or “capability”
For Jacques Tarondeau, capacities are defined as “routines for implementing assets to create, produce and/or offer products or services on a market, routines that are immaterial by nature” [TAR 03].
Capability is the ability to perform an action [MOR 08b]. In terms of actors within the organization, capability is, for these authors, “the sum of competences and capacities that can potentially lead to achievement, the highest level of competence” [MOR 08b]. Grant sees “organizational capability” as the result of the integration of knowledge into productive activities [GRA 96]. Making competence executable and profitable in this way will generate distinctive competences and dynamic “capabilities” for the organization. Whatever term used, the authors agree that capacity is endowed with a value that is clearly superior to all other possible resources within the organization.
1.3.1.3.4. Knowledge
For Jean-Noël Lhuillier, erudite knowledge is a set of knowledge regarding a broad field and without any notable lack [LHU 05]. Knowledge makes up the main codes for interpreting incidental messages that will allow the extraction of new information that will interfere with knowledge: the Latin term cum prehendere (comprehend) means to put with, to incorporate. We understand incidental information when it fits well into our existing codes, when it complements them without contradicting them. Competences are operational knowledge acquired in a situation and validated, usable for action [MEI 97]. If this operational knowledge covers a broad field, it then makes it possible to deal with quite unforeseen situations. Jean-Noël Lhuillier introduces the key concept of knowledge for competent actors, “competent” as defined by Le Boterf, where this qualifier designates those who have “a disposition to act in a relevant way with respect to a specific situation involving resources” [LEB 98]. Thus, through relevance, this disposition becomes a capacity. According to Le Boterf, competence is seen as knowing how to act, but it is necessary to be able to act and to want to act. In relation to an organization, we can talk of strategic competence (for the organization), if it is useful for the core business (defined from the core competences), vulnerable (difficult to acquire for the organization but easy to lose) and rare (difficult to acquire for competitors). Creative ideas are “emerging new knowledge” that “require informal experimentation” ([ROB 97], quoted in [LHU 05]).
1.3.2. The DIKW knowledge evolution chain
The knowledge evolution chain is a sequence of cognitive activities that act on knowledge processing procedures in the company. The most famous is the so-called DIKW (Data, Information, Knowledge, Wisdom) chain, which describes all the transformations to move from perceived reality to data, information, knowledge and then to wisdom. As this last concept is not well developed within the context of the company, we have opted for “metacognition“. A complete study of this chain can be found in [ROW 07].
Figure 1.5 describes the DIKW knowledge evolution chain at four levels: data, information, knowledge and metacognition.
Figure 1.5.DIKW chain
1.3.2.1. Level 1: data
Digitized information and simple signs are binary messages [LHU 05], which are addressed by the information theory [SHA 49]. Data are very simple information, still quite objective, but already cognitively interpreted (examples: ASCII code, hieroglyphs, ideograms): they are defined as raw facts [BIE 00].
Data is “the recording, in a code agreed upon by a social group, of the measurement or tracking of certain attributes of an object or event” [BRU 08]. For these authors, a datum can be qualitative or quantitative. It contains no intention (thus situated at level 0 of the teleological scale) or project, which gives it its objectivity character and therefore has no meaning in itself. Data are defined [VER 96] as something given, granted or admitted by all, that is, as elements on which something can be discussed or can allow an implication. Other authors have argued that data are guaranteed true facts, which are the raw material of evolved elements [DAV 85].
It is only by giving meaning to this datum that the individual will obtain information that will allow them to say “that they know” or “that they have retained” something. Giving meaning is therefore achieved by expressing a purpose: from the Greek τελος (finality) and λογος (reason), teleology is the study of purpose [JUL 84]. Human history can be considered according to the order of causes (causal approach) or according to its goal (teleological approach): in the usual sense, τελος means the end, the completion (in the sense of ultimate accomplishment or fulfillment), while in the philosophical sense τελος represents the final cause [GOB 00]. The teleological explanation based on the whole is opposed to the mechanistic explanation based on the parts. Here, the purpose will be backed by activation, by putting into action of knowledge.
1.3.2.2. Level 2: information
The only unambiguous definition of information is the mathematical definition given by Shannon [SHA 49], according to a probabilistic point of view of information produced by a system. The calculation of the amount of information received through a set of messages is made from the arithmetic mean of the probability of occurrence of each message [ERM 18a].
According to Jean-Noël Lhuillier, elementary information is more global and more interpreted than data, and is made up of the elementary meanings conveyed by the media (sentences, voice inflections, cinematographic effect, etc.) [LHU 05].
According to [BRU 08], information is a collection of data organized to give shape to a message, most often in visible, pictorial, written or oral form, so that it reduces uncertainty and conveys something that triggers an action. “Information produces a new point of view on events or objects, which makes visible what was invisible” [BRU 08].
