Knowledge and Ideation
Description
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.
More details
Other editions
Additional editions
Person
Pierre Saulais is a research associate at the Institute for Knowledge and Innovation, Bangkok University, Thailand. His research focuses on knowledge-based innovation, as well as the generation and extraction of inventive knowledge.
Content
Foreword xiii
Preface xvii
Part 1 Inventive Knowledge and Inventive Intellectual Corpus 1
Chapter 1 Nature of Inventive Knowledge 3
1.1 Knowledge levels 3
1.1.1 Knowledge in everyday life 4
1.1.2 Scientific knowledge 4
1.1.3 Knowledge in the Japanese intellectual tradition 4
1.1.4 Knowledge according to cognitive science 5
1.2 The limits of knowledge 6
1.3 Value chain and knowledge evolution chain 7
1.3.1 The knowledge value chain inspired by Porter 7
1.3.2 The DIKW knowledge evolution chain 16
1.4 Inventive knowledge concepts 21
1.4.1 Current and fruitful ideas 21
1.4.2 Depth of inventive knowledge 22
1.5 Cognitive and social dimensions of the knowledge actor 22
1.5.1 From erudite (scholar) to expert 23
1.5.2 From expert to inventor 23
1.6 Conclusion 24
Chapter 2 Representation and Analysis of Inventive Knowledge 25
2.1 The concept of dematerialized knowledge object 25
2.1.1 Founding principle 25
2.1.2 Illustration by electromagnetic wave detection object 26
2.1.3 Application to the description included in patents 27
2.2 Cartography or mapping 28
2.2.1 Introduction 28
2.2.2 Information mapping 28
2.2.3 Knowledge mapping 29
2.3 The map 30
2.3.1 Introduction to the map 30
2.3.2 Types of maps 31
2.4 Cognitive mapping 32
2.5 The cognitive map 32
2.6 A reasoned procedure for analyzing inventive knowledge 35
2.6.1 Introduction 35
2.6.2 Work on a knowledge structure 36
2.6.3 Example of an invention file 37
2.7 Conclusion 40
Chapter 3 Knowledge: Bridge between Innovation, Invention and Intellectual Property 41
3.1 Innovation 41
3.1.1 Multidimensional aspect of innovation 41
3.1.2 Innovation procedures and processes 42
3.2 Invention and the ability to invent 44
3.2.1 Concept of inventiveness 44
3.2.2 Concept of creativity 44
3.2.3 Combining creativity and inventiveness 46
3.3 Intellectual property rights 46
3.3.1 General information on intellectual property rights and copyright 46
3.3.2 The patent 47
3.3.3 Summary 48
3.4 Analysis of the links between invention, innovation and inventive intellectual corpus 48
3.4.1 Links between industrial property rights and innovation 48
3.4.2 Links between industrial property rights and invention 49
3.4.3 Links between invention and intellectual property rights 51
3.4.4 Links between innovation and intellectual property rights 51
3.4.5 Links between invention and innovation 51
3.4.6 Links between innovation and invention 51
3.4.7 Reciprocal links of the inventive activity and the inventive intellectual corpus 51
3.5 The nature of the bridges between knowledge domains 53
3.5.1 The perspective of economists 54
3.5.2 The knowledge management perspective on innovation 54
3.5.3 The perspective of KBI (Knowledge-Based Innovation) 55
3.6 Conclusion 55
Chapter 4 Knowledge Capital and Inventive Intellectual Corpus 57
4.1 Knowledge capital and intellectual corpus 57
4.1.1 Knowledge capital 57
4.1.2 Intellectual corpus 57
4.2 Inventive intellectual corpus 64
4.2.1 Dematerialized nature of the inventive intellectual corpus 64
4.2.2 Epistemic diagram of the inventive intellectual corpus 64
4.2.3 Inventive intellectual corpus versus intangible capital 65
4.2.4 Inventive intellectual corpus and creation of inventive knowledge 65
4.2.5 Traces in the inventive intellectual corpus 68
4.3 Projection of the inventive intellectual corpus on the inventive knowledge map ® 69
4.4 Conclusion 71
Part 2 Knowledge-Based Innovation 75
Chapter 5 Innovation Dynamics and Innovation as a Mode of Innovative Problem Solving 77
5.1 Innovation dynamics 77
5.2 Using knowledge to find innovative solutions 79
5.2.1 Relationship between knowledge management and innovation within the general framework 79
5.2.2 Relationship between knowledge management and innovation within the context of research and development activities 83
5.2.3 Known knowledge management methods instrumenting innovation 83
5.3 Overview of some common methods and techniques 84
5.4 Innovation and knowledge evolution by the principle of divergence-convergence 85
5.5 Innovation and knowledge evolution by the principle of analogy 86
5.6 Innovation and knowledge evolution by the principle of expansion 87
5.7 Generalization: global problem-solving process 88
5.8 Conclusion 89
Chapter 6 Innovation in Ideation Mode 91
6.1 The concept of ideation 91
6.2 Knowledge-based innovation (KBI) field 91
6.2.1 Relationship between knowledge management and innovation 92
6.2.2 Management by the strategic capabilities portfolio 92
6.2.3 Knowledge-based innovation as a process 92
6.2.4 Two key hypotheses 93
6.2.5 Systemic evolution 94
6.2.6 Path dependency 96
6.3 Principle of emergence 97
6.3.1 Need for a new principle for creativity 97
6.3.2 Principle of emergence 98
6.4 Theoretical model of knowledge evolution (the "chaotically" inspired model of knowledge evolution by emergence) 100
6.4.1 Step 1: knowledge, a complex system 100
6.4.2 Step 2: knowledge creation, an evolution of the knowledge system 101
6.4.3 Step 3: description of knowledge evolution by another complex system 102
6.4.4 Step 4: generalization of the evolution process to any complex system evolving over time 102
6.5 Theoretical model of inventive knowledge creation (step 5) 105
6.6 Instantiation of the "chaotically" inspired model of knowledge evolution by the ICAROS ® method (step 6) 107
6.7 The purpose of ideation for innovation 110
6.8 Conclusion 110
Chapter 7 Implementation of the ICAROS ® Method: Case Study 113
7.1 Introduction to the case study 113
7.2 Funnel model 113
7.3 Presentation of the experiment context 114
7.3.1 Concept of Knowledge and Technology Areas Portfolio 115
7.3.2 Adaptation of the Knowledge and Technology Areas Portfolio concept to the company under observation: the Knowledge and Technology Areas Portfolio 117
7.4 Preliminary step: constitution of cognitive stimulus 118
7.4.1 Structuring of the intellectual corpus by knowledge domain 118
7.4.2 Development of cognitive stimulus 124
7.5 Course 130
7.5.1 Individual stimulation session 131
7.5.2 Seminar 137
7.5.3 Dissemination 147
7.6 Conclusion in the form of lessons learned 147
Part 3 Inventive Activity and Visibility of Inventive Potential 151
Chapter 8 The Inventive Potential of a Company 153
8.1 Reminder on inventive activity 153
8.2 Notion of inventive potential 154
8.3 Annual innovation and invention activity file 154
8.4 Concept of making the inventive potential visible 156
8.5 Inventive data knowledge base 158
8.6 Introduction to the activation of inventive knowledge extracted from inventive intellectual corpus 158
8.7 Conclusion 160
Chapter 9 Managerial Applications 161
9.1 Reasoned contribution to technical strategic decision-making support 161
9.2 Strategic surveillance 162
9.2.1 Introduction 162
9.2.2 The place of strategic surveillance in overall performance steering 162
9.2.3 Knowledge management and environment surveillance 165
9.2.4 Interaction between knowledge capital and its environment 166
9.2.5 Knowledge-based strategic surveillance 168
9.3 Information system on patent portfolio management 172
9.3.1 Introduction 173
9.3.2 The patent file considered as a knowledge object 173
9.3.3 Description of the patent information system 174
9.3.4 Descriptive sheet of a patent file 177
9.3.5 Presentation support for the inventor's working file 178
9.3.6 Applications 178
9.4 Valorization of inventive activity associated with intangible assets 183
9.4.1 Limits of automated analysis of technical information contained in a patent portfolio 184
9.4.2 Limits to the quality of the drafting of patent files 186
9.4.3 Identification of the knowledge generated by the inventive activity involved in the patent 187
9.5 Publication policy 187
9.6 Determination of the inventive activity for the research tax credit 188
9.6.1 Industrial research and development 188
9.6.2 Characteristics of the research tax credit in France 189
9.6.3 Application of inventive knowledge engineering methods 191
9.7 Reasoned contribution to innovation management 195
9.8 The knowledge worker 196
9.8.1 Knowledge worker definitions 196
9.8.2 Characteristics of the knowledge worker 196
9.8.3 The knowledge worker in their relationship with the law 197
9.8.4 Knowledge Manager 199
9.9 A new profession: the inventive activity expert 202
9.10 The cognitive scientist and inventive activity expert pair 203
9.11 Need for a change in culture 203
9.11.1 Compatibility of conventional companies with the development of creativity 203
9.11.2 New knowledge-based organization 204
9.12 Conclusion 204
Part 4 Perspectives 207
Chapter 10 Knowledge Assessment Based on Knowledge 209
10.1 Introduction 209
10.2 Fundamental principles of knowledge management 212
10.2.1 The virtuous circle of knowledge management 212
10.2.2 Notion of critical knowledge 213
10.2.3 Reminder: ascent along the knowledge evolution chain 214
10.3 Reminder on the social mechanism for stimulating creativity and reflexivity 215
10.3.1 Reminder on the model of "chaotic" evolution 215
10.3.2 Instantiation of the creativity process: the ICAROS ® method 215
10.4 Transposition to the knowledge assessment field 216
10.4.1 Application of the fundamental principles of knowledge management 216
10.4.2 Application of the social mechanism of stimulation 217
10.5 Case study (2019-2020 academic year) 218
10.5.1 Context 218
10.5.2 Objectives of the action research 219
10.5.3 Preparation of the framework 219
10.5.4 Precautions taken with regard to students 221
10.5.5 Example of exercise subject terms 221
10.5.6 Analysis of the score database 224
10.5.7 Benefits of the analysis in the institution 231
10.5.8 Lessons learned and perspectives 231
10.6 Conclusion 232
Chapter 11 Towards an IKM ® : Inventive Knowledge Management 235
11.1 Introduction to the second level of the ICAROS ® method 235
11.1.1 Reminder on the first level of the ICAROS ® method 235
11.1.2 The second level of the ICAROS ® method 236
11.1.3 Notions of creativity 238
11.1.4 Contribution of creativity and inventiveness to ideation 246
11.2 Knowledge-based ideation 248
11.2.1 Introduction to the Idea according to Plato 248
11.2.2 Knowledge-based ideation and supervenience 249
11.2.3 Gestalt theory 252
11.2.4 Synthesis of knowledge-based ideation 258
11.3 Inventive profile engineering 259
11.4 Perspectives from the academic point of view 261
11.4.1 Inventive knowledge creation process as a study object in itself 261
11.4.2 Theoretical approach to knowledge by the physical sciences 261
11.4.3 Extension of the exploration to non-creativity 262
11.4.4 Reminder on the path hypothesis 262
11.5 Conclusion 263
Glossary 265
References 281
Index 295
1
Nature of Inventive Knowledge
All men by nature desire to know (in ancient Greek, t? e?d??a?); 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,...
