Knowledge Management in Innovative Companies 2 E-Book

Pierre Saulais

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 276

Veröffentlichungsjahr: 2020

Beschreibung

The status of knowledge management (KM) as a mature science has long been recognized in the academic world. However, in the economic arena, its connection with companies and organizations has been more gradual. Jean-Louis Ermine established a theoretical and practical framework for KM in Knowledge Management: The Creative Loop, which was also published by Wiley. In this second volume, practical examples are illustrated with real case studies. Modeled on the four-step operational approach inspired by the creative loop , this book includes four sets of real case studies each one following the basic presentation of the fundamental material. Knowledge Management in Innovative Companies 2 is especially useful for practitioners, as there are numerous illustrations based on best practices for each specific KM step and for global project implementation. Indeed, the last chapter is dedicated to the implementation of a global KM corporate project.

Leseprobe

Table of Contents

Cover

Preface

1 Knowledge Transfer and Knowledge Sharing

1.1. Articulation of Chapter 1

1.2. Introduction to knowledge transfer and sharing

1.3. The case of a banking company

1.4. The Sonatrach case

1.5. The CEFRIO intergenerational knowledge transfer project and the case of Hydro-Québec.

1.6. Case study on choosing a knowledge transfer method

1.7. Case study in agroecology

1.8. Lessons learned from the case studies

2 Innovation from the Knowledge Base

2.1. Articulation of Chapter 2

2.2. Introduction to knowledge-based innovation

2.3. The case of ONERA

2.4. The case of an automotive company, PSA Peugeot Citroën

2.5. The case of a defense company

2.6. Introduction to knowledge-based surveillance

2.7. An example for environmental monitoring

2.8. A case of CEA monitoring in the nuclear field

2.9. The case of a Renault monitoring unit

2.10. A methodology for capitalizing on reasoning

2.11. Lessons learned from the case studies

3 Case Study of a Global KM Project

3.1. Articulation of Chapter 3

3.2. Introduction

3.3. From awareness to the launch of an ambitious professional activity project

3.4. Operational deployment of the project

3.5. The implementation of the KM plan

3.6. Conclusion – key success factors and perspectives

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1. Analysis grid for knowledge transfer coded according to [HAR 12]

Table 1.2. Formalization of exchanges in order to develop a shared context for t...

Table 1.3. Overview of highly critical strategic skills

Table 1.4. Summary table of “business” knowledge areas

Table 1.5. Examples of knowledge management actions for a knowledge domain

Table 1.6. Examples of knowledge management actions for a strategic competency (...

Table 1.7. General structure of our scripting

Table 1.8. Characteristics of each generation

Table 1.9. Main behaviors

Table 1.10. Knowledge transfer methods

Table 1.11. Criteria and characteristic areas of their range of variation

Table 1.12. Example of an evaluation for classroom training. For a color version...

Table 1.13. Case study rating

Table 1.14. Types of interactions to be strengthened between stakeholders in sus...

Table 1.15. Hierarchy of critical knowledge in organic agriculture

Chapter 1

Table 2.1. Knowledge to be explained according to the type of surveillance

Table 2.2. Detailed plan of the different phenomena and concepts modeled

Table 2.3. Classes of critical information

Table 2.4. Surveillance requests

List of Illustrations

Chapter 1

Figure 1.1. Model of the knowledge transfer process

Figure 1.2. Key transfer elements

Figure 1.3. Generational characteristics ([ERM 10])

Figure 1.4. Map of strategic competencies in the “Contracts” domain. For a color...

Figure 1.5. Map of the business know-how of the “Contracts” Domain

Figure 1.6. Knowledge transformation methods within the project

Figure 1.7. Principle of learning content development

Figure 1.8. General diagram of pedagogical modeling with IMS-LD

Figure 1.9. General structure of the scenario

Figure 1.10. Definition of activity steps from MASK models

Figure 1.11. Definition of learning activities from MASK models

Figure 1.12. From the knowledge book to educational engineering

Figure 1.13. Rewriting pedagogical educational scenarios

Figure 1.14. Rewriting into educational elements

Figure 1.15. Age pyramid at Hydro-Québec

Figure 1.16. Graphical comparative analysis of transfer methods. For a color ver...

Figure 1.17. Main actors in knowledge management in direct contact with conventi...

Figure 1.18. Role of actors in the knowledge management tool

Figure 1.19. Concept model for agricultural machinery

Figure 1.20. Weed control task model

Figure 1.21. Organic farming phenomenon model in large-scale farming (Burgundy p...

Figure 1.22. Description of a crop succession

Figure 1.23. Wheat Technology Route Activity Model

Figure 1.24. Content of the KOFIS knowledge tool in organic field crop agricultu...

Figure 1.25. Example of the two spaces [I] and[K]

Figure 1.26. KOFIS IT architecture

Chapter 2

Figure 2.1. The “Innovation Maturity Model” grid

Figure 2.2. The innovation procedure

Figure 2.3. From the problem to the solution

Figure 2.4. From ideation to innovation

Figure 2.5. The model of “chaotic” evolution through the emergence of systems

Figure 2.6. The history model from DRASC to ONERA

Figure 2.7. The lineage model

Figure 2.8. Example of an argument

Figure 2.9. Organic model of door frames

Figure 2.10. Door frame services

Figure 2.11. Classification of door frames

Figure 2.12. Performance of door frames. For a color version of this figure, see...

Figure 2.13. Benefit history. For a color version of this figure, see www.iste.c...

Figure 2.14. Example of an antagonism model

Figure 2.15. Use of knowledge capitalization in innovative design

Figure 2.16. The funnel model for innovation

Figure 2.17. The process of knowledge-based creativity (ICAROS® method)

Figure 2.18. Representation in the form of a cognitive map for the “Algorithms” ...

Figure 2.19. The process of interaction between knowledge and the environment

Figure 2.20. The projection phase. For a color version of this figure, see www.i...

Figure 2.21. The distortion phase. For a color version of this figure, see www.i...

Figure 2.22. The identification phase. For a color version of this figure, see w...

Figure 2.23. The feedback phase. For a color version of this figure, see www.ist...

Figure 2.24. The representation phase. For a color version of this figure, see w...

Figure 2.25. The knowledge creation phase. For a color version of this figure, s...

Figure 2.26. Generic model of the phenomena to be instrumented. For a color vers...

Figure 2.27. Structure of the monitoring domain into three strategic axes for in...

Figure 2.28. Projection activity model

Figure 2.29. 2-P parasitic effect of hydrogen consumption by oxygen

Figure 2.30. Analogy grouping for pollution abatement technologies

Figure 2.31. Mapping of strategic domains of information research

Figure 2.32. Question asked

Figure 2.33. Reasoning of experts

Figure 2.34. Principle and application of a flexible sheath

Figure 2.35. Structuring the reasoning base

Chapter 3

Figure 3.1. The approach

Figure 3.2. The knowledge value chain

Figure 3.3. The MASK method

Figure 3.4. Adaptation of the global approach to IRSN

Figure 3.5. Interactions and contributions of the Sectors and Directorates

Figure 3.6. Indicators. For a color version of this figure, see www.iste.co.uk/s...

Figure 3.7. Capacity analysis against objectives

Figure 3.8. Critical Knowledge Analysis

Figure 3.9. Map of the knowledge repository

Figure 3.10. A portal for sharing IRSN’s knowledge. For a color version of this ...

Figure 3.11. A book from the IRSN collections

Guide

Cover

Table of Contents

Begin Reading

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Smart Innovation Set

coordinated by Dimitri Uzunidis

Volume 27

Knowledge Management in Innovative Companies 2

Understanding and Deploying a KM Plan within a Learning Organization

Pierre Saulais

Jean-Louis Ermine

First published 2020 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 Ltd27-37 St George’s RoadLondon SW19 4EUUK

www.iste.co.uk

John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA

www.wiley.com

© ISTE Ltd 2020

The rights of Pierre Saulais and Jean-Louis Ermine to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.

Library of Congress Control Number: 2019957523

British Library Cataloguing-in-Publication DataA CIP record for this book is available from the British LibraryISBN 978-1-78630-505-3

Preface

Knowledge management (KM) is a field that is now reaching maturity.

In the academic world, KM has established itself as an autonomous field, with a constant and impressive growth in the number of academic publications devoted to it, with dozens of international scientific journals dealing with the subject, with a large number of congresses throughout the world and with the creation of dedicated learned societies. In France and in the French-speaking world, it is AGeCSO (Association pour la Gestion des Connaissances dans la Société et les Organisations) that brings together research laboratories interested in this field. I helped to create this association and had the honor of being its first president. This active association is now recognized and it organizes an annual congress (in France or Quebec), the 12th of which was held in France (Clermont-Ferrand) in 2019.

In the economic world, KM has gradually taken its place in companies and organizations. In some organizations, new knowledge management processes have been put in place, specific structures have been created around this theme, specific expectations have been integrated into strategic objectives, etc. Gradually, the economic world is adapting to this famous “knowledge economy”, which represents the economy of the future. After hesitant and often chaotic beginnings, KM is also maturing in companies. A strong structuring element is the arrival of international standards, which now guide the KM approach of companies around the world. These include section 7.1.6 of ISO 9001:2015, the IAEA’s nuclear safety standards and, above all, ISO 30401, which defines the requirements for a knowledge management system. The ISO 30401 standard was published in November 2018 for the English version and in January 2019 for the French version.

The road towards KM maturity has been long and sometimes difficult. I started it more than 20 years ago, first at the University of Bordeaux, until 1991, when I started with artificial intelligence, then at the CEA (Commissariat à l’Énergie Atomique et aux Energies Alternatives), where I was in charge of a knowledge management unit from 1994 (we were not yet talking about knowledge management at the time!), until 2000. It is in particular by working on large and fascinating projects in this organization that the first versions of the MASK method discussed in this book were developed. From 2003, I was able to continue my work on KM at the Institut Mines-Télécom and collaborate on the NKM (Nuclear Knowledge Management) project of the International Atomic Energy Agency.

In 1999, I participated in the creation of a professional association, the Club Gestion des Connaissances, a French knowledge management club which I chaired for 17 years. This association, whose founding members are PSA Peugeot Citroën, Microsoft France, Cofinoga and Bull/Osis, brings together some 50 very diverse companies in 2019. Its objectives are to promote inter-sectoral exchanges, develop a network of contacts and build a shared operational reference system. After 20 years of existence and work, the Club has developed a KM methodological reference framework widely used by its members and which has served as a working document in the IAEA and ISO 30401 standardization commissions. It can therefore be said that the Knowledge Management Club has contributed, at both the national and international levels, to the maturity of the field in general.

At the dawn of the 2020s, which are beginning to change the (almost paradigmatic) framework of knowledge management, it seemed useful to me to draw the lessons learned from this journey, so as not to reinvent elements that have already been the subject of much reflection and experimentation. It is a bit like applying the principles of knowledge management to yourself!

A first step was to establish, in the light of these lessons, a theoretical and a practical framework for the field. This is the subject of the first book, Knowledge Management: The Creative Loop, published in 2018 by Wiley/ISTE [ERM 18a]. This book does indeed have two distinct parts: one on the theory of KM and one on the practice of KM.

To go further and satisfy even more KM practitioners in organizations, it seemed useful to Pierre Saulais and myself to illustrate this first book with real cases, which I experienced with other actors in research, industry and/or the Club Gestion des Connaissances. This was not an easy task. It was necessary to search for documents sometimes well buried in the corners of hard disks, in storage places more or less improbable or to call upon more or less recent memories (including mine)! It was necessary to compile, reread, rewrite and structure the content according to the plan in Part 2 of the book [ERM 18a], in order to ensure its true illustration. Two tools have helped us considerably in this task. The first tool is the basis of the CNRS’s open HAL archives, where I have deposited the original writings concerning the corresponding cases included in this book. Thus, the reader will be able to freely consult the additions desired by referring to the HAL identifiers provided in the bibliography. The second tool is, of course, the KM methodological reference framework for Club Gestion des Connaissances, which includes many case studies and from which we have extracted some of the most significant studies.

To make the link with [ERM 18a], we have written an introductory chapter using the KM framework and, at the beginning of each chapter dedicated to a case study theme, we have taken it upon ourselves to provide a brief introduction to the subject dealt with in that theme.

We still have to thank the many people who are mentioned in the references, with whom I collaborated and/or who created these applications, leaving behind testimonies that will undoubtedly inspire the next generation of KM.

We hope that this set of cases will be enlightening for KM practitioners, who will be able to discover the innovative possibilities offered by structured and equipped approaches, outside the box.

Jean-Louis ERMINE

Honorary President of the Club Gestion des Connaissances and

Honorary President of the Association pour la Gestion des Connaissances dans la Société et les Organisations

January 2020

1Knowledge Transfer and Knowledge Sharing

1.1. Articulation of Chapter 1

The purpose of this chapter is to explore conceptually and practically the third step in the virtuous cycle of knowledge management. described in Volume 1, Chapter 1, which is dedicated to the transfer and sharing of the organization’s knowledge.

In section 1.2, we recall the main concepts of transfer and sharing operations that are sufficient to fully understand the case studies.

The practical exploration of knowledge transfer and sharing will then be done through case studies, respectively of a banking company (section 1.3), Sonatrach (section 1.4), Hydro-Québec (section 1.5), analysis of the choice of a knowledge transfer method (section 1.6) and an agricultural field (section 1.7).

Section 1.8 summarizes the lessons learned from these five case studies on knowledge transfer and sharing.

1.2. Introduction to knowledge transfer and sharing

This paragraph is based on the study written by Thierno Tounkara [TOU 13] (sections 1.2.1 to 1.2.5) and that written by Jean-Louis Ermine [ERM 10].

1.2.1. Introduction

The concept of knowledge transfer was introduced by [TEE 77] in the classic case of technology transfer. It can be defined as the process by which an organization regenerates and maintains a complex, causal and ambiguous set of routines in a new context [SZU 96]. This process is a key element of the knowledge management cycle and allows organizations to absorb and make optimal use of critical knowledge. We are interested here in this process as an intra-organizational transfer of knowledge.

Knowledge transfer (or sharing) is an exchange process based on a binary relationship that depends on the contexts in which the actors operate. A knowledge transfer action is therefore characterized by the target audience (receivers), by the source that provides the content and participates in the transfer, by the characteristics of the knowledge that is transferred and by description of the environment (technical, social, organizational, cultural, etc.) in which this transfer takes place. A transfer process is easily described by a model (Figure 1.1) and thus provides the reference model for transfer or sharing operations (see Volume 1, Chapter 1). It is not a priori a unidirectional model from the holders to the receivers, because many crossinfluences and retro-adjustments are implemented in its implementation.

Figure 1.1.Model of the knowledge transfer process

This model makes it possible, for any transfer action, to specify in detail which elements are to be taken into account in the implementation. It is extremely useful for the success of the transfer. A large number of criteria can be established that can be used to characterize these processes. This is the purpose of this paragraph.

Research on knowledge transfer in general focuses on three themes [HAR 12, DAL 11, GUP 00, ZAC 99, SIM 99, SZU 96, ZAN 95]:

– factors that affect knowledge transfer: these are parameters to measure the degree to which knowledge can be easily communicated, understood and transferred;

– knowledge transfer modes or processes that address the respective transformation between tacit and explicit knowledge;

– evaluation and measurement of the performance of knowledge transfer, with the aim of developing indicators to measure the effectiveness of knowledge transfer.

We deal here with the first two themes. We focus here on knowledge transfer where knowledge codification is a possible step in knowledge sharing and transfer, including the use of knowledge engineering techniques for knowledge codification and the development of organizational memories. We can thus observe the effects of codification on the factors that affect knowledge transfer. We then propose an approach that provides optimal continuity between knowledge capture using knowledge engineering methods and knowledge transfer at the individual and organizational levels.

1.2.2. Factors influencing knowledge transfer

We can group the factors influencing knowledge transfer into four dimensions:

– characteristics of knowledge;

– knowledge transfer mechanisms;

– the absorption capacity of the receptors;

– cultural and organizational contexts.

1.2.2.1. Characteristics of knowledge

With the characteristics of knowledge, we can measure different aspects that can be facilitators or barriers to knowledge transfer.

The work of [ZAN 95] and [SIM 99] highlights three characteristics that affect knowledge transfer: their tacit nature, the complexity and specificity (or degree of contextualization) of knowledge.

Tacit knowledge versus explicit knowledge

Polanyi described tacit knowledge as “things we know, but cannot express” [POL 67] and can therefore only be transferred through interaction. Tacit knowledge is not easily expressed or formalized and is difficult to translate into words, texts, drawings or other symbolic forms. In fact, tacit knowledge is the property of those who possess it: it can be easily expressed by one person, but another may find it very difficult to explain.

Tacit knowledge is generally considered to be more valuable than explicit knowledge and requires more cognitive effort on the part of transmitter and receiver [DAL 11], [HAR 12].

Explicit knowledge is associated with declarative knowledge, consisting of descriptive elements [GAR 97]. Explicit knowledge is a set of elements collected in tangible form, such as texts, sound recordings or graphic representations.

Complexity

Knowledge complexity can be defined as the number of tools and operations used in the knowledge transfer procedure [PTR 90]. Operations are actions based on implicit conventions from past experience that can embody knowledge translation within an organization [SZU 96].

Therefore, the greater the number of operations required to interpret and appropriate knowledge, the more difficult it can be to transfer this knowledge [ARG 00].

Specificity or degree of contextualization

Specificity describes the degree to which knowledge (and the operations in which it is integrated) can satisfy the beneficiary of the knowledge transfer (the receiver). In other words, “specificity” represents the degree to which knowledge is dependent on many different contexts of use [ZAN 95]. The more knowledge is adapted to the receiver’s context, absorbed and understood by him, the more valuable it is.

For example, knowledge that is closely linked to local experiences and culture can be a barrier to transfer and difficult to transplant to another environment.

Key transfer elements (ECT) [ERM 10]

In a knowledge transfer action, it is important to characterize the difficulties specific to the flow of knowledge from transmitter to receiver. This characterization consists of identifying the difficult points in the transmission of knowledge in the field. This identification is mainly done in cooperation with experts in the field, who de facto always have experience of transmission to less experienced people and who are familiar with the difficult points that are generally a problem for novices. To assist in this identification, a grid is used to classify what are called “key transfer elements”, an example of which is given in Figure 1.2. These elements are classified according to whether they involve theory, technique or practice, and in general in two classes: the key points to know and the classical errors to avoid [CAS 04]. Identification of these characteristics is a valuable aid for any transfer device.

Figure 1.2.Key transfer elements

1.2.2.2. Knowledge transfer mechanisms

Transfer channels

Communication mechanisms and information flows stimulate knowledge transfer in organizations. The existence and richness of transmission channels are success factors for knowledge transfer [GUP 00].

Knowledge transfer channels can be informal or formal, personal or impersonal [HOL 98].

Informal mechanisms (such as informal seminars or coffee break conversations) are part of socialization and are more effective in small organizations [FAH 98]. However, such mechanisms may involve some loss of knowledge due to the absence of a formal coding of knowledge.

Formal transfer mechanisms (such as training sessions) can ensure a wider dissemination of knowledge, but they can inhibit creativity.

Personal channels (such as learning) can be more effective in disseminating highly contextual knowledge, while impersonal channels (such as knowledge repositories) can be more effective in providing knowledge that can be easily codified and generalized to other contexts.

Information technology can be beneficial for all four types of knowledge transfer channels.

Transfer devices [ERM 10]

The transfer of knowledge is a rich issue that has many tools [ROS 08]. There are many methods of knowledge transfer (Companionship, Twinning, Tutoring/Mentoring, Community of Practice, Training, etc.) supported by many technologies (CMS [Content Management System], Weblog (or blog), SPIP (Publishing System for the Shared Internet), e-learning platforms (e-training), portals or knowledge servers etc.). Unfortunately, the process, method and technology of transfer are often confused.

We are interested here in transfer processes that use, as initial support, a body of codified knowledge (obtained, for example, through the capitalization of knowledge using knowledge engineering techniques [see below]).

This raises the question of how to design a sociotechnical system, modeled by the process described in Figure 1.1 and based on a body of codified knowledge, resulting for example from an operation capitalizing on one more expert (see Volume 1, Chapter 3). It is a question of adapting often classic devices to this context. Among the tracks currently being followed, here are three significant examples:

The transfer process based on the socialization of a body of codified knowledge

Two distinct processes can be put in place for this purpose:

– expert/novice co-modeling: this involves putting an expert and one or more novices together (with a knowledge engineer as facilitator), with the objective of using the knowledge modeling technique to capitalize on the expert’s knowledge. The know-how is thus represented on a common basis, which allows novices to learn;

– direct transfer of the corpus of capitalized knowledge: the knowledge models created during capitalization provide a structured, intense and rich “digest” of the corpus of knowledge to be transmitted. It is a formulation of the expert’s knowledge that allows him to explain it in a structured and logical way. From this representation, the expert can, easily and quickly, explain to novices, during training sessions, the essential part of his know-how. This can be done with the help of a knowledge engineer. It has even happened that the knowledge engineer who produced the knowledge book alone performs a direct transfer session to the target audience, without the presence of the expert.

More generally, a knowledge capitalization, built with experts from a knowledge community, can be entrusted to that community, which must ensure its dissemination, maintenance and sharing. Knowledge is then fully socialized.

The transfer process based on a knowledge server

A knowledge server is a website that provides a knowledge community with a body of knowledge and provides access to all knowledge resources related to the corpus, as part of a professional activity (URL links, documentation, work groups, databases, software, collaborative spaces, etc.). We also talk about a knowledge portal or a professional activity portal.

Designing a knowledge server raises particular problems, compared to designing a traditional website. These are essentially problems of cognitive ergonomics, where the progression within the site must follow mental diagrams that correspond to professional activities logic. The design methods currently used proceed in two stages: creation of a knowledge directory, where all resources are encapsulated, in the sense of object languages, in “knowledge capsules”, then the structuring that distributes the knowledge capsules according to a professional activity logic (or several, if sites are to be obtained for distinct uses). It is only during the creation of the site that “use” elements are integrated, which cannot be encapsulated in knowledge elements.

The transfer process based on a learning system

A body of knowledge, resulting from capitalization, is organized in such a way as to represent know-how in a specific field. This is practical knowledge, acquired from problem-solving experiences. This corpus is generally not sufficient in itself to ensure the transfer of the knowledge it capitalizes on. As is often the case, the transfer can be carried out traditionally through an associated training system. The way in which the corpus has been designed greatly facilitates the educational engineering required to design a learning device. In particular, it allows you to:

– design the pedagogical path to be followed by the learner(s), according to their level of learning, the evolution of their learning, etc.;

– produce teaching materials based on a knowledge book, in the form of quizzes, level tests, evaluation tests, etc.;

– specify teaching tools that can be integrated into learning materials, such as e-learning.

1.2.2.3. The absorption capacity of the receptors

According to [GUP 00], absorptive capacity can be interpreted as a key element of the knowledge transfer mechanism.

Absorption capacity can be defined as “the ability of a company to recognize the value of new external information, to assimilate this information and to apply it” [COH 90].

It seems very difficult to control absorption capacity, because knowledge must go through a recombination mechanism in the mind of the knowledge receiver. This recombination depends on the cognitive ability of the recipient to process incoming stimuli [VAN 98].

1.2.2.4. Cultural and organizational contexts

Cultural and organizational factors

Inter-organizational knowledge transfer (across organizational boundaries) seems more complex than knowledge transfer within the organization. There are several reasons for this:

– cultural distance can hinder partners’ understanding and the transferability of knowledge;

– organizational distance (centralized vs. decentralized, innovators versus followers, entrepreneurial vs. bureaucratic) can increase the difficulty of transferring knowledge through inter-organizational relationships [SIM 99].

We limit ourselves here to the case of the knowledge transfer context within an organization.

Intergenerational transfer: the generational profile of an organization

The Club Gestion des Connaissances, in France, has worked on the generational characteristics of the source and target groups of a transfer, which can determine successes and failures depending on the method used and the modalities of the intergenerational transfer (Figure 1.3).

Figure 1.3.Generational characteristics ([ERM 10])

It is remarkable to see that, contrary to a long-held idea, characterizing a generation amounts to far more than referring only to age. According to the former idea, a generation would be a set of people born at approximately the same time. As generations follow one another at specific intervals, each generation would be characterized by a major innovation that would destroy the old heritage of innovation brought by the previous generation. The criteria for characterizing a generation would then be the year of birth and the “technical contribution”. But this vision, called positivist, has long been contested (see [MAN 28]). A qualitative, non-measurable approach can define a generation as a set of people with the same structuring tendencies. For there to be a generation, there must be unity of generation, with socialization based on structuring principles. This definition of generation has an economic aspect, and is a factor of social dynamics, as well as having a significant sociospiritual aspect.

Thus, the generational characteristics of the grid in Figure 1.3 include quantitative and qualitative criteria, linked to the individual (age, of course, but also professional and training background), linked to the social environment and linked to the ruptures or changes that people may have experienced in the company. In some projects, this grid has made it possible to draw up a company’s “generational profile” and to determine the success or failure factors for knowledge transfer between various generations (as defined in the analysis grid) in this company. The establishment of a generational profile of a company is still a little explored and yet very promising avenue (for knowledge transfer, but also for internal communication, human resources management, etc.).

1.2.3. Knowledge transfer methods

To better understand knowledge transfer, it is important to explore two complementary approaches to knowledge: socialization exchange and codification.

1.2.3.1. Knowledge transfer by socialization versus codification

We can share and transfer knowledge through socialization exchange, which is a mechanism for personal communication and interaction. It is a socialization mechanism (based on tacit knowledge) as described by [NON 95] in the SECI knowledge management model.

Knowledge coding is the process of transforming knowledge into a tangible and explicit medium, such as a document, so that knowledge can then be disseminated much more widely and at a lower cost.

1.2.3.2. Knowledge transfer models

We present here two theoretical models with different perspectives. These models provide a conceptual framework for many knowledge transfer processes. They have been discussed and validated by academics and professionals [DAL 11, HAR 12].

These two models allow us to better understand the role of knowledge coding in the knowledge transfer mechanism.

The SECI model

Nonaka and Takeuchi’s SECI model has proven to be one of the most robust in the field of knowledge management. This model focuses on the conversion of knowledge between tacit and explicit knowledge. It describes how knowledge is accumulated and transferred in organizations in four modes: socialization, externalization, combination and internalization.