Knowledge Management Systems: Concepts, Technologies and Practices 1801173494, 9781801173490

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Knowledge Management Systems

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KNOWLEDGE MANAGEMENT SYSTEMS: Concepts, Technologies and Practices SHABAHAT HUSAIN Department of Library and Information Science Aligarh Muslim University, India

JEAN-LOUIS ERMINE Institut Mines-T´el´ecom Business School Universit´e Paris-Saclay, France

United Kingdom – North America – Japan – India – Malaysia – China

Emerald Publishing Limited Howard House, Wagon Lane, Bingley BD16 1WA, UK First edition 2021 Copyright © 2021 Shabahat Husain and Jean-Louis Ermine Published under exclusive licence by Emerald Publishing Limited Reprints and permissions service Contact: [email protected] No part of this book may be reproduced, stored in a retrieval system, transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without either the prior written permission of the publisher or a licence permitting restricted copying issued in the UK by The Copyright Licensing Agency and in the USA by The Copyright Clearance Center. Any opinions expressed in the chapters are those of the authors. Whilst Emerald makes every effort to ensure the quality and accuracy of its content, Emerald makes no representation implied or otherwise, as to the chapters’ suitability and application and disclaims any warranties, express or implied, to their use. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-80117-349-0 (Print) ISBN: 978-1-80117-348-3 (Online) ISBN: 978-1-80117-350-6 (Epub)

To Whom Who taught me what i knew not

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Table of Contents

List of Figures and Tables About the Authors Foreword Preface

ix xiii xv xvii

Chapter 1 A Conceptual Approach to Knowledge Management

1

Chapter 2 An Overview and Trends in Knowledge Management

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Chapter 3 Knowledge Management: Processes and Models

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Chapter 4 The Virtuous KM Cycle, a Global Approach to Managing Knowledge

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Chapter 5 The Key Processes for KM: The Daisy Model

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Chapter 6 Knowledge Management System: A Case Study of Sonatrach, National Oil Company, Algeria

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Chapter 7 Knowledge Management System Standardization: An Overview

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Chapter 8 Knowledge Management International Standards: ISO 9001, 30401, and IAEA Safety Standards

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Chapter 9 Artificial Intelligence and Knowledge Management

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Chapter 10 Evaluation of Knowledge Management System

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Index

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List of Figures and Tables

Figure 1.1. Figure 1.2. Figure 1.3. Figure 1.4. Figure 3.1. Figure 3.2. Figure 3.3. Figure 3.4. Figure 3.5. Figure 3.6. Figure Figure Figure Figure Figure Figure Figure Figure

3.7. 3.8. 3.9. 4.1. 4.2. 4.3. 4.4. 4.5.

Figure 4.6. Figure 4.7. Figure 4.8.

Knowledge Generation. Data, Information, Knowledge, and Wisdom (DIKW) Pyramid. Spiral of Scientific Method. The Knowledge Framework for Knowledge Society. Diagrammatic Representation of the Knowledge Management Process. Boisot I-Space KM Model (Adapted). EFQM KM Model Virtuous Cycle. EFQM KM Model Key Components (Adapted). SECI Model (Spiral) (Adapted). Sense-making, Knowledge Creating, and Decision-making. Organizational Knowing Cycle. Workflow of Choo’s KM Model. Husain–Ermine AI-KM Model. Knowledge Pyramid. Knowledge Value Chain. Knowledge Process Wheel. Virtuous Knowledge Management Cycle. The Virtuous Knowledge Management Cycle as a Continuous Action of Progress. An Example of Strategy Map. An Example of Assesment of Critical Capacities (Extract). An Example of Knowledge Map.

2 3 4 8 38 43 46 47 49 53 55 56 57 62 63 65 66 68 70 71 72

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List of Figures and Tables

Figure 4.9. An Example of Critical Knowledge Assessment. Figure 4.10. An Example of Strategic Alignment. Figure 4.11. Strategic Assessment Process with Method for Analyzing and Structuring Knowledge (MASK II) in the First Phase of the Virtuous Knowledge Management Cycle. Figure 4.12. Example of Ph´enomena Model: Spent Fuel Pools Drain out by Integrity Loss. Figure 4.13. Example of Concept Model: Radionuclides Transfer in the Atmosphere. Figure 4.14. Example of History Model: History of Safety for Spent Fuel Pools. Figure 4.15. The Capitalization (or Codification) Process with Method for Analyzing and Structuring Knowledge (MASK I), in the Second Phase of the Virtuous Knowledge Management Cycle. Figure 4.16. The Knowledge Transfer Process. Figure 4.17. The Knowledge-based Innovation Process. Figure 4.18. The Creativity Process for Knowledge-based Innovation with Method for Analyzing and Structuring Knowledge (MASK IV) in the Fourth Phase of the Virtuous Knowledge Management Cycle. Figure 5.1. Daisy Model. Figure 5.2. Nonaka’s Cycle of Knowledge Conversion. Figure 5.3. Water Cycle. Figure 5.4. Two Different Points of View of an Organization Regarding Its Environment. Figure 5.5. Process of Interaction with the Environment. Figure 5.6. Knowledge-based Innovation Process. Figure 5.7. Giordan’s Allosteric Learning Model. Figure 5.8. Three Types of Learning Process. Figure 5.9. Training Process as a KM Process. Figure 6.1. Strategic Capacities of the PED Department (Extract). Figure 6.2. Knowledge Domains Map of the PED Department (Extract).

74 75

77 79 80 82

84 85 86

89 92 94 97 98 100 106 109 110 115 128 129

List of Figures and Tables

Figure 6.3.

Knowledge Criticality Diagram of a Knowledge Domain. Figure 6.4. Influence Matrix of Knowledge Domains and Strategic Capacities, First Level. Figure 6.5. Influence Matrix in the Second Level of Cross-analysis. Figure 6.6. Simulated Phenomena in Reservoir Engineering. Figure 6.7. Task Model in Reservoir Engineering (Extract). Figure 6.8. Conceptual Model of the Overall Learning Design Structure (IMS, 2003). Figure 6.9. Domain Model Generates Various Possibilities of Scenarization. Figure 6.10. General Framework Defining from MASK Models. Figure 6.11. Steps Defining from MASK Models. Figure 6.12. Activities Learning Defining from MASK Models. Figure 7.1. The System Analysis Process. Figure 7.2. Effects of Standardization. Figure 7.3. The Numbering System of ISO Certification. Figure 9.1. Concept Map of Artificial Intelligence–Based Knowledge Management. Table 1.1. Table 3.1. Table 3.2. Table 6.1. Table 8.1.

Explicit versus Tacit Knowledge. List of Select Organizations with Knowledge Management System. Models of Knowledge Management. General Learning Scenario Generated. Knowledge Management.

130 131 132 133 134 136 138 138 139 139 146 148 149 176 6 41 42 140 160

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About the Authors

Shabahat Husain is a Professor of Library and Information Science and has served as Chairman, Dean, University Librarian at Aligarh Muslim University, India, and President, Indian Library Association, during 40 years of his active service. Besides his contributions to several International Conferences, Shabahat is a founder editor of “Collnet Journal of Scientometrics and Information Management” (Taylor & Francis) and has so far authored 50 research articles and eight books, including “Library Classification: Facets and Analyses” (McGraw Hill, 1993) which is considered as the authoritative text on the subject.

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About the Authors

Jean-Louis Ermine is Professor Emeritus at Institut Mines-T´el´ecom Business School, Universit´e Paris-Saclay, France. Author of eight books and more than a 100 peer-reviewed articles, Jean-Louis is also founder Honorary President of the “French Knowledge Management Club,” President of “French Academic Association for KM,” was a part of the French delegation for ISO on Knowledge Management (2018/2019) and has served as an expert consultant in Business Knowledge Management both in France and other countries.

Foreword

It is heartening to note that two well-known Knowledge Management (KM) Experts, one from India and another from France, have probably for the first time joined their hands to share their insightful knowledge of the subject coupled with vast practical experience in form of the present book for global readership. I have had the chance to glance through the book in its pre-publication stage that has enabled me to write this foreword. Even though the field of KM has emerged since the mid-90s, its popularity had ups and downs, not because its value was in question but because it sometimes lacked organizational strategic anchoring, that was eventually confirmed by the publication of an international standard (ISO 30401:2018-Knowledge Management Systems). The enviable development of KM International standard, with Prof. Ermine being the part of the committee, has renewed the interests in KM activities, as a consequence of its reinforced legitimacy and value addition superiority. Realizing the need of the hour, the authors have discussed in-depth, both the practical as well as the strategic issues that need to be addressed for KM to work with and fetch additional value to the organization. The book spanning almost all the aspects of the subject, not only presents the evolution and vivid description of core theories, discernible by current trends of KM, but also discusses its predictable future by continually increasing application and integration of Artificial

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Intelligence techniques with KM practices. The concepts presented are well illustrated by various case studies. As per Peter Drucker, “what gets measured gets managed”, the book ends with addressing measurement issues and questions as to how best to measure the application of KM and the value it brings to the organization while showing the knowledge risks it mitigates, enabling KM adoption more likely to succeed and be sustainable. I congratulate Prof. Shabahat Husain and Prof. Jean Louis Ermine for the yeoman work in bringing out such a wonderful book that will undoubtedly go a long way for those who study the subject for teaching and research and also for the private and public enterprises where KM is applied for commercial gains.

27 February, 2021

Dr Vincent Ribiere Founder and Managing Director Institute for Knowledge and Innovation South East Asia (IKI-SEA) Bangkok University, Thailand

Preface

The Latin dictum “Scientia potentia est” generally attributed to Francis Bacon connotes “Knowledge is Power.” In the same vein, A.P.J. Abul Kalam, exPresident of India, and a world-renowned Scientist, endorsed “knowledge for the prosperity and power” for one and all. Rightly so, the recent moves toward what has come to be often called “Knowledge Society” consider knowledge as an essential ingredient in the progress and welfare of nations, particularly in view of economic Globalization and Branding of products for customers’ satisfaction. Consequently, two interrelated management concepts, namely Total Quality Management (TQM) and Quality Management System (QMS), are involved in the enrichment of the quality standards, whereas Knowledge Management has, of late, emerged as a strategic approach for the implementation of the objectives and the means of the organizations to manage their knowledge assets which otherwise remained unnoticed before the emergence of the concept of Knowledge Management Systems (KMS). It aims to create applied knowledge for effective decision-making in order to bring professional efficiency to help organizations achieve competitive advantage over others. Consequently, the installation of KMS during the last two decades has become increasingly frequent, more so in business organizations than in the institutions of higher learning. Nevertheless, the introduction of an International Standard known as ISO 30401:2018-Knowledge Management Systems lends credence to the aforesaid happenings confirming subject maturity. The great and growing interest in the application of KM and KMS in private and public enterprises have encouraged universities and colleges around the globe to commence specialized UG and PG courses leading to doctorate degrees. The book is expected to serve the requirements of academics and practitioners both. I take this opportunity to thank my learned colleague, Jean-Louis Ermine, Professor Emeritus and a scholar in his own right, for shouldering the responsibility as a coauthor of the book. I also appreciate the efforts of my worthy student

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and colleague, Prof. Naushad Ali P.M., in whose collaboration, I started the research on Knowledge Management in the Department of Library & Information Science, A.M.U. Aligarh in 2010, which culminated into UGC’s SAP, a major research project (2013 to 2018). Last but not the least, no words of thanks will be enough for my wife and family members for their continual support in all my academic endeavors so far. February 1, 2021 Prof. Shabahat Husain Formerly Chairman, DLIS; Dean, Faculty of Social Sciences, & University Librarian, A.M.U.Aligarh President, Indian Library Association (2016-2019) [email protected]

Preface

The Knowledge Capital of an organization is its greatest wealth. It ensures its sustainability, competitiveness, and stability. During the past twenty years of its development, Knowledge Management has emerged as a strategic approach to the implementation of the objectives and the means of the organization to capitalize, share, and create knowledge, through a new relationship between the “People,” “Information,” and “Communication Systems.” For the same reasons, Knowledge Management is being implemented in organizations steadily, for it effectively responds to the fundamental problems, now increasingly compounding with the ever-growing phenomena of globalization, population ageing, and digitalization. Consequently, an international standard (ISO 30401) confirming its strategic anchoring has been published. Recent progress toward societal approaches, often called “Knowledge Society,” now tends to consider knowledge as a fundamental factor of the development and well-being of nations. The present scenario may thus be considered as the dawn of a major evolution in managerial, socioeconomic, and political thought around the globe. At such a juncture, this book explains the main concepts of the subject, providing key facts to understand the necessity and strategic planning of KMS, both in public and private organizations. The book will be useful in understanding the challenges faced in implementing such systems with the strengths and limitations of the working environment. I thank my colleague, Prof. Shabahat Husain, already a well-known person in the field, to give me an opportunity to share my experience and understanding of the subject with his own in bringing out the present book together. It was a real pleasure to join together our efforts and competencies during the course of completion of this venture. My contribution to this book is far from being an individual effort but is the result of collaboration with a large number of scholars of the French Academic KM Association, industrial practitioners of the French KM Club, numerous public and private organizations who trusted me to experiment with new ideas

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during an extremely productive professional journey of the last two decades that, in fact, helped me to learn a great deal. Thanks to everyone who shared in my journey.

1 February, 2021

Jean-Louis Ermine Professor Emeritus Institut Mines-T´el´ecom Business School Universit´e Paris-Saclay 9 rue Charles Fourier 91011 Evry Cedex, France [email protected]

Chapter 1

A Conceptual Approach to Knowledge Management 1. Introduction The word knowledge has been defined and described in many different ways, such as a justified true belief; facts, information, and skills acquired through experience or education; a sum total of what is known on a given point of time; etc. In a psychological context, whenever one thinks an idea is generated, conservation of such ideas by the human mind contributes to what is called knowledge. Generally, the process of knowledge involves two parties; one who knows or intends to know out of curiosity or through targeted research is called knower, i.e. man; and the other which is known already or known through research/investigation is called knowee, i.e. things or concepts. When the knower comes in contact with knowee to know more and more, knowledge grows (Fig. 1.1). Knowledge has been growing ever since the man has become Homo sapiens, i.e. man, the thinker. It may be imagined that primitive man would have started the process of learning using his different senses, of sight, touch, hearing, and smell. As the necessity is the mother of invention, the Homo sapiens have helped in the deposition of ideas, the precursors of knowledge. Such ideas conserved by human beings at a particular point of time constitute what is called knowledge, which is recorded and stored in various forms whether digital or nondigital (such as books, periodicals, journals, conference proceedings, reports, magazines, newspapers, video, blogs, wikis, etc.), so that it can be passed on to coming generations. As discussed above, when man interacts with his surroundings, knowledge grows. This type of interaction may take place in the following three ways: (1) When a man interacts with nature; the knowledge, thus generated is known as Natural Sciences, (2) When a man interacts with self, the knowledge, thus gained is called Humanities, and (3) When a man interacts with society, the knowledge resulting from such interaction is called Social Sciences.

Knowledge Management Systems, 1–12 Copyright © 2021 Shabahat Husain and Jean-Louis Ermine Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-80117-348-320210001

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Knower i.e. Man

Knowee i.e. Things or concepts

Knowledge

Fig. 1.1.

Knowledge Generation.

Thus, three important branches of knowledge are formed. Whatever the case may be, the bulk of knowledge keeps on growing as more and more ideas are added to it. Today, the bulk of published output in whatever form has increased so much that it seems impossible to even a super human being to master just one a small area of knowledge. In such a situation, specializations/super-specializations in different subjects constituting the bulk of knowledge are the only answer to the problem of keeping oneself updated in his own specialized field. That is where the process of division of knowledge into well-defined areas sets in (Husain, 2005).

2. Data, Information, Knowledge, and Wisdom In the context of data, information, knowledge, and wisdom (DIKW), the famous quotation of T.S Eliot explains the functional relationship between wisdom, knowledge, and information: Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information? (Eliot, 1934). The functional relationship between the four concepts has been shown as a pyramid by light and dark shades in Fig. 1.2.

•

Data: Data forming the base of the pyramid comprise unorganized, insignificant, meaningless symbols representing raw facts in bits and pieces unless they are collected and arranged in a certain manner. For example, the figures entered in the spreadsheet constitute raw data. To explain the concepts further, consider the following example: Classroom analogy: Students’ age: Ranges from 13 to 20; Students’ names: Ben, Anne, Poulter, Charlie, John, Morris

•

Information: When data are organized in such a way that it can be interpreted to answer a given question, then it is called information. For instance, a relational database management system (RDBMS) provides information on the basis of the data stored in it.

A Conceptual Approach to Knowledge Management

3

WISDOM

KNOWLEDGE

INFORMATION

DATA

Fig. 1.2.

•

•

Data, Information, Knowledge, and Wisdom (DIKW) Pyramid.

In the above analogy: The age of six students, namely Ben, Anne, Poulter, Charlie, John, Morris, is 13, 18, 17, 16, 19, 20, respectively. Knowledge: The application of information, built on data, turns out to be knowledge. The latter is applied to get to the desired goal. In the above analogy: Anne is the youngest student of the class, while Morris is the oldest. Wisdom: When a judgment is arrived at by the application of knowledge, it is called wisdom. In the above analogy: Anne, the youngest student of the class, is likely to have more stamina, and therefore, may be selected to represent the class in a given sports meet, while Morris, the senior-most student, may be asked to monitor the class.

3. Development of Universe of Knowledge Knowledge has always been the creation of the human mind since the dawn of civilization. Whatever has survived through observation, experimentation, and experience have contributed to the recorded knowledge. The process of development of knowledge is in a state of what is called as “dynamic continuum,” which is defined as “turbulently” growing at every moment and made up of an “infinity of points” (Ranganathan, 1951). Ranganathan’s faceted classification theory was developed on the understanding of the importance of knowledge production and the impact of newly generated knowledge on classification schemes. (Ferreira, Maculan, & Naves, 2017)

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Fig. 1.3. Spiral of Scientific Method. Source: Ranganathan (1967). Figure reproduced with Permission from “Sarada Ranganathan Endowment for Library Science.” A number of methods have been identified and discussed to study the growth of the universe of knowledge in the relevant literature, but the one given by Ranganathan still holds water in the modern era of knowledge explosion. The same is discussed in succeeding paragraphs (Ranganathan, 1957). The spiral of the scientific method (Fig. 1.3) starting in the clockwise direction is characterized by a never-ending spiral movement. Four cardinal points namely Nadir, Ascendant, Zenith, and Descendant are identified in the process of the growth of knowledge. The nadir marks the accumulation of facts, obtained by observation, experiences, and experimentation. In the same manner, the ascendant symbolizes the accumulation of inducted or empirical laws resulting from the facts accumulated at the nadir, by inductive logic and other aids from statistical calculus. Fundamental laws manifested by the zenith are formulated with the aid of intuition of some degree or other so as to understand all the inducted or empirical laws, accumulated at the ascendant, as compelling implications. The descendant marks the accumulation of the deduced laws got from the fundamental laws at the zenith, with the aid of deductive logic including general semantics and all kinds of mathematical calculus. These four cardinal points result in four quadrants numbered from I to IV, respectively, i.e. Quadrant I between descendant and nadir; Quadrant II between nadir and ascendant; Quadrant III between ascendant and zenith; and the Quadrant IV between zenith and descendant. Thereafter, the next cycle of knowledge generation as depicted by the spiral of the scientific method begins.

4. Types of Knowledge Generally, the documented form of “knowledge” is regarded as the only form available, whereas the fact is that of the total knowledge gained continuously

A Conceptual Approach to Knowledge Management

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through observation, experiences, experimentation, and expertise, only some part of it gets documented, whereas about 70% of it remains embedded in the minds of the persons concerned. The documented knowledge form is commonly known as explicit knowledge, and the one which remains confined to the minds of people is recognized as tacit knowledge.

4.1 Explicit Knowledge The explicit or documented knowledge is acquired out of formal or informal education by making use of various sources of information. Being formal in nature, explicit knowledge may be found in different ways including print, electronic, and other formal means and is similar to a priori knowledge which comes “from earlier.” It is based on sound reasoning and is considered more authentic. In the present electronic age, explicit knowledge can be tapped by using various information retrieval systems. Explicit knowledge is disseminated exhaustively and expeditiously through various types of libraries and Online Databases.

4.2 Tacit Knowledge The concept of tacit knowledge may be traced back to 1967 when Michael Polanyi’s philosophical theory of knowledge mooted that “we know more than we can tell.” The theory, thus, elicits the concept of tacit knowledge in so far as it is understood without openly expressing it (Virtanen, 2009). Tacit knowledge is much personal for it usually resides in the minds of people and remains with them until it is shared. It is similar to a posteriori which means “from what comes later” and that is accumulated through study and experience, and developed through the process of socialization. It involves using five senses for gaining experience followed by the application of logic to develop understanding. Once acquired, it remains deeply ingrained in human minds in the form of concepts or skills gained out of the experience, generalization, or reasoning. Tacit knowledge being technical or cognitive in nature, is made up of mental models, values, beliefs, perceptions, insights, assumptions and is usually grouped according to content, context, and orientation (Smith, 2001). The use of tacit knowledge in many fields of research and activity proves its applicability in a broader spectrum of academic disciplines including Philosophy, Psychology, Sociology, Management, Economics, etc. In contrast to Philosophers and Psychologists, Sociologists and Economists have different perceptions of tacit knowledge. Whereas Sociologists put emphasis on personal contacts to pass on unspoken words, the Economists believe it to be passed on by engaging those who have already needed it (Collins, 2010). To achieve the optimum results, a good value of return on investment (ROI), and for the reason that of the undeniable value of this knowledge in being the prerequisite for making good decisions, organizations must identify and manage their institutional tacit knowledge (Hansen et al., 1999; Kumar & Gupta, 2012). It is, therefore, of good motivation for organizations to necessitate taking initiatives

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to realize proper techniques in the direction of handling their internal knowledge and the proper ways for use of their own intellectual capital, a cycle of which promotes them to generate new knowledge within the organization.

4.3 Explicit vis-a-vis Tacit Knowledge Subject experts apply both types of knowledge in many ways to solve the problems, although explicit knowledge in contrast to the tacit knowledge is available in organized form making it readily accessible, whereas the latter is unorganized and remains untapped. Communication of tacit knowledge is impossible unless and until it is brought on record. In fact, the organizations make use of the two types of knowledge, called organizational knowledge, to capitalize, share, and create new knowledge, through a process known as Knowledge Management (KM). The phenomena of globalization, population aging, and digitalization have compelled organizations to implement KM steadily, for it effectively serves to capitalize share and create new knowledge, through a novel relationship between the “People” and “Information and Communication Systems.” Smith has identified 10 general categories in which tacit and explicit knowledge can be used in workplace as given in Table 1.1.

Table 1.1. Explicit versus Tacit Knowledge. Explicit Knowledge

Tacit Knowledge

“Academic knowledge” or “knowwhat” that is described in formal language, print, or electronic media, often based on established work processes, use people-to-documents approach.

“Practical, action-oriented knowledge” or “know-how” based on practice, acquired by personal experience, seldom expressed openly, often resembles intuition.

Work process – organized tasks, routine, orchestrated, assumes a predictable environment, linear, reuse codified knowledge, and create knowledge objects.

Work practice – spontaneous, improvised, web-like, responds to a changing, unpredictable environment, channels individual expertise, creates knowledge.

Learn – on the job, trial and error, self-directed in areas of greatest expertise; meet work goals and objectives set by organization.

Learn – supervisor or team leader facilitates and reinforces openness and trust to increase sharing of knowledge and business judgment.

Teach – trainer designed using syllabus, uses formats selected by organization, based on goals and needs of the organization, may be outsourced.

Teach – one-on-one, mentor, internships, coach, on-the-job training, apprenticeships, competency based, brainstorm, people to people.

A Conceptual Approach to Knowledge Management

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Table 1.1. (Continued) Explicit Knowledge

Tacit Knowledge

Type of thinking – logical, based on Type of thinking – creative, flexible, facts, use proven methods, primarily unchartered, leads to divergent convergent thinking. thinking, develop insights. Share knowledge – extract knowledge from person, code, store, and reuse as needed for customers, e-mail, electronic discussions, and forums.

Share knowledge – altruistic sharing, networking, face-to-face contact, videoconferencing, chatting, storytelling, personalize knowledge.

Motivation – often based on need to Motivation – inspire through perform to meet specific goals. leadership, vision, and frequent personal contact with employees. Reward – tied to business goals, Reward – incorporate intrinsic or competitive within workplace, nonmonetary motivators and rewards compete for scarce rewards, may not for sharing information directly, be rewarded for information sharing. recognize creativity and innovation. Relationships – may be top-down from supervisor to subordinate or team leader to team members.

Relationships – open, friendly, unstructured, based on open, spontaneous sharing of knowledge.

Technology – related to job, based on availability and cost, invest heavily in information technology (IT) to develop professional library with hierarchy of databases using existing knowledge.

Technology – tool to select personalized information, facilitates conversations, exchange tacit knowledge, invest moderately in the framework of IT, enable people to find one another.

Evaluation – based on tangible work Evaluation – based on demonstrated accomplishments, not necessarily on performance, ongoing, spontaneous creativity and knowledge sharing. evaluation. Source: Smith (2001).

5. Information and Knowledge Society The Information and Knowledge Society (KS) rests on researches combining information and communication technology (ICT), multimedia and cognitive science, and others. This theme is emerging from strategic problems occurring in the real world such as massive intergenerational knowledge transfers, serious risks of knowledge loss with massive retirements, development of competitiveness based on innovation, knowledge-based economy, political and social problems due to cognitive divide, massive intrusion in the social community of ICT, and so on. No global attention has been paid, up to now, to the relationships between those

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numerous problems that are, however, separately studied in multiple research perspectives. A common originality linking these programs is the questions that they are obliged to ask regarding the future of our societies and so regarding the visions one has of those societies – conceptual perspectives – and to participate through the production of programs to the development of the global framework that is essential of this future. The interest for the new theme of KS, related to Information Society, appears to be imposing. It appears as an emerging and strategic topic for the near future in India too, as it is for other countries in the world.

5.1 Knowledge Framework for Knowledge Society The KS is related to deep and strategic problems in national and international organizations, as Knowledge Crash, Knowledge Economy (KE), and KM, and of Information Society and Technology. It can be seen (Fig. 1.4) in the present framework that we are trying to evolve a mechanism to address the subject through several points of view, such as:

• •

Knowledge as a fundamental resource for social development (KS); Knowledge as an economic good for prosperity and competitiveness (KE);

Fig. 1.4.

The Knowledge Framework for Knowledge Society. Source: Munshi and Ermine (2011).

A Conceptual Approach to Knowledge Management

• • •

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Knowledge as an organizational capital (KM); Knowledge as a strategic risk for societies and firms (Knowledge Crash); Knowledge sharing and capitalization supported by Social Information Systems (Information Society and Technology) (Munshi & Ermine, 2011).

5.2 From Knowledge Society to Knowledge Management The recent concept of “Knowledge Society” gives a new vision of our societies where knowledge becomes the fundamental resource for socioeconomical development. The development of that KS implies important transformation in the social and economical networks, and no one knows precisely the consequences in terms of evolution of the social link, role of elderly people, building of new “virtual territories,” etc. The revolution of “Information Society” started in the 1990s, has failed in the sense that it has provided only technical solutions worldwide and not the adequate environment for the different societies to create intellectual wealth for a sustainable and harmonious development. Hence, came the KS era. All the questions on the digital or the cognitive divide are tightly related to those problems. A KS must have a new type of economy. This new economy is called “Knowledge Economy.” Knowledge is henceforth considered as a new source of wealth within firms and organizations. Knowledge is considered as a fundamental immaterial asset of the firm and as one of its main strategic resources. However, the management of this asset gives rise to numerous problems because of its characteristics. Knowledge is hardly controllable (involuntary spill over) and, on the other hand, difficult to access and share. It is an inexhaustible resource and is not destroyed by its usage. It accumulates in the organization. It is through the processes of accumulation, exploitation, and dissemination of knowledge that the human organizations can progress and create added value. So, some of the key questions that arise are: what are the issues of management of this knowledge asset today, how to manage and how to protect knowledge in the best possible manner, how to measure this immaterial asset, and how to value the knowledge of the organization, etc.? KE implies a managerial perspective in the organizations; KM has the objectives of formalizing and transferring specific knowledge and know-how in the organization, capitalizing, and operating this knowledge to increase organizational performance. There are also, in that domain, important challenges, for instance: what are the best organizations for KM, how to manage communities of practice, how to stimulate innovation through KM, what are the best strategies for KM, how to preserve the knowledge capital of the organization, etc.?

5.3 Knowledge-related Concepts As “knowledge” has been defined in this chapter in its conceptual framework, the aforesaid knowledge-related notions or concepts are explained hereunder at least into their basic connotations:

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5.3.1 Knowledge Society The advent of ICT has resulted in the enormous production of data with excellent speed. The data which are made up of raw facts can be structured to create information, which in turn can be converted to knowledge by linking it through the cognitive model. The same knowledge can be used by the society to improve the conditions of life. The KS is, however, different from the information society. While the information society is concerned with the use, production, and dissemination of information, the KS, on the other hand, is capable of transforming information into resources for the betterment of mankind. According to Kalam, a nation qualifies as a KS, if it deals with knowledge creation and knowledge deployment effectively. A society can be declared as prosperous if it has the ability to create and maintain the knowledge infrastructure to enhance skills and increase productivity. 5.3.2 Knowledge Economy The KE (or the knowledge-based economy) mainly depends upon the creation, sharing, and use of available information or knowledge in the production of supplies and services. The ICT with the increasing use of computer technology as applied in the information age has brought spectacular changes in the process of sharing information. It is a well-known fact that the business enterprises always look for the bright and forward-looking minds on any price they want, for they form invaluable intellectual assets of the organization. Such bright minds generally do not stay for long, for better opportunities always come in their way. It simply means losing out the intellectual capital of the enterprise to the detriment of the enterprise every now and then. It is, therefore, imperative to create a culture of social learning wherein the staff of the organization remains well informed about what their fellow staffs have accumulated out of their individual knowledge and experience. In the present times, the sharing of such an intellectual capital is the success story of business enterprises that treat knowledge as an economic good and do not confine themselves to the resources of production only. In fact, the realization of such a sign of KE has resulted in the creation of specialized jobs in industries ranging from researchers to programmers to software developers, etc. 5.3.3 Knowledge Management All said and done about intellectual capital and KE, the next challenging task is to make the most of the aforesaid knowledge and reflect the same in the performance of the enterprise, whether public or private. From a more managerial perspective, KM is a growing issue in companies, with the objectives of formalizing and transferring specific knowledge and know-how in the organization, capitalizing, and operating this knowledge to increase organizational performance. There are also, in that domain, important challenges, for instance, what are the best organizations for KM, how to manage communities of practice, how to stimulate innovation through KM, what are the best strategies for KM, how to manage knowledge in an extended enterprise, and so on.

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Knowledge is, however, gained through observation, experiences, experimentation, and expertise, and some part of which gets documented, whereas its major part (70%) remains embedded in the minds of the persons concerned. In other words, the documented knowledge is commonly known as explicit knowledge, whereas the one embedded in the minds of workers is recognized as tacit knowledge until documented. Organizations, therefore, must identify and manage their institutional tacit knowledge, by exploring best strategies for use of their own intellectual capital, a cycle of which promotes the generation of new knowledge. 5.3.4 Knowledge Crash Like a double-edged sword, both public and private enterprises are suffering from two different phenomena viz., the increasing number of retirees on one hand (Population Aging) and the number of losing out bright minds for better prospects (Brain Drain) on the other. In either case, organizations and societies suffer from what is called as Knowledge Crash. In view of the 70% of tacit knowledge deeply embedded in human minds as skills, innovative practices, and expertise, such a strategic risk is compounded to the detriment of the interests of the organizations. 5.3.5 Knowledge Sharing and Capitalization The only answer to the problems of Population Aging and Brain Drain amounting to Knowledge Crash is to find out appropriate tools and techniques for capitalizing upon and sharing of documented and undocumented knowledge for enhancing the performance of the organization. For the purpose, organizations have to make use of web technologies like social networks, such as Facebook Twitter, LinkedIn, Instagram etc.; and content management systems such as Web 3.0 (Semantic web), Web 4.0 (Intelligent Web), etc.

6. Conclusion Knowledge and Society are distinct and yet inseparable. Society is made possible by knowledge that may include both philosophical and practical knowledge. Today’s knowledge is as authentic as it was yesterday, although people may have varied opinions of what knowledge is and what it is not. This day we do not have armchair philosophers and unscientific theories but valid knowledge. It has sound arguments, developed by erasing any possible voids of confusion. This world has made it necessary for all of us to know the things, especially those involved in our day-to-day exercises and in our endeavor to follow. The development of organized societies put greater knowledge demands. The present era focuses on the consolidation of knowledge and its dissemination in complex ways. To have the firsthand knowledge of this intricate knowledge network makes one’s sustenance easier and likely his way of living. Employing the positive outcomes of knowledge, the peace stability and order of society are ensured in this modern era.

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However, we have the other face of the knowledge as well. It has been juxtaposed for centuries. Every time in society and even in this modern era, combat is essential and indispensable. The negative face has once again redesigned, forcing the other side to struggle and come up with new shields. This process of knowledge goes on from centuries altogether. But fact remains that each struggle opens up new pathways to add the existing knowledge. The sole process can be consolidated in the quest, order, and development.

References Collins, H. M. (2010). Tacit and explicit knowledge. Chicago, IL: University of Chicago Press. Eliot, T. S. (1934). Choruses from the Rock. The complete poems and plays of TS Eliot. Retrieved from http://www.otupload.orangeoval.net/uploadsbackup/1368712062.pdf Ferreira, A. C., Maculan, B. C. M. D. S., & Naves, M. M. L. (2017). Ranganathan and the faceted classification theory. Transinformação, 29(3), 279–295. doi:10.1590/ 2318-08892017000300006 Hansen, M. T., Nohria, N., & Tierney, T. (1999). What’s your strategy for managing knowledge? Harvard Business Review, 72(2), 106–116. Husain, S. (2005). Library classification: Facets and analysis (2nd rev ed.). New Delhi: B.R. Publishing. Kumar, S., & Gupta, S. (2012). Role of knowledge management systems (KMS) in multinational organization: An overview. International Journal of Advanced Research in Computer Science and Software Engineering, 2(10), 8–16. Munshi, U. M., & Ermine, J. L. (2011). Information and knowledge society: Some futuristic perspectives. Journal of Knowledge & Communication Management, 1(1&2), 1–10. Retrieved from https://www.indianjournals.com/ijor.aspx?target5ijor: jkcm&volume51&issue51and2&article5001 Ranganathan, S. R. (1951). Philosophy of library classification. Copenhagen: Munksgaard. Ranganathan, S. R. (1957). Library science and scientific method. Retrieved from http://hdl.handle.net/123456789/28515 Ranganathan, S. R. (1967). Prolegomena to library classification. Bangalore: Sarada Ranganathan Endowment for Library Science. Retrieved from https://dspace.gipe.ac.in/xmlui/bitstream/handle/10973/19232/GIPE-011296.pdf?sequence53 Smith, E. A. (2001). The role of tacit and explicit knowledge in the workplace. Journal of Knowledge Management, 5(4), 311–321. doi:10.1108/13673270110411733 Virtanen, I. (2009). How tacit is tacit knowledge? Proceedings of the ER PhD Colloquium 2009. Retrieved from https://ceur-ws.org/Vol-597/paper-3-1.pdf

Chapter 2

An Overview and Trends in Knowledge Management* 1. Introduction Knowledge Management (KM) is the process through which organizational knowledge can be created in value-added form and made available to those whosoever need it within the organization to create quality products or services. Knowledge is gained through the experience and expertise of the staff of the organization. The recorded experiences embodied in document form (explicit knowledge) constitute only a small percentage (about 30%) of the whole, whereas the remaining part keeps embedded in the minds of staff/experts (tacit knowledge). KM promotes different knowledge processes involving acquisition, creation, packaging, application, and reusing of knowledge. It also provides a collaborative knowledge-sharing platform with the objectives to enhance learning and performance in the organization (Kumar & Gupta, 2012). KM though finds its usability and applicability in all types of organizations, but as witnessed from the literature, it is mostly found operational in business organizations. An overview of the literature available on KM and Knowledge Management System (KMS) clearly shows certain research and development trends that appertain to the conceptual development of KM, KMS, KM Models, KM Tools, and KMS as applicable both in profit-making and nonprofit-making organizations.

2. Overview and Trends An analysis of the extant literature on KM helps us to identify the following trends while tracing the development of KMS in a variety of organizations such as Libraries, Education, Health Care, Law Firms, Police Departments, Energy Sector, Banking, Commerce, Disaster Management, etc., as elaborated hereunder: *

Husain, S., & Gul, R. (2019). Research Trends in Knowledge Management: Past, Present and Future. ICISDM 2019: Proceedings of the 2019 3rd International Conference on Information System and Data Mining, April 6–8, 2019, Houston, Texas, USA. pp. 208–217. Retrieved from https://doi.org/10.1145/3325917.332594. Under permission from ACM publisher of the proceedings: Author Rights & Responsibilities (acm.org).

Knowledge Management Systems, 13–32 Copyright © 2021 Shabahat Husain and Jean-Louis Ermine Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-80117-348-320210002

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2.1 Conceptual Development of KM KM is a subject of broad and current interest both in profit-making and nonprofitmaking organizations. Call (2005) puts emphasis on broader understandings of KM and how to successfully implement it in any organization. From their research, Efficiency, Adaptability, and Flexibility are noted as the three characteristics of a successful organization. As KM is an innovative concept, Saulais and Ermine (2012) seek to elucidate the link between KM and innovation, which furthermore links intellectual corpus and creativity. KM in one way or the other may usefully be incorporated in the midst of organizational creativity and innovation (Basadur & Gelade, 2006). Due to KM, organizations are alive with their best available knowledge which helps to improve the organizational performance. In the same vein, Mills and Smith (2011) conducted the study in which 500 questionnaires were distributed to make a connection among the performance of an organization and the KM resources, out of which only 189 from the management department were considered useful. The structural modeling equation was used to know the correlation between certain resources of KM and organizational performance. Few knowledge resources, e.g., organizational structures, and knowledge applications were found clearly associated with organizational performance. A knowledgeable manager of the organization plays a key role in achieving organizational goals. In fact, they are engaged in various knowledge processes which lead to greater effectiveness, both for organizational and outfitted processes. These processes engross some sort of alliance to get the best out of existing knowledge. For Armistead (1999), leadership qualities play an important role in KM practices. The above statement was concretized by the study performed by Singh (2008) who investigated the link and impact of the way of management on KM practices in a software organization. The study involved the collection of data by using two psychometric instruments, i.e. Organizational Leadership Questionnaire (OLQ) and Knowledge Management Assessment Tool (KMAT). As many as 331 knowledgeable workers having at least one year of experience were chosen. An analysis of the data revealed that directive and compassionate ways of management extensively and pessimistically bond with the capability of KM practice, whereas conferring and delegating ways of leadership have a positive and considerable link to Managing Knowledge. Furthermore, only delegating mode of leadership behavior in the software firms of India is important for foreseeing conception in addition to the organization of intellectual assets for the viable benefit. Many researches have been conducted across the globe to study KM and its practices in commercial and government sectors in the present time. In the same context, Baquero and Schulte (2007) explored the state of KM practices in the above-mentioned sectors of Columbia. 50 organizations cooperated by sharing the quantitative data, collected through a survey of their search field. Besides some organizations having less support of KM practices, there are a few others that endow with exemplars in KM. However, the fact remains that KM is not

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only responsible to manage the intellectual assets but also has direct linkage with the security as well. Randeree (2006) conducted a study examining the connotations of KM for security. Of the various studies, it was revealed that less focus has been given on security, in both research settings and practical applications of the KM framework.

2.2 Development of KMS KMS is an amalgamation of content, experience, and process management. The content may be of academia or of “outside community” or from any “digital library.” The experienced management is a part of KMS in which members of the organization as knowledge workers must share their organizational knowledge to participate in the process of KM, while as, process management is the practice wherein the sources acquired from its networking result in accessing knowledge foundation so as to be a cycle and a reference for users (Rohendi, 2012). Kumar and Gupta (2012) discussed KMSs with their role in global organizations that include core concepts like knowledge creation and sharing. The given case examples explained the process of knowledge creation and also laid the special emphasis on tabbing tacit knowledge by highlighting different methods and techniques applied for the same, e.g. the organized discussion with the peers which in turn creates the environment for working together (tutor–pupil), the process of learning through conversations, or by sharing the experiences (storytelling), etc. Alavi and Leidner (1999) studied the KMS, along with the issues, challenges, and benefits involved with it. The analysis of current practices and outcomes of KMSs were provided. It was found that interests in KMS are quite high across a variety of industries. The concern revolves around achieving the accurate knowledge and garners support to KMS. A KMS can be successful only when used effectively to meet the knowledge goals of an organization. He, Qiao, and Wei (2009) explored the use of KMS, putting emphasis on social relations and on its importance in the use of KMS by the employees in a Chinese company. Poston and Speier (2005) conducted a similar study to examine the effect of content ratings and credibility indicators on KMS users. It was revealed that ratings have a strong influence on KMS search and evaluation processes thus affecting decision performance. In the developed world, maximum efforts are being made to develop practical systems for managing their knowledge. Some have put forward the blueprint of the system, and few have developed the practical KMSs. Similarly, Khalifa, Yu, and Shen (2008) developed a model to study the impact of KMS on organizational performance. The same was tested upon 100 core organizations with the core aim to make the model more effective and useable. Structural equation modeling was used for data analysis. Results revealed that KMS pays direct as well as indirect effects on the performance of organizations. Besides the use of KMS in business or academic organizations, it can also be used to tackle the problems raised during emergency situations. For example,

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during Hurricane Katrina, federal agencies developed two KMSs. Because of the KM tools and concepts incorporated into these systems, data were shared and the interaction through board resulted in much better services at the time of crisis (Murphy & Jennex, 2006). In addition to this, Hassan, Hayiyusuh, and Nouri (2011) conducted the study regarding the “implementation of KMS for support of humanitarian assistance/disaster relief (HA/DR) in Malaysia,” which made people aware of what is going on around them by providing situation awareness and decision-makers with current information to react immediately to reduce damage. The tripartite paper dealt with: (1) disasters occurred in Malaysia, (2) discussion on KM and KMS, and (3) model and framework from the previous study. Discussion on KM and KMS revealed that a common platform must be available for all knowledge processes including knowledge discovery, capitulation, and networking in Malaysia. KM framework was adopted to support HA/ DR in Malaysia, by a centralized knowledge base through which the KMS model was able to update, share, and acquire information needed before crisis, during crisis, and post crisis to explore the organizations involved in HA/DR. It was also found that the success of KMS for the use of HA/DR depends on the willingness of organizations to share knowledge. Furthermore, on the current basis of “Scientech Documentation and Information Center/Chinese Academy of Agricultural Sciences (SDIC/CAAS),” a schema was presented on the analysis and design of KMS of SDIC/CAAS to augment the competitive ability and organizational performance on a cost-effective basis (Sijing, 2006). Edwards, Shaw, and Collier (2005) discussed the role of technology in associations by conducting a study on a sample of 78 people, associated with 10 different organizations for workshops, with the main aim to be aware about the present status of KM in these organizations. It revealed that only three organizations have adopted technology-based explanation to pro-KM problems. For the purpose, the three organizations were found to use general information Technology tools (IT) rather than specific KM tools. Apart from this, Lin and Tseng (2005) studied another aspect called KM gaps. The study proposed a framework to demonstrate the management gaps. Findings from the study highlighted many reasons for KM gaps, which may be avoided by the proposed fundamental approaches and corrective actions to enhance the success of the implementation of KMSs. A survey on KMS and its relevance in the field of social sciences was conducted at the School of Social Sciences, the University of Kashmir, in order to record the consciousness and the use of KMS by the different category of respondents, i.e. faculty members and research scholars of the said School. Results congregated through questionnaires revealed that 60% of respondents are aware of KMS and the remaining 40% are not. Among 60% of aware users, 45% have used KMS, while 55% have not used any. Yet, 86.2% of respondents strongly agreed that KMS is useful for the researchers and other information seekers working under different domains of social sciences, while 13.8% not agreed so strongly. Further, 91% of users strongly agreed upon the fact that KMS in social sciences must be developed to benefit the people working under its different branches, whereas, remaining 9% agreed on the same (Husain & Gul, 2018).

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2.3 Development of KM Models To heighten organizational performance through KM, different models have been developed on various aspects of KM. However, the fact remains that no single KMS model was found suitable for all different organizations simply because divergent estimation of knowledge management processes, implemented and validated in real-world settings, have resulted in lots of models with different structures. Nevertheless, taking the complex and dynamic nature of knowledge into account, some of the KM Models in active use have been discussed in Chapter 3. In the same vein, Handzic (2011) proposed a conceptual integrated sociotechnical KM model with three interrelated concepts. The survey-based study examined the validity of the projected model, tested by 185 senior civil servants. It was brought into being that in advancing the knowledge within the public administration organization, social factors play a greater role than the technical ones. At the same time, leadership quality was also pointed as an essential enabler of organizational KM. In addition, a “three-layer reference model” for KMS was developed in order to identify the processes used for support by any KM support system, which were further made to be used for modeling the dynamics, and also for developing a framework, and for the development of blueprints for information and communication technology (ICT) based KMSs (Abou-Zeid, 2002). Beveren (2002) presented a model of knowledge acquisition, which avows that knowledge can only be created within the brain after its proper processing with prior knowledge. It was revealed that the center of attention of KM should be on human resource strategies and also for networking of essential information that promotes inventiveness and advancement among workers of an organization. Similarly, the practical approach to KM that has been proved useful to many organizations in achieving their goals was created on the basis of eight building blocks, namely knowledge goals, knowledge measurement, knowledge identification, knowledge use, knowledge acquisition, knowledge preservation, knowledge development, and knowledge distribution (Probst, 1998). It is necessary for every organization or firm to evaluate the model before its implementation. Similarly, Haslinda and Sarinah (2009) critically evaluated the different KM models and found that few KM models vary from the basic assumptions just before the more complex and complicated assumptions. Cristea and Capatina (2009) also did a similar type of study, highlighting some widely used KM models. They also described the most important characteristics of each model with its usefulness in the economic environment.

2.4 Development of KM Tools In the present knowledge-ridden era, various tools have been developed to make the work processes easier than before. Al-Aama (2013), on the basis of a study, carried out at “Jeddah Municipality in Saudi Arabia developed Knowledge Management taxonomy.” The classification lists the tools used to enhance KM processes and more specifically specify the tools for their respective process. The developed taxonomy can be used to choose the exact tools that cover all KM processes i.e. “knowledge creation, knowledge maintenance, knowledge distribution, and knowledge revision.” Bhatt, Gupta, and Kitchens (2005) explored the

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relationship between the use of one of the KM tools (groupware) and KM processes. Data gathered from the managers of 1,000 firm divisions at Fortune through a telephonic survey reported that these tools were significantly related to the majority of KM processes. On the other hand, it was also found that it is only the email, which plays a vital role in knowledge distribution. With the rising interest in different strategies for managing knowledge, the intranet is considered as information as well as a strategic management tool (Edenius & Borgerson, 2003). Many other cost-effective tools can also be used for managing organizational knowledge. Social media is also numbered in these tools, which are used for enhancing the work process. Forcier, Rathi, and Given (2013) conducted a survey of “two public libraries in Canada” to look into their understanding of the concept of KM and how they make use of social tools in KM. Findings revealed that social media can be considered as many useful tools for the purpose of promotion and also for enhancing collaborative work within the organizations. In the same context, Chua and Banerjee (2013) also made a study of Starbucks, an international coffeehouse chain. The study was focused to analyze the utilization of social media to prop up consumer KM. Data gathered from different sources (such as newspapers, newswires, magazines, scholarly publications, books, etc.) revealed the three most important findings. Firstly, the wide ranges of social media tools were deployed by Starbucks for certified knowledge manager. Secondly, the transformation of passive customers to active ones was witnessed through the use of this tool. Also, using effective strategies to improve customer’s disinclination for voluntary knowledge sharing is, thus, encouraging its use. Razmerita, Kirchner, and Sudzina (2009) made a new model of KM for managing personal knowledge by using Web 2.0 tools which not only facilitates dynamic responsiveness but also acts as a medium or a platform that allows or gives privilege to an individual for managing all their knowledge processes. In yet another study, Ray (2014) examined the barriers to KM and also explained how social media will overcome those barriers. National culture and social media were conceptually linked to overcoming KM barriers. In addition to this, Grace (2009) put light on the usefulness of wikis with their role in the process of management and sharing of knowledge. This paper reported the results from the analysis of review and case studies for the execution of wikis in organizations and proposes a concrete blueprint for adopting wikis. The reasons behind the use of wikis were made known and also the paper highlighted security control issues and technical issues encountered. With regard to the selection of appropriate tools, most organizations choose Wikis as a tool for KMS. But, before implementing the wikis, one should be aware of its loopholes as well. In the same context, Kiniti and Standing (2013) conducted the study, aimed to highlight and investigate the main problems having the influence of accomplishment wikis as a KMS. From the previous studies conducted on the use of wikis as KMS in organizations, six main problems came to be known are: …lack of a clear purpose for the wiki, wiki usability, integrating wikis into established work practices, social issues, role of management and organizational culture that supports knowledge sharing and collaboration.

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Some cases were also witnessed where wikis as a KMS has failed to meet the optimum goals of the organization. KM supported by the technologies has a great role to play in the present era to fulfill the knowledge needs of any organization. Lindvall, Rus, and Sinha (2003) addressed a technological aspect of the KM while studying the available software systems that support different KM activities. Based on the potential, the KM tools were categorized into classes, responsibilities, and operations for processing knowledge. Another study carried out in Iran was aimed at evaluating 20 government websites of the public domain to know the KM mechanism. “Knowledge access, creation, and transfer” (K-ACT) model was used, alongside an application checklist to make known their utility. It was found that these websites are very poor in terms of functionalities and use (Behzadi, Isfandyari-Moghaddam, & Sanji, 2012). The success of KMS depends on the manager responsible for the whole KM work process. Organizations, which are aided by these tools of information, can utilize the management of all resources more efficiently. Total quality management (TQM) is such a tool that focuses on quality and in turn benefits the organizational performance. Johannsen (2000) while studying TQM from a KM perspective examined TQM-based management tools from the KM point of view encompass colossal outcome on the performance of any association, particularly in view of knowledge creation, accumulation, and sharing the process.

2.5 Development of KMS in Nonprofit-making Organizations As the concept of KM has basically developed and applied in business organizations but keeping in view the importance of KM in nonprofit-making organizations as well, the various researches conducted on different aspects under different domains of social importance, e.g. Libraries, Education, Health Care, Law Firms, and Police are explored hereunder: 2.5.1 Knowledge Management and Libraries Nonprofit organizations like libraries may create an environment that promotes knowledge sharing. Teng and Hawamdeh (2002) tried to find how KM can be effectively applied to National Library Board (NLB), Singapore, to augment learning capacity etc., promoting a sociable society with a mission to deliver a world-class library system. Various studies have been made and are being conducted on KM and libraries, and different approaches are used to cover every aspect associated with it. Libraries as KM centers directly or indirectly have its impact on the organization it serves. In the same viewpoint, Parker, Nitse, and Flowers (2005) put emphasis on libraries, to be active as KM centers highlighting some of the components. Libraries, whether public, academic, special, or any other, act as a vital part of its parent organization, thus needing to manage both the external as well as internal knowledge either by creating institutional repositories or developing KMSs. Yi (2008) discovered the same from the study in which the respondents both directors and students were from the eastern part of the United States.

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Although the lesser number of students agreed with this statement than directors, on the same hand maximum of them believed that KM must be implied in libraries for developing the system. In context to academic libraries, librarians must ensure the quality of services and the likelihood of adopting KM for service innovations in their libraries (Islam, Agarwal, & Ikeda, 2005). When one talks about academic excellence, one thing that strikes the mind is the role of the library pertaining to that institution. Academic excellence is directly related to the library, so the library must meet the pace of the dawn of technologies and must incorporate special tools and processes which support information professionals to deliver the best services to the users (Rowley, 2003). The academic Institutes also include special institutes, developed to serve the special audience. In these institutions the knowledge generated within the organization or outside it is of much use, keeping its importance into consideration, Gul (2017) developed “A Conceptual Knowledge Management System Model for Special Libraries.” For his research paper, the author conducted case studies of different special libraries, interviews, and also an extensive survey of both offline and online literature. It was revealed that the special libraries under purview (namely Tulsi Das Library, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, Indian Institute of Advanced Study, Shimla, Satyanand Stokes Library of Y.S. Parmar University of Horticulture and Forestry, Solan) have not established KMS till date. So, the final findings of the study came up with a conceptual KMS model for special libraries of which the concepts and relations were developed to describe the implementation of KMS in special libraries. In all types of resource centers, an intranet is regarded as both information and strategic management tool and is utilized as a valuable means for KM. Mphidi and Snyman (2004) in their study investigated the utilization of intranet by the academic resource centers, and discovered that these centers take less advantage of the intranet and there is much room for improvement in regards to tools and processes within all of these libraries. On the other hand, Jain (2007) investigated KM practices in the select academic libraries of East and South African countries; it was made clear that most of the libraries are practicing information management, and the majority of 65% of participants considered themselves as information managers. As far as knowledge repository is concerned, only 35% of participants indicate that they had a central knowledge repository in their organizations. Akin to this, Sarrafzadeh, Martin, and Hazeri (2006) conducted the study with the main motive of knowing the perspectives of library and information science (LIS) professionals on KM and examined the “benefits, opportunities, and threats” of KM on the line of work. The outcome of the study revealed considerable awareness among LIS professionals, to the extant of being fully aware of positive implications of KM for oneself as well as for the profession. Sarrafzadeh, Martin, and Hazeri (2010) also found in LIS fraternity possessing a positive attitude for introducing KM in libraries to bring them closer to their parent organization and for their survival in an increasingly challenging environment. As far as KM in libraries is concerned, the professionals working in the libraries are usually from the LIS field, so that they must be conscious of KM from the root level. Roknuzzaman and Umemoto (2009) made it known that

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library practitioners have different perceptions regarding the ways of knowing and understanding the concept. To make the basics clear to the budding librarians and information scientists, the integration of KM in the LIS curriculum is a must. Hazeri, Martin, and Sarrafzadeh (2007) from their Web-based survey and indepth interviews with LIS professionals from LIS schools tried to put some light on the subject by interviewing a group of professionals in this field and gaining their perspectives and opinions of the subject matter. Their study revealed a considerable interest within the LIS community in expanding the LIS curricula by the integration of KM in it.

2.5.2 Knowledge Management and Education Most organizations understand the importance of knowledge hence, taking initiatives to manage it by using various tools and techniques. As the KM concept cropped up in the corporate sector, it is mostly exploited in business organizations. Fortunately, the world of academics has also realized the significance of managing their knowledge assets. Consequently, various studies have been carried out on many facets of KM. Arntzen, Worasinchai, and Ribi`ere (2009) conducted a study in order to know how KM processes could contribute to improving educational performance. The study stated the main aim of setting up of KM initiatives in Bangkok University. Findings from the study explored that the benefits to the university from KM were encouraging and also there was an improvement in the educational community through creating such kind of an environment which supports cross-organizational learning and knowledge-sharing processes. In addition to this, Tian, Nakamori, and Wierzbicki (2009) studied the knowledge creation component of KM processes, in order to know the benefits of using KM to enhance knowledge creation in academia. To attain the research rationale, a survey was carried out at the Japan Advanced Institute of Science and Technology (JAIST) in two phases. The first phase was about KM and academia highlighting current situations, and also the requirements of researchers and the focus in other phase was on how KM supports creative processes of intellectual output. From the result of the first phase survey, some barriers were identified with regards to the technology and the people associated with knowledge creations, etc. In phase II in accordance with academic knowledge creation process, critical as well as important questions were evaluated by respondents in the study. Tikhomirova, Gritsenko, and Pechenkin (2008) also described several KM initiatives which had been taken at “Moscow State University of Economics, Statistics, and Informatics (MESI).” Findings reported from the interview revealed that quality management and an e-learning system have been successfully established by MESI, and also, it is on the verge of implementing “Total Quality Management.” The induction of ICT has addressed a major change in the academic world. Shoham and Perry (2009) conducted a study of universities of Israel to investigate the realization and management of technological changes including: “introduction of online instruction, e-learning, and enterprise resource planning technology” during the last seven years. The study discovered a valid mechanism, on the basis of which, a model was proposed that can be used to manage these changes in

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the Universities of Israel. Academic institutions are taking initiatives to enhance the work process of their institution by implementing such systems. Mansourvar and Norizan (2010) also tried to discover the university needs to the Web portal as a tool for students to fulfill their educational needs. A survey was, therefore, conducted to gather their requirements for incorporation into the portal. Blackman and Kennedy (2009) described the relationship between governance and KM in an Australian university. They used interview and observation method as appropriate tools for data collection from the key governance committees. From the findings of the study, it was illustrated that strategic success and effective governance shows dependency upon opposite KM activities. In an example of the case study, the committee members were found more stuck on processes that even do not efficiently allow the knowledge creation and transfer. As technology emerges, it becomes easy for organizations to manage their knowledge. Web-based KMSs (WBKMSs) are among the preferable tools that are used to create, share, use, and reuse existing knowledge. Rah, Gul, and Wani (2010) illustrated the requirements of the university libraries and went on to propose a WBKMS for them. WBKMS was surveyed from which the framework of the model was developed keeping all the aspects of the present model under consideration. Most institutions/organizations are conducting research on the management systems, and many of them have developed the model or prototype or portals for making it easy to access and share the knowledge for the betterment of their organizations. Rohendi (2012) also conducted the study at the Indonesia University of Education with the prime aim of developing a blueprint of a management system for them. The steps taken showed the way to the development of a product which itself was a KMS. Several phases of the KM life cycle such as “knowledge creation, capturing, organizing, refining, and knowledge transfer” were explored and discussed. KMS was developed by considering content management, experience management, and process management using a “(waterfall) approach method supported by Microsoft SharePoint software” with Web-based system development, user authentication, upload facility, searching facility, etc. The preliminary stage of the execution of the system was knowledge caption that was obtained from various sources. After the completion of system implementation, it was suggested that the policy ought to be designed by the university through which scholastic society must be rewarded for sharing the knowledge, igniting their hope, and willingness to share the knowledge.

2.5.3 Knowledge Management and Health care The emergence of KM makes it easy to manage internal as well as external knowledge, hence achieving the optimal goal of the organization. Both private and government organizations are using different techniques and tools to manage knowledge for obtaining better results. Besides, health is one sector, among the others, which relies heavily on knowledge. It is though a challenging task to implement KM into health care, yet it is necessary for health-care providers to implement it to multiple factions and branches of the health-care community, thus enhancing wellness and medical care to their patients in all sectors of health care.

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KM provides multiple advantages that are worthwhile to organizations that use KM tools for management (Morr & Subercaze, 2010). KMS is the best among all Information Retrieval Systems to improve the firm’s competitiveness while keeping the costs to a minimum. Hung, Huang, Lin, and Tsai (2005) conducted the study, in which affiliates of the “Taiwan Pharmaceutical Marketing and Management Association” were used as the study sample. From the research, 32 variables were delineated in the process of completion of the KMS. After proper investigation, at least seven critical issues were determined to exist as follows: …a benchmarking strategy and knowledge structure; organizational culture; informational technology; employees’ involvement and training; the leadership and the commitment of senior management; learning environment and resource control; and evaluation of professional training and teamwork. Husain and Gul (2017) performed a case study of the All India Institute of Medical Sciences (AIIMS) and its online portal with the aim to develop a KMS in Health Sciences fulfilling the requirements of both doctors and patients. The study was based upon “Interviews,” offline and online “Extensive Literature Survey,” and “Observations.” The findings of the study came out with a KMS that takes care of both doctors’ knowledge needs and patients’ health care.

2.5.4 Knowledge Management and Law Firms In the legal profession, the experiences of the experts are considered more valuable than the documented knowledge. Therefore, KM is becoming crucial in law firms to be successful in this challenging environment. The profession of law is among those prominent fields where KM plays a fundamental role in capturing, storing, manipulating, and making appropriate knowledge available to an appropriate person at the appropriate time. Lawyers, who were often adversely compared to “white-collar” workers, e.g. medical practitioners and tax accountants due to their lack of terminology and information, now have access to the technology of KM to enhance their knowledge and nomenclature to excel in their profession. Gottschalk (1999) puts emphasis on the lessons learned from Norwegian law firms while examining KM in these firms. Two approaches were proposed which includes initial field study and survey of Norwegian law firms, but only the initial field study was conducted wherein semistructured interview was carried out, in which both organizational and individual questions were asked to 14 employees of Thommessen Krefting Greve Lund (TKGL), eight attorneys and six staff persons. The questionnaire was also filled by each respondent during the interview. Furthermore, the base of the study was made actual by conducting a comparative study of TKGL and Schjodt, where Schjodt scores were lesser than TKGL. During the study, different posts were identified by both the firms as Knowledge Manager and IT Coordinator. In future studies, the second phase of the venture has to look for the advantage of law firms, the value of indefinable resources, and the use of IT to support KM.

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From a technological perspective, insofar the law firms are concerned, KM is aimed at providing staff with the best possible tools at least cost to support their routine work and fulfilling their needs. There are different stages of KMT in law firms associated with lawyers; four of them are “lawyer to technology, lawyer to lawyer, lawyer to information, and lawyer to application.” They also presented a stage model, i.e. the period of development of KMT within law firms, from exploratory empirical investigation of Norway. Findings from the research stated that the majority of the law firms are at 3rd phase, i.e. lawyer to information (Gottschalk & Karlsen, 2009). As we are aware that KM is a continuum of different tools and techniques, IT plays a main role in the success of KM in an organization. IT support for KM is of four kinds: The first category is concerned with end-user tools that are made available to knowledge workers, the second category is information about who knows what, the third is information from knowledge workers, and the last category is information systems solvingknowledge problems. The outcome revealed is from a pragmatic study of law firms of America. Among all the premeditated firms, in most of the firms, the current project was concerned with the “end-user tools,” while only a few were implementing systems for solving-knowledge problems. From the discriminate analysis of all firms, it was indicated that the number of lawyers and IT professionals was important determinants of the category of KM technology projects. There is a good implication of KM and KMS in different types of organizations in general and in law firms in particular (Gottschalk & Khandelwal, 2003). It is quite difficult to manage all the knowledge assets of a firm. Various studies have been done, and some are in process. Plessis (2011) in a similar way provided insight of KM in law firms and the impact of legal services provided to the clients. It was made known by the study which was carried out in two steps. Firstly, the findings of the literature were presented in the course of which the role of information KM in altering the legal environment was explored. And lastly, a pragmatic study in South African law firms was conducted, and findings were presented based on their KM practices.

2.5.5 Knowledge Management and Police The importance of KM in almost every sector including the ones of public domain, e.g. health, disaster management, police, etc., can hardly be overemphasized. Seba and Rowley (2010) associated with the organization of knowledge especially in the public sector and conducted four different case studies of United Kingdom Police Force Department, taking into account interviews with 10 separate senior police officers in three police forces. The researchers also surveyed the national agency responsible for drafting policies to investigate the initiatives taken for the

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management of knowledge, the strategies planned, and promotion of sharing it by encouraging the staff for collaboration as per their domain. Three-stage thematic analyses of the interview transcripts were undertaken. First transcripts were analyzed individually for key themes in each force, followed by common themes across individual respondents within one police force. Finally, a general picture of Knowledge Management approaches was surfaced by making comparisons between the analysis of the three forces and that of the national policing improvement agency. It was revealed that even not a single organization has a concrete KM policy, although they do try to symbolize KM in their processes (strategies, processes, and training methods). It was witnessed that knowledge networking is a major concern in these departments because of culture, strength, and unpredictable detection of worth for KM.

2.6 Development of KMS in Profit-making Organizations KM helps organizations to meet organizational goals by managing their knowledge assets. In business, KM has the task of managing the activities of knowledge workers for better product service to their customers (Gao, Li, & Clarke, 2008). In fact, the implications of KM are considered to be one of the basic components of every firm. All organizations including banks are nowadays being in a race of this unending competition. Banks must take initiative to incorporate KM in their business process to survive in the race. Cader et al. (2013) conducted the study regarding “knowledge management in Islamic and conventional banks in the United Arab Emirates (UAE)” to investigate the present or to which extent the KM is practiced in these banks. The study begins with a research literature review, followed by interviews with senior executives in eight different banks, wherein three were from Islamic and five from conventional banks using a structured questionnaire. Due to their experiences in banking and operations management, they were selected for participation, and data, thus, obtained were recorded using tape recordings and field notes. According to findings revealed from the study, Islamic banks were found more actively involved in KM than those of conventional banks. No doubt, both differ in the involvement in KM, although they had the same goals and objectives of capturing knowledge, transferring it, and most importantly it is sharing. But unfortunately, not even a single bank was having a knowledge officer nor was any one of them fond of KM ethnicity. Besides managing the internal knowledge of an organization, the external knowledge that lies within the customer knowledge can also be managed, and this has been found more useful from different studies. Taherparvar, Esmaeilpour, and Dostar (2014) conducted the study to examine the customer KM effect on firm performance and continuous innovation in private banks of Guilan, Iran. The data

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were collected from managers using questionnaires, whereas structural modeling was used for testing the hypothesis. The reports gathered from the findings revealed that customer’s knowledge has a positive impact on innovation speed and innovation quality. In Nigeria, a study of “central bank” was conducted to investigate the development of the KM strategy, in which a “two cleft approach of communities, practice, and a functional portal” was used to impel KM. The need for aligning the KM strategy with a business strategy for the success of KM was strongly felt (Oluikpe, 2012). Akin to this, an empirical study was carried out to make known how those organizations working in fewer business settings can benefit from KMS. Findings highlighted that KMS could be of more significance, such as for better decision-making, for promoting innovations hence, leading organizations to betterquality positions (Elaid et al., 2006). Another study on the “perceptions of knowledge management and intellectual capital in the banking industry” was performed with the main motive to tab the perceptions of KM, intellectual capital, relevancy, and supposed assessment of such secretarial variables in the banks. The research followed the qualitative approach, while the analysis was developed after the interviews with the top-level management of different banks. Findings conferred the verification of most of the theoretical KM and intellectual capital literature and also identified the value given to KM and intellectual capital by the banks (Curado, 2013). The approaches of an international organization to the development of KM were reviewed by Ringel-Bickelmaier and Ringel (2010), and their study begins with the analysis of the evolution of particular approaches supported with different case studies. From the findings of the study, all institutions were found at a nice position of information management encompassed with initiatives to set active KMSs. It was also found that only certain international associations like “the United Nations Development Programme (UNDP) or the World Bank” had integrated external and internal knowledge. While in other organizations, tacit and external KM was noted to a less significant level, highlighting that there is still room for improvement in these organizations. Lee and Hong (2002) discussed the KM life cycle from knowledge capture to knowledge utilization and also strategies used to develop an entrepreneur KMS with the support of IT. Squier and Snyman (2004) performed a study to know the present status of the KM execution program in three “South African financial organizations.” The study was carried out using a questionnaire and face-to-face interviews for collecting useful data. After the proper results obtained, pointed the usefulness and effectiveness of KM in creating and organizing corporate knowledge, most importantly the tacit knowledge. In addition, Koh, Gunasekaran, Thomas, and Arunachalam (2005) evaluated the need for KM in a call center. After a thorough study of a few already existing models, they developed a new KM model. For analysis of the important KM processes in companies, a conceptual model was developed which analyzed 12 dynamic companies from the industrial sector. Findings have recorded 79 instruments with which knowledge is practically organized. It was also found that there are provisions for KM on a strategic and tactical level, but they have not been developed as such (Beijerse, 2000). Apart

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from this, China has taken an initiative of compiling all the business knowledge by developing a single KM platform called “iBridge” with the main purpose to promote knowledge sharing and collaboration across the world, which will encourage greater levels of innovation. The developed KMS is enabled with many new technical and social tools including blogs, community forums, etc. (Grossman, 2008).

3. Conclusion In the present era, every organization needs its own KMS to support its work processes and develop decision-making power, sense building, and learning by providing access to its explicit as well as tacit knowledge, thus helping the organizations to achieve their set goals. However, prior to the successful implementation of KM, one must be aware of the KM trends operational in different domains of the research management of knowledge. The present chapter has, therefore, focused upon bringing the research trends in KM/KMS into the light to all the knowledge dealers. It will not only enable the researchers to know the overall patterns of research on the topic but also enable them to find out the gaps in the research to be dealt with in the future.

References Abou-Zeid, E. (2002). A Knowledge management reference model. Journal of Knowledge Management, 6(5), 486–499. doi:10.1108/13673270210450432 Al-Aama, A. Y. (2013). Technology knowledge management TKM taxonomy: Using technology to manage knowledge in a Saudi municipality. Vine, 44(1), 2–21. doi: 10.1108/VINE-12-2012-0052 Alavi, M., & Leidner, D. (1999). Knowledge management systems: Issues, challenges, and benefits. Communications of the Association for Information Systems, 1(1), 7. Retrieved from https://core.ac.uk/download/pdf/301377233.pdf Armistead, C. (1999). Knowledge management and process performance. Journal of Knowledge Management, 3(2), 143–157. doi:10.1108/13673279910275602 Arntzen, A. A. B., Worasinchai, L., & Ribi`ere, V. M. (2009). An insight into knowledge management practices at Bangkok University. Journal of Knowledge Management, 13(2), 127–144. doi:10.1108/13673270910942745 Baquero, T., & Schulte, W. (2007). An exploration of knowledge management practices in Colombia. Vine, 37(3), 368–386. doi:10.1108/03055720710825663 Basadur, M., & Gelade, G. A. (2006). The role of knowledge management in the innovation process. Creativity and Innovation Management, 15(1), 45–62. doi:10.1111/ j.1467-8691.2006.00368.x Behzadi, H., Isfandyari-Moghaddam, A., & Sanji, M. (2012). E-government portals: A knowledge management study. The Electronic Library, 30(1), 89–102. doi: 10.1108/02640471211204088 Beijerse, R. P. U. (2000). Knowledge management in small and medium‐sized companies: Knowledge management for entrepreneurs. Journal of Knowledge Management, 4(2), 162–179. doi:10.1108/13673270010372297

28

Knowledge Management Systems

Beveren, J. V. (2002). A model of knowledge acquisition that refocuses knowledge management. Journal of Knowledge Management, 61(1), 18–22. doi:10.1108/ 13673270210417655 Bhatt, G., Gupta, J. N. D., & Kitchens, F. (2005). An exploratory study of groupware use in the knowledge management process. Journal of Enterprise Information Management, 18(1), 28–46. doi:10.1108/17410390510571475 Blackman, D., & Kennedy, M. (2009). Knowledge management and effective university governance. Journal of Knowledge Management, 13(6), 547–563. doi:10.1108/ 13673270910997187 Cader, Y., O’Neill, K. K., Blooshi, A. A., Shouq, A. A. B., Al Fadaaq, B. H. M., & Ali, F. G. (2013). Knowledge management in Islamic and conventional banks in the United Arab Emirates. Management Research Review, 36(4), 388–399. doi: 10.1108/01409171311314996 Call, D. (2005). Knowledge management – not rocket science. Journal of Knowledge Management, 9(2), 19–30. doi:10.1108/13673270510590191 Chua, A. Y. K., & Banerjee, S. (2013). Customer knowledge management via social media: The case of Starbucks. Journal of Knowledge Management, 17(2), 237–249. doi:10.1108/13673271311315196 Cristea, D. S., & Capatina, A. (2009). Perspectives on knowledge management models. The Annals of “Dunarea de Jos,”, 355–366. Retrieved from https://core.ac.uk/ download/pdf/26762758.pdf Curado, C. (2013). Perceptions of knowledge management and intellectual capital in the banking industry. Journal of Knowledge Management, 12(3), 141–155. doi: 10.1108/13673270810875921 Edenius, M., & Borgerson, J. (2003). To manage knowledge by intranet. Journal of Knowledge Management, 7(5), 124–136. doi:10.1108/13673270310505430 Edwards, J. S., Shaw, D., & Collier, P. M. (2005). Knowledge management systems: Finding a way with technology. Journal of Knowledge Management, 9, 113–125. doi:10.1108/13673270510583009 Elaid, A., Jack, K., Goulding, S., Halkias, D., Cader, Y., Neill, K. K. O., & Goulding, J. S. (2006). A case study on knowledge management implementation in the banking sector. Vine, 362, 211–222. doi:10.1108/03055720610683013 Forcier, E., Rathi, D., & Given, L. M. (2013). Knowledge management and social media: A case study of two public libraries in Canada. Journal of Information and Knowledge Management, 124, 1350039. doi:10.1142/S0219649213500391 Gao, F., Li, M., & Clarke, S. (2008). Knowledge, management, and knowledge management in business operations. Journal of Knowledge Management, 12, 3–17. doi:10.1108/13673270810859479 Gottschalk, P. (1999). Knowledge management in the professions: Lessons learned from Norwegian law firms Journal of Knowledge Management, 33, 203-211. doi: 10.1108/13673279910288699 Gottschalk, P., & Karlsen, J. T. (2009).Knowledge management in law firm business. Journal of Small Business and Enterprise Development, 163, 432-442. doi:10.1108/ 14626000910977152 Gottschalk, P., & Khandelwal, V. K. (2003). Determinants of knowledge management technology projects in Australian law firms. Journal of Knowledge Management, 74, 92–105. doi:10.1108/13673270310492976

An Overview and Trends in Knowledge Management

29

Grace, T. P. L. (2009). Wikis as a knowledge management tool. Journal of Knowledge Management, 13, 64–74. doi:10.1108/13673270910971833 Grossman, M. (2008). An emerging global knowledge management platform: The case of iBridge. Vine, 38(4), 525–534. doi:10.1108/03055720810917750 Gul, R. (2017). A Conceptual model of Knowledge Management System model for special libraries. In Information access in knowledge society: Changing paradigms (pp. 357–365). Handzic, M. (2011). Integrated socio-technical knowledge management model: An empirical evaluation Journal of Knowledge Management, 152, 198–211. doi:10.1108/ 13673271111119655 Haslinda, A., & Sarinah, A. (2009). A review of knowledge management models. The Journal of International Social Research, 29, 187–198. Hassan, N. A., Hayiyusuh, N. A., & Nouri, R. (2011). The implementation of knowledge management system KMS for the support of humanitarian assistance/ disaster relief HA/DR in Malaysia. International Journal of Humanities and Social Science, 1(4), 103–112. Retrieved from http://www.ijhssnet.com/journals/Vol._1_ No._4;_April_2011/14.pdf Hazeri, A., Sarrafzadeh, M., & Martin, B. (2007). Reflections of information professionals on knowledge management competencies in the LIS curriculum. Journal of Education for Library & Information Science, 483, 168–186. He, W., Qiao, Q., & Wei, K. K. (2009). Social relationship and its role in knowledge management systems usage. Information and Management, 463, 175–180. doi: 10.1016/j.im.2007.11.005 Hung, Y.-C., Huang, S.-M., Lin, Q. P. & Tsai, M.-L. (2005). Critical factors in adopting a knowledge management system for the pharmaceutical industry. Industrial Management & Data Systems, 105, 164–183. doi:10.1108/02635570510583307 Husain, S., & Gul, R. (2017). Knowledge management system in health sciences: Design and development In Proceedings of the International Conference on Knowledge Generation, discovery, sharing, and networking in the 21st century 2017, February (pp. 27–37). Husain, S., & Gul, R. (2018). Knowledge management system: Relevance in social sciences. Journal of the Indian Library Association, 54 (2), 1–6. Retrieved from http://ilaindia.net/jila/index.php/jila/article/view/260 Islam, A. M., Agarwal, N. H., & Ikeda, M. (2005). Knowledge management for service innovation in academic libraries: A qualitative study. Journal of Library Management, 36(1/2),40–57. doi:10.1108/LM-08-2014-0098 Jain, P. (2007). An empirical study of knowledge management in academic libraries in East and Southern Africa. Library Review, 56(5), 377–392. doi:10.1108/0024253 0710750572 Johannsen, C. G. (2000). Total quality management in a knowledge management perspective. Journal of Documentation, 561, 42–54. doi:10.1108/eum0000000007108 Khalifa, M., Yu, A. Y., & Shen, K. N. (2008). Knowledge management systems success: A contingency perspective. Journal of Knowledge Management, 12, 119–132. doi:10.1108/13673270810852430 Kiniti, S., & Standing, C. (2013). Wikis as knowledge management systems: Issues and challenges. Journal of Systems and Information Technology, 15, 189–201. doi: 10.1108/13287261311328895

30

Knowledge Management Systems

Koh, S. C. L., Gunasekaran, A., Thomas, A., & Arunachalam, S. (2005). The application of knowledge management in call centers. Journal of Knowledge Management, 94, 56–69. doi:10.1108/13673270510610332 Kumar, S., & Gupta, S. (2012). Role of knowledge management systems KMS in multinational organization : An overview. International Journal of Advanced Research in Computer Science and Software Engineering, 210, 8–16. Retrieved from http://www.ijarcsse.com/ Lee, S. M., & Hong, S. (2002). An enterprise-wide knowledge management system infrastructure. Industrial Management & Data Systems, 102, 17–25. doi:10.1108/ 02635570210414622 Lindvall, M., Rus, I., & Sinha, S. S. (2003). Software systems support for knowledge management. Journal of Knowledge Management, 7, 137–150. doi:10.1108/136732 70310505449 Lin, C., & Tseng, S. M. (2005). The implementation gaps for the knowledge management system. Industrial Management & Data Systems, 105, 208–222. doi:10.1108/ 02635570510583334 Mansourvar, M., & Norizan, M. Y. (2010). Web portal as a knowledge management system in the universities. World Academy of Science, 4(10), 2113–2119. doi: 10.5281/zenodo.1061489 Mills, A. M., & Smith, T. A. (2011). Knowledge management and organizational performance: A decomposed view. Journal of Knowledge Management, 15, 156–171. doi:10.1108/13673271111108756 Morr, C. E., & Subercaze, J. (2010). Knowledge management in health care. In Handbook of research on developments in E-health and telemedicine: Technological and social perspectives (pp. 490–510). Retrieved from http://liris.cnrs.fr/Documents/ Liris-3768.pdf Mphidi, H., & Snyman, R. (2004). The utilization of an intranet as a knowledge management tool in academic libraries. The Electronic Library,22(5), 393–473. doi: 10.1108/02640470410561901 Murphy, T., & Jennex, M. E. (2006). Knowledge management systems developed for Hurricane Katrina response. In Proceedings of the 3rd International Conference on Information Systems for Crisis Response and Management ISCRAM 2006, May. (pp. 615–624) Retrieved from http://www.iscram.org/dmdocuments/S2_T3_3_ Murphy_Jennex.pdf Oluikpe, P. (2012). Developing a corporate knowledge management strategy. Journal of Knowledge Management, 166, 862–878. doi:10.1108/13673271211276164 Parker, K. R., Nitse, P. S., & Flowers, K. A. (2005). Libraries as knowledge management centers. Library Management. 26(4), 176–189. doi:10.1108/01435120510596035 Plessis, T. . du. (2011). Information and knowledge management at South African law firms. Potchefstroom Electronic Law Journal,14(4), 233–258. doi:10.4314/pelj.v14i4.8 Poston, R. S., & Speier, C. (2005). Effective use of knowledge management systems: A process model of content ratings and credibility indicators. MIS Quarterly, 292, 221–244. Probst, G. J. B. (1998). Practical knowledge management: A model that works. PrismCambridge Massachusetts, 17–30. Rah, J. A., Gul, S. & Wani, Z. A. (2010). University libraries: Step towards a webbased knowledge management system. Vine, 40, 24–38. doi:10.1108/03055721011 024900

An Overview and Trends in Knowledge Management

31

Randeree, E. (2006). Knowledge management: Securing the future. Journal of Information Management, 10(4), 145–156. doi:10.1108/13673270610679435 Ray, D. (2014). Overcoming cross-cultural barriers to knowledge management using social media. Journal of Enterprise Information Management, 27(1), 45–55. doi: 10.1108/JEIM-09-2012-0053 Razmerita, L., Kirchner, K. & Sudzina, F. (2009). Personal knowledge management: The role of Web 2.0 tools for managing knowledge at individual and organisational levels. Online Information Review, 33, 1021–1039. doi:10.1108/14684520 911010981 Ringel-Bickelmaier, C., & Ringel, M. (2010). Knowledge management in international organisations. Journal of Knowledge Management, 14(4), 524–539. doi: 10.1108/13673271011059509 Rohendi, D. (2012). Development model for knowledge management system KMS to improve university’s performance case studies in Indonesia university of education. Journal of Computer Science, 9(1), 1–6. Roknuzzaman, M., & Umemoto, K. (2009). How library practitioners view knowledge management in libraries. Library Management. 30(8/9), 643–656. doi:10.1108/ 01435120911006593 Rowley, J. (2003). Knowledge management – the new librarianship? From custodians of history to gatekeepers to the future. Library Management, 24(8), 433–440. Sarrafzadeh, M., Martin, B., & Hazeri, A. (2006). LIS professionals and knowledge management: Some recent perspectives. Library Management. 27(9),621–635. doi: 10.1108/014351206610715527 Sarrafzadeh, M., Martin, B., & Hazeri, A. (2010). Knowledge management and its potential applicability for libraries, Library Management, 31(3), 198–212. doi: 10.1108/01435121011027363 Saulais, P., & Ermine, J. L. (2012). Creativity and knowledge management. VINE: The Journal of Information & Knowledge Management Systems, 42(3/4), 416–438. doi:10.1108/03055721211267521 Seba, I., & Rowley, J. (2010). Knowledge management in UK police forces. Journal of Knowledge Management, 14(4), 611–626. doi:10.1108/13673271011059554 Shoham, S., & Perry, M. (2009). Knowledge management as a mechanism for technological and organizational change management in Israeli universities knowledge management as a mechanism for technological and organizational change management in Israeli universities. Higher Education, 57, 227–246. doi:10.1007/sl0734008-9148-y Sijing, L. (2006). Analysis and design of knowledge management system. In J. S. Edwards, D. Shaw, & P. M. Collier (Eds.), Asian federation for information technology in agriculture (pp. 172–178). Singh, S. K. (2008). Role of leadership in knowledge management: A study. Journal of Knowledge Management, 12(4), 3–15. doi:10.1108/13673270810884219 Squier, M. M., & Snyman, R. (2004). Knowledge management in three financial organisations: A case study. ASLIB Proceedings, 56(4), 234–242. doi:10.1108/ 00012530410549268 Taherparvar, N., Esmaeilpour, R., & Dostar, M. (2014). Customer knowledge management innovation capability and business performance: A case study of the banking industry. Journal of Knowledge Management, 18(3),591–610. doi:10.1108/JKM-112013-0446

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Teng, S., & Hawamdeh, S. (2002). Knowledge management in public libraries. ASLIB Proceedings, 543, 188–197. Tian, J., Nakamori, Y., & Wierzbicki, A. P. (2009). Knowledge management and knowledge creation in academia: A study based on surveys in a Japanese research university. Journal of Knowledge Management, 13, 76–92. doi:10.1108/1367327091 0942718 Tikhomirova, N., Gritsenko, A., & Pechenkin, A. (2008). University approach to knowledge management. Vine, 38(1), 16–21. doi:10.1108/03055720810870851 Yi, Z. (2008). Knowledge management for library strategic planning: Perceptions of applications and benefits, Library Management,29(3), 229–240. doi:10.1108/ 01435120810855331

Chapter 3

Knowledge Management: Processes and Models 1. Introduction The knowledge capital that ensures sustainability, competitiveness, and stability of an organization constitutes its biggest wealth. During the past two decades of its development, knowledge management has emerged as a strategic approach to the implementation of the objectives and the means of the organization to capitalize upon and share organizational knowledge to serve the purpose of creating value-added new knowledge, through a new relationship between the “people” and “information and communication systems.” Consequently, the installation of knowledge management systems (KMSs) during the last two decades has become increasingly frequent, more so in business organizations than in the institutions of higher learning, particularly in view of economic globalization and branding of products for customers’ satisfaction, and also because it effectively responds to the fundamental problems, now compounding with the phenomena of population aging and digitalization. Knowledge management involves a conscious attempt on the part of an organization to explore, collect, evaluate, systematize, and share its organizational knowledge for increasing collaboration among knowledge managers and workers to enhance efficiency. Of the two types of organizational knowledge, the documented/explicit knowledge may include policies, procedures, documents, databases, etc., whereas the implicit knowledge may include unexploited expertise of the staff gained through vast experience. A formal process of KM involves identification of company’s information that can be helpful to other employees who need it. Formal procedures used for the purpose include the following:

• • • • •

Lessons learned during a project’s execution Best practices followed A well-established infrastructure Networks for transferring knowledge between employees Tools to facilitate the process.

Knowledge Management Systems, 33–60 Copyright © 2021 Shabahat Husain and Jean-Louis Ermine Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-80117-348-320210003

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After capturing organizations’ knowledge, it is shared for maximizing the quality of the product (Kumar & Gupta, 2012). By transforming explicit and implicit knowledge of the organization into exploitable form, KM provides a new dimension of strategic information management that may enable to reach new heights of excellence (White, 2004). While dealing with the “KM in Academic libraries,” Preeti Jain (2007) gave 17 essential features of KM policy document for an organization that may serve as a road map to answer questions such as what, why, how, and who? Some of them are as follows: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Organizations need a KM policy. Its main purpose is to achieve organizational goals efficiently. It focuses on creativity and innovativeness. It requires a system to capture staff tacit knowledge. It facilitates calls for updating knowledge and important documents. There is an emphasis on identification of expertise. It is founded on a strong culture of knowledge sharing. It is based on a strategic plan. It embraces both tacit and explicit knowledge. It is related to change management, so success depends on a learning environment.

As KM is generally regarded as a social activity, some people call it a KM culture, while some call it KM mechanics, as it requires I.C.T. facilities and tools.

2. Definitions Some standard definitions of KM are given hereunder: Knowledge Management is the systematic management of an organization’s knowledge assets for the purpose of creating value and meeting tactical & strategic requirements; it consists of the initiatives, processes, strategies, and systems that sustain and enhance the storage, assessment, sharing, refinement, and creation of knowledge. (Frost, 2010) The way in which knowledge is organized and used within a company, or the study of how to effectively organize and use it. (Cambridge online dictionary) Efficient handling of information and resources within a commercial organization. (Oxford living dictionary, Online) A business discipline dealing with the tracking of knowledge built up from experiences and developed into workflows and processes. (Collins English dictionary, Online)

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KM addresses policies, strategies and techniques aimed at supporting an organization’s competitiveness by optimizing the conditions needed for efficiency improvement and collaboration among employees. (Sousa & Hendriks, 2006)

3. Knowledge Management Processes KM converts organizational knowledge into value-added form which is made available to whoever needs it for the creation of quality products and services. KM provides a means to exploit, create, and share documented knowledge (explicit) as well as the embedded knowledge (tacit) that remains in the minds of staff/experts. The two constitute what is called an organization’s knowledge capital, which when put to action provides stability, sustainability, and competitiveness by creating better quality products and services. For the same reason, organizations are implementing KM steadily, for it’s the only way to tackle effectively the deep-seated problems being faced by the business enterprises due to globalization. Organizations, whether public or private, use both digital or print form of knowledge to serve their patrons by enhancing the access to the documented knowledge (explicit knowledge) of the organization, and also by creating a system whereby the undocumented knowledge (implicit knowledge) created out of practice-in-action may be shared by and among the staff engaged in the central productive activity of the enterprise. Unfortunately, the knowledge gained out of observations and experiences of the staff largely remains untapped, unless maneuvered through a manual or automated System. For instance, one may think of the quantum of knowledge gained by the medical practitioners, engineers, legal experts, managers, academicians, researchers, and psychologists, etc. working in public or private sectors. The knowledge gained remains embedded in their mental faculty, of which only a small part gets documented in the form of books, articles, research reports, prescriptions, project reports, etc., whereas a large portion of their knowledge remains unexploited. A KMS takes care of not only explicit knowledge but also implicit knowledge by developing a scheme that enables the staff members/students/researchers of any organization to participate in the exchange of information, more so in the process of sharing their experiences especially relevant to meet exigencies of the situation or requirements of a project. To site an example, the Minnesota department of transportation (MnDOT) piloted a project titled “Knowledge books preserve expertise of retiring workers” by which the expertise of retiring workers was successfully preserved. MnDOT successfully piloted a European knowledge retention method to preserve the expertise of retiring workers, who are subject experts. The department produced and preserved information accumulated as tacit knowledge in multimedia sources of information apart from written material on relevant topics such as concrete pavements, asphalt pavements, and steel bridges for use by future engineers, as 31% of MnDOT’s workforce will be eligible for retirement by 2023.

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The departure of highly experienced engineers, administrators, and other staff may carry with it a significant loss of accumulated knowledge and expertise pertaining to MnDOT’s highway and bridge infrastructure, best practices, successful and unsuccessful approaches to construction and maintenance, and other topics. (https://mntransportationresearch.org/2020/07/16/knowledge-books-preserve-expertise-of-retiring-workers/) In the same context, Dalkir (2005) contended KM is the deliberate and systematic coordination of an organization´s people, technology, processes, and organizational structure in order to add value through reuse and innovation. This coordination is achieved through creating, sharing, and applying knowledge as well as through feeding the valuable lessons learned and best practices into corporate memory in order to foster continued organizational learning. To Davenport, KM is a continuous development comprising of many processes that may include “acquisition, creation, packaging, and application or reuse of knowledge.” (Davenport, 1993) However, KM processes/cycles (https://km-cycle.weebly.com/major-approaches-1. html).can work effectively, if knowledge/knowledge sources are recognized and found out; implicit knowledge is converted into explicit form; networks and practices are introduced; knowledge is tested and transferred to knowledge repository of the organization, so that it becomes part of “corporate memory.” According to Wiig (1997), activities like Socialization, Seminars, Discussion, Research, Brain Storming etc. prompt the process of creation of the two types of knowledge. The next steps include identification and collection of knowledge, followed by proper observation and scrutiny after which it is processed for storage. Now the practice-in-action phase of the KM process entails knowledge retrieval either by technology or collectively for acquisition and proper application of the same. Thus giving birth to new knowledge and restarting the cycle of KM process. As per Zion Market Research (2018), …the global Knowledge Management market, which was estimated at 206,900 (USD Million) in 2016 and is predicted to accrue earnings worth 1,232,000 (USD Million) by 2025, is set to record ‘Compound Annual Growth Rate’ (CAGR) of nearly 4% over 2016–2025. (https://www.zionmarketresearch.com/report/ knowledge-management-market) It is therefore imperative for any business to be successful, the relevant data are converted into valuable information, which in turn provides knowledge for vital decision-making. A good KMS constantly updates organizational knowledge at all levels at the right time by applying the basic principle of minimum input and maximum output.

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The KM process involves the following four steps: (1) (2) (3) (4)

Discovery of Knowledge Capturing of Knowledge Sharing of Knowledge Application of Knowledge

3.1 Discovery of Knowledge This step involves culling out of both types of knowledge using raw data or oral communication. For the purpose, observations, interviews, surveys, data mining, scanning, etc. are conducted to gather important information, which is further refined and analyzed to convert it into useful knowledge by contextualization, experts’ views, and packaging.

3.2 Capturing of Knowledge Recovery of explicit or tacit knowledge from organizational entities including persons and documents are called knowledge capturing, which entails both externalization (i.e., converting tacit knowledge into explicit knowledge through documentation, verbalization etc.) and internalization (i.e., translating explicit knowledge into tacit knowledge by a process known as “learning by doing” whereby documented knowledge is used to create implicit knowledge).

3.3 Sharing of Knowledge Akin to the definition of a library service “Right information to the right user at the right time,” knowledge sharing as a vital component of the KM process also implies “Right knowledge to the right person at the right time.” The knowledge sharing in the organization may be affected through communities of practice, information flow, push messaging, presentations, lectures, forums etc. The use of ICT goes a long way in sharing knowledge effectively.

3.4 Application of Knowledge The fourth and the final step in KM processing is the application of knowledge so assiduously discovered, captured, and shared among the communities of practice and knowledge workers. Application of knowledge also referred to actualization of knowledge assists in solving business problems and making business decisions. Recent developments in machine learning and artificial intelligence have facilitated in the knowledge application by using knowledge-base software and automation tools. Nevertheless, a KMS provides a higher “return on investment” (ROI) by comparing favorably to its cost with improved customer satisfaction and cheap customer support costs.

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The four steps in the KM process have been shown by light/dark shades in Fig. 3.1.

•Learning by doing •Externalization •Internalization

•Data input •Surveys •Mining •Scanning •OCR

Discovery

Capturing

Application

Sharing

• Machine Learning • Artificial Intelligence

Fig. 3.1.

•Push messaging •Information flow •Communities of Practice •Lectures • Forums

Diagrammatic Representation of the Knowledge Management Process.

4. Building Knowledge Management System KMS supports the techniques and processes that make the platform for promoting all knowledge processes starting from the knowledge generation, organization, storage, distribution, and utilization of the institution’s digital knowledge assets. KMS is an amalgamation of technology, people, and processes. A significant chunk of knowledge is retained by the people, who should be encouraged to look for the new knowledge for its sharing and proper application. The processes involved include searching, recording, sharing updating, and application of new knowledge for the refinement of the systems. Technology assists in all the aforesaid processes. The success of KMS relies on the proper balance between these three KMS components.

4.1 KMS Modules The L&T InfoTech Company in a “Presentation on Knowledge Management System” (https://www.youtube.com/watch?v5gI2H0d0C410 Accessed on January

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21, 2021) has described the approaches for building KMS under the heading of KM modules along with their features as given hereunder: (1) Document Management Module with the following features

• • •

Document Repository Upload/Download/Edit/Move/Copy/Delete documents Approval workflow and version control.

(2) Content Management Module with the following features

• • •

Upload user-generated or contributed articles Wiki and blog features FAQS

(3) Expert Profiles Module provides necessary space for sharing personal and professional details (4) Collaborative features Module provides Discussion forums, messaging, RSS, Polls, who-is-online, chat, Broadcast, Meeting planner (5) Utilities Module provides

• • • •

Full-text search across the content from all the four main modules Push-based search User management Site usage reports

The KMS model developed for Indonesia University of Education consisted of three components, namely, content management, experience management, and process management, that follow the following KM cycle: (1) Knowledge Acquisition; knowledge within the academic community of UPI should be collected and categorized according to the specified category, (2) Knowledge Recording: Knowledge must be recorded and stored on storage media by using standard technology platforms and applications that are used in the organization, (3) Knowledge Storage; new knowledge which has been digitalized will be stored on a centralized database system, (4) Knowledge Sharing; is the process that allows the sharing of knowledge and valuable information stored on the system through the use of Internet or intranet (Munir & Rohendi, 2012).

4.2 KMS Applications Depending upon the means and expertise available, an organization decides to create its own application, or uses one that is commercially obtainable. The IT team of the company or vendor may make use of database development tools like

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“Access,” “SQL,” or “WordPress.” Some of the commercially available KMS applications for different purposes are listed below1: (1) Customer Relationship Management System (keeps track of all interactions with customers from a lead to life span) (2) Applicant Tracking System (HR KMS focused on recruiting and hiring) (3) Shared Project Files (allows teamwork on a project) (4) Feedback Database (collects feedback from customers and employees) (5) Research Database (conducts research on competitors/products) (6) Lessons Learned Database (information from past projects and business practices) (7) Communities of Practice (information from groups who discuss problems, opportunities, lessons learned, and other information gained from users) (8) Internal Knowledge Base (organizational information store for its employees) (9) External Knowledge Base (marketing information for people external to the organization. Research on KM conducted at the Department of Library and Information Science, Aligarh Muslim University, at the behest of UGC’s major research project under SAP (DRS-I) during 2013–2018 produced scores of articles apart from Conference proceedings, at least one Dynamic KMS (http://liskms.com/) and a few Doctorate theses. Word Press open source software was used for developing the Dynamic KMS as well as a model of KMS, being part of the thesis (http://hdl.handle.net/10603/236062).

4.3 Corporate Intranets A network that is accessible to the authorized users is called intranet. A company uses intranet for information security as well as to enable its staff members to share or access files and keep them informed of its policies. LAN and/or WAN technologies are used for the purpose of developing intranet. Modern Corporate Intranets are equipped with search engines, user profiles, blogs, and mobile apps to perform a variety of purposes in different divisions and departments and sections.

4.4 Examples As a part of the research project, an online search of KMSs in use at international and regional levels, followed by a physical survey of KMSs at national level was conducted to identify select public and private organizations, as detailed below: (1) Ford The Ford Motor is a multinational company that has been making use of best practices of KM since long. Ford has since used a web-based knowledge 1

Knowledge Management Examples: Systems and Types (yourdictionary.com).

Knowledge Management: Processes and Models

(2)

(3)

(4)

(5)

41

base software for maintaining quality standards of its products. It helped reduce its warranty cost by $1 billion. GE (General Electric) This is also an American multinational company for more than 125 years that has successfully implemented best KM techniques. Unlike others, the KM approach of the company is people-centric promoting expertise and tacit knowledge exchange, which ultimately resulted in a significant upsurge of market capital of the company from 17% to 84%. Amazon Amazon, the world’s most successful e-commerce company, has been excelling at KM since the late 1990s. The company applies many-core KM principles and practices to serve customers, irrespective of the fact what, when, and where they want as quickly and easily as possible, in addition to catering to the needs of its employees. Pratt & Whitney (P&W) P&W is another American multinational aerospace manufacturer, which uses a KMS effectively for competitive advantage, by breaking KM activities into Knowledge Creation, Storage, Transfer, and Application, with an aim to holistically consider KM as a system of people, processes, and technology. Some years ago, P&W realized that half of their engineers would soon retire. They were rushing to find ways to preserve the knowledge held by its staff. The problem was effectively treated through KMS that allowed the company to save over $25 million. World Bank Washington-based United Nations’ financial institution that has been using KM’s principles, techniques, and best practices to effectively manage the knowledge generated through “World Bank Projects,” not only on the basis of working on capturing explicit knowledge but also on the more qualitative aspects of knowledge such as discussions and opinions to maximize its impact on clients, which to a large extent are the low- and middle-income countries pursuing capital projects after taking a loan from World Bank.

Some examples of KMSs have been listed in Table 3.1.

Table 3.1. List of Select Organizations with Knowledge Management System. S. No

1 2 3 4 5

Name of the Organization

Amazon Ford GE (General Electric) Pratt & Whitney World Bank

Scope

International do do do do

Type

Private do do do Not for Profit

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Table 3.1. (Continued) S. No

6 7

8 9 10

Name of the Organization

IBM ICIMOD KM (International Centre for Integrated Mountain Development, Nepal) TERI KM, (The Energy and Resources Institute, Delhi) EXIM Bank of India (GOI, Ministry of Finance) Bharti Airtel Limited

Scope

Type

do Regional

Public Not for Profit

National

Private

National

Public

National

Private

5. Knowledge Management Models The work done on theoretical models of KM has, though established convincingly in academic and professional fields, no single KMS model, however suitable for different organizations because of the fact that divergent estimation of KM processes, implemented and validated in real-world settings, has resulted in lots of models with different structures. Yet, taking the complex and dynamic nature of knowledge into account, some of them in active use have been given in Table 3.2.

Table 3.2. Models of Knowledge Management. S. No

Name

1

Boisot I-Space KM Model

2

European Foundation for Quality Management (EFQM) KM Model Wiig Model for Building and Using Knowledge Nonaka and Takeuchi Knowledge Spiral Model Von Krogh and Roos Model of Organizational Epistemology Choo Sense-Making KM Model Husain and Ermine AI KM Model

3 4 5 6 7

Developed By

Max Henri Boisot European companies Karl Wiig Nonaka and Takeuchi Von Krogh and Roos Choo Husain and Ermine

Country Year

UK

1987

Europe 1989 USA

1993

Japan

1995

Norway 1995 Canada 1998 India/ 2021 France

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5.1 Boisot I-Space KM Model Max Henri Boisot, a British architect and management consultant, also served as a Professor of Strategic Management at ESADE Business School in Barcelona. He gave a conceptual framework of information space, called I Space, wherein Data, Information, and Diffusion constitute important parameters of the dynamic flow of knowledge in an organization. Boisot, interestingly, considered organizations parallel to living organisms, wherein the process of growth and development of knowledge assets are constantly changing. He thus emphasized a dynamic KM strategy for managing knowledge assets. The conceptual framework of “I-Space KM Model” distinguishes between data and information. According to him, information is pulled out from the data on the basis of earlier know-how. The hitherto diffused data are converted into information, through a process that may be easy and less time consuming when data are available in plenty, or it may be difficult and more time-consuming when their availability is scanty. The latter requires a shared context for its diffusion (Dalkir, 2017, p. 82). The easily cultivated information is diffused easily and vice versa. Boisot I-Space KM Model, developed in 1987, can be envisaged as a threedimensional cube, comprising the following facets, as shown in Fig. 3.2: (1) from uncodified to codified, (2) from concrete to abstract, (3) from diffused to undiffused.

UnCodified

(Codification)

Diffused (Diffusion) Codified Concrete

Fig. 3.2.

Undiffused (Abstraction)

Abstract

Boisot I-Space KM Model (Adapted). Source: Dalkir (2017).p p Knowledge Management by Kimiz DalkirED.3.pdf. Figure reproduced with permission from MIT Press [email protected].

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The Boisot KM model proposes the adoption of a “Social Learning Cycle (SLC)” and its use in I Space. The SLC that uses the I Space to model the dynamic flow of knowledge, efficiently links “content,” “information,” and “knowledge management.” The three-dimensional process involves the following: (1) Codification linked to categorization and classification (2) Abstraction linked to knowledge formation (3) Diffusion linked to information access and transfer In fact, the process includes a series of six stages: (1) Scanning: The available data (diffused) are scanned through the insights of knowledge workers. (2) Problem-solving: Rationality to the insights provides the necessary knowledge structure for problem-solving. In other words, knowledge gets “codified.” (3) Abstraction: the newly codified knowledge is generalized to make it applicable to a wide range of conditions. The knowledge then becomes more “abstract.” (4) Diffusion: the newly created well-codified abstract knowledge is shared with a given population. The knowledge has now become “diffused.” (5) Absorption: the newly codified knowledge, when applied to a variety of situations, generates new learning experiences, meaning thereby that absorbed knowledge, in turn, produces learned behavior of individual knowledge workers. It thus becomes “uncodified,” or “tacit” knowledge once again. (6) Impacting: the abstract knowledge becomes embedded in concrete practices of the organization, and thus becomes “concrete.” The aforesaid description of the model presents a picture of knowledge assets which is on one extreme of I Space is “most abstract,” “least codified,” and “undiffused form,” whereas on the other extreme we have knowledge that is “least abstract” (more concrete), “most codified,” and “most diffused.” This type of knowledge possesses the highest level of entropy but has the least possibility for performing value addition. The knowledge assets of an organization pursuing competitive advantage are moved into the region of minimum entropy. Another significant aspect of the SLC is that the data are filtered to produce meaningful information which when applied to different situations gives rise to new experiences that produce new data to start a new cycle of knowledge creation (http://www.innovators.edu.pk/ node/205).

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5.2 European Foundation for Quality Management (EFQM) KM Model European Foundation for Quality Management (EFQM) was recognized in 1989 as a nonprofit making foundation in Brussels. The purpose of the foundation was to somehow bring competitiveness in the European economy. Introduced in 1992, the EFQM Excellence Model also known as KM Model, serves organizations of all sizes in the public as well as the private sectors, as a tool for self-assessment by understanding gaps and finding out their solutions for future business planning. Researchers have proved the correlation between the adoption of such holistic models and achieving superiority in organizational results. Being a traditional model of quality assessment, it serves as a bridge between organizations’ KM processes and their expected results in achieving sustainable excellence. Organizations find out their current excellence level with the help of the EFQM model framework that ultimately allows them to improve their efforts, keeping in view the organization’s objectives and the requirements of all the stakeholders. Based on the feedback and experiences of the users, revision of the model is done frequently, the last being in 2013. Components of the Model: The EFQM KM Model consists of the following three components: 5.2.1 Fundamental Concepts of Excellence2 The following eight concepts not only serve as the foundation of sustainable superiority of any organization but are also described as the attributes of excellence by the top management:

• • • • • • • •

Adding value for customers Creating a sustainable future Developing organizational capability Harnessing creativity and innovation Leading with vision, inspiration, and integrity Managing with agility Succeeding through the talent of people Sustaining outstanding results

5.2.2 Model Criteria3 The Excellence Model is based on a framework of nine criteria, of which five are termed as “Enablers” while four are “Results.” The “Enablers” criteria help an organization to take necessary steps to achieve the required “Results” as per the predecided aims. In the process, KM acts as an enabler to translate the company’s goals into reality. While the first criteria are realized by proper leadership, strategy, people, partnerships and resources, processes, products and services, the 2

Fundamental Concepts (efqm.co.uk). Criteria (efqm.co.uk).

3

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second one is recognizable through customer results, people results, society results and business results, as shown hereunder: Leadership People Enablers

Policy and strategy Partnerships and resources Processes

People results

Results

Customer Society results Performance results

The abovementioned criteria, namely, “enablers” and “results” used in the framework of the EFQM Excellence Model follow one another. In fact, a virtuous circle is followed wherein “enablers” produce “results”; whereas feedback received from “results” facilitate “enablers” to improve for better “results,” thus helping organizations in achieving sustainable excellence in their performance as given in Fig. 3.3. The modus operandi of enablers of KM process in EFQM Model for achieving sustainable excellence of the organization is shown by a line diagram in Fig. 3.4.

Results

Enablers

Enablers

Results

Fig. 3.3.

EFQM KM Model Virtuous Cycle.

Knowledge Management: Processes and Models Enablers

Results People results

People

Leadership

Strategy

Partnerships & Resources

47

Processes, Products & Services

Customer Results

Business Results

Society Results

Fig. 3.4. EFQM KM Model Key Components (Adapted) (The European Foundation for Quality Management (EFQM) KM Model | kmwiki (wordpress.com)). 5.2.3 RADAR Logic4 The acronym RADAR stands for Results-Approaches-Deploy-Assess-Refine. It serves as a leading management tool for the continuous assessment and improvement of the performance of an organization. As per Radar Logic an organization should do the following:

• • • •

Determine the Results that are aimed at as part of its strategy. In other words, what are we trying to achieve? Prepare for and deploy an integrated set of appropriate Approaches to deliver the required results at present as well as in future. In other words, how do we try to achieve the set aim through immaculate planning and its execution? Deploy the approaches methodically to guarantee implementation as a part of the execution of the plan. In other words, the deployment of approaches must address questions: how/where/when? Assess and Refine the applied approaches by constant examination and scrutiny of the achieved results and continuing learning activities. In other words, how do we assess that the deployed approaches have been working and what improvements can be made still?

For the purpose of immaculate analysis, the RADAR elements can be split up into a chain of attributes that will help in finding out as to what we expect the organization to demonstrate in terms of both “enablers” and “results.” For assessing the enablers, we look at the adopted approaches, their deployment within the organization, and how are they assessed and refined to help achieve efficiency and effectiveness over time. While assessing the results, two things are to be looked into: (1) their relevance to the organization’s strategy and (2) their usefulness in reviewing the progress against the key objectives. Jaroslav Nenad´al (2020), (1415–5777-1-PB.pdf accessed on January 12, 2021), while making a comparative analysis of EFQM (2012) and EFQM (2019) 4

Radar Logic (efqm.co.uk).

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concludes that the latest version of the EFQM Model is not better or worse in comparison to the version from 2012 as advantages and weaknesses are mutually balanced and only a near future will show if this model is a valuable tool for digital transformation of the organizations and their management systems. The EFQM KM Model is used by a large number of organizations across Europe. In recent years, more and more countries started implementing the Model, especially across the Middle East and South America.

5.2.4 Nonaka–Takeuchi SECI Model Ikujiro Nonaka, known for his study on Knowledge Management, is a Professor Emeritus at the Graduate School of International Corporate Strategy of the Hitotsubashi University, Japan, whereas Hirotaka Takeuchi is a Professor of Management Practice in the Strategy Unit at Harvard Business School. The SECI model was originally developed by Ikujiro Nonaka (1990) and later further refined by Hirotaka Takeuchi. (Xu, 2013). In fact, both of them have coauthored quite a few worth mentioning articles including the Nonaka–Takeuchi model (1995) of accumulation of tacit knowledge. In the SECI Model, Nonaka has elucidated as to how tacit and explicit knowledge are converted into the knowledge assets of the organization. SECI is an acronym of the four underlying processes named as Socialization, Externalization, Combination, and Internalization. The model is founded upon two types of knowledge, namely, explicit or documented form of knowledge (i.e., available in print and digital format) and implicit knowledge (i.e., experience, skill, and capability-based) which remains confined to the individuals. Whereas explicit knowledge can be retrieved, stored, and transferred to others easily, implicit knowledge is difficult to tap, verbalize, and transfer to other people without practicing. The formation of knowledge in this model involves two dimensions, i.e., epistemological and ontological. The former converts tacit knowledge to explicit knowledge and vice versa, while the latter stresses the knowledge transfer from individuals to groups and organizations. In addition to that, the model assumes the creation of knowledge by conversion between tacit and explicit knowledge. It thus recognizes four modes of knowledge conversions in organizations. As the knowledge is continuously tapped and converted by practicing, collaborating, interacting, and learning in all the organizations, the four dimensions envisage a “spiraling knowledge” process, as shown in Fig. 3.5. The four knowledge dimensions recognized and presented by this model can be combined together and transferred, to enable sharing of knowledge in the organization. The four dimensions, forming an acronym “SECI,” are given hereunder:

• • • •

Socialization (tacit to tacit), i.e., indirect way, Externalization (tacit to explicit), i.e., indirect to direct way Combination (explicit to explicit), i.e., direct way, and Internalization (explicit to tacit), i.e., direct to indirect way

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Dialogue

Field building

Linking explicit knowledge

Learning by doing

Fig. 3.5. SECI Model (Spiral) (Adapted). Source: Dalkir (2017). (Knowledge Management by Kimiz DalkirED.3.pdf. Figure reproduced with permission from MIT Press ([email protected])) Various modes of knowledge conversion are discussed as follows:

•

•

Tacit to Tacit (Socialization) In this dimension, tacit knowledge is gained through socialization. In fact, it is the transfer of tacit knowledge from one individual/group to another individual/group through practice, guidance, and observation. Many times knowledge is obtained through dialogue or interaction. Working in the same environment, spending time together, and sharing experiences face-to-face lead to acquiring such knowledge. Meetings and brainstorming can support this kind of interaction. A hands-on training during apprenticeship helps learn the tacit knowledge required to develop their expertise as compared to the documented knowledge available in the organization. Tacit to Explicit (Externalization) The process of converting tacit knowledge into explicit knowledge is called externalization. When this is done, the knowledge sorts itself out in such a way that it could easily be made common, allowing others to share, so that it becomes a part of new knowledge. The mechanism of externalization could be intricate because codifying tacit knowledge sometimes becomes difficult, if not impossible. When it is possible, the same is expressed in the form of concepts, images, and words in manual or other documents, making it feasible for the

50

•

•

Knowledge Management Systems staff of the organization to share among themselves. Externalization might result in some sort of tangible changes in the existing product, or development of a new product altogether. For example, a trainee in web designing after a few months learned specific techniques to make user-friendly websites. He may get specialized in software like Joomla or WordPress etc. and decides to write a manual explaining the steps involved in web designing using particular software. He may then share it with his colleagues to receive feedback on his work. The trainee has thus codified his tacit knowledge into the manual created by him. Explicit to Explicit (Combination) The third dimension of knowledge sharing in an organization is affected by the combination of explicit to explicit knowledge, thereby generating new knowledge. The latter is not necessarily collected from the internal resources, but also from outside the organization. The resources of explicit knowledge may include books, documents, memos, and online databases, etc. The information at this stage may be systematized, integrated, and edited to produce new knowledge like a project report for instance. This type of explicit knowledge created out of absolute explicit knowledge is then shared by the other staff of the organization so that it becomes part of their practice again. Explicit to Tacit (Internalization) Internalization is the fourth and the last knowledge sharing dimension of an organization. It involves how explicit knowledge is converted into tacit knowledge. In general, internalization is a part of the continual process of learning of all experts and knowledge workers, by what is called as “learning by doing.” In an organization, apart from the individual worker’s ability to learn, the internalization process is greatly facilitated by the documented sources of knowledge made available to the employees who learn during reading and ultimately by doing. The knowledge so gained by the individual soon becomes an asset for the organization, after it is modified suitably and applied appropriately. In the above example of a trainee-turned expert in web designing software, the person concerned may be asked to share his tacit knowledge with the other workers in the organization or may be requested to lecture the students of the web designing in some training institutes.

This process is sturdily allied to “learning by doing” by which people create implicit knowledge from documented knowledge resulting in the continuous conception of knowledge and may thus start the spiral again (Santos, 2012).After internalization, the process continues at a new “level,” hence the “spiraling knowledge creation” as characterized by the SECI model Nonaka and Takeuchi (1995). Mclean, Laird D. (2004) concluded his review stating that the theory of organizational knowledge creation put forth by Nonaka and Takeuchi (1995) is indeed a well-conceptualized theory, but it is an emerging one that will grow more robust over time. Subsequently Nonaka and Konno (1998) developed the SECI model by introducing the Japanese concept of “Ba,” which means a “place” or “space” in a shared context, providing a platform to converse or interact for a common cause

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or good. All the four knowledge dimensions of SECI model viz Socialization, Externalization, Combination, and Internalization may be thought of in the shared space (Ba) of the organization where knowledge is created and utilized mutually.

5.3 Von Krogh and Roos Model of Organizational Epistemology 2.2.1 Georg von Krogh a native of Norway is a Professor at the Federal Institute of Technology in Zurich (ETH Zurich) where he holds the Chair of Strategic Management and Innovation. Johan Roos, a Swedish currently serving as Chief Strategy Officer at Hult International Business School, is known for his work on intellectual capital. The von Krogh and Roos KM model (1995) is the first to take an approach of organizational epistemology. It emphasizes that knowledge is to be found both in the minds of the individuals and in the links they form with others, thus demarcating between individual knowledge and social knowledge. The model while examining the frail nature of KM in organizations identified five concerns that slow down or even thwart KM strategies. They are as follows:

• • • • •

Mindset of the staff members Communication and connections between the staff Organizational setup Interconnections between the members Human resource management

In view of the above impeding factors, knowledge enablers should be put in place to encourage the development of individual knowledge, knowledge sharing between the staff members, and preservation of valuable knowledge-based content. Subsequent to an epistemology approach vis-`a-vis KM, the model highlights a clear difference between personal and public knowledge. As per the model, below features must be studied:

• “Why and how the knowledge gets to the employees of a company • Why and how the knowledge reaches the organization • What does it mean knowledge for the employee/organization • What are the barriers to organizational knowledge management.”

(Cristea & Capatina, 2009)

Cognitive processes include opinions, beliefs, knowing, recalling, judging, and problem-solving. Individuals in the organization gather information from the external environment and also internally by using their senses. It helps them to create mental models on the basis of their cognitive proficiencies, which are to be tapped somehow to get the best of the organizational knowledge.

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Von Krogh and Roos in their model of organizational epistemology have followed the principles of conexionist approach, that underlies the existence of knowledge not only in the minds of the people but also in the connections (links) between them. In contrast to the cognitive the approach of acknowledging knowledge as an “abstract,” the conexionist approach lays emphasis on the role of the “Knower” (the collective mind), without which it is impossible to perceive organizational KM, a statement that is akin to the significance of tacit knowledge within the organization. The connection (links) between the group of persons results in the formation of the network thus representing the core of organizational KM. It can be said …that conexionist approach seems to be a good base for a theoretical knowledge management model, mainly because of the fact that the link between knowledge and the ones who possess it seems to be permanent (Cristea & Capatina, 2009, p. 357). The conexionist approach may be augmented through a process known as “knowledge activation” that involves promotion of human relationships, discussions, and sharing local knowledge.

5.4 Choo Sense-making KM Model Chun Wei Choo, a Professor in the Faculty of Information at The University of Toronto, presented a Sense-making KM Model in 1998. It works on the premise that the organizations use information strategically in sense-making, knowledge creation, and decision-making. (Choo, 1998). All the said processes are the main elements in pointing the organization’s knowledge vision (Souza, Queiroz, & Chipp, 2009). The model may serve as a guide for the assortment of informational elements, for facilitating their application to organizational procedures. All the three models of information usage have been explained in the Fig. 3.6. As a matter of fact, KMS in any organization is prone to resonate with the changes in the external environment that affect the inflow of information, thereby bringing necessary changes in its working. The three interconnected processes underlying Choo Sense-making KM Model are as follows:

•

Sense-making: This phase attempts to study the ecological changes caused by human influences that create a dynamic and complex environment. Any change external to the organization affects the inflow of information, thereby, prompting necessary transformation in its working, like interpreting the reported changes in the trends and circumstances concerning customers, challengers, suppliers, and other players. The first stage of the model, i.e., sense-making stage, involves the

Model Senses making

Process Environmental change Enactment, selection, retention Enacted interpretations “Looking backward”: Retrospective sensemaking

Modes

Interactions

Belief-driven processes Beliefs

Action-driven processes Interpractices

Enactments

Sense-making Knowledge-gap situation: Tacit, explicit, cultural knowledgeKnowledge conversion, building, linking New knowledge “ Looking across many levels”: Multi-Level Learning from individuals, groups,organisations

Knowledge conversion Knowledge building

Cultural knowledge

Knowledge linking Explicit knowledge

Tacit knowledge

Knowledge Creating

Decision making

Choice situation Alternatives, outcomes, preferences Rules, routines Decisions “Looking ahead”: Goal-directed, future-oriented

Rational Process Political Anarchic

Preferences

Rules

Routines

Decision Making

Sense-making, Knowledge Creating, and Decision-making. Source: Choo (2001). Figure reproduced with permission from Chun Wei Choo ([email protected])

53

Fig. 3.6.

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Knowledge Creating

54

•

•

•

Knowledge Management Systems study of alterations in the external environment responsible for bringing out necessary changes in the working of the organization. The inflow of information during the cyclic nature of ecological changes has to be carefully studied by applying a certain rationale, facilitating the selection process, followed by the retention process for the benefit of the organization. This is known as “retrospective sense-making” that follows “environmental change.” It, therefore, follows that whereas the “environmental change” which is a “belief-driven process,” includes enactment, selection, retention, and enacted interpretation; the “retrospective sense-making” which is “action-driven sensemaking” involves “looking backwards.” It thus creates a dynamic environment wherein any organization resonates with the alterations in the external environment by adopting necessary changes in its working. Knowledge Creation: The knowledge creation stage of the model involves the conversion of tacit knowledge of the individuals through dialogues, discussion, sharing, storytelling, etc. allowing the organization to acquire, organize, and process information to create new knowledge through the process of organizational learning. This knowledge facilitates organizations to modify their old products and services or create new ones by building new abilities and capabilities. This stage thus involves knowledge conversion, knowledge building, and knowledge linking. Decision-making: The new knowledge, created in the preceding step, helps in prudent decisionmaking processes based on rationality in the formulation of new strategies of the organization, by choosing the best and the most reasonably available options. The new spectrum of choices offered by the new knowledge and competencies are evaluated and then the selection is made on the basis of rational decision-making models, thus enhancing the company’s rational decision-making processes to develop innovative strategies. According to Choo, “Decision making is precipitated by a choice situation, an occasion in which the organization is expected to select a course of action. Depending on the degree of uncertainty about the goals to be pursued, and the degree of uncertainty about the methods and procedures available to attain these goals, an organization adopts one of four modes of decision making, namely Boundedly rational mode, Process mode, Political mode, and Anarchic mode.” The Organizational Knowing Cycle: In the knowing cycle, an uninterrupted information flow is maintained between the three phases, namely, sense-making, knowledge creating, and decisionmaking. Consequently the outcome of information use in one mode provides the elaborated context and the expanded resources for information use in the other modes, as shown in Fig. 3.7.

Organizational Knowing Cycle. Source: Choo (2001). Figure reproduced with permission from Chun Wei Choo ([email protected])

Knowledge Management: Processes and Models

Fig. 3.7.

55

56

Knowledge Management Systems The workflow of Choo Sense-making KM Model is shown in Fig. 3.8.

Streams of experience Sense-making Shared meanings Shared meanings

Knowledge New knowledge, new capabilities External information and knowledge

Decision making Goal-directed adaptive behaviour Next knowing cycle

Fig. 3.8. Workflow of Choo’s KM Model. Source: Dalkir (2017). Knowledge Management by Kimiz Dalkir ED.3.pdf Figure reproduced with permission from MIT Press ([email protected]). 5.5 Husain and Ermine AI-KM Model It may be reiterated at the outset, that KM is not like all or nothing concept; even a small step takes it closer to the stated goal. The KMS of the organizations use their documented information and intellectual capital to carry out every day jobs, future assignments, and responsibilities. The models of KM put forward from time to time, though established convincingly in academic and professional fields, have formed a theoretical basis for the current techniques and practices being followed for knowledge management. To name a few, the Boisot I-Space model proposed a “SLC and its use in I-Space”; the EFQM KM Model served as a bridge between organizations’ KM processes and their expected results in achieving sustainable excellence; Nonaka–Takeuchi SECI Model elucidated as to how tacit and explicit knowledge is converted into the knowledge assets of the organization; the von Krogh and Roos model took an approach of organizational epistemology; the Choo Sense-making Model works on the premise that the organizations use information strategically in sense-making, knowledge creation, and decision-making. Similarly, MASK technique developed by Jean Louis Ermine (2016), has been implemented in many public and private enterprises of the USA (https://mntransportationresearch.org/2020/07/16/knowledge-bookspreserve-expertise-of-retiring-workers/). The present “Husain–Ermine AI-KM Model” (2021) is a holistic representation (Fig. 3.9) of a KMS that encompasses currently available techniques and

Fig. 3.9.

Husain–Ermine AI-KM Model.

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technologies for, what may be called a “Socio-Technical Knowledge Processing System.” This system comprises KM Actors (facilitators and enablers); KM Processes (Knowledge Capitalization, Sharing, Organizational learning, Creation, and Assessment); and Technical systems (AI Tools and Techniques). Whereas the strategic planning is conducted by the facilitators, the enablers carry out the various KM operations, necessary for enriching the Knowledge Capital of an organization. For each operation, certain tools and techniques are employed, some of which are the results of the application of AI in KMS. The aforesaid KM processes, tools, and techniques have been dealt with throughout the book. Technical systems include tools, meant for information processing, AI, communication, social network management, analysis, and evaluation. The AI-KM Model presents a picture of macroprocesses that enable the transformation of the organization’s knowledge capital, into a continual process of value creation by establishing a Knowledge Value Chain (KVC). (Ermine, 2013). The latter is a chain of fundamental intellectual tasks (cognitive activities) that transform Data in Information (structuration), Information in Knowledge (learning), Knowledge in Competence (experience), and Competence in corporate Capacity (strategic vision).The KVC may be interpreted as a continuum of knowledge processes adding new value at each step. As per the AI-KM Model, the first step involves the process of establishment of a KM framework by top management, by setting out the objectives, strategy, followed by the implementation of the KM plan. The step requires a complete analysis of the existing corporate Knowledge Capital and is, therefore, supervised by one of the members of the top management, appointed as Chief Knowledge Officer (CKO) for the purpose. The second step of strategic planning is entrusted to a number of actors capable of carrying out the implementation of various assigned tasks effectively to obtain the desired results. Some of them are key players such as the Knowledge Managers, responsible for the success of the project, while the other Knowledge Workers hold the knowledge that is the main resource of the KMS. Yet another group of actors such as Middle Managers, Information Systems and AI Systems Managers, Support staff, HR, and Documentalists necessarily participate in the success of the project. The remaining steps as given in the AI-KM model, which are progressive and nonexhaustive in nature, are mainly concerned with the implementation of KM processes, supported by operational methodologies and tools. Many of these tools are inherited from classic AI techniques (e.g., Organizational Learning and Training, Classification, Natural Language Processing, Problem-solving, etc.). The devices thus developed to perform definite KM oriented tasks are the real Knowledge Processing Devices. These devices can converge into a novel full-fledged KMS, to be called as Husain–Ermine AI-KM Model in a coherent and global way.

6. Conclusion In the present age it can be perceived that those dealing with knowledge for research and development, and also those in corporate sectors are on their toes to

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contribute to the whole bank of knowledge in terms of research articles, books, monographs, etc., thus giving birth to new knowledge. On the other side, the most important process, i.e., management of the tacit knowledge, is not given due attention, if not totally ruled out in some organizations at least. It is, therefore, important for all organizations to initiate a KM process to get the most benefit out of it by adopting one of the well-known models of KM.

References Choo, C. W. (1998). The knowing organization: How organizations use information to construct meaning, create knowledge, and make decisions. New York, NY: Oxford University Press. Choo, C. W. (2001). The knowing organization as learning organization. Education 1 Training, 43(4/5), 197–205. Cristea, D. S., & Capatina, A. (2009). Perspectives on knowledge management models. The Annals of “Dunarea de Jos”, 355–366. Retrieved from http://mpra.ub. uni-muenchen.de/25358/1/MPRA_paper_25358.pdf#page5356 Dalkir, K. (2005). Knowledge management in theory and practice (pp. 4). Burlington, MA; Oxford: Butterworth Heinemann. doi:10.1002/asi.21613 Dalkir, K. (2017). Knowledge management in theory and practice (3rd ed.). Cambridge, MA: MIT Press. Davenport, T. H. (1993). Process innovation: Reengineering work through information technology. Boston, MA: Harvard Business School Press. D, A. N. R. C., Souza, R. R., Queiroz, J. G., & Chipp, H. (2009). Knowledge management Implementation : A process design proposition at Brazil’s ONS (national operator of the interconnected power system). Electronic Journal of Knowledge Management, 7(5), 593–604. Retrieved from https://issuu.com/ academic-conferences.org/docs/ejkm-volume7-issue5-article209/2 Ermine, J.-L. (2013). A knowledge value chain for knowledge management. Journal of Knowledge & Communication Management, 3(2), 85–101. Frost, A. (2010). Knowledge management tools. Retrieved from http://www.knowledge-management-tools.net/knowledge-management-definition.html. Accessed on January 25, 2021. Jain, P. (2007). An empirical study of knowledge management in academic libraries in East and Southern Africa. Library Review, 56(5), 377–392. doi:10.1108/0024253 0710750572 Knowledge Management. (2013). In Collins English dictionaries. Retrieved from https://www.collinsdictionary.com/submission/7723/knowledge1management. Accessed on January 25, 2021. Knowledge Management. (2016). In Cambridge dictionary. Retrieved from http:// dictionary.cambridge.org/dictionary/english/knowledge-management. Accessed on January 25, 2021. Knowledge Management. (2017). In English oxford living dictionaries. Retrieved from https://en.oxforddictionaries.com/definition/knowledge_management. Accessed on January 25, 2021.

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Kumar, S., & Gupta, S. (2012). Role of knowledge management systems (KMS) in multinational organization : An overview. International Journal of Advanced Research in Computer Science and Software Engineering, 2(10), 8–16. Mclean, L. D. (2004). A review and critique of Nonaka and Takeuchi’s theory of organizational knowledge creation. University of Minnesota, USA. Retrieved from https://pdfs.semanticscholar.org/a947/e9975d5daf665f6cdc115e49121431887527.pdf. Munir, & Rohendi, D. (2012). Development model for knowledge management system KMS to improve university’s performance case studies in Indonesia university of education. Journal of Computer Science, 9(1), 1–6. Retrieved from https:// ijcsi.org/papers/IJCSI-9-1-1-1-6.pdf. Accessed on January 21, 2021. Nenad´al, J. (2020). The new EFQM Model: What is really new and could be considered as a suitable tool with respect to quality 4.0 concept?. Quality Innovation Prosperity, 24(1), 17–28. Retrieved from 1415–5777-1-PB.pdf. Accessed on January 12, 2021. Nonaka, I. (1990). Management of knowledge creation. Tokyo: Nihon Keizai Shinbunsha. Nonaka, I., & Konno, N. (1998). The concept of Ba: Building a foundation for knowledge creation. California Management Review, 40(3), 40–54. doi:10.2307/ 41165942 Nonaka, I., & Takeuchi, H. (1995). The knowledge creating company: How Japanese companies create the dynamics of innovation. (pp. 71–72, 89, 284). New York, NY: Oxford University Press. Santos, T. (2012). Metaversia – A MOOC model for higher education. Retrieved from http://bit.ly/metaversiathesis. Sousa, C. A. A., & Hendriks, P. H. J. (2006). The diving bell and the butterfly: The need for grounded theory in developing a knowledge-based view of organizations. Organizational Research Methods, 9(3), 315–338. doi:10.1177/1094428106287399 White, T. (2004). Knowledge management in an academic library: Based on the case study KM within OILS. 70th IFLA General Conference and Council, Buenos Aires, August 22–27. Retrieved from http://eprints.ouls.ox.ac.uk/archive/00000815/01/ Tatiana_White_KM_ article.pdf Wiig, K. M. (1997). Knowledge management: Where did it come from and where it will go? Expert Systems with Applications, 13(1), 1–14. Xu, F. (2013). The Formation and development of Ikujiro Nonaka’s knowledge creation theory. In G. von Krogh et al., (Eds.), Towards organizational knowledge: The pioneering work of Ikujiro Nonaka (pp. 60–76). Basingstoke: Palgrave Macmillann.

Chapter 4

The Virtuous KM Cycle, a Global Approach to Managing Knowledge 1. Introduction 1.1 From Knowledge Society to Knowledge Management (Munshi & Ermine, 2011) As developed in chapter 1, the recent concept of Knowledge Society (KS) gives a new vision of our societies where knowledge becomes the fundamental resource for socioeconomical development. The revolution of Information Society, started in the 1990s, has failed in the sense that it has provided only technical solutions worldwide and not an adequate environment for the different societies to create intellectual wealth for sustainable and harmonious development. Hence, came the KS era. All the questions on the digital or the cognitive divide are closely related to those problems. A KS must have a new type of economy called Knowledge Economy (KE). The knowledge that is henceforth regarded as a new source of wealth and a fundamental immaterial asset within firms and organizations is one of the main strategic resources. The processes of accumulation, exploitation, and dissemination of knowledge within organizations create added value and hence the progress. KE implies a managerial perspective in the organizations, whereas Knowledge Management (KM) has the objectives of formalizing and transferring specific knowledge and know-how in the organization in order to capitalize and operate this knowledge for the enhancement of organizational performance. This, however, entails important challenges. For instance:

• • • • • •

What are the best corporate structures for KM? How to manage communities of practice? How to stimulate innovation through KM? What are the best strategies for KM? How to preserve the Knowledge Capital of the organization? Etc.

1.2 The Knowledge Value Chain (Ermine, 2013a) Knowledge is impossible to define as a strict and operational concept. For KS, what is important is “knowledge in action,” that is, to say how knowledge brings Knowledge Management Systems, 61–90 Copyright © 2021 Shabahat Husain and Jean-Louis Ermine Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-80117-348-320210004

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added value to a company, a social group, or a society. Bringing added value with knowledge is a complex process that mobilizes basic building blocks organized in what can be called a Knowledge Value Chain (KVC) that introduces the notion of knowledge through the concept of a value chain. As compared to the tremendous development of KM during the past 20 years or so, the concept of KVC is of recent origin (Carlucci, Marr, & Schiuma, 2004). A new kind of KVC that chains fundamental intellectual tasks (cognitive activities) for knowledge workers has been proposed here. In the process, the value chain by adding the value of each activity in an overall knowledge creation has been justified. This KVC clarifies the relationships between the concepts of data, information, knowledge, and competence. It is built on the celebrated DIKW model (see chapter 1), where wisdom is not seen as a “philosophical” concept, but an operational organizational concept. We interpret that model as a succession of cognitive activities to transform data up to the most adding value for the organization. Fig. 4.1 shows the different layers of the building of knowledge from data up to capacity and the different steps of transformations of a layer into the following layer.

• •

Information Information is a structured set of data. The terms used in that structure are understood by professionals of the domain. Knowledge Knowledge is a structured set of pieces of information linked by a cognitive model that puts them into context and that is justified. Usually, when the cognitive model is tacit, then knowledge is essentially personal. Sometimes, the model can be elicited and shared.

Added Value Creativity

Experience

Learning

Comprehension

Collective Capacity

Individual Competence Knowledge Information

Data

Memorization

Fig. 4.1.

Knowledge Pyramid.

The Virtuous KM Cycle, a Global Approach to Managing Knowledge

• •

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Competence Competence is the accumulation of experience by putting in practice, individually and efficiently, and knowledge is an operational activity in order to achieve the required objectives. Capacity Capacity is the integration of a set of individual competences in order to achieve the strategic goals of the organization.

The Knowledge Pyramid is the support of a KVC, considering that each level of the pyramid is the output of a transformation of the lower level. The KVC is then a chain of successive transformations of data into information, information into knowledge, knowledge into competence, and competence into capacity. The added value is calculated by the value added in each transformation, according to the nature of each transformation. To define more precisely the transformations in the Knowledge Pyramid or the KVC, we briefly define the transformation chain: the starting point of the transformation chain is reality, as a set of things possessing actuality, existence, or essence, which exists independently of human awareness:

• • • • •

Transforming reality into data is acquiring signs (signals) through perceptive filters via observation; Transforming data into information is coding data trough conceptual filters via a structuring activity; Transforming information into knowledge is building models through theories via learning; Transforming knowledge into competences is implementing a set of practices through action via experience; Transforming competences into capacities is building a strategy (knowledge strategy) through strategic filters (strategic alignment) via a vision. We can summarize the KVC and its management as in Fig. 4.2.

Capacity Management Competence Management

Cognitive Value Chain

Experience

Knowledge Management

Learning

Information Management

Comprehension

Data Management

Memorisation Explicit

Data

Creativity

Tacit

Information Knowledge

Individual Competence

Collective Capacity

Wisdom

Knowledge Value Chain

Fig. 4.2.

Knowledge Value Chain.

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1.3 The Knowledge Processes (Carlucci et al., 2004) Implementing practical solutions for building knowledge societies requires operational processes. A Knowledge Process Wheel is a taxonomy of KM processes that takes into account the dynamic nature of knowledge and provides guidelines to manage the Knowledge Capital of an organization. As an example, six different types of processes that can be described within a Knowledge Process Wheel are given as under: (1) Knowledge Creation: It is the most important of all processes, as it accumulates the Knowledge Capital which is the essence of any knowledge-based organization. (2) Knowledge Identification: It is basically concerned with analysis and structuring and appertains to such questions as:

(3)

(4)

(5)

(6)

• •

What is the relevant knowledge to creating a KS? What are the basic knowledge needs? Knowledge Codification: Capturing tacit knowledge is called Knowledge Codification, which is quite complex in nature, for such knowledge lies in the brain of the knowledge holders without their being aware of it. Knowledge Sharing: Once a knowledge corpus is identified and a knowledge repository is built, sharing that knowledge within a community requires a lot of efforts, ranging from building the community to the facilitation of the access through infrastructures. Knowledge Dissemination: Dissemination of knowledge for the majority of concerned persons, with the basic philosophy, the right information to the right user at the right time, has been coined as the last mile stone, which implies information technology (IT) infrastructures and design processes. Knowledge Evaluation: As a part of the process, knowledge evaluation is important and necessary to assess the shared knowledge by using different grids of evaluation.

The above description of Knowledge Process has been summarized in Fig. 4.3.

1.4 The Virtuous KM Cycle (Ermine, 2018) The Virtuous KM Cycle is a model for implementing a knowledge-based approach for KM that relies upon a four-step process consisting of: assessment of Knowledge Capital, organization of Knowledge Resources, implementation of Knowledge Processes, and enrichment of Knowledge Capital. The first step involves an in-depth analysis of the organization’s Knowledge Capital by addressing the opportunities and threats attached to the different knowledge domains. The second step deals with the means such as the organization of resources, methods followed, and tools employed in order to develop the opportunities or reduce the threats for the concerned domains. The third step underlines the design and implementation of the means for organization of

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Knowledge Creation

Knowledge Identification

Knowledge Evaluation

Knowledge Capital

Knowledge Dissemination

Knowledge Codification

Knowledge Sharing

Fig. 4.3.

Knowledge Process Wheel.

knowledge, involving methods and the tools that will consistently address the opportunities and tackle the threats of each domain. The fourth step is the ultimate objective of the Knowledge Process that minimizes the risks, to preserve and enrich its Knowledge Capital. The Virtuous KM Cycle establishes a comprehensive set of processes, as illustrated by the help of the Knowledge Process Wheel that will create value from the Knowledge Capital of the organization, according to the KVC. Fig. 4.4 shows Virtuous KM Cycle including four steps of Knowledge Processes.

1.4.1 Step 1: Strategic Assessment of the Knowledge Capital and KM Plan Elaboration The Knowledge Capital of an organization constitutes its most strategic assets; especially the tacit knowledge, which by its very nature is vulnerable and can be easily lost with the person holding it. Preservation and transfer of such an asset must, therefore, be planned meticulously so as to design and integrate the same as a strategic process of the organization. The strategic process involved should be able to address questions such as:

• • • • •

Are knowledge domains really in danger of being lost? Are they really strategic? Who keeps this knowledge in custody? What possible and pertinent actions are to be taken for its preservation? How to integrate the action plan with the strategic objectives of the organization? etc.

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Strategic assessment of the Knowledge Capital and KM plan elaboration

4

2

Evolution of the Knowledge Capital

Organization of the Knowledge Resources

3

Implementation of the Knowledge Processes

Fig. 4.4.

Virtuous Knowledge Management Cycle.

The aforesaid questions necessitate an audit of the knowledge resources, guided by the strategy defining the missions of the organization. Proposing an action plan for managing the Knowledge Capital comes next for the preservation, sharing, transfer, and evolution of knowledge that is aligned with this strategy. This first step requires strategic analysis of Knowledge Capital, whose objective is to identify the critical knowledge domains in the organization and the adequate actions to reduce their criticality. The KM plan elaborated in this way identifies the KM processes required for each knowledge domain. 1.4.2 Step 2: Organization of the Knowledge Resources Consequent to the identification of knowledge domains in the first step, a wide range of knowledge resources can be discovered; hence the need to put them in order and establish how they are organized. The first types of resources to be codified include databases, information and document resources, software resources, Web resources, etc. The second types of resources that are not codified include tacit knowledge of experts and specialists, knowledge communities (e.g. communities of practice), networks, etc. As it happens, that huge knowledge corpus is usually scattered in various sites, links between knowledge chunks are often missing while tacit knowledge is not sufficiently elicited. Consequently, there is no comprehensive view of the knowledge corpus (tacit or explicit) associated with each knowledge domain, and thus, the knowledge is far from being easily accessible. Moreover, it is an uphill task to

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map the resources, design a coherent repository to facilitate their organization, allow their maintenance, and ensure their availability. This implies often adding new knowledge resources and artifacts to the repository. 1.4.3 Step 3: Implementation of the Knowledge Management Processes The next step is to organize the utilization of the knowledge resources in the daily routine of the knowledge workers to enable them to share, transfer, and acquire knowledge in order to efficiently discharge their operational and/or decisionmaking tasks. As business processes are implemented to support their operational activities, KM Processes, as per Knowledge Process Wheel, have to be implemented to support knowledge utilization in the business processes, as required in the KM plan. 1.4.4 Step 4: Evolution of the Knowledge Capital The final goal of KM is to create an ingenious organization by cultivating resourcefulness and creativity. Therefore, the KM process shall result into the enhanced capability of the organization to enable its knowledge resources to evolve in a strategic way for the creation of new knowledge. For the purpose, the KM process must use all the resources created in the previous steps to foster corporate knowledge evolution. Finally, as the business and the processes are continuously evolving, KM processes should stay relevant and ensure the creation of the right type of resources. A certain mechanism based on surveys, follow-ups, and evaluations, etc., may have to be practiced to measure as to how useful is the knowledge for the benefit of the organization. For the purposes of quality management, the Virtuous KM Cycle supports the continuous progress of the organization by the implementation of the KM processes. After each cycle, the Knowledge Capital is organized, preserved, disseminated, and updated. It is then ready for a new cycle to perpetuate. The continuity of the progress is represented by the classical PDCA (Plan–Do–Check–Act) or Deming Wheel (Fig. 4.5).

1.5 The KM Virtuous Cycle with the MASK Method MASK (method for analyzing and structuring knowledge) is a global methodology, starting from the highest level in the organization (the strategy) to build step by step some operational solutions, in a coherent KM road map for the organization. This methodology is complete, from strategy to information system, and therefore, its implementation requires an important effort of the concerned organization. It can also be partially implemented, according to the problem addressed. MASK methodology has been experimented and refined continuously worldwide during the last 20 years. Today, the MASK approach, built on the basis of constant crossfertilization between theory and practice, is now strong enough to be deployed on a very wide range of knowledge problems facing industries. The development of MASK started as a research project in early 1990, in France, at the Universit´e de Bordeaux. It was implemented (and still is), on a wide

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Continuous Improvement

Plan

KM Virtuous Cycle

Do

Act

Check Knowledge Management

Fig. 4.5.

The Virtuous Knowledge Management Cycle as a Continuous Action of Progress.

´ range of applications, from 1993 to 2000 at the Commissariat a` l’Energie Atomique (French Atomic Energy Commission), sometimes on very important projects both in size and strategic objectives, such as uranium enrichment, nuclear weapons, and nuclear safety. Later versions of MASK were developed with research institutes, such as Universit´e de Technologie de Troyes, Institut Mines-T´el´ecom (Business School) within Universit´e Paris-Saclay. Cross-fertilization of theories and practices followed within two nonprofit organizations has proved gainful for scientific and operational validation. One of these organizations is the Club Gestion des Connaissances (French Knowledge Management Association), bringing together many francophone companies, of all sizes and domains, involved in KM, founded in 1999. The second organization is an academic society called AGeCSO (Association pour la Gestion des Connaissances dans la Soci´et´e et les Organisations, Association for Knowledge Management in the Society and the Organizations), rallying a lot of research laboratories in the French-speaking world, which was founded in 2008. Quite aware of the requirements that this new subject would require continuous experimentation in the field, the development of MASK has been supported from the beginning by many research and industrial projects, concerning KM in private and public organizations in France, e.g. industry, energy, transportation, defense, banks, small and medium enterprises (SMEs), and abroad, e.g. Algeria, Canada, United States, Brazil, Asia, and United Nations. MASK I is the first module, developed in the initial stages. It is dedicated to the codification of tacit knowledge, by using techniques of knowledge modeling (Knowledge Books). The second module that was then added, called MASK II, is

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dedicated to strategic analysis of the Knowledge Capital of an organization and the elaboration of a KM plan. The third module, MASK III, is dedicated to Knowledge Transfer, whereas the fourth module, MASK IV, is dedicated to Innovation. Those four modules constitute a pragmatic (although incomplete) implementation of the Virtuous KM Cycle. They provide validated frameworks, processes, and tools to realize a KM System, according to the recent KM standards.

2. Strategic Assessment of the Knowledge Capital and KM Plan Elaboration The strategic assessment of the Knowledge Capital involves an audit of organizational knowledge in order to construct a KM action plan for the management of this capital. The audit must be conducted carefully in order to ensure the success and sustainability of the organization’s KM project. The MASK II approach is good enough to perform that task, as it can be simplified and adapted as per the organization’s needs. For instance, MASK approach is generally concentrated on the assessment of critical knowledge domains. The audit of the Knowledge Capital takes place in the following three main steps.

2.1 Step 1: Assessment of Critical Capacities A collective capacity integrates a set of individual competencies to accomplish the strategic objectives of the organization. The analysis of critical capacities consists of identifying and qualifying the capacities required by the organization in carrying out its missions and ultimately reaches its strategic operational objectives. In the MASK II approach, the objectives of the organization are modeled by actors of the strategy, including people who are deeply involved in elaborating the organization’s strategy, such as unit directors or members of the management committee. The model used to represent the objectives is a strategy map that is a tree of strategic topics, objectives, and subobjectives, with a limited number of topics, generally varying from 4 to 6. Each topic is broken down into several objectives that may be further broken down into several subobjectives. As an illustration, Fig. 4.6 illustrates a strategy map built for a KM project in IPEN (Energy and Nuclear Research Institute), which is the largest research institute of the National Nuclear Energy Commission (CNEN) of Brazil. The Radiopharmacy Center created within IPEN was able to transform the research center into an industrial production center completed by some profit center elements. Its mission is to produce and distribute radiopharmaceutical products for nuclear medicine (diagnosis and therapy) in Brazil (see the case study in Ricciardi, Baroso, & Ermine, 2006). In the next phase, a strategy map is presented to managers for identifying and qualifying the capacities required for the organization to reach the objectives presented in the map. The managers consider the topics one by one to indicate

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Fig. 4.6.

An Example of Strategy Map.

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Fig. 4.7.

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An Example of Assesment of Critical Capacities (Extract).

their capacities to mobilize for reaching out to the objectives under consideration. Each of the capacities, so identified, is evaluated qualitatively by its level of criticality, i.e. very critical, somewhat critical, or not very critical. A capacity is assessed as “more critical” or “less critical” depending on its degree of rarity or strategic nature, difficulty in its acquisition, and implementation. A synthesis is then carried out to eliminate redundancies, standardize formulations, classify capacities, and summarize arguments. Such capacities are now represented on the strategy map, where each capacity is assigned a criticality coefficient, which is elaborated by using the criticality evaluations completed during the previous stage. Symbolically, a capacity is represented in red, orange, or green based on its degree of criticality. An example taken from an imaginary Air Cargo Company is provided in Fig. 4.7.

2.2 Step 2: Assessment of Critical Knowledge Domains An assessment of critical knowledge domains consists of identifying and qualifying the organization’s various knowledge domains. The latter is defined as a corpus consisting of homogeneous groups of the people carrying out one or more activities in the organization, dealing with a set of documented and/or tacit knowledge of workers, constituting a “knowledge network” in the organization. The first phase of the assessment implies the construction of the knowledge domains map (or knowledge map). The knowledge map is a representation, given by the knowledge actors as to how the knowledge domains are structured and develop skills that are useful and necessary to operate the different business processes. This map is broken down into knowledge axes (or themes), domains, and subdomains. The objective of the map is to represent different knowledge

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Fig. 4.8.

An Example of Knowledge Map.

domains (sometimes called the “knowledge portfolio”) in the organization in a clear, shared, and easily understandable way. Fig. 4.8 illustrates a knowledge map built for a KM project in Hydro-Qu´ebec, Canada, one of the biggest electricity producers and suppliers of North America. With 20,000 employees on its roll, Hydro-Qu´ebec faces problems due to massive retirements and departures of the experienced staff for good. The case study under reference was carried out in a unit of the electricity company as a part of a larger study, “Succession Management Plan” (expertise management plan), conducted by the Human Resources Department (see Ermine et al., 2006). The next phase of the assessment process involves criticality analysis of each knowledge domain of the map, performed by chosen referents or knowledge actors in their area. In view of the vastness of knowledge map, it is necessary to choose a level of granularity in the map that does not require too many referents. The criticality analysis is systematically carried out with a criticality grid (often called Critical Knowledge Factors or CKFs). For example, a CKF grid can use the following questions for evaluation on a scale from 1 to 4: (1) Rareness of knowledge:

• • • • •

How many people hold the knowledge of a given domain and how much are they available? Are there outsourcing solutions for such domains? If yes, whether these domains can be outsourced for strategic and organizational reasons? While outsourcing, can the organization concerned be declared as a leader in that particular knowledge domain? How vast the knowledge domain is? What is the degree of confidentiality of the domain?

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(2) Usefulness for the organization:

•

Is the identified domain useful from the viewpoint of the organization’s strategic operations? • What is the value of the knowledge domain for the interested parties? • Is the identified domain expected to be emerging or evolving in the future? • Can the knowledge domain be reused in another context after some modifications? • What is the usage level of the knowledge domain? (3) Difficulty in acquiring knowledge:

•

What is the general level of knowledge necessary enough to perform the activity? • How difficult is it to build or rebuild networks of contacts (internal and/or external), necessary for the knowledge domain? • What is the level of elicitation/codification of the knowledge? • What volume of codified information (documents, databases, etc) is available in the domain? • What is the life span of knowledge? How long will it take for that knowledge to become useless or unutilized in the organization? (4) Difficulty in exploiting knowledge:

• • • • •

What is the level of depth of the knowledge domain? What is the degree of complexity of the knowledge domain? What is the level of difficulty to acquire knowledge in the domain for the organization? In case knowledge has accumulated over a long time, will it require mastering of its historical evolution? What is the degree of dependence between the knowledge domain and its environment?

The criticality assessment of each identified domain consists of assigning a score, as per each criterion used, followed by calculating an average criticality score for each domain. The more critical the domain, the higher its score. The bottom line is an independent assessment of each domain. An example taken from an imaginary Air Cargo Company is provided in Fig. 4.9 wherein knowledge areas colored in red, orange, or green are shown depending on their level of criticality. As the critical knowledge map itself is not good enough for decision support, it must be documented in a simplified way for each knowledge domain, so that it gives a qualitative assessment of the criticality of the domain, by summarizing the arguments and also the possible action proposals, made during the assessment. This step simplifies the description of the criteria and allows a quick analysis of the critical nature of the domain. Such types of synthesized documents may be produced for the whole set of knowledge domains under study.

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Fig. 4.9.

An Example of Critical Knowledge Assessment.

2.3 Step 3: Strategic Alignment and Action Planning This step aims to compare both strategic and operational visions, so as to make relevant recommendations on KM actions or devices to be implemented. Such recommendations are based on cross-analysis of the strategic capacities (characterized by the strategic map in terms of the capacities and their criticality) and the critical knowledge analysis (characterized by the map of the knowledge domains and their criticality). A cross-vision between strategy and business is called the strategic alignment, which allows the identification of “strategic dissonances” or disharmony as follows:

• •

Cognitive biases in the representation of the strategy that the strategy and knowledge workers have; Cognitive biases in the representation of the strategy that the actors have, in regard to the impacts of the objectives on professional knowledge in the operational processes.

This step involves the identification of the potential influence of the strategic vision on the operational vision and vice versa. It uses a double-entry table, an influence matrix, in which the influences between knowledge domains and capacities are indicated. Each knowledge domain and each capacity having a criticality score are assigned a simple weighted average. This type of rating is indicative of the strategic importance and criticality of the elements.

The Virtuous KM Cycle, a Global Approach to Managing Knowledge

Logistics

Customers services

Exploitation

Management Transportation Capacity of designing of the loading plan design and an adapted areas management information of delocalized system airport

Capacity to integrate local costs in the information system

X

X

X

X

X

X

X

X

X

X

X

X

X

X

Production

X

Commercial offer

X

X

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Capacity to recruit quickly and efficiently all over the world

X

X

Impact of objectives on knowledge

Impact of knowledge on objectives

Fig. 4.10.

An Example of Strategic Alignment.

An illustration of strategic alignment on the basis of an imaginary example of Air Cargo Company is given in Fig. 4.10. The capacity “transport plans to design and localized management of airports” had been initially assessed as “not very critical,” but the influence matrix shows that this capacity involves most of the knowledge domains (except “production”) in the organization. The alignment, therefore, reclassifies it to be called as “very critical.” Likewise, the knowledge domain “commercial offer” was initially assessed as not very critical. However, the influence matrix shows that all the organization’s objectives require that knowledge domain. The alignment, therefore, qualifies to be called as “very critical.” As a sequel, the large amount of information collected during the assessment can be summarized on the basis of strategic alignment, so as to make recommendations for a KM action plan. This assumption is based on the fact that the arguments, collected throughout the assessment at the knowledge or strategic level, are content-wise quite rich and may comprise many suggestions. Such recommendations concerning the future KM action plan are defined for each knowledge domain and strategic capacity and documented:

•

•

For the knowledge domains, on the basis of synthetic documents produced during the critical knowledge analysis and by highlights identified (elements discussed during the assessment that characterize the criticality of the domain as need for knowledge sharing, tool, unsuitable training device, absence of knowledge capitalization program, strong technicality of the domain, etc.); For the strategic capacities, on the basis of arguments collected during the assessment with the actors of the strategy.

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The recommended KM actions are prioritized according to criticality of the involved knowledge domain or strategic capacity. Recommendations may be of many types and forms, as given below for the sake of instance: (1) Acquisition of knowledge:

• • • • • • • •

HR process: Recruitment Professional tracks Expertise tracks Learning process Training Mentoring Corporate university (2) Knowledge search

• •

Search for knowledge and information Technical and scientific watch (3) Knowledge Creation

• • • •

Creative processes Research and development process Innovation process Prospective (4) Knowledge sharing

• • • • •

Collaborative work Working groups Communities of practice Knowledge communities Sharing tools (5) Codification of knowledge

• • •

Codification of tacit knowledge Lessons learned Writing knowledge documents (6) Structuring the knowledge repository

• • • •

Structure of databases and documents Design of knowledge servers Implementation of search engines Definition of the knowledge repository

2.4 Summary Strategic assessment of the Knowledge Capital and KM plan elaboration, forming the first phase of MASK in the Virtuous KM Cycle, can be summarized by the diagram given as Fig. 4.11:

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Strategy actors

Critical Capacity Assessment

Critical capacities and knowledge

Strategic alignment

Decision and KM action plan

Critical Knowledge Assessment

Knowledge actors

• Knowledge to acquire (recruiting, training, learning …) • Knowledge to observe (Competitive intelligence, environmental scanning …) • Knowledge to create (innovation, R&D …) • Knowledge to share (Collaborative work, Communities …) • Knowledge to transfer (Capitalisation and transfer)

Fig. 4.11. Strategic Assessment Process with Method for Analyzing and Structuring Knowledge (MASK II) in the First Phase of the Virtuous Knowledge Management Cycle.

3. Creating New Knowledge Resources from Tacit Knowledge: Knowledge Books The audit conducted in the previous phase (MASK II) demonstrates that more often than not various knowledge domains are composed of tacit knowledge, embedded in the heads of a group of critical knowledge workers, some of whom may leave the organization for good. Such knowledge is under threat of being lost and, therefore, must be documented or transferred to other people. The MASK I approach presented here is to collect this knowledge in an explicit form in order to obtain a “knowledge corpus” that is structured and tangible enough to constitute an essential resource of any knowledge transfer device. Such an operation responsible for the transformation of Knowledge Capital by adding new knowledge resources of great value is called capitalization (or codification). This process of converting the tacit knowledge into explicit knowledge has been termed as externalization by Nonaka, who defines it as follows: it is a process that is the quintessence of knowledge creation because tacit knowledge becomes explicit as metaphors, analogies, concepts, assumptions or models (Nonaka & Takeuchi, 1995). This process takes part in the second phase of the virtuous KM Cycle by creating and structuring new knowledge resources.

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3.1 Knowledge Modeling Knowledge modeling is a technique which started in the 1970s and 1980s for artificial intelligence purposes and has now been considerably developed to constitute a new kind of engineering discipline, called “knowledge engineering.” MASK I adapt well-known knowledge models and also offer some others that are more original for knowledge elicitation. This is a Common KADS (knowledge acquisition and documentation structuring)-like approach (Schreiber et al., 1999). MASK I, which is based on a theory of knowledge adapted to engineering (Ermine, 2013b), supports analyses, representation, and structuring of a knowledge corpus with templates. Knowledge, perceived as information, provides a given meaning in a given context. There are, therefore, three fundamental points of view to model knowledge, namely information, sense, and context (symbolized by the equation K 5 ISC). Each one is split into three other points of view, namely structure, function, and evolution, thus yielding nine points of view, as detailed below: (1) For Information, The three points of views are:

• • •

The structural aspect, modeled by the data structures, The functional aspect by the data processing, and The evolution aspect by dating and versioning. (2) For Sense, The three points of views are:

• • •

The structural aspect is modeled by concept networks, The functional aspect by cognitive tasks, and The evolution aspect by lineages. (3) For Context, The three points of views are:

• • •

The structural aspect is modeled by phenomena, The functional aspect by activities, and The evolution aspect by historical context.

The following paragraphs give a brief account of a simplified description of some models, taking examples extracted from a case study conducted in the nuclear domain within the French Institut de Radioprotection et de Sˆuret´e nucl´eaire, IRSN, (in English: Institute of Radioprotection and Nuclear Safety) (see Couturier & Jorel, 2016). The examples have been translated, modified, and simplified for comprehension. IRSN is a public organization with industrial and commercial activities. It is the French national organization in nuclear and radiation risks, and it combines together expertise, particularly for French nuclear safety authorities, and research and development. IRSN employs around 1,750 persons comprising many specialists, engineers, researchers, physicians, agricultural engineers, veterinary, surgeons, and technicians, as well as experts in nuclear safety, radiation protection, and control of sensitive nuclear materials. IRSN aims

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at “enhancing nuclear safety.” The knowledge at IRSN concerns itself with three values of the institute, viz. Knowledge, Independence, and Proximity. The related KM issues ensure the development, sustainability, and efficiency of IRSN strategic knowledge, in order to maintain the quality and relevance of its expertise in anticipation to its scientific needs, and also to promote the knowledge transfer between generations. For more details of the method MASK I, see Ermine, 2018, and for more case studies of MASK I, see Saulais and Ermine, 2018.

3.1.1 The Phenomena Model The phenomena model describes the general phenomena constituting the fundamental knowledge of the domain of activity under consideration. These phenomena include the events, to be controlled, known, triggered, optimized, inhibited, or moderated in the concerned operational activity. Fig. 4.12 taken as an example from a project dealing with “storing nuclear fuels in cooling pools” consists of capitalizing on knowledge about potential incidents and accidents in fuel storage devices. In nuclear power plants, the leftover fuel after the production of electricity is replaced and temporarily stored near the stations concerned in what is called a “cooling pool,” so that its radioactivity is dissipated before its final storage in a specialized center. To ensure their safety, such devices require prior safety analyses in all conditions. The example describes a phenomena model of one of the risks attached to “spent fuel pools.” In an online system, the hyperlinks allow the attachment of a lot of information and documentation. Examples : - Drain-out at level 1 - Drain-out at level 2 - Drain-out at level 3

If there is a fuel handling in progress, even with a drain-out at level 1, a dewatering may occur with consequences at level 3 Influence parameters : • Residual power of the fuel • Communications between vaults • Leak profile (size, position) • Fuel handling in progress

Initiating event : - Alignment error of a circuit connected to the pools - Integrity loss of an element involved in pool watertightness - Maintenance error

Pool

Pool • Drain-out level 1 Level of the suction pipe of the cooling system • Drain-out level 2 Level of the civil engineering of the vaults adjacent to the pool • Drain-out level 3 Total drain-out

• Consequences depend on the level of drain-out (see link)

Water flow

• Level 1 : Water loss with biological protection • Level 2 : Water and biological protection without dewatering of the fuel assembly • Level 3 : Complete loss of water with dewatering of the fuel assembly

Example : Tricastin 4 19 October 1989

Fig. 4.12.

Example of Ph´enomena Model: Spent Fuel Pools Drain out by Integrity Loss.

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3.1.2 The Activity Model This model is built upon an analysis of the activity of the system that uses or produces the knowledge. The activity model is further broken down into major phases (subactivities) of activity under consideration. The major phases are linked by exchanges of data flow, material flow, energy flow, etc. This is a classical flow diagram (there are examples in that chapter at Figs. 4.10, 4.14, and 4.17).

3.1.3 The Concept Model The concept model represents the conceptual structuring of an expert, specializing in a particular area. This structure symbolizing semantic networks is given in the form of classification of concepts or objects of the domain. The IRSN example of a concept model (Fig. 4.13) describes the main physical phenomena that control the transfer (dispersion, deposition) of radionuclide into the atmosphere. Such a transfer depends upon the evolution of the radioactive gases and particles released by a nuclear installation, in time and space, in an accidental release situation. It may be noted that the Knowledge Book under reference is a pedagogical introduction to the subjects, covered in great detail in scientific publications, as well as in some reference books. It is, therefore, intended for a nonspecialist audience, nevertheless aware of the issues in the field.

Atmospheric stability classes

It is a classification of physical type (in a classification dedicated to modeling, there is for example a class "very stable")

Instable class

Sunny day without clouds (hot ground)

Atmospheric stability classes

Neutral class

Low speed wind

• These conditions are called "convective conditions". • The heat flow is strong and oriented upwards. • The ascending vertical component is important compared to the horizontal component • Significant spread of pollutants outside the surface layer

Cloudy day

High speed wind

• For this class, the vertical component is weakened, and the horizontal component is augmented. • The heat flow is low and upwardly oriented. • The horizontal propagation is dominant.

Depending on the turbulence and the thermal gradient, the atmosphere is described as unstable, neutral or stable. The characterization of atmospheric stability is very important for the empirical description of the effects of dispersion.

Stable class

Night without cloud (heat flow downwardly oriented)

Low wind

• . For this class, there is attenuation of thermal turbulence. • The heat flow is weak and oriented towards the ground, • Low horizontal flow • Little spread. There is an accumulation in the surface layer

Illustration of dispersion phenomena related to stability classes

Fig. 4.13.

Example of Concept Model: Radionuclides Transfer in the Atmosphere.

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3.1.4 The Task Model The task model is the representation of a problem-solving method implemented in specific circumstances. The graphical model is similar to those used in cognitive ergonomics to describe a user’s tasks. 3.1.5 The History Model The history model that describes the evolution of knowledge integrates the evolution of given knowledge in a given context, necessary for this development. It allows the understanding of overall guidelines that led to the development of knowledge to the currently perceived state. The example in Fig. 4.14 is again about “storing nuclear fuels in cooling pools.” It may be noted that the evolution of safety analysis of spent fuel pools has been influenced with the advancement of knowledge in this domain, events that have unfolded around the world, and even the advancements in the production of electricity in the power plants. The history model provides a diagram and a graphic syntax to model a history of the events, making it possible to codify expert discourse. 3.1.6 The Evolution Model The evolution model, linked to the previous one, describes the evolution of ideas, concepts, technical solutions, etc., in the form of a genealogical tree that reflects the reasons for such developments.

3.2 The Capitalization Process The final product of the capitalization process is called a “Knowledge Book,” a metaphorical term designating a set of structured knowledge elements, representing not only knowledge diagrams and the associated text but also publications in form of digital or print format. The development of a Knowledge Book follows the following steps: 3.2.1 Step 1: Scoping The purpose of the scoping phase has twin purposes, viz. to delimit the knowledge domain on which the Knowledge Book is built and to identify modeling phases that will be useful to the objective. It allows the feasibility of the project to be validated and to work out a plan. 3.2.2 Step 2: Realization of the Knowledge Book

• •

The realization of a Knowledge Book is a complex process involving the following tasks: Coconstruction of the knowledge models with the cooperation of knowledge holders (experts, specialists), who may provide a set of models during the interview along with relevant documents or references.

Safety approach for the pools

Safety is assessed by a deterministic approach limited to internal events of cooling loss.

ten-year reassessment of reactors: 2005: 900 MW 2009: 1450 MW 2012: 1300 MW

Extended deterministic approach

Deterministic and general probabilistic approach

Considering: • maintenance operations, • aggressions

Study of drain-out scenarios (deterministic and probabilistic)

• Modifications for maintenance operations: replenishment of PTR pumps and redundant instrumentation; • Modification for the aggressions, notably total loss of cold source but not plane crash ; • first reflections about drain-out phenomenon

1994 EDF: increase in pool’s residual power

Cooling and storage capacities

Limited capacities

Safety objectives for the ponds

Simultaneous operation of two PTR circuit paths (limited modification )

No objectives specific to pools

Fig. 4.14.

Considering extreme aggressions (earthquake, flood)

2011 Project « Life extension of NPPs »

Increased storing capacities

Increased cooling capacities

2001: 3 rd generation safety operation

2011 Fukushima

For 2020-2025 due to a refusal to increase storage in existing pools (they must tend toward 3rd generation criteria)

3rd generation safety objectives for pools Practical elimination of mixing the fuel in a non-confined building; •diversification of cold source; • reinforcement of prevention plans and drain-out expertise; • consideration of all aggressions (including plane crash)

Example of History Model: History of Safety for Spent Fuel Pools.

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Limited deterministic approach

• • •

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2002 first safety reassessment of pool design

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• • • • •

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Grouping of such knowledge models with the diverse elements of knowledge provides “knowledge chunks.” Building consensus between the knowledge contributors. Designing and production of the Knowledge Book with a definite architecture and presentation. Legitimizing the Knowledge Book’s content with the help of a committee composed of peers recognized by the organization. Approval of the Knowledge Book by the hierarchy to enable the organization its fair usage.

3.2.3 Step 3: Share the Knowledge Book The sharing of the Knowledge Book is all about the success of the knowledge transfer operation. It ensures the availability of new knowledge for application in business practices and to develop further. 3.2.4 Step 4: Evolution of the Knowledge Book Knowledge is always evolving, and it is necessary to implement a supervising process for the Knowledge Book’s evolution. It is a specific process that is not reducible to a simple classic maintenance operation. It requires several tasks:

• • •

Identify new emerging knowledge. Submit and validate the new knowledge to be integrated into the Knowledge Book. Modify the Knowledge Book and validate its evolution.

As Knowledge is a dynamic continuum, it is necessary to develop and implement a supervising process for the evolution of Knowledge Book. The process is not confined to a routine maintenance operation but requires several tasks, as given below:

3.3 Summary Fig. 4.15 summarizes the capitalization process with a Knowledge Book as output. This is an activity model as described above (flow diagram).

4. Knowledge Transfer 4.1 The Transfer Process Once the knowledge is capitalized in a Knowledge Book (or other similar knowledge-based documents), which provides a consistent, structured, and highly added-value corpus, this book must not stay “on the shelf,” but be transferred to some specific people in the organization. Knowledge transfer is an exchange process based on a binary relationship that depends on various contexts in which

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Knowledge Management Systems Scoping the Knowledge Book project

Interested parties

-Title of the book - Knowledge sources - Elements for modeling - Work plan

Realization of the Knowledge Book Validated Knowledge Book -Knowledge holders (experts, specialists) - Knowledge engineers

- Sharing procedures and tools - Information system

Share the Knowledge Book Shared Knowledge Book - Information system actors - Knowledge users and contributors

Evolution of the Knowledge Book Scalable Knowledge Book

Fig. 4.15. The Capitalization (or Codification) Process with Method for Analyzing and Structuring Knowledge (MASK I), in the Second Phase of the Virtuous Knowledge Management Cycle. the actors act. A knowledge transfer action is, therefore, characterized by: the target, the source that provides knowledge content and participates itself in the transfer process, the knowledge content that is transferred, the description, and the characteristics of the environment (technical, social, organizational, cultural etc.) in which this transfer takes place, the transfer and learning activities of the actors. A transfer process is easily described by a model (one of the models cited in MASK I) and, therefore, provides a reference model for the approach of transfer operations, as exemplified in Fig. 4.16. This model allows the successful implementation of any transfer action, for it accurately defines what items are to be taken into account in the implementation process.

4.2 The Transfer Devices The transfer of knowledge involves two important aspects, namely the method and technology. The methods for knowledge transfer include mentoring, tutoring, the community of practice, training, learning, etc., supported by many technologies such as CMS (content management system), blogs, shareware, e-learning platforms, portals, or knowledge servers, etc. The Knowledge Books (in a very generic sense) is the mainstay in the knowledge transfer processes. In fact, it requires designing a “sociotechnical” system, which may use the Knowledge Book as a basic corpus. It often adapts

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Contextual parameters influencing Knowledge Transfer

Justification of the Knowledge Transfer

Knowledge holders

Targeted population

• Transfer activities

• Learning activities

Benefits of the Knowledge Transfer

Transferred knowledge

Fig. 4.16.

The Knowledge Transfer Process.

classic devices in the context of Knowledge Books. Three significant examples are given hereunder.

4.2.1 Transfer Process Based on the Socialization of a Knowledge Book With regard to the transfer process based on the socialization of a Knowledge Book, the following two different processes can be implemented: (1) Expert/novice comodeling: In order to capitalize on the expert’s knowledge, an expert may be accompanied by one or several novices with a knowledge engineer as a moderator, as per MASK I modeling technique, for instance. The expertise, representing a common basis, thus allows novices to learn. (2) Direct transfer of the Knowledge Book: The models created during the design of the Knowledge Book provide an intensive and extensive knowledge corpus to be transferred in a structured and logical form. Such a transfer to the novices is possible by involving experts who can perform the task effectively in the shortest possible time by conducting training sessions in the organization. The knowledge engineer who has played a key role in the development of Knowledge Book could even undertake a direct transfer session to the audience without involving an expert. More often, a Knowledge Book, built with the help of experts for a given knowledge community, may be entrusted to that community in order to promote its dissemination, maintenance, and sharing. Only then, the Knowledge Book will be known to have been fully socialized.

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4.2.2 Transfer Process Based on a Knowledge Server/Knowledge Portal A Knowledge Server is a website that provides access to a given community, the knowledge corpus (a Knowledge Book, for example) in addition to all knowledge resources related to the corpus, in the framework of a specific domain (uniform resource locator (URL) links, documentation, workgroups, databases, software, collaborative spaces, etc.). It is also known as a Knowledge Portal. 4.2.3 Transfer Process Based on a Learning System The Knowledge Book, a product of knowledge engineering, is an organized knowledge corpus representing proficiency in a specific domain. This practical knowledge acquired by problem-solving experiences as contained in the Knowledge Book may not be enough to ensure the transfer of the knowledge that it has capitalized. The transfer can often invoke training methods facilitated by pedagogical engineers (see an example in Benmahamed & Ermine, 2007, or in Chapter 6). It allows:

• • •

The learning tracks may be designed for the learners as per their respective levels (the learning evolution). Teaching materials to be created from a Knowledge Book, in the form of quizzes, level tests, assessment tests, etc. Pedagogical tools to be specified and integrated to support e-learning.

5. Knowledge-based Innovation The correlation between creativity and knowledge resources in an organization is called Knowledge-Based Innovation (KBI), which follows the process illustrated in Fig. 4.17.

Creativity Identifying innovation laws and potential innovation pits

History of ideas, Lineages of innovative solutions, experiences ….

« Knowledge Drilling »

Inventivity Innovative propositions

Innovation

Innovative design

Knowledge Capital

Knowledge Capital

Fig. 4.17.

The Knowledge-based Innovation Process.

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The KBI process has two main phases:

5.1 Knowledge Drilling (or “Knowledge Archeology”) as a Support for Creativity Creativity is the next stage in the evolution of knowledge resources in an organization. The creativity of ideas follows the guidelines defined by past developments (along knowledge trajectories that can be traced by analyzing knowledge resources) including choices, decisions, discoveries, lessons learned, etc. Knowledge drilling is a detailed analysis of the history of past ideas and innovations that have either led to significant changes in the organization or rejected for one reason or the other. The analysis of history is then extrapolated to identify some potentially useful ideas for future innovation. Of the many creativity methods, only a few are based on knowledge drilling. For instance, brainstorming methodologies are the most popular creativity tools, as they include a stage of divergent thinking that includes distancing from the problem at hand, calling on subjectivity, analogy, and imagination to come back to the problem later from a different angle, followed by a segment of convergent thinking (transforming ideas into solutions that respond to the initial problem, using logical reasoning). It is not, in general, a KBI method. As creativity methods do not a priori provide a means of realizing the chosen idea, it requires a supplementary process to provide a design for innovative knowledge that can be patented as an invention. The latter process is called inventiveness.

5.2 Creation of Innovative Knowledge as a Support for Inventiveness Inventiveness is a process of transforming creative ideas into effective knowledge in order to design new products, new services, and making improvements, etc. Inventiveness involves research and development and constitutes a key process for KM because it elicits effective knowledge in form of documents, studies, and patents, etc., as new knowledge resources. MASK IV being the last module deals with the last phase of the Virtuous KM Cycle concerning the evolution of the Knowledge Capital, through creativity (Saulais & Ermine, 2012). The creativity process occurs just before the KBI process. It begins with the representation of explicit elements of the inventive part of the Knowledge Capital, obtained by knowledge drilling. This representation is then used to prompt reflection by knowledge actors about the potential evolution of knowledge in some specific domains of their organization. In the next phase of the creativity process, the prospective elements so obtained are successively presented to different groups, technical peers, other experts in the domain, such as representatives of technical strategy and organization strategy. This type of collective coconstruction is the product of the constructive reflection of participants based on their past and current knowledge. The last phase is the dissemination of the prospective vision to the knowledge communities belonging to different domains for a shared vision of

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the future and also to the innovation managers for transforming prospective ideas into operational knowledge to devise innovations. 5.2.1 Step 1: Analysis of the Tangible Intellectual Capital (Cognitive Stimulus Elaboration) The process of analysis of the tangible intellectual capital starts with knowledge drilling, which is quite a lengthy and tedious process. Its goal is to seek inventive tracks from the past explicit Knowledge Capital (patents, articles and documents, study reports, internal notes and white papers, presentations, training material, experts’ interviews, etc). The tracks are dated and attached to one of the knowledge domains of the subject, in order to develop a summary model of inventive tracks, based on this corpus. Models like knowledge maps, historical time charts, or genealogical trees of lineages can be used. 5.2.2 Step 2: Stimulation of the Experts’ Creativity Subsequent to the reconstitution of the inventive tracks, individual sessions of stimulated creativity are organized with recognized experts. Each session starts with an expert’s presentation pertaining to the inventive tracks, explored as a result of the previous step. The analysis of the tracks by the experts affects extrapolation and thereby elicits a prospective vision of the domain, which is later on summarized by the expert. 5.2.3 Step 3: Collective Coconstruction of the Prospective Elements (Stabilization and Emergence) In the third step, the prospective elements are successively presented to the following groups:

• • • •

Peers, who must react to the prospective technical material proposed by the domain representatives, Experts in the field, who know how the technical object operates on the customer site and who plays the role to provide the technical perspective of the customer, Actors in the interdisciplinary technical strategy, whose role is to state the technical policy of the organization or a technological field, Strategy actors, whose role is to provide marketing elements and product policy.

This type of interaction is generally organized in a one-day strategic seminar, that serves to provide:

• •

The focal points, which means the major problems that determine future challenges, The action plans for each focal point, the implementation of which is based on the current situation/environment as well as future trends.

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Potential subject of innovation and involved knowledge domains

Knowledge drilling.

Inventive tracks from the past decades in the domains

Stimulation of the experts’ creativity

Prospective vision of the involved domains

Collective co-construction of the prospective

Shared and synthetic prospective vision for innovation To R&D and technical networks

Shared prospective vision

To innovation managers

Innovation management

Dissemination

Fig. 4.18. The Creativity Process for Knowledge-based Innovation with Method for Analyzing and Structuring Knowledge (MASK IV) in the Fourth Phase of the Virtuous Knowledge Management Cycle.

5.2.4 Step 4: Dissemination This step consists of dissemination and sharing of the previously constructed synthetic prospective vision with communities of technical experts and also with management and innovation leaders in the organization. The objective of this step is to make technical, commercial, and strategic propositions for the development of innovative products or services in order to lead the innovation process to its final stage.

5.3 Summary Fig. 4.18 summarizes the creativity process for KBI.

6. Conclusion Knowledge is becoming a fundamental factor of development in human societies, requiring a new type of economy for every type of organization, known as KE, which is implemented in organizations, through KM. The latter comprises a new type of management activities that prioritizes an important and strategic asset called the Knowledge Capital. KM is a global strategic approach which essentially aims at increasing the added value of the Knowledge Capital. In a pragmatic way, KM is implemented by a set of processes that aim to capitalize, share, and create knowledge of the organization. This set can be organized in a virtuous cycle which allows us to master the development of a complete and integrated KM system and to be part of a process of continuous progress. MASK

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is a validated operational method that provides processes and tools for some knowledge issues of the KM virtuous cycle as critical knowledge assessment, elaboration of a KM plan, a codification of tacit knowledge, knowledge transfer, and KBI. KM is now a globally recognized activity within organizations, wherein it can be successful only if it fits into its culture and management perfectly.

References Benmahamed, D., & Ermine, J.-L. (2007). Knowledge management techniques for know-how transfer systems design. The case of an oil company. In S. Hawamdeh (Ed.), Creating collaborative advantage through knowledge and innovation (pp. 15–34). London: World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6337. October. Carlucci, D., Marr, B., & Schiuma, G. (2004). The knowledge value chain: How intellectual capital impacts on business performance. International Journal of Technology Management, 27(6/7), 575–590. Couturier, J., & Jorel, M. (2016). Implementation of a global knowledge management system at IRSN. Third International Conference on Nuclear KM, IAEA, 7–11, November. Ermine, J.-L. (2013a). A knowledge value chain for knowledge management. Journal of Knowledge & Communication Management, 3(2), 85–101. doi:10.5958/j.22777946.3.2.008 Ermine, J.-L. (2013b). Knowledge management with the MASK method. In U. M. Munshi, V. K. Sharma (Ed.), Knowledge management for sustainable development. Medtech. Ermine, J.-L. (2018). Knowledge management: The creative loop. London: Wiley and Iste Ed. Ermine, J.-L., Boughzala, I., & Tounkara, T. (2006). Critical Knowledge Map as a decision tool for knowledge transfer actions. Electronic Journal of Knowledge Management, 4(2), 129–140. Retrieved from www.ejkm.com Munshi, U. M., & Ermine, J.-L. (2011). Information and knowledge society: Some futuristic perspectives, Journal of Knowledge & Communication Management, 1(1), pp. 1–10. Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. New York, NY: Oxford University Press. Ricciardi, R. I., Baroso, A. C. O., & Ermine, J.-L. (2006). Knowledge evaluation for knowledge management implementation, the case study of the radiopharmaceutical centre of IPEN. Journal of Nuclear Knowledge Management, 2(1), 64–75. Saulais, P., & Ermine, J.-L. (2012). Creativity and knowledge management. Vine, the Journal of Information and Knowledge Management Systems, 42(3/4). Saulais, P., & Ermine, J.-L. (2018). Knowledge management in innovative companies (Vols. 1–2). London: Wiley and Iste Ed. Schreiber, G., Akkermans, H., Anjewierden, A., de Hoog, R., Shadbolt, N., Van de Velde, W. (1999). Knowledge engineering and management, the CommonKADS methodology. Cambridge: MIT Press.

Chapter 5

The Key Processes for KM: The Daisy Model 1. Introduction Developments in micro- and macroeconomics around the world have brought out significant changes in business perceptions. Important levers outside the Taylorism vision of the productive tool and the work have emerged recently, that include customer relations, the information system, business intelligence, environmental scanning, quality, etc. Such activities have become quite crucial for the company for the purpose of procurement, marketing, etc. The company keeps on reorganizing in relation to its environment to meet new economic challenges. Knowledge Management attempts to connect the classic visions of basic activities with the new requirements in a coherent manner by combining critical knowledge, as an essential resource for the production of goods and services, with the resources provided by the increasingly significant economic and competitive environment. The knowledge-based approach developed in the foregoing chapters provides means of organizing coherent processes around the company’s Knowledge Capital, to which all key processes must contribute, and through which they cooperate. Some of these key KM processes will partly be described in this chapter, through different models, which includes a functional model as the Knowledge Process Wheel (Chapter 4, § 1.3, Carlucci et al., 2004), a life cycle model as the virtuous KM cycle (Chapter 4, § 1.4, Ermine, 2018). As a whole, every organizational process that brings added value to the organization’s Knowledge Capital is a key process for KM. Those processes are not always sensu stricto KM processes in the strict sense. They, however, strongly contribute to KM and must be considered as an important aspect in a global KM approach. This chapter deals with a new model, called “Daisy Model,” (Fig. 5.1), which tackles the problem of organizing key processes of KM. Some of these processes, such as capitalization and sharing, or creativity and learning, are designated as internal, whereas other processes such as competitive intelligence or environmental scanning are called external. The external processes commence from internal knowledge and feedback mechanism, as also from customer relations and marketing that act as a filter on the immense potentialities of the creation and evolution of knowledge in the organizations.

Knowledge Management Systems, 91–123 Copyright © 2021 Shabahat Husain and Jean-Louis Ermine Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-80117-348-320210005

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Capitalization and sharing

Environment

Selection

Knowledge Capital

Interaction

Environment

Learning and creation

Fig. 5.1.

Daisy Model.

Knowledge Management, in a wider sense, is the management of these key processes and the consideration of their relationship to the organization’s Knowledge Capital. In fact, the model is comprised of a “daisy’s heart,” which is the core knowledge and “daisy’s petals” consisting of four broad classes in a knowledge-based organization, as described hereunder:

• •

• •

The first process of “capitalizing and sharing knowledge” ensures the “recycling” of the knowledge resource in the company. The second process involves interaction with the environment. A system disconnected from its environment is regarded as a dead system. This is especially true for acknowledge-based organization, being fed by increasingly large in flow of information, coming from the organization’s environment. The process of transformation from information inflow into knowledge capital is complex. The process involves environmental scanning or competitive or strategic intelligence. Presently, it is addressed as the external information processing aspect, consisting of little interaction with the knowledge, specific to the organization. The third process relates to learning and creating knowledge, which as an endogenous and collective process forms the basis of the evolution of knowledge. It includes the issue of the “learning organization” and creativity. The fourth process consisting of “selection by environment” is an evolutionist process, par excellence, for it selects knowledge created/based on marketing/ acceptability criteria, that are both economical and sociotechnical. It includes marketing issues, customer relations, etc. The Knowledge Management aims to integrate this type of problem into a strong relationship with the organization’s critical knowledge, especially professional knowledge.

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In view of the global approach for KM to define a set of coherent methods and tools to manage the above-mentioned processes in order to achieve the set objective of Knowledge Management, the four petals of the Daisy Model mark the starting points. The process of refinement of the model can develop the four petals into several other petals. This aspect of implementation of a Knowledge Management System is called as “plucking the petals off the daisy”! It is noteworthy that all the aforesaid processes are available in the organizations in one form or the other, like an intuitive/formalized way, or in a simple/ sophisticated form. The problem, however, remains of cooperation to achieve and implement common objective activities hitherto perceived as disparate and sometimes even peripheral to core business of the organization. Analogically, the capitalization process is equivalent to documentary department, or even archives, the sharing process is akin to an IT department, the interaction process is comparable to an external information provider, the learning process is similar to a training department, the creation process is analogous to an innovation department, and the selection process is parallel to a marketing department. These departments work independently without any interaction for enhancing business interests of the organization. In the succeeding paragraphs, the working of Daisy Model will be dealt with the processes, bringing added value to the organization’s Knowledge Capital.

2. The Capitalization and Sharing Process 2.1 The Cycle of Knowledge Conversion As a prelude to the discussion of the Daisy Model, the famous theory of Nonaka and Takeuchi, forming the basis of Nonaka–Takeuchi SECI Model (1995), may be recalled (Chapter 3).The model is founded upon two types of knowledge, namely Explicit or Documented form of knowledge (i.e. available in print and digital format) and Implicit knowledge (i.e. experience, skill, and capabilitybased) remains confined to the individuals. Whereas explicit knowledge can be retrieved, stored, and transferred to others easily, implicit knowledge is difficult to tap, verbalize, and transfer to other people without practicing. The demarcation between the two types of knowledge strongly influenced all KM-related research and current knowledge management approaches. In the SECI Model, Nonaka has elucidated as to how tacit and explicit knowledge are converted into the knowledge assets of the organization. SECI is an acronym of the four underlying processes named as Socialization, Externalization, Combination, and Internalization, that represent the four modes of conversion between tacit and explicit knowledge, as explained below:

• •

Socialization at workplace results into knowledge accumulation from tacit to tacit knowledge. Externalization or Elicitation underlies transformation from tacit knowledge to explicit knowledge, resulting from metaphors, concepts, hypotheses, models, and analogies, etc.

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On the basis of the above processes, capitalization and sharing of knowledge may be described as the Cycle of knowledge conversion (Fig. 5.2), that achieves one of the objectives of Knowledge Management of preserving and keeping alive the organization’s Knowledge Capital. The process of knowledge transfer may involve two ways: (1) A direct transfer is effected through a process termed by Nonaka–Takeuchi as socialization. This type of knowledge transfer takes place without elicitation. The prime example of this kind of process is companionship, where learning takes place through direct contact with the expert, by way of “Observation” and “Impregnation.” It is though an ideal way of sharing knowledge but is frost with many practical and financial problems that include the availability of experts, frequent transfer to knowledge workers,

Indirect Transfer

Sharing Explicit Knowledge

Explicit Knowledge ICT

Encoded knowledge

Appropriation

Elicitation

Learning Teaching …

Direct Transfer Tacit Knowledge

Tacit Knowledge Knowledge communities

Fig. 5.2.

Nonaka’s Cycle of Knowledge Conversion.

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apart from turnover and the total cost, etc. Other methods of transferring tacit knowledge are based on networks, in particular, Communities of Practice or Communities of Interest, working groups, etc. The problem raised by different types of knowledge communities is the integration of the knowledge inflows into the organization’s Knowledge Capital to meet the production objectives. (Nonaka & Takeuchi, 1995) (2) An indirect transfer, a partial alternative to the direct transfer may be broken down into three subprocesses:

•

The first subprocess called elicitation consists in bringing out a part of tacit knowledge (collective or individual) in a visible informational form, also known as knowledge codification, which because of its “barrier of the tacit,” will always be far from being complete, though a large number of methods and tools have off late been developed for this task. There are several approaches for elicitation A first type of approach is knowledge transcription. Some tacit knowledge can be explained simply, by transcribing it, in a more or less structured way. For instance, the first rule in the implementation of quality systems is to “write what we are going to do,” that is feedback experience, lessons learnt, and also about all in-house publications, including “secondary documents” that synthesize the knowledge contained in the given documents. Yet another type of approach is knowledge engineering, a technique originated in the design and development of KM-related expert systems (or knowledge-based systems), meant to reproduce expert reasoning on specific areas of knowledge. It was quickly realized that while powerful technologies were available to build such systems, the greatest difficulty lied in the ability to transfer knowledge from one or more human experts into a computer program. Knowledge Engineering has therefore put in place methods to collect knowledge, most often from interviews, and to structure it, generally from models (Dieng et al., 1998; Schreiber et al., 2000). These methods can therefore be used with profit to make explicit, from interviews with the knowledge holders, part of the tacit organization’s Knowledge Capital of the organization. Knowledge Engineering uses often knowledge modeling techniques, which transform tacit knowledge into explicit knowledge by using modeling tools. Modeling is though a difficult process to implement, yet it is far more powerful as compared to simple transcription. Knowledge modeling can be done

– –

either by observing the studied systems and making a formal model, that may be mathematical, physical, automatic … or semi-formal, used for functional analysis, or systems analysis, or by directly questioning the “sources of knowledge,” by implementing specific representation techniques by the help of experts and specialists over a corpus of documents under analysis. For instance,

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•

•

the description of MASK I in Chapter 4 shows a mixed approach related to Knowledge Books, in which knowledge modeling structures are used to codify knowledge linked to already existing information corpus. The second subprocess is called sharing. Mention may be made that tacit knowledge elicitation is only of interest if the knowledge is shared by the interested parties within the organization. Information technologies (especially intranet) provide a significant ground of sharing collective Knowledge Capital. “Motivational” communication and adoption of proper procedures also play an important role. Indeed, knowledge sharing in an organization is not a natural process. Tailor-made procedures are adopted to initiate this process, which too often tends to be confused with those of dissemination (especially dissemination of information). Sharing is a two-way process where people (“knowledge workers”) contribute as much as they get. Of course, this process cannot be conceived without offering adequate “incentives,” necessary to promote sharing, as per the commonly used statement that: “one must acknowledge in order receiving knowledge.” The third subprocess, known as appropriation, validates the explicit knowledge for what it is used in action (called “actionable knowledge”), that ultimately contributes to achieving the organization’s objectives. For this, people need to recreate their tacit knowledge, based on shared explicit knowledge, which will serve them specifically in their work. It is the guarantee that this appropriation is carried out which ultimately guarantees the proper functioning of the (indirect) knowledge cycle. Explicit knowledge is worth nothing if it is not shared; shared knowledge is worth nothing if people do not appropriate it. There are a few structured and innovative approaches to acquiring knowledge. Experimentation (personal or collective) and training are classic levers in this process.

2.1.1 Water Cycle Metaphor The knowledge capitalization and knowledge sharing processes are the two faces of the same coin. Both are therefore inseparable and yet complementary and continuous, as a result of the cycle of knowledge conversion, involving the constant transfer and reappropriation of knowledge for better capitalization. For the sake of illustration, the cycle of knowledge conversion may be compared with the Water Cycle, as a model and metaphor to communicate concepts of interdependence and intradependence. The well-known hydrological cycle (Fig. 5.3) describes the continuous process of interdependence collection, evaporation, condensation, and precipitation of water. Similarly, the tacit knowledge may be compared with clouds, whereas elicitation of this knowledge is akin to the process of precipitation, where water vapors get condensed and collected into a liquid phase on the ground. The process of knowledge sharing is like surface runoff, forming streams, rivers, reservoirs on the planet. The process of appropriation (converting explicit knowledge into tacit) is comparable with the phenomenon of evaporation, which converts water back to its vapor phase.

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Water Cycle. Source: https://sciencestruck.com/water-cyclefor-kids (2020).

Finally, the process of internal circulation of tacit knowledge is equivalent to the processes of convection and other meteorological phenomena which form cloud masses, depending on the environment. The bottom-line of the aforesaid comparison is to emphasize the renewable nature of the “knowledge resource,” which like “water” is an inexhaustible resource (provided the cycle is not disturbed), which renews itself in an identified cycle. The process of evolution and innovation connected to the Daisy Model may be compared with the water cycle metaphor, to the contribution of external water streams in a given hydrological system. Thus, it emphasizes that the knowledge resource is not only renewable but also increases or improves in volume when it is used. Knowledge is a strategic resource for the company, as the water resource is in the whole water cycle.

3. The Process of Interaction with the Environment 3.1 The Knowledge Capital, a Key Support for Interaction with the Environment An organization is constantly evolving like a system in action. The same is true of its Knowledge Capital. The capitalization and sharing process described in the previous paragraph is endogenous, i.e., internal to the company. It brings added value to the already existing Knowledge Capital in the organization. Because of the actors who detain knowledge, this capital, however, appears isolated from the environment of the organization. On the contrary, it is strongly linked to it. This linkage allows knowledge to be enriched by external contributions through a process similar to environmental scanning or business intelligence processes (cf.

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for instance Stoffels, 1994; Sabherwal & Becerra-Fernandez, 2010). It enables relevant information to be drawn from the socioeconomic competitive environment, which transforms it into useful knowledge for the organization. In drawing relevant information from the environment, the first problem is the positioning of an organization in relation to its environment. From the topological point of view, the company and its environment are two distinct subsystems, with strong interaction, but without any predominance of one over the other. A right balance of interaction between the environment and the organization is a fundamental factor of stability (Fig. 5.4). The hypothesis is supported by the following two facts:

• •

The organization has actors capable of reacting, fighting, understanding, and controlling the environment to counterbalance a “locked-in syndrome.” The organization has original and specific resources to respond effectively to the demands of its environment.

Among the resources available in an organization, Knowledge Management focuses upon the “knowledge resource,” that provides essential support to the organization in understanding and controlling its environment. The environment of a given organization is perceived, made available through the accessibility of information for planning and strategic development of an organization’s knowledge capital. The risk factors involve inadequacy and/or misrepresentation of information. Therefore, it is not enough to scrutinize the information inflow and be satisfied with the gathered information. It is here that the insight of the leaders or the actors coupled with their experience will come to play a significant role. Right questions at the right time will help in the transformation of the gathered information into knowledge.

Topological analytic view

Relational systemic view

Organization Organization

Environment

Environment

Fig. 5.4.

Two Different Points of View of an Organization Regarding Its Environment.

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First of all, knowing what you want requires knowing what you know. The quality of the obtained information often depends on the quality of the question and also the relevance of the question based on its cohesion with the knowledge already acquired. Secondly, the information collected must be used optimally as feedback to create operational knowledge for decision-making or production. It is not enough just to disseminate the information, unless until it becomes part of the complex process confronting the collected information with the existing knowledge, so that a new dimension with a new meaning is brought out. It may be emphasized that understanding and controlling a company’s environment also means understanding oneself and comparing one’s knowledge with the external availability of information. The said process of confrontation will be discussed in the succeeding paragraphs.

3.2 Description of the Process of Interaction with the Environment To understand its environment, to make decision and/or take action, an organization goes through the following three phases:

• •

•

Projection Projection involves the elaboration of the information retrieved. It is the matching of information that needs the perceived or perceptible environment. It results in a query addressed to the organization’s environment. Intelligence This phase, the name of which is inherited from military terminology, starts from data/information collection for the development of a corpus of information. It is made up of several processes: distortion, identification, and relevant feedback (see below). Knowledge creation It brings together two complementary stages:

– –

Representation that consists of synthesizing the obtained corpus of information Sensemaking that is a process of interpretation and creation of “actionable” knowledge.

The above information activities are basic, and therefore, the key to their success lies in the six subprocesses (Fig. 5.5), as described below: 3.2.1 Projection (Elaboration of the Information Retrieval) The process of Projection is the mapping of the company’s knowledge (explicit or tacit) by its actors about the perceived or perceptible environment. This phase involves the Knowledge Capital, which is the internal vision of the company, linked to the outside world. Company’s internal vision is based on the beliefs and representations that prevail in the organization, on the bases of the work culture and knowhow of the actors. The process works on the surmise that the projection

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Distortion

Identification

Projection

Environment

Organization

Knowledge creation

Relevant feedback

Representation

Fig. 5.5.

Process of Interaction with the Environment.

process makes Knowledge Capital of an enterprise better known and better exploited to serve the basic objectives of KM. To prove the point, one may look into cognitive studies about the decisionmaking processes that represent the environment as tacit, which is sometimes modeled by “cognitive maps.” The resulting mapping is intuitive and personal. In a more methodological and/or collective approach, the projection can be done by setting up “projection vectors,” which are an explicit and simplified representation of knowledge like an explicit filter, search profiles, Internet queries, etc. The projection may also be done by the confrontation of tacit representations within the organization that is made possible by the creation of a specific group for elaboration of information retrieval, etc. The projection scenario of the Knowledge Capital is comparable to the acquisition of new knowledge by the researchers of the subject of botany, say for example about the concept of a “Tree.” Initially, the research community defines a tree as a living organism comprised of roots, stem, leaves, flowers, and fruits. 3.2.2 Distortion (Weak Signals Discovery) Distortion, defined as weak signals discovery, may occur after the first phase of information retrieval. Differences arise in the perception of the actors of the organization about their beliefs and what they have observed in the environment.

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This is because the structuring of knowledge in the organization does not exactly correspond with the information retrieved from the environment under observation. From an organizational perspective, the informational mismatch, between what they have perceived from organizational knowledge with that of the one retrieved from the environment, can be analyzed, discussed, and defined collectively. The use of sophisticated information retrieval tools may convert initially obtained weak signals into consolidated information. To continue with the analogy of the “Tree,” after having projected into the informational environment the concept of tree, composed of “Wood” and “Leaves” the organization’s actors note the existence of plant species that differ in the representation of their knowledge system:

• •

Species consisting of wood and thorns or needles Species consisting of wood, leaves, and flowers.

3.2.3 Identification (Analysis of Weak Signals) The identification of weak signals constitutes the fundamental factors of distortion. This phase forms the basis of decision-making. In a collective approach over vast domains, the identification of weak signals in the environment is essentially done through distributed interpretations, that necessitate a feedback mechanism, to be followed before decision-making. In the botany example, the distortion can be identified as coming from the notion of “flower” and that of “thorn” or “needle” which does not fit into the initially defined representation in the existing knowledge system.

3.2.4 Relevant Feedback Relevant feedback is a priori elimination of singular domains that are irrelevant. On a personal level, it is simply common sense. While dealing with important corpus of information, it is necessary to analyze the identified results by brainstorming and/or exploration with information tools that consists in launching a simple projection/reaction loop by an adapted modification of the projection vectors as per the conducted analysis. Information retrieval, with standard technologies, often collects a large number of informational documents. This corpus has two characteristics: noise (irrelevant information) and silence (relevant information that is not captured). The goal of relevant feedback is to decrease noise and reduce silence, as much as possible. Continuing with the example of botany, the research community may decide in favor of concept of “flower” that can be advantageously added to that of tree that is considered useful for the study. Conversely, it will be quite unnecessary to pay attention in anything coniferous. The corpus of information is then adjusted. In the process the week signal about the coniferous trees were present, but had to be rejected during collection of in the first corpus of information.

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3.2.5 Representation A representation is built by mathematical (statistical) and/or cognitive operations (representations) on singular areas to have particular reading grids. In an intuitive approach, if the feedback is adequate and well dimensioned, a natural overall perception is sufficient for adequate action. Without using a proper methodology, risks are, however, involved in having a linear reading and information overflow resulting in a random reading grid that can lead to deviant actions. In a more tool-based approach, representation can be achieved by using infometric tools or through collective targeted readings. In the aforesaid analogy from botany, the corpus of the observation obtained in the refinement of the concept of the tree makes it possible to distinguish a class of plants which has two subclasses: The trees (deciduous) and the flowers. The other class, containing conifers, is a class recognized as being distinct from this one. Classification is thus established, as more similarities between deciduous trees and flowers are recognized than between deciduous trees and conifers. This points the difficulties of a relevant classification. Although many automatic classification tools or classification aids work for large bodies of information, any classification obtained in this way must then be validated by the next phases. 3.2.6 Knowledge Creation This phase is concerned with the creation of endogenous information and knowledge by involving cross-checking of information, initiating a process of interpretation and creation of the so-called “actionable” knowledge. The latter is based on the representation of the environment resulting from the projection/ feedback loop described here. Apart from a rather simple phase of creating endogenous information, the process is unfamiliar, at the heart of the problem of “cognitive organization,” which is a kind of “collective semiosis” that still needs to be elaborated. In the example from botany, the perception of different characteristics/classes in the body of information obtained after interaction processes can be justified and detailed further by serious studies, research programs, etc. Based on the initial signals/information, organization’s actors’ conduct studies, analyze, validate, and create new knowledge in the field of botany. For example, the following two classes are distinguished in botany:

• •

Angiosperms: They are the plants with their seeds enclosed in fruits (hidden seeds) and which may include both trees and flowers (but also the cactus, which are, however, spiny plants!) Gymnosperms: These are plants with uncovered seeds, carried by a fertile flower. One example is conifers whose seeds are carried by the scales of pine cones.

The example of a “Tree” from botany finally gives credence to the assumption, that simple well-asked questions, followed by an analysis of the information obtained and a real work of reflection generates knowledge with significant added value.

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3.3 Knowledge Management Issues in Interaction Process Within the interaction process, the two phases in direct synergy with the Knowledge Capital of the organization are the phase of projection, at the initial stages of the confrontation with the environment; and the phase of integration of knowledge, involving the culmination of environmental feedback. The latter is a step in enriching the Knowledge Capital, whether tacit or explicit. Despite all that, many questions remain unanswered, such as how new knowledge, useful to the organization, is created while observing the environment? How are the creativity and the capacity for innovation achieved? How does the organization monitor this creation? How does the organization capitalize this knowledge in its knowledge base? The projection phase is concerned with how the Knowledge Capital represents the internal vision of the company. This phase is based on the culture and the knowledge specific to the actors, who interact with the environment. Needless to emphasize that the Knowledge Capital should be better known and better exploited for better results. This brings into consideration the common-sense rule of “Know thyself” to maximize the benefits of the surrounding environment. Unfortunately, the two activities, i.e., projection and feedback coming under the purview of Knowledge Management for organization of Knowledge Capital, are least mastered in business intelligence or environmental scanning activities. At this juncture, the following two observations can be made about the processes followed: (1) In the projection process

•

The problems faced by the experts and managers to precisely define the information they need. • The failure to express information-seeking needs in current processes. The above observations are corroborated by the studies conducted by Stubbart, 1982, who has highlighted two factors for failure in the projection the process as follows: • the inability to define the information needs • the tendency to reduce the observed environment, which may be detrimental for the organization as the full opportunities emerging in its environment are not looked into. (2) In the knowledge integration process: Classical business intelligence or environmental scanning activities are often reduced to the following three stages:

• • •

search for information processing of the information collected storage and dissemination of processed information

As already pointed out, the effectiveness of this activity could be optimized by adopting certain mechanisms that facilitate the transition from the state of information to the state of knowledge. Unfortunately, such mechanisms are yet to be developed fully.

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Knowledge Management can therefore bring definite added value to environmental scanning activities, which are often strategic for the organization. Relevant questioning can be supported by the good management of the knowledge already existing in the organization, and the analysis of external information can be translated into useful and operational knowledge if this analysis is carried out by mobilizing knowledge actors and the already existing Knowledge Capital needs to be enriched.

4. The Knowledge Creation Process 4.1 Knowledge Creation as a Process of Evolution of the Knowledge Capital The Knowledge Capital of a company changes constantly and rapidly. Managing this capital also means managing this change. Two essential processes play a key role in this evolution. The first is the internal (endogenous) creation of knowledge, driven by creativity and determining innovation in the organization. The second is learning, in particular so-called “organizational” learning, which makes it possible to integrate and remodel new knowledge constantly and collectively, that makes Knowledge Capital a living subject. The Daisy Model is comprised of an endogenous process of evolution, i.e., “Learning and creation.” The two will be dealt with separately as follows: The underlying hypothesis in the evolution of the Knowledge Capital is the identification of the process of creativity, for the creation of new knowledge, enriches the capital. However, a counter hypothesis dwells upon the belief that “it is better to forget what we know to be more innovative” and that “do not waste time analyzing the past and the present to find new ideas, amounting to spontaneous generation” hypothesis rather than to evolutionary hypothesis. If innovation contains an evolutionary subprocess, it represents “evolutionary” type hypotheses, which in the subject Economics is called “path dependency” (David, 2000; Nelson & Winter, 1982). Innovation is a process of “endogenous and cumulative technological creation” which is the very nature of the Knowledge Capital accumulated in an organization that predetermines the evolutionary path of this knowledge (even evolution of the organization itself). As such, there is no pure creation of knowledge capital as dictated by only external constraints, but it is due to the evolution of ideas, by assimilation, accommodation, mutation, etc. The evolution of ideas takes place within the organization from its “genetic patrimony” which is made up, among other things, of its Knowledge Capital. It is therefore existing knowledge that conditions future ideas and thus leads to innovation. The path dependency hypothesis, therefore, requires analyzing the evolution of a Knowledge Capital vis-`a-vis its history to better control its future evolution. It considers that innovation (of a product, process, or mode of the organization) comes from substantial new recombination of skills, of knowledge already existing in the assets of the organization. That kind of knowledge management is an essential element for innovation. It has to discover the laws of evolution specific to each organization to update the combinations potentially the most important, most innovative.

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A good understanding of the knowledge evolution in the organization, therefore, promotes the emergence of new ideas. An analysis of the history of ideas can lead to the discovery of laws of evolution, specific to the company, providing guides for future development (discovery of innovation factors, unexplored paths, etc.). Studies conducted in the past enlighten conditions that were the triggering factors of innovation; the good coordination of internal knowledge and know-how with information from the environment (environmental scanning, competitive intelligence, etc.) can promote creativity. Understanding knowledge evolution laws and factors will make it possible to use them as a lever to improve the creativity and innovation processes implemented in the organization. The correlation between knowledge creation and organization’s Knowledge Capital is called Knowledge-Based Innovation (KBI). An example of a knowledge-based method to solve problems is the famous TRIZ method, a Russian acronym for the Theory of Resolution of Inventive Problems, developed by G.S. Altshuller in the 1980s (Savransky, 2000). The theory is dedicated to the resolution of technical problems that require innovative solutions. This method shows that it is possible to find inspiration in other domains to solve similar problems. TRIZ is the archetype of the knowledge-based innovative design method. The technique used is sophisticated because the method accounts for the existing ideas in databases of millions of patents. KBI process is described in the following paragraphs, while more details can be found in Ermine (2018).

4.2 Knowledge-based Innovation Process In a pragmatic way, the path dependency hypothesis can be expressed by the fact that innovative knowledge appears, at least partially, from a recombination of existing knowledge of the organization, and the knowledge developed internally and/or acquired externally. The Knowledge Capital therefore serves as the foundations on which creativity develops. The Knowledge-Based Innovation process may be defined as a process based on the knowledge acquired within the organization, which is further revitalized. 4.2.1 The Creativity Process Currently most of knowledge creation processes are based on the so-called creativity techniques. The working groups, that aim to provide new ideas, use these techniques for evaluation, and integration of new knowledge by using an innovative design activity for validating and implementing the idea (demonstrators’ model, prototype, etc.) up to the level of obtaining an innovative concept. A more classical follow-up of innovation consists of “industrializing” innovation, that results into innovative knowledge, called inventiveness process (Fig. 5.6). The well-known techniques to support creativity are widely used. They focus on a variety of aspects of creativity including techniques for idea generation, divergent thinking, methods of reframing problems, and changes in the affective environment and so on. Those methods are described in PMI (2013).

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Creativity

History of ideas, Genealogy of solutions, Experiences ….

Identify evolution laws and potential innovation pits

Knowledge drilling

Innovating propositions Innovating design

Innovation

Knowledge Capital

Knowledge Capital

Fig. 5.6.

Inventiveness

Knowledge-based Innovation Process.

There are two types of techniques: (1) Idea search techniques These techniques help a particular group, working on a defined problem to find applicable solutions. In strict sense of the term all such techniques lead to creativity. They generally take place in five stages:

• • • • •

The wording: its goal is to have an adequate “feeling” of the problem (often with the “client” needing the solution) Impregnation: it transforms the problem into an “obsession” so as to look for any type of stimuli, may contribute in to solving the problem Distancing: it aims to focus upon the reality of the problem with new (even unusual) insights. This step mobilizes the resources that may perceive the problem in images and to work upon these images Crossing: it consists of using all the perceptions and ideas of the previous phase to bring them back to the reality of the problem Assessment: it consists of carrying out, for each selected idea, a kind of technical assessment to assess its value.

The technique known as brainstorming is a famous example of this kind of technique. (2) Projective techniques These techniques help working groups to discuss on a given theme, without necessarily looking for solutions or directly applicable ideas (Group discussions, What if…, Arbitrary stimuli etc.). The most popular creativity tools, as brainstorming methodologies, include a phase of divergent thinking (distancing from the problem at hand, calling on subjectivity, analogy, and imagination to come back to the problem later from a different angle) and a phase of convergent thinking

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(transforming ideas into solutions that respond to the initial problem, using logical reasoning). They do not use already existing knowledge to stimulate creativity; it is unfortunately often a question of “forgetting everything you know”! In our hypothesis, this process of creativity is therefore incomplete. There are, however, approaches that use existing knowledge to stimulate creativity as the TRIZ method, the theory of inventive problem solving. We describe this kind of technique called knowledge-based techniques. (3) Knowledge-based techniques The knowledge-based techniques have two main phases:

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Knowledge drilling (or “knowledge archeology”) (Chapter 4) Creativity is the evolutionary process of knowledge resources in a company. The evolution of ideas follows the guidelines defined by past developments, along knowledge trajectories that can be traced by analyzing knowledge resources: choice, decisions, discoveries, lessons learned, etc., that were produced in the past. Knowledge drilling is a detailed analysis of the history of past ideas and innovations that lead to significant changes in the organization or that were rejected for some reason. The analysis of this history is then extrapolated to identify some potentially useful ideas for future innovation. This phase provides a shared and relevant vision of the evolution of ideas and concepts as it happened in the organization. The identification of innovation pits and of innovation laws

The previous phase is an opportunity to discover, by cross-checking histories, some roadmaps that have not been explored or poorly explored. It is also an opportunity to see the factors that have prevailed in past innovations, the major laws that have made it possible to innovate. These factors and laws are very often dependent on the corporate culture, and can be revealed on this occasion. This provides roadmaps which enable to set up work environments and give milestones to creative working groups as mentioned in the previous paragraph. This knowledge-based discovery process is upstream of the knowledge creation process described in Fig. 5.6. A case study in a defense industry, using that KBI process, can be found in Saulais and Ermine, (2012).

4.2.2 The Inventiveness Process Creativity methods do not a priori provide a means of realizing the chosen method. A supplementary process is required to provide a design and innovative knowledge that can be patented as an invention. This is the process of inventiveness. Inventiveness is a process of transforming creative ideas into effective knowledge in order to design new products, new services, improvements, etc. It often involves the activity of research and development. It is a key process for KM because it elicits effective knowledge (documents, studies, patents…) that must be capitalized on as new knowledge resources.

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5. The Learning Process 5.1 Introduction To consider learning as a KM process is to open a Pandora’s Box: so much has been said and done in this area. However, learning is by definition a process of knowledge building, and therefore of knowledge creation. Regarding KM implementation, the question of learning processes cannot be avoided in the organization. Several points of views have to be considered:

• •

There is a duality between individual and collective learning. If these two types of learning are indisputably linked, KM is more involved in the collective learning process, based on the organization’s Knowledge Capital and which has strong consequences on the organization itself. There is the contextual dimension of learning. In individual terms, the learning context is viewed in very different ways, and sometimes even ignored. In organizational terms, the notion of “learning organization” is a recent notion originating in the transformation of the technical, economic, and social environment of organization which induces constant pressure for change (continuous innovation, increased competitiveness, globalization, and complexity). The organization must not only improve the knowledge of its employees but transform itself as a collective learning system that continuously learns and changes to achieve its objectives. In conclusion, learning will be conceived here as collective and oriented toward the strategic change of organizations.

5.2 Individual Learning Despite what has been said above, it is however interesting to recall a few concepts on individual learning, because they can be a fruitful source of reflection for the organizational dimension (Giordan 1995, 2012).

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A first conception of learning, which is still very widespread, is that of a simple process of registration. This is the result of the provision or transmission of information. This is an elementary model of a sender (source of information) who has knowledge and a receiver who memorizes the transmitted messages. In an organization, information servers replicate this process (not only at the individual level but also at the collective level). A second conception of learning, also very widespread, is called “behavioral”: learning is a process of adaptation (through positive or negative “reinforcements” – rewards/punishments) in order to respond adequately to a given situation. Training in operational tasks, classic “predetermined solution” exercises (the archetype of which is MCQ, Multiple Choice Questions), is of this type. A third concept is what one might call “creative learning” It is based on the creativity and interpersonal skills of individuals. It is based on discovery,

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understanding. Learning is not a simple memorization of information, nor the result of conditioning due to the environment. It is a mental activity of a subject facing a situation, whether his capacity for action is effective or symbolic, material or verbal. It is a process of emergence resulting from questions, from initial ideas, from usual ways of reasoning, where the subject retains only what touches or catches him. This emergence is possible if the subject grasps what he can do with this new knowledge – we speak of intentionality – if he manages to reformulate his mental structure – we speak of elaboration – and if this new knowledge brings him a “bonus” giving him awareness in terms of explanation, prediction, or action – we speak of metacognition. (Giordan, 2012). We can thus sketch a relatively elaborate learning process as in Fig. 5.7.

5.3 Collective Learning Considering an organization as capable of developing structures and specific mechanisms to modify its Knowledge Capital (knowledge, know-how, various techniques, and practices) is recent. It has been developed in famous works like ¨ (1996), Senge (1990). Argyris and Schon Organizational learning processes are directly linked to the Knowledge Capital and its management in the organization. They are essentially operated by actors who act as agents of an organization in conformity with the existing roles and rules. They always have a “productive” target, oriented by the objectives of the

Put into perspective (Intentionality)

Elaborate

Understand : Compare Connect Integrate Interpret Transform

Mobilize Memorize : Code Store Deduct

Generate added value: act, explain, decide (metacognition)

Fig. 5.7.

Giordan’s Allosteric Learning Model.

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company, in response to a problematic situation. They are always supported by an investigation in the collective Knowledge Capital: tacit knowledge of individuals or groups of individuals, files, procedures, documents, organization charts, up to the physical objects and devices that may be references and indicators to individuals. On the other hand, the ways of accessing to the Knowledge Capital, of using it, as well as the capitalization and sharing of acquired and transformed knowledge are not part of the problems of organizational learning, which is more a supporting practice for change than a knowledge management practice. Classically, there are three types of learning process as shown in Fig. 5.8.

5.3.1 Single-loop Learning It is operational learning that modifies the strategies for action or their characteristics, but does not affect the organization’s reference framework, standards, or values. This learning is essentially focused on obtaining results: it is about achieving the existing objectives as well as possible, while maintaining organizational performance within the limits set by existing values and standards. Classically, this learning, which generally originates with a dysfunction to be resolved, attempts to solve the problem posed by a joint analysis and a modification of uses. Unfortunately, things are not so simple and this mode of action triggers “inhibitory loops” that often result in the inability of change. Indeed, the

Usage standards Reference values

Actions

Consequences Analysis/Corrections

Simple loop learning

Usage standards Reference values

Actions

Consequences Analysis/Corrections

Analysis/Corrections

Double loop learning

Construction of reference frameworks

Usage standards Reference values

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Consequences Analysis/Corrections

Analysis/Corrections

Reflexive learning

Fig. 5.8.

Three Types of Learning Process.

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analysis, even though designed to be positive, faces facts distorted by the existing uses of the actors, generating defensive reactions, first at the individual level then at the organizational level, establishing defensive routines which are then very difficult to challenge. 5.3.2 Double-loop Learning Double-loop learning induces a change in the “program” that guides change; it requires a questioning of organizational standards and a restructuring of the general reference framework. It aims at modifying individual uses, rather than “simply” solving the problem, as in the simple loop. The organization may then question the uses, the values on which it is based. It is then necessary to work on the mental and organizational structures that systematically hinder attempts at change, by discussions with the actors, and by examining without bias the real data. Double-loop learning depends on dissemination of valid information, and public or consensual verification of shared assertions and assumptions. It is on this last point that double-loop learning depends on a potential knowledge management system, through knowledge sharing, the involvement of all the relevant actors on the problem, and with consensual sharing and control. 5.3.3 Reflexive Learning This is a level of operation of the organization that systematically facilitates double-loop learning, where it “learns to learn.” This level involves the recognition of the principles which help to change the reference frameworks, the standards. Such a system is based on the establishment of a reflexive process that allows

• • • • •

To work on mental models: how the visions, beliefs in the organizational culture determine observations and influence decisions? To have a shared vision: how to build a shared comprehension for a project and for commitment? To have a systemic vision to articulate points of view and systems in action. To develop the “collective intelligence” of teams. To work on personal development of individuals.

These last points show how an organizational learning system can be strongly linked to a knowledge management system.

5.4 Human Resources Management 5.4.1 Introduction The term learning is quite ambiguous because it covers so many meanings. If learning is defined as the ability to acquire knowledge, it takes many forms for an individual, but even more for an organization. All of the processes described in this chapter are therefore learning processes, and many more can be also described.

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However, it is an obvious fact that knowledge is created, lives, and is disseminated in the organization mostly by people. These people have knowledge and learning skills. Therefore, a common way for an organization to acquire new knowledge and/or to make it operational is to recruit and/or manage the human resources for their “cognitive” activity. Knowledge acquisition in an organization is then inseparable from human resources (HR) management (“Employment in the knowledge-based economy is characterised by increasing demand for more highlyskilled workers” [OECD, 1996]).However, this connection is not as simple as it seems. The notion of “Human Capital” is now a well-established notion. It is the set of knowledge, skills, competences, and other qualities possessed by an individual and relevant to economic activity (Becker, 1993) (cf. OECD, 1996, 1999). We can speak of Human Capital for an organization, but this refers rather to the sum of the cognitive capacities of individuals, which they have acquired through their education. The link with the organization’s Knowledge Capital is certain, but it is not clearly defined, and it would be wrong, from the perspective of KM, to build a HR management approach mainly on this notion. However, important links need to be clarified with competence (or skills or talent) management, training, and recruitment.

5.4.2 Competence Management Managing cognitive resources in an organization is tricky because it relies on people’s ability to appropriate, use, and develop collective knowledge to transform it into competence, individual or collective. Knowledge Management is therefore inevitably linked to competence management, as competences are resources that can be mobilized in the production of a good or a service. 5.4.2.1 Individual Competence and Collective Competence. Many definitions exist in the literature. The meaning given to the notion of competence varies according to the fields of concern or the application of the definitions. It is always seen as an ability to mobilize knowledge or skills that must be implemented to solve problems according to the production need and the mission assigned by the organization. Competences are always linked to a given task, activity, or set of activities. It is a notion linked to those of activity and knowledge. Competences are knowledge in action. The competence capital of an organization cannot be reduced to the sum of the individual competences of its staff. Collective competence emerges from the cooperation and synergy existing between individual competences. The organization itself, as a whole, can be viewed as a “competence system” Indeed, more and more, the company tends to organize itself and operate as a" network of competences." Its performance will depend on its ability to mobilize and combine the competence resources of its actors. The concepts of individual competence and collective competence are specific to the organization, and are strongly linked to its productive activity, which itself is highly dependent on the knowledge and know-how of the actors.

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Knowledge management and competence management are therefore articulated: Knowledge management is seen as the management of cognitive resources in a production activity, and competence management is seen as an action of identification, enhancement, and development of these cognitive resources. The pivotal notion of this articulation is that of process or activity. 5.4.2.2 The Strategic Positioning of Competence Management. There are two modes of management in the organizations, which lead to very different visions of how to identify, valuate, and develop competences:

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Management monitored by the hierarchical organization, which is a traditional management method. Objectives, guidelines, responsibilities, information, etc., are transmitted through the only official channel of the hierarchy. The operational implementation of competence management is therefore the responsibility of hierarchical bodies at all levels. Management by activity, by workflows, which is a management transverse to the hierarchy, since it is based on “processes” which organize the different activities in relation to the production objectives of the organization. Through workflows, the competences are more simply and clearly identified, because they are perceived as resources (cognitive resources) for the activities, components of the processes.

The management mode implies the competence management mode. The competence management resulting from management by the organization is delegated to middle managers, and is therefore carried out operationally according to (at best!) local strategies in the structural units. This has a number of clearly identified consequences and biases:

• • • • •

Lack of cohesion incompetence management Individualization of competences Obstacles to a globalizing strategy Management experienced as a constraint in relation to the perception of a skill that seems “naturally acquired” on the workplace The actors/decision-makers of the competence management (middle managers) do not necessarily master either the knowledge content (knowledge, skills, attitudes, etc.) or the way of doing things of the domain

Competence management guided by workflows is a different approach, which attempts to overcome the mentioned shortcomings. 5.4.2.3 Competence Management Based on Processes. A process is a set of activities organized to achieve an organization’s objective (generally production). This is a workflow that goes from the initial activity of the process (receipt of customer request, for example) to the final activity (delivery of the product/ service, for example). An activity is defined as a homogeneous set of tasks and roles, transforming inputs, coming from the environment or from a previous activity, into results that bring added value to the process.

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Process analysis is a standard exercise in numerous organizations. However, in order to link competence management with knowledge management, it is necessary

• •

To define each activity in relation to its added value in the process (visible on the result of the activity) To define the roles involved in each activity (e.g., decide or, for another role, apply a purchasing strategy for production items). A role is defined by an expected result, the objects (in a generic meaning) handled by the role (e.g., production items…), and a level of action which defines the activity (decide, apply…).

This type of modeling has the natural consequence of distributing the KSA (Knowledge, Skills, and Attitude) necessary for the activities, in the different roles. The emphasis here is on roles, which facilitates the dynamic appropriation of the competence/knowledge necessary and sufficient to perform these roles. A role is thus differentiated from the functions and persons performing these roles. Sometimes it takes more than one role to achieve the expected result of an activity. The role is defined in relation to an objective in a context at a given moment. In summary, the organization has on the one hand a structural environment for the implementation of flows and activities (departments, services, etc., generally called “units”), on the other hand, a human environment. The organization is at the service of the processes, which are the production supports. Functions are visible in units, but they are, as we have seen, at the service of the role, and the role carries the KSA. KSA are positioned through the process/ activity/role structure. There is thus a need to connect traditional competence management based on a KSA approach and Knowledge Management. This fundamental connection requires answering two basic questions:

• •

“how to gather the knowledge useful for a role”? (Enabling Knowledge) “how to deliver this knowledge in time, place, and quality"?

For this, two additional operations must be carried out in connection with the processes modeling, which are described above:

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The identification of competences: this involves giving a name to a competence and attaching it as a resource to one (or more) activity of the process. The knowledge capture or capitalization: this involves formalizing, if necessary, the content of knowledge, know-how necessary and sufficient to play the roles associated with the skills identified.

As an example, a case study of this methodology can be found in Ricciardi et al. (2006).

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5.5 Training and Recruitment Acquiring and delivering knowledge obviously also calls on the traditional levers of human resources management, such as training and recruitment. 5.5.1 Training Training is a basic tool for enriching the organization’s Knowledge Capital. Unfortunately, this tool is not often optimized for the management of this capital. Training plans are most often built according to a classical logic of competence management delegated to middle managers. It is very often based on a “training catalog” which hardly reflects the dynamics and complexity of the organization’s knowledge. However, it is possible to consider training as a real process of Knowledge Management. To illustrate this, we will give an example of such a process. The process is initiated by a necessary change in the organization: restructuring, strategy, competitiveness, etc. The first phenomenon resulting from this change is the impact on the Knowledge Capital, which requires to analyze the impacts of this change and their criticality, and to translate them into needs of new knowledge that has to be acquired. Then comes a transfer process, where it is necessary to find the most appropriate training resources to acquire this knowledge. The last phase of the process is the appropriation of this knowledge by the concerned actors, which is validated not only by the evaluation of the knowledge acquisition but also by the operational performance evaluation (Fig. 5.9). This process is therefore closely linked to the Knowledge Capital, first at the beginning through an impact study, and then at the end, through controlled feedback that effectively enriches the Knowledge Capital. As an example, a case study in the nuclear domain of such a process can be found in Jorel (2020).

Knowledge Acquisition Change

Training device

Impact

•Impact study •New knowledge required

Knowledge Capital

Fig. 5.9.

Appropriation

Evaluation system : - of acquired knowledge - of operational performance

Knowledge Capital

Training Process as a KM Process.

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5.5.2 Recruitment The recruiting process is a key part of human resource management. It may be far from a KM process as it can be interpreted as a process of acquiring individual skills. But it is indeed a process of acquiring individual skills to enrich collective skills, and thereby the organization’s Knowledge Capital. Then it cannot be disconnected from KM preoccupations. The recruitment process is a connection between a system of available external skills and a system of internal skills that is lacking. Recruitment is not just about selection procedures. There are three major subprocesses. The organization “projects” its need onto the external system, first by analyzing the need and then transcribing it into a “job offer” This is often the classic “job/profile” transcription of the requirement. The second subprocess is a search for adequacy (recruitment campaign and selection procedures). The third is the on boarding process (of the new employee), which feeds back into the internal skills system. Note the strong parallel between this process and the process of interaction with the environment (§ 3). The same biases occur in that process:

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In the “projection” subprocess, the organization’s need for competence is strongly transformed depending on the type of organization of the company, on the perception the recruiter has of the competence and the perception that he has from the labor market. The job definition can be too rigid or too strict, the transformation of the required job into a profile is very subjective, the ignorance of the reality of the labor market also leads to a gap between the required job and the requested profile. This results in standardized and rigid job/profile formulations (diploma, experience, specialty, standard personal characteristic, etc.) which ignores the complexity of a real “skills market,” made up of persons that are all different and have specific knowledge. In the search for adequacy, recruitment is often done to minimize costs (salary cost of course, but also production loss due to the lack of job, integration cost, etc.) and risks (uncertainty about the effectiveness of the future employee), without taking into account, for example, the aspect of enriching the organization’s Knowledge Capital. It results in the search for a competence that is as quickly operational as possible. That contradicts KM principles where “actionable knowledge” does not exist and is largely built up within the company. The integration process in traditional recruitment is often reduced to its simplest expression (company visits and presentations, for example), the knowledge aspect being delegated, for example, to a training process. It is also clear that, very often, it is a process more of formatting than of sharing. The objective is to integrate the individual by giving him the required qualifications, and little to integrate his specific knowledge and know-how into the Knowledge Capital. From this short analysis of the recruitment process with respect to the KM, we can distinguish three types of management of this process: The “Staff Management” type: starting from a need to fill a vacant position, it is based on a formalized and fixed concept of competence, a mechanistic

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translation of the job/profile, a strong reference to the hierarchy of diplomas. It is focused on a desire to minimize the cost of recruiting and integrating the individual into the position in the short term. The process is repetitive and linear, the recruitment technique is most often “preselection by CV and interview." The “Human Resources management” type. Competence is seen in a broader way. There is an agreement to transform external competences into those required by taking into account, for example, on-job training upstream, or by anticipating integration. The labor market is seen not as a simple applicant, but with a more complex dimension. The “Knowledge Management” type. Competence is fundamentally seen as “knowledge in action.” By recruiting, the organization acquires a “potential for action” in a market of “raw” knowledge. Recruitment starts from an analysis and a well-founded representation of the knowledge, skills, and attitudes available in the Knowledge Capital

6. The Selection Process 6.1 Introduction The evolutionary model of Knowledge Capital, which we develop throughout this book, implies selection processes acting as filters that select, from among the possible evolutions, those most suited to the environment. The selection processes are a central issue in evolutionary theories of economics (David, 2000). These contradict the idea, very widespread in classical theories (and in common intuition), that the economic environment (the market) is capable of effectively eliminating any company (any innovation) that does not behave according to a profit maximization hypothesis, thus reducing the selection to a single determining criterion. In fact, selection is a more complex process, which depends on many factors, and it is important to grasp the diversity of the ways by which innovations are filtered, some tried and rejected, others accepted and propagated. Factors such as the nature of the market, the effects of public policies, access to financial resources, the available Knowledge Capital, etc., are all determinants that explain the diversified technological trajectories of organizations. Selection processes must be defined that effectively select new knowledge created internally in the organization, which should lead to successful innovations. These processes are based on knowledge. There are two types of knowledge useful in these processes. First, of course, the knowledge accumulated on the innovative design of the new products, services, or concepts, and its relationship with prior knowledge (differential compared to other products). Second, market knowledge, which helps manage the fit between new ideas and potential markets. On the “product knowledge” side, the most used tools are value analysis and functional analysis. On the “market knowledge” side, we find marketing studies and other market analyzes. Other more “systemic” methods also exist.

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It is clear that the link between these two types of knowledge is often rather tenuous. On the one hand, innovators develop knowledge that is naturally linked to the organizational knowledge, on the other hand, analysts develop knowledge of the market that are not always sufficiently linked to the organizational knowledge. For example, satisfaction questionnaires and customer feedback analysis do not always imply technical experts. For these reasons, and certainly many others, the success rate in the market for new products (services, concepts, etc.) is incredibly low (less than 20%). This is not necessarily inevitable. This requires refining the processes for selecting knowledge (or concepts, to speak a language closer to innovation) in the company, by skillfully integrating and synergizing the specific knowledge of the organization, and the knowledge that can be acquired through the markets. It requires integrating these selection processes into a global Knowledge Management strategy. We will give some examples. Note, however, that presently the selection process is not very well known.

6.2 Customer Relationship Management In implementing a “customer-oriented” business strategy (Customer Relationship Management or CRM) (cf. for instance Butler & Maklan, 2019), there is a real change. The company is moving from an organization focused on production, based on its skills and internal assets, to an organization where, from design to distribution, the relationship with the customer is always present, in order to identify as many elements as possible of competitive differentiation. It is therefore, more than “putting the customer at the center of its concerns” as the managerial slogans proclaim, to set up a process of organizational learning about the customer (see § 5). In the intangible capital of the company, in addition to the Knowledge Capital, “Customer Capital” has a special and increasingly important place: with the evolution of market models (end of mass production, increased competitiveness, diversification of products, increase in variety, continuous innovation, etc.), the customer is a precious resource and his relationship with the company’s production is increasingly complex. The design of a “customer orientation” therefore requires articulating knowledge and production know-how with knowledge about the customer and the market. The objective is to respond to market changes with the right innovative products for the right targets. The sustainable competitive advantage is obtained by

• • •

Guaranteeing growing customer satisfaction through increased attention and responsiveness to market changes Improving the business performance of the company through loyalty (retaining a customer is cheaper than winning them, and generally generates a large part of the turnover) Creating products, services, and distribution channels optimized for a customization of the offer

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Thus, by simply managing a customer database, the organization will build up a real knowledge base on its existing and potential markets in conjunction with knowledge about its products. However, building such a knowledge base is still problematic, as it is not well connected to classical knowledge management:

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•

•

Satisfaction is generally managed from surveys. These surveys are carried out by specialized or external services. The questioning and analysis of these surveys are often carried out without real connections with company’s experts (thus the company’s knowledge base), which makes their effectiveness limited and their feedback difficult. To caricature, take the example of a customer of an automobile who would report that the car “pulls on the left” which would be reported to the car steering system department, while a specialist warned with a more precise question, and/or a more detailed analysis, would quickly diagnose a problem with the ergonomics of the driver’s seat! Loyalty is often managed using data from a transactional model, such as “frequency of purchases of the brand in relation to the total number of purchases,” which is based on a unilateral commitment of the consumer to the company that remains dominant in the supply system. This simplistic model is evolving toward a relational model based on a sustainable mutual commitment. This last type of model requires a management based on much more knowledge and data than the first one. It is indeed necessary to structure data on the physical dimension of the supply system (coming from the production system), and also on its psychological and affective characteristics and put them in relation with data from the customer system which characterizes not only the characteristics of the customer but also purchasing situations and purchasing behavior strategies. Developing such knowledge bases, both at the product and customer level, requires significant collaboration between the sales departments and products specialists and experts of the organization. Customization is essentially managed from segmentation. It involves analyzing behavior, understanding customer needs and expectations in order to establish a range of products and establish appropriate marketing and communication plans. Here again, these activities are generally carried out by specialized services whose connection with production units and their knowledge is often weak. The segmentation does not always correspond very well with the organization’s innovation capacities, and the feedback on the performance of the marketing campaigns is not necessarily a lever of creativity for the design of innovative products.

6.3 Usage-centered Selection The selection process is a process of adaptation of the organization to its environment. An effective selection process requires an inversion of the organization’s logic: from the logic of the designer/producer to that of the user. Integration of a product/service in the market is a complex phenomenon, which depends on many

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interdependent factors. The in-depth comprehension of some of these factors contributes to the establishment of an effective selection process, contributing to the success of the organization’s innovations. For instance, in the previous paragraph, it has been shown that the in-depth comprehension of the product/ market relationship is key for competitiveness. In that case user is seen as a customer. For more than 20 years, following the evolution of the markets, numerous sociological surveys have shown that there are two types of comprehension of the product/market relationship, resulting in two types of innovation management:

•

•

The “technical” design, which provides the new product with a mobilizing effect that involves a change in the roles and usages of users. This has been particularly visible in recent years with ICT (Information and Communication Technologies), with the widespread belief that the introduction of these new technologies is a fundamental factor in organizational change. With this design, the innovation process remains very product-oriented, characterized by technical functionalities. This is a unilateral supply-building process. It guides the classic processes of designing new products. The “sociotechnical” design which considers that the product by nature is neutral. It is not its technical potential that mobilizes users, but the way in which it is included into a whole social system, cultural, technical, organizational, relational… and what are the user’s representations and practices of this inclusion. Therefore, innovation is user-oriented, and products are characterized by usages (and no longer functions). It is then possible to study their “acceptability” by certain social categories. The design process is performed in strong synergy with users not seen as customers. This is called usage-centered (or user-centered) design. This kind of design methodology was first described for software systems by Norman & Draper, 1986, and is well known now in this domain as UX for “User experience” (Platt, 2016).

As an example of usage-centered selection process, the pioneering CAUTIC method (Design Assisted by Use for Technologies, Innovation and Change) designed by P. Mallein illustrates the sociotechnical approach (Mallein &Toussaint, 1994). This method tests a new concept, product, or service to increase its chances of success in the market. It produces knowledge on user needs and on market segmentation that complements traditional methods (market analysis, value analysis, etc.). It anticipates the reactions of the early adopters to innovation by identifying the way they think of its usage. The key to the method is the identification of “usage meanings” that is, the meanings that users attribute to the innovation, in relation to their own value systems. Any innovation is faced with

•

Existing techniques: does the users have the opportunity to integrate the new technique into their usual technical background? Appropriation occurs

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•

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through the use and enhancement of certain simple and practical functions (trivialization) or the users must learn all the functionalities of the device and comply with an ideal use (idealization). Existing practices: does the users have the possibility of integrating the innovation into their current practices? The use of innovation allows the marginal development of new practices, which gradually take shape (hybridization), or else this use is radically new and replaces preexisting practices (substitution). The private and professional identity of the users: can the users act, through the use of the innovation, on their social identity? The use takes place in the personal issues, tactics, and imaginaries of the users (active identity) or the users are assigned to a social identity over which they have no control (passive identity). The private and professional environment of the users: can the users adapt the innovation to their environment? The use of innovation is parallel to some ongoing developments in different “social fields” (family, relational life, work organization, etc.) (social evolution) or else the use is going to radically transform social relations (social revolution).

This analytical grid is used to build an effective collaboration between design engineers, product developers, and the actors responsible for bringing innovation to market. It thus creates a useful junction between the knowledge of the organization and that of its environment, allowing better control of the selection process.

7. Conclusion The Daisy Model, as described here, provides a broader, more strategic view of KM. Its objective is not to model “knowledge processing” but to identify organizational processes that add value to the organization’s Knowledge Capital. These processes can be very diverse: information processing, human resource management, marketing, innovation, etc. The analysis through the Daisy Model transforms these to adjust and orient them toward KM objectives, in a global and coherent strategy. The resulting improvement is an important vector of change for the organization which can thus expand its efficiency, competitiveness, and creativity.

References ¨ D. A. (1996). Organizational learning. New York, NY: AddisonArgyris, C., & Schon, Wesley. Becker, G. (1993). Human capital: A theoretical and empirical analysis (3d Edition). Chicago, IL: University of Chicago Press. Butler, F., & Maklan, S. (2019). Customer relationship management, concepts and technologies (4th ed.). London: Routledge.

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Carlucci, D., Marr, B., & Schiuma, G. (2004). The knowledge value chain: How intellectual capital impacts on business performance International Journal of Technology Management, 27(6/7), 575–590. David, P. A. (2000). Path dependence, its critics and the quest for historical economics. In P. Garrouste & S. Ioannides (Eds.), Evolution and path dependence in economic ideas: Past and present. Cheltenham: Edward Elgar Publishing. Dieng, R., Corby, O., Giboin, A., & Ribi`ere, M. (1998). Methods and tools for corporate knowledge management. RR-3485, INRIA. inria-00073203f Ermine, J.-L. (2018). Knowledge management: The creative loop. London: Wiley-ISTE Ed. Giordan, A. (1995). New models for the learning process: Beyond constructivism? Prospects, 25(1), 101–118. Giordan, A. (2012). The allosteric learning model and current theories about learning (Trans. Nadine Allal).(Consulted 08/2020). Retrieved from http://www.andregiordan.com/apprendre/The-allosteric-learning-model-and-current-theoriesabout-learning1.pdf Jorel, M. (2020). Case study of a global KM project. In P. Saulais & J.-L. Ermine (Eds.), Knowledge management in innovative companies 2. Hoboken, NJ: WileyISTE Ed. Mallein, P., & Toussaint, Y. (1994). L’int´egration sociale des technologies d’information et de communication: Une sociologie des usages (Social integration of communication and information technologies: a sociology of uses). Technologies de l’information et soci´et´e, 6(4), 315–335. Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Cambridge, MA: The Belknap Press of Harvard University Press. Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. New York, NY: Oxford University Press. Norman, D. A., & Draper, S. (dir.) (1986). User centered system design: New perspectives on human-computer interaction. Hillsdale: Lawrence Earlbaum Association OECD Publications. (1996). The knowledge-based economy. Paris. OECD Publications. (1999). Measuring what people know: Human capital Accounting for the knowledge economy. Paris. Platt, D. S. (2016). The joy of UX: User experience and interactive design for developers. Boston, MA: Addison-Wesley. Project Management Institute (PMI). (2013). A guide to the project management body of knowledge (PMBOKÒ guide). Newtown Square, PA: Project Management Institute, Inc. ISBN 978-1-935589-67-9. Ricciardi, R. I., Barroso, A. C. O., & Ermine, J.-L. (2006). Knowledge evaluation for knowledge management implementation, the case study of the radio-pharmaceutical centre of IPEN. International Journal of Nuclear Knowledge Management, 2(1), 64–75. Sabherwal, R., & Becerra-Fernandez, I. (2010). Business intelligence: Practices, technologies, and management. Hoboken, NJ: Wiley Ed. Saulais, P., & Ermine, J.-L. (2012). Creativity and knowledge management. Vine, the Journal of Information and Knowledge Management Systems, 42(3/4), 416–434. Savransky, S. (2000). Engineering of creativity: Introduction to TRIZ methodology of inventive problem solving (p. 394). Boca Raton, Fla: CRC Press.

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Schreiber, A. Th, Akkermans, H., Anjewierden, A., Dehoog, R., Shadbolt, N., … Wielinga, B. (2000). Knowledge engineering and management: The CommonKADS methodology (1st ed.). Cambridge, MA: The MIT Press. Senge, P. M. (1990). The fifth discipline. New York, NY: Doubleday/Currency. Stoffels, J. D. (1994). Strategic issues management: A comprehensive guide to environmental scanning. Oxford: Pergamon Press. Stubbart, C. (1982). Are environmental scanning units effective. Long Range Planning, 15(3), 139–145.

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Chapter 6

Knowledge Management System: A Case Study of Sonatrach, National Oil Company, Algeria* 1. Introduction Sonatrach, a national state-owned oil company of Algeria is engaged in research, pipeline transport, processing, and sales of hydrocarbons and their derivatives. Founded in 1963, it is known today as the largest company in Africa with 154 subsidiaries. Sonatrach is the 12th largest oil consortium in the world, operating over the entire oil value-chain with approximately 120,000 workers. The company produces 30% of the Gross National Product (GNP) of Algeria. Sonatrach has some oil concessions in Libya, Mauritania, Peru, Yemen, and Venezuela. Of late the company has also diversified into petrochemistry and the desalination of seawater. The Sonatrach Group’s top management launched a knowledge management (KM) project as a strategic project. This project was based on a global vision of the company with the support of general management, on the strength of local, concrete actions aimed at producing significant profits in the short term. It aimed to preserve the strategic potential of the tacit knowledge acquired over the years by the company’s people. The study presented here concerns:

• •

The strategic assessment of knowledge and The professional expertise transfer strategy for the Sonatrach oil group.

The knowledge mapping studies initiated during the first stages of the project constituted the backbone of a future vision of the Sonatrach oil group. Similarly, KM has demonstrated to be a powerful and indispensable tool for the future Sonatrach Corporate University.

*

Most of the description in this chapter is based on a PhD research project undertaken by Djilali Benmahamed (PhD) and Jean-Louis Ermine (Benmahamed & Ermine, 2007a, 2007b). This study is based on a practical application in Sonatrach, which was funded by the company.

Knowledge Management Systems, 125–144 Copyright © 2021 Shabahat Husain and Jean-Louis Ermine Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-80117-348-320210006

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The primary aim of the project was to demonstrate the feasibility of a capitalization approach in the oil industry, as well as to describe the conditions for the success of the project in such an environment. The company’s expectations were underlying the perpetual usage of the strategy and devices and their deployment throughout the group. In view of the size and complexity of the company, the project focused upon the operational activities within Sonatrach, in particular the upstream activities pertaining to research, exploitation, and production of hydrocarbons. The project chose targeted knowledge within a structure that constituted a nodal point and an essential upstream activity, involving strategic, and critical know-how in the Petroleum Engineering and Development Department (PED). The research project consisted of designing and testing a KM methodology, based on the concepts mentioned above, as per Sonatrach’s strategy to ensure the sharing and transfer of the most critical knowledge. To impress upon the importance of the project and also to ensure the active participation of the concerned persons/actors, the project was presented to them in the initial phase. In the first phase of the project, the best professional knowledge which is critical regarding the strategy of the company and the vision of the concerned knowledge workers were identified. It also helped in putting the knowledge holders (knowledge workers) at the center stage of any process of thinking/acting. It practically involved four steps:

• • • •

Group working sessions involving those concerned with discussion on a particular issue, Individual interviews with nearly 20 people concerned in various processes (knowledge actors/experts), Interviews with managers to explain the strategy, and Readings of reference documents.

A total 80% of the interviewed knowledge actors had an average of 20 years of experience in their competences domains with highly qualified professional profiles. The interviews and group working sessions took place at the PED, together with Sonatrach top managers. During the interviews, some support tools such as profile cards, recordings, evaluation grids for criticality, etc. have been used along with interviewing techniques. During group working sessions, facilitation techniques (brainstorming) and projections (video projector) helped the group to merge their views and ideas for better knowledge sharing. Once formalized, all the results have been validated with the participants. The second phase of the project relies on the work of the first phase that has pointed out chunks of critical and strategic knowledge, of which a larger part is tacit knowledge, residing in the brain of the knowledge workers, akin to the point of criticality. Knowledge Engineering techniques are invoked to reduce this criticality factor using KM’s elicitation process to test the outcome of phase 1. The chosen domain for knowledge elicitation is the so-called “reservoir engineering” domain, while the elicitation method is MASK, a well-tried knowledge modeling

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method. This results in a set of models formalizing knowledge after developed from interviews with the knowledge holders. The models constitute a “knowledge book,” after supplementation by all the related information, documents, and sheets, etc. It capitalizes and disseminates a body of knowledge in a given domain, through descriptive sheets, memos, publications, hyperlinks, multimedia content video, audio, images, etc. The third phase of the project consists of capitalizing knowledge by applying the knowledge engineering method, followed by carrying out a pedagogical observation with the help of acquired knowledge models. The groups of learners involved in the process will evolve into communities of practice, once the knowledge is shared. The first possible use of a knowledge book is to make it available to professional actors in a dedicated “knowledge space,” integrated into the company’s information system. This is what we call “knowledge server” or “professional portal.” This type of system is an element that contributes to organizational learning since it provides stakeholders with professional know-how that improves practices in work situations. Another use of a knowledge book is to use the MASK models for pedagogical scenarios, describing the approaches to sharing and appropriating the modeled knowledge. This makes it possible to define the content of e-learning devices. Knowledge servers and Computer Environments for Human Learning, such as e-learning, are the technical support systems for the transfer of professional knowhow. During the vocational training, the knowledge book, representing knowhow and best professional practices, provides most of the content for the planned systems. This approach differs from a certain classical learning problem, which focuses more on the learner than on expert knowledge.

2. Strategic Assessment of Knowledge 2.1 The PED Department, a Significant Source of Strategic Know-how For the strategic assessment of the knowledge, a particular department/center of the organization serves as a main source of information. the and PED department, which operates in almost the entire E&P (Engineering and Production) value chain, manages the widest variety of petro technical data, as it collects and stores relevant data generated by other departments. It, therefore, serves as a center of interactions with various entities in the upstream activity. For the strategic assessment of the knowledge, a PED must fulfill the following missions:

• • • •

Studies and definition of development options in each basic engineering field. Planning and surveillance of operations (drilling and workover) and production. Technical surveillance and implementation of new techniques (“short radius,” horizontal drilling, etc.). Design and definition of development and exploitation plans for the deposits (operated by Sonatrach and its associates).

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Realization of technical and economic studies to develop existing or discovered reserves. Evaluation of reserves in all oil fields throughout Algeria, preparation of production, and injection forecasts based on the reserve situation, the level of development of the oil fields, and the capacity of the facilities. Evaluation of opportunities for the acquisition and development of capital, through own efforts and/or project partnerships in Algeria and abroad.

In view of the above, the PED department was chosen for the project under study.

2.2 Strategic Capacity Analysis of the PED Department The Sonatrach strategy map of the PED department, developed through several meetings and interviews, highlights the capacities that correspond to the vision of the managers of the PED department. This set of capacities is linked to the strategy for the identification of core business capacities. The strategic analysis highlights the strategic capacities required to achieve the company’s objectives. Fig. 6.1 illustrates the results.

2.3 Assessment of Knowledge Domains of the PED Department The first step involves consensual mapping of the knowledge domains, specific to the PED department by engaging concerned professional experts. The various activities in each knowledge domain are grouped, to structure them by means of representation, followed by validation with the experts, in an interactive manner. A knowledge map is thus generated. The progress of the map development depends upon the pace of the interviews. At a time, the different versions of the maps were validated during the project. This iterative validation took the form of a coconstruction to ensure ownership by the interviewees. Once this step was completed, a map (partial) was obtained, as shown in Fig. 6.2.

Fig. 6.1.

Strategic Capacities of the PED Department (Extract).

Knowledge Domains Map of the PED Department (Extract).

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Fig. 6.2.

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This map represents a description of the know-how of the PED department at macroscopic level to facilitate access. On the basis of this map, a criticality study was carried out by using the classical Critical Knowledge Factors grid that encompasses the Rareness of knowledge, Usefulness for the organization, Difficulty to acquire knowledge, and Difficulty in exploiting knowledge. The criticality assessment of a domain consists of assigning a score based on each criterion of the analysis grid. The more critical the domain, the higher the score. Each area was assessed independently of the others. The results for each domain were summarized graphically in a Kiviat diagram (Fig. 6.3). The results of this criticality study allowed completion of the knowledge domains map, in which the critical areas are identified by color coding (red, orange, and green). This visualization was useful for presentation to top management, as it can easily justify a complete analysis file carrying verbatim of interviews, scoring system, summary notes, Kiviat diagrams, etc. However, the following two results were obtained at this stage: (1) a strategic capacities map involving top management was obtained on the basis of the analysis of the company’s strategy and (2) a map of critical professional knowledge domains was obtained on the basis of analysis involving the technical managers of the units. The two maps, while representing two different points of view without being contradictory, will be useful in synthesizing them in the next step.

Availability 20 Dependence on the 14.0 2 Externalization environment 19 History of knowledge 3 Leadership 3.0 18 Difficulty of appropriation 4 Originality 2.0 17 Complexity 16 Depth

1.0 0.0

5 Confidentiality 6 Strategic adequacy

15 Speed of obsolescence

7 Value creation

14 Importance of tangible sources of knowledge

8 Emergence/Evolution

13 Tacit character of Knowledge 9 Adaptability 12 Mobilization of networks 10 Utilization 11 Difficulty of identification of the source

Fig. 6.3.

Knowledge Criticality Diagram of a Knowledge Domain.

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2.4 Strategic Alignment Cross-analysis of the abovementioned “critical knowledge domains” and “strategic capacities” involved in PED activity is called “Strategic Alignment” which is realized in two steps as follows: The first step is to link the professional knowledge domains to strategic capacities in an influence matrix as shown in Fig. 6.4. In the second step, each influence noticed in the matrix is analyzed by exploiting the main branches and their subbranches in the knowledge maps. Fig. 6.5 is an example of an analysis of the links between the knowledge domain “Reservoir Engineering” versus the strategic capacity of “Exploiting Hydrocarbon Reserves.” From a qualitative point of view, the results represent the professional knowledge domain identified as critical by operational managers and meeting the capacities required by the strategy. As the most critical knowledge was to be selected, it was, therefore, necessary to weight the analysis by the critical knowledge factors. In Fig. 6.5, the know-how appearing in the last line was not taken into account because it was not critical, whereas lines 1 to 4 represent both critical know-how and strategic capacities, out of which, only line 2 “Reservoir Simulation,” represents critical know-how involved in the maximum number of strategic capacities. It was thus selected as the most critical know-how that could best meet strategic requirements.

Domains

Geophysics

Geology Economy

Reservoir

Hydrocarbon reserves

X

Production

X

Drilling, exploration & seismic activity

Call for tenders for exploration blocks

Fig. 6.4.

X

X

Production

engineering engineering

X

X

X

Influence Matrix of Knowledge Domains and Strategic Capacities, First Level.

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Hyd. Res.

Reservoir

Reservoir

Res. Eng.

management

surveillance

PVT

X

Reservoir simulation

X

EOR assisted recovery

X

Reservoir management

X

Well tests

Fig. 6.5.

Petrophysics

X

X

X

Influence Matrix in the Second Level of Cross-analysis.

2.5 Conclusion of the Strategic Assessment of Knowledge All the critical know-how selected so far is reliable and verifiable because it is the traceable result of the application of global methodology (strategic mapping, knowledge mapping, criticality analysis, strategic alignment, etc.) involving all stakeholders of the company. It makes it possible to discriminate between knowledge within very important initial knowledge capital (the case study retained 15% of the knowledge initially identified), on the basis of objective criteria open to discussion. The strategic assessment revealed the very tacit nature of a large number of critical professional knowledge that justifies the next phase of the KM project.

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3. Capitalization of Tacit Knowledge 3.1 Knowledge Elicitation To reduce the criticality factor involving risk of tacit knowledge being lost, the project adopted an elicitation process applied to a chosen domain, by using the MASK I method, described in Chapter 4. Such a chosen domain is called “reservoir engineering” domain. The application of the MASK I method resulted in a set of models representing the knowledge domain, elaborated during the interviews with the experts of the domain. The whole process involved 10 experts, spending about 150 man-hours over a period of three months. These sets of models were equipped with important information, documents, files attached to the elicited knowledge, called a “Knowledge Book,” which carried with it the know-how of the domain “reservoir engineering,” as practiced in the PED division in Sonatrach. The knowledge representation in a Knowledge Book based on MASK I is a graphic presentation of professional knowledge consisting of:

• •

Domain knowledge, that is represented as a phenomenon model (Fig. 6.6) and concept tree Reasoning knowledge that is described as a process model and task tree (Fig. 6.7). Influence • Uncertain data for wells (contacts, petrophysics evaluation) • Uncertain data

Source

Target

Geological model Triggering event : • Requirement to determine the existing fluids

• Evolution from the structural model to a stratigraphic (static) model • Upscaling

• PVT • SCAL (Special Core Analysis)* • Contacts (WOC : Water Oil Contact, GOC : Gas Oil Contact)

Geological model

• Workflow of the reservoir model ( conventional or enhased**) Modelisation software : Eclipse, VIP, ATHOS

• N (STOOIP) = (A*Φ*NTG*Hu)/Bo

Consequences : • Existing fluids classified and valued • Ready for the simulation phase

Evaluation STOOP (Standard Oil in Place)

Quantity of fluids (oil, gas, GPL, condensed …) under standards condition (1bar ,15°c )

*Capillar pressure Pc and relative permeability (oil Kro, water Krw et gas Krg) ** uncertainty managemnet due to the stratigraphic structure

Fig. 6.6.

Simulated Phenomena in Reservoir Engineering.

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Analyze the coherence logcarrots

Communicate the results to the project team Report with the missing data and data classification

Collect the data from the lab

•PVT •Pc •Kr

Analyze the quality of the data

• Excel • VectorizedImplicit Program (LANDMARK -Halliburton) • dTPVT (VIP)

Fig. 6.7.

Collect the data at the well

Analyze the records

Collect the description of the carrots

Create tables CPP

• Logs at the well • tests • etc.

Task Model in Reservoir Engineering (Extract).

Each modeling phase is based on interviews with specialists, using cognitive techniques. It results in the “coconstruction” of the models with the knowledge holders. The collected contents are then reviewed to reach some quality standards, followed by their completion with the help of different information and document sources. A validation phase is finally organized with all the contributing experts. The Knowledge Book thus made for the project for the PED expertise carried about 200 pages, accompanied by an index. It took 6 months to develop the K. Book, which was soon recognized in PED as a real professional reference for reservoir engineering. However, the final destination of the project’s Knowledge Book lies in its encapsulation followed by its dissemination through various devices, in order to get the most valuable benefits out of it.

3.2 E-KBook: An Electronic Knowledge Book The Knowledge Book is the final output of a Knowledge Engineering process. However, to ensure knowledge dissemination, sharing, and enrichment, it was decided to upload Knowledge Book in electronic form (e-KBook) on the company’s intranet. This required three-dimensional applications of hypermedia as given below:

• • •

The content: the information presented as texts, drawings, diagrams, pictures, videos, etc. The browsing network: consisting of browsing links representing nodes as the pieces of information of the content. The user interface: the frontend.

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The e-KBook offers different possibilities and viewpoints for exploring knowledge.

• • •

Phenomenon models assist to understand the physical properties and phenomena of Reservoir Engineering. The activities decomposition facilitates understanding and follow-up of various processes in Reservoir Engineering The interactions between activities and phenomena help in understanding and mastering of the complexities of reservoir engineering.

e-KBook aims at providing a part of the Knowledge Book to different users, whose profiles may vary depending on the objectives (capitalization, vulgarization …)

4. Transformation of the Knowledge Book into E-learning for Professional Knowledge 4.1 Introduction The process of knowledge transfer is evidently based on the training of knowledge workers in the organization because the basic purpose of a Knowledge Book is to capitalize upon critical professional know-how. The phenomena of capitalizing and learning are the two faces of the same coin. The KM project was, therefore, geared to transfer professional knowledge by constructing learning activities from Knowledge Book through a device called “e-PLearn” (e-platform of learning). Learning activities facilitate learners/employees of the organization to internalize knowledge. The process of constructing such a learning activity requires identification and modeling the scenarios that the employees will have to perform, the different tasks they will have to perform, the different roles to be distributed, etc. When the knowledge, to be learned by the employees, is a part of the company’s KMS, building the learning activities scenarios from the data stored in the Knowledge Book appears natural. The KM project, understudy, builds such a process more precisely, by constructing learning scenarios using MASK methodology and to represent them using the IMS-Learning Design (IMS-LD) language (IMS, 2003). The following description dwells upon the matching between the different MASK models and the construction of different components of an IMS-LD scenario.

4.2 IMS-learning Design Learning Design is an advancement of e-learning, which not only deals with the content but also captures the “learning processes” through collaborative learning activities. The process of Learning Design has thus emerged as one of the most significant recent developments in e-learning. From the perspective of standards/ specifications, IMS Global Learning Consortium has recently released the IMS

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Learning Design specification (IMS, 2003), based on the work of the Open University of the Netherlands (OUNL) on “Educational Modelling Language” (Koper, 2001), a notational language to describe a meta-model of instructional design. The OUNL coordinates an international EML/IMS Learning Design implementation group known as the Valkenburg group (Tattersall, 2003), although OUNL has intended to no longer continue developing EML, instead, it will focus upon new IMS Learning Design specification. The suggested three levels of representation by IMS-Learning Design allow the specification and implementation of a great diversity of e-learning teaching contents (Fig. 6.8):

•

•

At Level A, Learning Design specifies a time-ordered series of activities to be performed by learners and teachers (role), within the context of an environment consisting of learning objects or services. Analysis of existing design approaches revealed that this was the common model behind all the different behaviorist, cognitive, and (social) constructivist approaches to learning and instruction. For more advanced learning purposes, properties and conditions, and notifications are required. Levels B and C of the Learning Design Specification provide: Properties, specified at Level B, needed to store information about a person or a group of persons (role). In the case of a student, progress may be stored, perhaps in a

Condition

Method

*

Learning objective

*

Global elements

1-* Properties

*

Scenario

Prrequisite

* Pedagogical activity

1-*

*

Activity support

Act Person

1-*

1-*

*

Environment

Role-part

*

*

Role

Activity

Outcome

Notification

1-*

Learner Professor

Activity structure

Fig. 6.8.

Learning object

Service

Conceptual Model of the Overall Learning Design Structure (IMS, 2003).

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dossier; for a teacher, information of grades may be stored. Conditions, also being a part of Level B, confine the actual evolution of the educational scenario. They are set in response to specific circumstances, preferences, or the characteristics of specific learners (e.g., their prior knowledge). The idea is of course that randomness allows the student to freely explore the materials. Notifications, specified in addition to the properties and conditions of Level B and C, are the mechanisms to trigger new activities, based on an event during the learning process. For instance: the teacher is triggered to answer a question when a question of a student occurs; or the teacher should grade a report, once it has been submitted. For the present project, we will limit ourselves to the “A” level, i.e., the general design of the scenario as time-ordered activities.

4.3 Matching MASK/IMS–LD 4.3.1 Identifying General Scenarios Before a MASK I project, as described in Chapter 4, we generally use a model, called “Domain Model,” to identify a knowledge domain to capitalize in a given framework, in order to propose a global vision of the modeled knowledge. In other words, it allows identification of the general scenario(s) of the learning activities at the initial stage. The continuation of the same will result in deepening of each element representing a flow. This process underlines a principle based on the perception of a field, like a decomposition of the phases wherein each phase can, in turn, be broken up into other phases of the lower level. The idea is thus to describe the various headings of the teaching scenario by traversing these phases. The total framework will be defined starting from the phenomena model to the succession of the decompositions which will give an indication about the granularity level of the teaching schematization. A phenomena model can, thus, provide several scenarios guided by various possible principal activities models (Fig. 6.9). 4.3.2 Defining the Scenarios from Principal Activities Model The general scenario can be detailed further as follows:

• • •

The inheritance model allows defining different scenario elements, such as global prerequisites including global teaching objectives, etc. and the principal activities model including the different activity steps. (Fig. 6.10) The general activities model allows making the different activity steps more precise by defining different characteristics, such as the step number, the title, and in particular, the different actions to be scheduled later in the process. (Fig. 6.11). The different activity submodels along with their referent tasks and concept models allow different features of the learning activities (such as the different roles, the teaching objectives, and the intended production, etc.) to be more precise (Fig. 6.12).

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Domain Model

Principles of teaching engineering

IMS – LD Norm

Selected activities principal model

Concepts model

Tasks model Knowledge to be transmitted

Learner

Resources & constraints

IMS Learning Design teaching scenarios (Teaching objectives, Pre-requisites, Roles, etc.)

Fig. 6.9.

Domain Model Generates Various Possibilities of Scenarization.

MASK modeling elements Knowledge inheritance model Title

Scenarisation elements Teaching scenario general structure Timing

Consumed knowledge

Title Global prerequisites

Produced knowledge

Global teaching objectives Didactic principles & synopses

Principal activities model Principal activity n° X

Fig. 6.10.

1..n

Activity 1 Sort

step

General Framework Defining from MASK Models.

As the objective of the learning is to work out accurately a given procedure, the activity can be further detailed using the tasks model of the considered activity (particularly, the task model that describes the “expert” problem resolving strategy).

4.3.3 Example of Matching MASK/IMS–LD Once “Reservoir Engineering” MASK I models capitalizing knowledge carried out and validated by the experts (syntax and contents), we applied

Knowledge Management System MASK modelling elements

139

Scenarisation elements

Principal activities model Step Principal activity n° X

1..n

1..n Step Description N° of step

Activity title

Step title End step Conditions Action

Elementary activity n° X

1..n

Fig. 6.11.

1..n

Scheduling

Steps Defining from MASK Models.

MASK modelling elements Final activity model Title Resources

Scenarisation elements Learning activity (action) Titre Materials suitable for training environment Teaching materials (Learning Objects)

Actors/Roles Inputs/outputs

Other Staff roles Teaching objectives

{ Knowledge, Knowledge to make, Knowledge to be } Tasks model 0..n (Strategy for solving problems + involved within the activity) Concepts model 0..n (Classification & description of + concepts defining the activity)

Fig. 6.12.

Production Prerequisite

Description

Activities Learning Defining from MASK Models.

our matching process as we have defined it. Table 6.1 presents a general learning scenario generated. This general scenario is based on six activity steps, each step being then refined into 24 activities. From the standard description of this material can be generated an IMS-LD description as an XML file.

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Table 6.1. General Learning Scenario Generated. Timing: Depending of the nature of Learning Learning activity general structure for “Reservoir Engineering” Course title: Learning the job “reservoir engineering.” Global teaching objectives: … Global prerequisites: Adaptability to development planning and economic and strategic context, fundamental basis of the job Didactic principles and synopses: alternate individual and collective learning steps alternate synchronous and asynchronous learning steps … Learning steps (Learning steps references and execution conditions) R´ef.

Starting with?

Waiting the end of learning step

1

Yes

/

2 3 4

No No No

1 2 3

5

No

3

Learning step title Preparation of the upstream project Project beginning Models construction Uncertainty Management Operating the optimal planning

Next Learning step 2

3 4 or 5 3 /

The constants of the learning scenario from the MASK models can be characterized by:

• • •

The obtained scenario, covering the key knowledge like that in the MASK models. Training process features (staff roles, durations of the meetings, environment materials, etc.) that are not taken into account at the time of MASK modeling. Definition of the level of granularity: MASK Models constitute “a block” of knowledge distributed on the various levels and models. In order to keep the direction of the knowledge-making, it is a constraint to adopt the same levels of granularity and decomposition.

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•

141

Some elements of the teaching scenario cannot be directly picked in the given MASK model but can be extracted from a combination of models. As an example, the description of the Learning activity requires elements “Knowledge-to make” and “Knowledge-to be” aspects as the corresponding model of the activity, descriptions of the task models, and those of concepts models by referent.

This work of definition of contents, design, and scenarization or schematization is intended to the actors of the field through a Computer Environment for the Human Training (CEHT), standard e-Learning, as described in IMS-LD. This requires additional work to reinforce the assets of such transition and to answer the difficulties and/or shortcomings recorded at the time of the transition. For future developments, the following can be considered:

• •

The expert can be called, during the interviews, to indicate some elements which he/she considers essential to explicit his mode of reasoning and/or his/her own way of resolution of problems, in order to include it in the e-PLearn device. Exploit the Knowledge Book rather than the simple MASK models. The Knowledge Book is, in fact, the final output of the MASK I, which includes all different models. The Knowledge Book has the advantage, to compare the models, in order to provide complementary descriptions which fill the gap between the theory and practice.

4.3.4 Quiz for Evaluation Evaluation of any system is the last stage of the project. For this purpose, quizzes have been used as questionnaires. The quizzes serve as tools to do the following:

• •

Validate the acquired knowledge, by putting questions pertaining to Learning objectives. This is known as “summative evaluation,” which is used to define the learner’s level and write the appropriate scenarios. Verify the learners’ progress, keeping in view the objective of the predefined program. This is the “formative evaluation,” which is used in e-PLearn as a limited time exercise for comments and answers, in order to be used as an autoevaluation tool.

The freeware “HotPotatoes” version 6 helped the implementation of the quizzes, which are nothing but a translation of phenomenon models, chosen with the collaboration of the experts (Fig. 6.6). The corresponding quizzes are built to highlight the key points. 4.3.5 E-PLearn: A Computerised Environment for Human Learning (CEHL) for Professional Learning E-PLearn is an intranet-based e-Learning environment that integrates learners in a professional situation, and provides them with a set of pedagogical objects and

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interactions, through the access to formative resources. The aim of e-PLearn in the organization is to transfer knowledge, applicable to employees in an operational context. Its pedagogic content must then be oriented toward an effective application. The pedagogical design of e-PLearn not only includes both contents as well as tasks but also “learning relationships,” that ultimately lead to actualization. On line, coaching is implemented from the perspective of dialogical communication. The learner, as soon as connected, can test his level to personalize his pedagogical agenda or identify himself as beginner, specialist or expert for “Reservoir Engineering,” by introducing quiz, and translation of the knowledge modeled by phenomenon models. E-PLearn aims at fostering the collective learning process for knowledge acquisition that is recognized as crucial by the previous assessments and capitalized by Knowledge Engineering techniques. Collaborative learning is implemented in order to:

• • •

Recognize the added value of knowledge built-in action Formalize knowledge to facilitate access by others Help flexibility in organization.

5. Conclusion 5.1 The Knowledge Management System Knowledge Management System (KMS) of an organization is often seen as a federation of various tools, with no strong integration. In that case, a coherent and integrated approach has been elaborated from the strategic analysis stage to the implementation of a KMS, which is now called a Knowledge Server. The latter is a professional portal, dedicated to a precise activity in the company, enabling access to every kind of knowledge resource, in an efficient and useful way. The knowledge resources include knowledge maps, knowledge books, technical repository (norms, files, procedures, quality processes, best practices …), learning content, etc. e-KBook and e-PLearn are the basic components of a Knowledge Server. The question of actualization and creation of knowledge (innovation) is crucial for every organization. Knowledge Server, as a tool for knowledge capitalization, sharing, transfer, and evolution, is built with the necessary help of the knowledge workers themselves. Therefore, it must also grow with them. The use of e-PLearn devices generates needs that push learners to communicate with each other in order to exchange their knowledge and experience for application in widely different environments as a learning community.

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The next step will be to support the progressive emergence of Communities of Practices or Knowledge in the sense of (Wenger & al., 2002).

5.2 The KM Process The key to the success of a KM project is the collaboration and coordination of concerned parties in the best possible participative way. The professionals are indeed the main producers and consumers of know-how. Their participation as codesigners is most essential for the appropriation of the methodology adopted by the project. The active participation approach for the pilot project involving both top managers and operational knowledge workers has largely reduced resistance to sharing and elicitation of knowledge. The mapping approach of the strategic assessment is aimed at the location of knowledge (review of knowledge areas, identification of expertise holders, etc.), evaluation and analysis of criticality of the knowledge capital (audit), and the visualization of critical knowledge and its alignment to Sonatrach strategy. This made it possible to identify know-how on which this strategy had some impact and therefore to identify the knowledge domains which have to be sustained and/ or developed by actions of transfer particularly via capitalization and learning. Model-based approaches meant for knowledge capitalization and knowledge sharing are of great interest in KM. The transition from knowledge engineering models to an electronic Knowledge Book and to e-Learning devices for human teaching is straightforward. The aforesaid concepts and processes involved have been made use of practically in a case study of Sonatrach, an international oil company of Algeria. It may be concluded that implementation of KMS is not an isolated project, but must be linked from the initial stage involving a sound strategic analysis and alignment, to a sophisticated specification using knowledge modeling, to the development of innovative software development. These key factors have been tested in that project and their positive implementation will lead to the operational development of the company for the better.

References Benmahamed, D., & Ermine, J.-L. (2007a). Knowledge management techniques for know-how transfer systems design. The case of oil company. In S. Hawamamdeh (Ed.), Creating collaborative advantage through knowledge and innovation. Series on Innovation and Knowledge Management (Vol. 5, pp. 15–34). London: World Scientific Publishing. Benmahamed, D., & Ermine, J.-L. (2007b). A knowledge server including tools for professional know-how transfer. ECKM 2007 (European Conference on Knowledge Management), Barcelona, 6-8 September 2007. IMS technical board: IMS learning design version 1 final specification. (2003). Retrieved from http://www.imsglobal.org/learningdesign/index.cfm. Accessed on February 2003.

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Koper, R. (2001). From change to renewal: Educational technology foundations of electronic environments. EML website. Retrieved from http://eml.ou.nl/eml-ounl.html Tattersall, C. (2003). EML and IMS learning design. Vancouver, BC: Presentation for the Valkenburg Group. Wenger, E., McDermott, R., & Snyder, W.-M. (2002). Cultivating communities of practice. Boston, MA: Harvard Business School Press. ISBN: 1578513308.

Chapter 7

Knowledge Management System Standardization: An Overview 1. Introduction The industrial revolution called as a turning point in the history of the world had brought a transition in the processes of manufacturing goods, which in consequence impacted every aspect of daily life of the people by bringing necessary changes through new scientific inventions, which include production of Electric Power, Medicines, Textiles, and Transportation means (Cars, Aero planes, Electric Engines), just to name a few. The two phases of industrial revolution that took place from 1760 to 1850 and from 1850 to 1914 had thus brought into sea changes in the economy of the countries worldwide. During the period, the manufacturing of goods basically shifted from homes/shops to large-scale factories or industries, whereas the agrarian and handicraft-based societies shifted to the locations where technical skills of the people could be utilized and developed further in meeting the goals of large-scale industries. Consequently, the industrial revolution had brought in significant social changes, through the process of urbanization. For any industry to sustain, it is of paramount importance to maintain the quality and consistency in the product or service they were delivering. For the purpose, it is necessary to undertake an in-depth study of the complex processes and practices followed right from the input stage to the output stage to increase effectiveness. The process known as “Systems Analysis” involves the following five stages: Planning, Analysis, Design, Implementation, and Support/Maintenance (Fig. 7.1). The continuous analysis followed by improved processing practices results in the maintenance of the quality products and services. As such standards may be created in any organization to guide the quality product or service by evolving a consensus between all the concerned parties, which is what, is called “Standardization.” Parties involved in building the consensus may include organization/Industry, Users of the products, Interest groups, Governments, and Standards Organizations (National/International).

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Sup ort

Planning

I plementation

nalysis

Desig

Fig. 7.1.

The System Analysis Process.

2. Benefits of Standardization Some of the benefits of the process of standardization include

• •

•

•

Elimination of ambiguity and guesswork Ambiguity and guesswork directly affect the quality and reliability of the products. Their elimination involves a well-defined structure of the organization and the set of procedures followed by each unit of the business. Confidence building The standardization leads to the overall confidence building among different levels of staff of the organization which include Top-level Management, Middlelevel Management, and Low-level Management. As the employees/technical staff of the organization has clarity about the procedures to be followed, it on the whole contributes to their self-assurance and self-confidence. Quality assurance The result of industrial revolution made it difficult for the factories/industries to sustain in the market, where the cutthroat competition naturally eliminates anything or everything, that is inferior to others. In such a situation quality assurance is the buzz word. Investors in the market always think upon return on investment which made standardization of products essential for all stakeholders. A process of standardization naturally centers on the quality products of an organization. Productivity enhancement An atmosphere created through standardization of procedures and practices in an organization is bound to enhance the productivity because the modus operandi or the protocol followed at every stage is already well defined after removal of ambiguities, whatsoever. The staff time that otherwise would have

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been wasted in the guesswork will contribute to the productivity. In addition to that, the repeated following of standard procedures also adds to the enhanced quality production.

3. Purpose of Standardization Although the work process of one firm may be different from the other, yet the basic aim of achieving the organizational goals remains the same. For the purpose, some type of standardization in the practices/procedures followed in the creation of products, and the technology followed is indispensable. The consistent quality of the standardized products not only attracts the consumers but also promotes the convenience of use for the buyer. Product standardization is useful for the following reasons:

•

•

•

•

Production effectiveness and price tag The set of protocols, applied in the production of goods, increase production efficiency while saving time and human resources, and maintain uniformity in the products with minimum efforts. The standardization of the products ensures that the cost of both production and maintenance is minimal. The set of guidelines followed by the firms ensure the best quality at minimum cost. Globalization and branding At present times people find no difficulty to consume the products made in foreign countries. The globalization of the business market has a great role to play with the marketing of products. It gives a chance to the firms to make their products with the same quality available to the world in which they are selling in their parent country. The availability of the products with consistent features across the local and international domains enables it to be recognized as a brand. There may be the products with the same composition but the consumer will go with the one which he/she trusts more on the basis of its quality and usage. Uniformity of features maintained through standardization helps in the branding of products. Customer satisfaction Customer satisfaction is the biggest concern for any product making firm. Consumers prefer to spend more on a standard product as compared to a cheaper customized item having the same composition. When applied to technology products, standardization is likely to be more convenient in maintaining the compatibility of technological types of equipment worldwide. Product standardization has a great influence on the marketability of an item. Quality and innovation Standardization has definite benefits over customization, as it makes sure that manufactured products are put up to a certain quality and uniformity in every aspect. Standardization makes rejection of a product with even a small error easier, thus helping in the maintenance of the standard quality of the products. Standardization of production protocols enables employees to work more smoothly and efficiently by giving more return on investment. The availability of a standardized infrastructure to work enhances the possibilities of innovation.

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Organizations

Effects of Standardization

Technology

Fig. 7.2.

Consumer

Effects of Standardization.

4. Steps to Standardization The tripartite process of Standardization involves Organizations, Technology, and Consumers as diagrammatically shown in Fig. 7.2.

• • •

Organizations: Standardization enables the competitive firms, irrespective of their type or size, to provide guidelines for establishing, applying, retaining, reviewing, and improving upon their products and services. Consumers: Consumers out of the standardization process are getting advantage of interoperability between products, by enabling them to match the components and choose the product of their choice. Technology: The standardization has a mixed impact on technology. On one side, standardization helps to rule the incompatible technologies out of the market, while it restricts experimentation of new ground-breaking technologies in the organization, on the other.

5. International Organization for Standardization On February 23, 1947, International Federation of the National Standardizing Associations founded International Organization for Standardization, to be called as ISO which is an Independent, International Non-Government organization.

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It was charged with the function of international standard-setting body composed of representatives from National Standard Organizations to ensure consistency. ISO with its headquarters at Geneva, Switzerland, presently comprises of 164 members. ISO certification has separate standards for every process/procedure/organization. The numbering system of ISO certification consists of three parts (Fig. 7.3 for example). The process of standardization has the following five goals: (1) (2) (3) (4) (5)

To To To To To

meet the requirements of stakeholders be useable by the organizations of different sizes be useable by the organizations in all sectors be understood clearly help application of QMS to the business processes

During the period of industrial revolution concerns have been raised with regard to quality management and quality assurance that may help in maintaining an efficient system of quality within the organization irrespective of their size. Consequently, ISO 9000 was published by ISO in 1987, this was based on BSI 5750 series of standards. The aim of ISO 9000 was to satisfy customers by following the regulations in order to improve consistently. ISO 9000, updated in 2000, 2008, and 2015, is a family of standards pertaining to quality management. The series consists of the following four standards:

ISO International Organization for Standardization

Fig. 7.3.

9001: Quality Management

2015 Year of Launching

The Numbering System of ISO Certification.

150

• • • •

Knowledge Management Systems

ISO 9001:2015: Quality Management Systems – Requirements ISO 9000:2015: Quality Management Systems – Fundamentals and Vocabulary (Definitions) ISO 9004:2018: Quality Management – Quality of an Organization – Guidance to Achieve Sustained Success (continuous improvement) ISO 19011:2018: Guidelines for Auditing Management Systems

6. ISO 9000 and Knowledge Transfers As ISO 9000 appertains to quality management, two interrelated concepts namely TQM and QMS need to be discussed here, as both are involved in the enrichment of the quality standards. A quality management system is a process of managing the quality standard of a product; it is a plan for its continuous measurement and improvement. On the other hand, Total Quality Management (TQM) is a management approach to long-term success through customer satisfaction by enabling the company’s staff to improve upon their products and services to meet the ultimate requirements of the customers. Though ISO 9000 and TQM serve as a common foundation for two different estimates to quality management, yet many authors including Taylor, 1995, have studied their interrelationship. However, Meegan and Taylor, 1997, find clear differences between the two, while Sun, 2000, Sun and Chen, 2002, have tried to study the performance of the organization, once ISO 9000 and TQM are implemented. The process of knowledge transfers brought within the purview of the ISO 9000 standards showed better consequences in the transfer process, by bringing greater control on the working practices and also on their delivery within the organization (Curkovic & Pagell, 1999). Szulanski, 1994, laid emphasis on the periodic review of the procedures under the certification process, for bringing out the performance problems. Following a common language, the standards instituted by the ISO 9000 impact upon knowledge encoding (B´e n´e zech et al., 2001). The existing common language is considered a prerequisite for assisting knowledge transfers between the sender and the receiver both (Huber, 1991). The fore discussed researches enable us to conclude that the implementation of ISO 9000 standards improves the knowledge transfers within the organization. During the period of the Industrial Revolution, the importance of standardization and its implementation became increasingly important in every sector especially in commerce and industry. In general, Standardization being a protocol or simply a set of guidelines allows organizations to maintain a quality standard of products with a fixed approach.

7. ISO 30401:2018 Knowledge Management Systems – Requirements Of late Knowledge Management has emerged as a strategic approach to the implementation of the objectives and the means of the organization to capitalize, share, and create knowledge, through a new relationship between the People and

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Information and Communication Systems. Recent moves toward what has come to be often called “Knowledge Society” tend to consider knowledge as a fundamental issue in the progress and welfare of nations. The present state of affairs may thus be considered as the dawn of a major evolution in managerial, socioeconomic, and political thought process around the globe. The advent of ICT has enabled organizations to manage their knowledge assets which otherwise remained unnoticed before the emergence of the concept of Knowledge Management Systems. A KMS is an ICT-enabled System meant for managing the tacit knowledge of an organization as well as making explicit knowledge available to the staff for optimal use. In the present times, when companies are hell-bent to provide a competitive advantage by offering better products and services at lower prices or value-added benefits, the implementation of such systems in every organization is crucial for their survival. Consequently, the installation of KMS during the last 20 years became increasingly frequent in order to capitalize, share, and create knowledge to effectively respond to the challenges posed by the observable facts like globalization, population aging, and digitalization. This had necessitated standardization of procedures and practices that can effectively tap and utilize the knowledge assets of the organizations. To lend credence to the aforesaid happenings especially during the past 20 years, ISO published a standard known as ISO 30401:2018 Knowledge Management Systems – Requirements in December 2018. The purpose of ISO KMS as it may be called is to provide a definite framework for establishing, maintaining, reviewing, and improving an affective System for KM organizations irrespective of their type and size or product and services they provide. The standard will help achieve consistency and maturity in KM practices, leading to the assessment of applied KM models in the organization on the basis of stated principles. As a matter of fact, ISO KMS basically serves the same purpose for which ISO was initially established. As we are aware, a KMS involves the following steps: (1) (2) (3) (4) (5)

Identifying Knowledge Capturing Knowledge Storing Knowledge Retrieving Knowledge Sharing Knowledge

The aim of KM is to create applied knowledge for effective decision-making brings efficiency in the processes involved to provide a competitive advantage. KM practices followed in any organization offer opportunities for learning, practicing, and exchanging information available explicitly or implicitly.1 One of the

1

ISO 30401:2018 Knowledge Management Systems. (2018). In ISO Knowledge Management Standard (ISO30401) – Brief Review. Retrieved from http://knowledgemanagementdepot. com/2018/12/08/iso-knowledge-management-standard-iso30401-brief-review/. Accessed on April 30, 2020

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most important aims of a KMS is to capture, use, and reuse tacit knowledge and ultimately convert it into explicit knowledge for the present and future benefit of the organization. The ISO KMS, thus, helps the organizations to effectively achieve the intended purpose for which a KMS has been developed. Like other ISO standards in different domains, ISO KMS was developed by the Technical Committee ISO/TC 260, Human resource management procedures as per the ISO/IEC Directives used for the purpose. The 20-paged standard published in 2018 is available for a cost CHF 118,00. Salient features of ISO KMS are available on the ISO website. The table of contents of ISO 30401:2018 Knowledge Management Systems given therein includes the following:2 Introduction describes the Purpose and importance of the ISO management system standard for knowledge management, followed by Guiding principles and Range of knowledge management. Scope provides guidelines for establishing, implementing, maintaining, reviewing, and improving an effective management system for knowledge management in organizations. Terms and definitions used for the purposes of the standard were defined. Context of the organization deals with Understanding the organization and its context, Understanding the needs and expectations of interested parties (stakeholders), determining the scope of the knowledge management system, Knowledge management system and Culture. Leadership includes Leadership and commitment, policy, roles, responsibilities, and authorities. Planning covers actions to address risks and opportunities, knowledge management objectives and planning to achieve them. Support deals with such aspects as Resources, Competence, Awareness, Communication, Documented Information. Operation deals with the planning, implementation, and control of the processes needed to meet requirements of the organizations and also to implement the actions. Performance evaluation that includes monitoring, measurement, analysis and evaluation, internal audit, and management review. Improvement covers such aspects as nonconformity and corrective action, continual improvement. Annexure A, B, and C are informative in nature, as they describe “The knowledge spectrum” – the range of knowledge management, Relationship between knowledge management and adjacent disciplines, and Knowledge management culture, respectively.

2

Table of Contents. (2018). In ISO 30401:2018(en) Knowledge Management SystemsRequirements. Retrieved from https://www.iso.org/obp/ui/#iso:std:iso:30401:ed-1:v1:en:e. Accessed on April 30, 2020.

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8. Conclusion Knowledge Management has emerged as the means of implementation of the organization’s aims and objectives to capitalize, share, and create knowledge in order to bring efficiency in the processes involved to enable it to provide a competitive advantage. The discipline, which is of comparatively recent origin, has now got established itself both in theory and practice. The publication of an ISO standard, i.e., ISO 30401:2018 Knowledge Management Systems – Requirements in 2018 bears testimony to the aforesaid statement.

References Be´ne´zech, D., Lambert, G., Lanoux, B., Lerch, C., & Loos-Baroin, J. (2001). Completion of knowledge codification: An illustration through the ISO 9000 standards implementation process. Research Policy, 30, 1395–1407. Curkovic, S., & Pagell, M. (1999). A critical examination of the ability of ISO 9000 certification to lead to a advantage. Journal of Quality Management, 4(1), 51–67. Huber, G. (1991). Organizational learning: The contributing processes and the literatures. Organization Science, 2(1), 88–115. Meegan, S. T., & Taylor, W. A. (1997). Factors influencing a successful transaction from ISO 9000 to TQM. The influence of understanding and motivation. International Journal of Quality & Reliability Management, 14(2), 100–117. Sun, H. (2000). Total quality management, ISO 9000 certification and performance improvement. International Journal of Quality and Reliability Management, 17(2), 168–179. Sun, H., & Chen, T. K. (2002). Comparing reasons, practices and effects of ISO 9000 certification and TQM implementation in Norwegian SMEs and large firms. International Small Business Journal, 20(4), 421–442. Szulanski, G. (1994). Intra-firm transfer of best practices project. Houston, TX: American Productivity and Quality Center. Taylor, W. A. (1995). Senior executives and ISO 9000: Attitudes, behaviours and commitment. International Journal of Quality & Reliability Management, 12(4), 40–57.

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Chapter 8

Knowledge Management International Standards: ISO 9001, 30401, and IAEA Safety Standards 1. Introduction As explained in Chapter 7, the need for standardization of Knowledge Management (KM) had been growing steadily parallel to the maturity of the subject, coupled with the crucial need of the business enterprises in the private and public sector to take into account the implementation of this strategic aspect, called KM in its operational terms. The legitimate question of “Why?” which arose more than 20 years ago has now been replaced by “How?” that dominates the primary concerns of the organizations. Consequently, many KM models have appeared in recent years to guide managers in the implementation of a KM system. However, as shown in Chapter 2, no single KM model is found to be suitable for all different organizations because of the fact that divergent estimation of KM processes implemented and validated in real-world settings have resulted in different structures. It is, therefore, imperative to have homogenous practices and a common framework to assist the implementation of KMS in organizations. An era of KMS standardization has come about since the mid of the last decade with concrete and significant results already in various fields of activity. Two advanced standardization projects have been discussed as follows: (1) International Organization for Standardization (ISO), in general, (2) International standardization in the nuclear field (International Atomic Energy Agency (IAEA)).

2. Knowledge Management in the ISO 9001 Standard ISO, the largest standardization body in the world, is a nongovernmental organization representing a network of national institutes from 165 countries. It aims

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to produce international standards, called ISO standards, implementable in the industrial and commercial organizations leading to the coveted seal of conformity to international standards. The ISO 9001 standard included a paragraph devoted to KM (ISO DIS 9001 § 7.1.6) since 2015. The requirements of the 2015 ISO version are well known by KM practitioners as follows:

• • •

Identify the required knowledge necessary for business processes and conformity for products and services; Maintain and disseminate the knowledge; Identify how to acquire or access additional required knowledge.

This standard (see the text below), as we can see, lays the foundations for the KM processes that must be implemented in companies. Notes 1 and 2 of the standard outlines, in a very rudimentary way, the concept of internal Knowledge Capital and the link with the existing external capital. They do not really distinguish information from knowledge and are allusive about the notion of tacit knowledge. However, the introduction of a paragraph on “Organizational Knowledge” in the international standard is a historic step for the recognition and implementation of KM in companies.

ISO/FDIS 9001:2015(E) 7.1.6 Organizational knowledge The organization shall determine the knowledge necessary for the operation of its processes and to achieve conformity of products and services. This knowledge shall be maintained and be made available to the extent necessary. While addressing changing needs and trends, the organization shall consider its current knowledge and determine how to acquire or access any necessary additional knowledge and required updates.

• •

NOTE 1: Organizational knowledge is knowledge specific to the organization; it is gained by experience. It is information that is used and shared to achieve the organization’s objectives. NOTE 2: Organizational knowledge can be based on: – internal sources (e.g. intellectual property; knowledge gained from experience; lessons learned from failures and successful projects; capturing and sharing undocumented knowledge and experience; the results of improvements in processes, products and services); – external sources (e.g. standards; academia; conferences; gathering knowledge from customers or external).

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3. Knowledge Management Standardization in the Nuclear Domain 3.1 Nuclear Knowledge Management The nuclear domain is very sensitive to the issue of preserving its Knowledge Capital globally. This domain is knowledge intensive, and it is particularly at risk of knowledge loss, for it covers highly technical and scientific aspects. Like in many other technical domains, a long-standing loss of interest of young generations toward scientific careers in general, combined with a long period of nonrecruitment, has created a very serious knowledge gap, accentuated by the aging demography affecting skilled people in that field. The well-known security and geostrategic constraints of the nuclear domain also add to the criticality of the risk related to knowledge. In the recent past, the nuclear domain has been experiencing an unprecedented renaissance, mainly in energy, for several reasons, such as increasing energy demands and the fight against climate change. The problem of knowledge is particularly acute at the level of its preservation as well as its dissemination and evolution. The aforesaid problems have attracted particular attention by international organizations in the nuclear domain, notably the IAEA, which being a United Nations (UN) organization, is at the center of international cooperation in the nuclear domain. The IAEA aims to promote the safe, secure, and peaceful use of nuclear technologies among its member states. The governing body of IAEA is the General Conference, led by the Board of Governors. Regarding the problem of knowledge, given the importance of the issue, the General Conference in 2002 in Vienna voted on a resolution, which was reiterated during the General Conference in 2014. Nuclear Knowledge Management Resolution, General Conference June 17-19, 2002, Vienna Declaration of Mohamed El Baradei, General Director of the IAEA (Excerpt) If we do nothing, we may be facing a situation by the end of the next decade in which the opportunity for a revival of nuclear power in terms of qualified personnel, safety, the expectations of developing countries and of our future will be lost together with knowledge and know-how built up over successive generations Consequent upon the Vienna declaration, a NKM (Nuclear Knowledge Management) section was created within the IAEA to fulfill the stated mission. The main objectives of the NKM section were as follows:

•

Increase member state awareness and understanding of risks and challenges of KM in the nuclear sector.

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Development and dissemination of good KM practices in all member states. Facilitate nuclear knowledge flow to developing countries and newcomers. The activities of NKM section consist of: Developing methodologies and guidelines to plan and implement KM programs in the nuclear domain. Facilitating training, networks and the exchange of experience in the nuclear domain. Assisting member states by providing products and services to maintain and preserve knowledge in the nuclear domain. Promoting the use of cutting-edge KM technologies and supporting member states interested in using them.

Declaration by John de Grosbois, Section head of Nuclear Knowledge Management, IAEA, 2015 Effective decision-making during design, licensing, procurement, construction, commissioning, operation, maintenance, refurbishment, and decommissioning of nuclear facilities needs to be risk-informed and knowledgedriven. Nuclear technology is complex and brings with it inherent and unique risks that must be managed to acceptably low levels. Nuclear facilities may have very long life-cycles with changing operational conditions. Our ability to take safe decisions and actions is continually being threatened by the risk of knowledge loss. To ensure safety, we have a responsibility not only to establish adequate technical knowledge and experience in our nuclear organizations but also to maintain it. This is the reason why nuclear knowledge management is so important. Recently, KM was integrated into the IAEA’s safety standards, which provide members states with the basic principles, standards, and guidelines to ensure safety in the nuclear domain. They reflect an international consensus on what constitutes a high level of safety to protect populations and the environment from the harmful effects of “Ionizing Radiation.” These standards are officially recognized by member states, which, though not mandatory, may adopt them directly and use them as a reference for audits of national standards, as well as a benchmark for their compliance. In any case, it is binding on member states for the purpose of accreditation by the IAEA and also for IAEA’s own activities. In view of the critical factor involved for safety in the nuclear domain, it is natural to introduce KM into the safety standards in the year 2016. This necessitated through revision of the safety standards from 2016 till date.

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Some examples of integration of KM in the safety standards are given hereunder:

3.2 Knowledge Management at the Top Level of Safety Standards Two standards are dedicated to the highest levels: governmental and top management; they include KM requirements as shown by the following examples: GSR PART 1 GOVERNMENTAL, LEGAL AND REGULATORY FRAMEWORK FOR SAFETY (REV.1) 2016 Requirement 18: Staffing and competence of the regulatory body § 4.13 A process shall be established to develop and maintain the necessary competence and skills of staff of the regulatory body, as an element of knowledge management. GSR PART2, LEADERSHIP AND MANAGEMENT FOR SAFETY 2016 Requirement 1: Achieving the fundamental safety objective Note 9: The maintenance of skills and knowledge […] will be the responsibility of the senior management. Requirement 9: Provision of resources § 4.27. The knowledge and the information of the organization shall be managed as a resource.

3.3 Knowledge Management at the Regulatory Level As a critical factor for nuclear safety, KM has been included with a lot of requirements in all the safety standards concerned with regulatory bodies and safety infrastructures as follows:

• • •

Organization, Management, and Staffing of the Regulatory Body for Safety (GSG-12); Functions and Processes of the Regulatory Body for Safety (GSG-13); Establishing the Infrastructure for Radiation Safety (SSG-44).

For instance, the Safety Standard Organization, Management and Staffing of the Regulatory Body for Safety (GSG-12), 144 pages have 11 occurrences of the term “KM” and 82 occurrences of the term “Knowledge.” It contains a complete description of the KM framework to implement in the regulatory body of the nuclear organization (see Table 8.1), as given below for example:

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Table 8.1. Knowledge Management. Purpose

Inputs

Process

Outputs Interfaces

Performance criteria

To ensure that knowledge relevant for the activities of the regulatory body is acquired, stored, preserved, and distributed (i.e. in general, managed as a very valuable resource of the regulatory body). Any information relevant for the regulatory body to discharge its responsibilities and to fulfill its functions. Special attention should be paid to the tacit knowledge that forms part of the experience of individuals (staff departures, retirements). (1) Periodically identify the regulatory body’s information needs; (2) Periodically review the existing knowledge base; (3) Identify needs for update of information; (4) Compare with existing knowledge base and identify gaps; (5) Identify and access internal and external sources of information and capture the necessary information to fill the gaps (essential for retirements and departures); (6) Convert information to knowledge of use to the regulatory body: (7) Store the information adequately and safely; (8) Ensure easy retrieval; (9) Inform the concerned individuals about changes and updates. Knowledge base; Comprehensive collection of up-to-date information. Planning; Human resources management (staff departures and retirement); Training and competence management; Research and development; External expert support; International cooperation. Accuracy and currency of information; Completeness of knowledge base; Ease of access to relevant information; Positive feedback of users.

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GSG-12 ORGANIZATION, MANAGEMENT AND STAFFING OF THE REGULATORY BODY FOR SAFETY 2018 § 3.20 Information and knowledge are part of the corporate memory of the regulatory body and should be managed the key resource that is embedded in the regulatory body’s processes, activities and functions §3.21 Processes should be established, from the early stages of development of the regulatory body’s integrated management system, to acquire, use, maintain, store and retrieve information and knowledge. These processes should be supported by specific tools and techniques, for example:

• •

Questionnaires, interviews and discussions, and reports (special attention should be paid to the transfer of knowledge when experienced staff leave or retire from the regulatory body); Databases, libraries, “knowledge portals” and archives.

§ 4.26 Administrative functions include […] Knowledge Management and library services, including access to specialized publications.

4. Knowledge Management in the ISO 30401 Standard 4.1 Introduction The ISO 30401 standard is an important milestone in the history of KM, for it is a general standard of KM applicable in all types of organization. The standard is dedicated to the definition and the implementation of a Knowledge Management System (KMS). Taking a holistic view, it can be integrated into a Global Management System, as defined by different ISO standards. The standard was published in November 2018 (English version). The general content is described in Chapter 5 of the standard. The objectives provided in the introductive part are:

• • •

The purpose of this ISO standard is to support organizations to develop a management system that effectively promotes and enables value creation through knowledge. A KMS aims to contribute to the achievement of a company’s strategic objectives through the preservation, dissemination, sharing, and evolution of its Knowledge Capital. The purpose of the standard is to set rigorous principles and requirements for KM.

In that §, we focus on Chapter 4 of the standard, which sets the requirement for the implementation of the KMS. That chapter is the fundamental part of building and assessing an operational KMS. We provide some recommendations to be compliant with the given ISO 30401 requirements.

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4.2 Requirements 4.1 and 4.2: Dwell upon Setting a KM Framework In the ISO 30401 standard, KM is addressed as a component in an integrated management system. Consequently, a prerequisite condition for any strategic KM plan in a company is to establish the framework of the KM project. This is the object of the requirements 4.1 and 4.2 ISO 30401 § 4.1 Understanding the organization and its context The organization shall determine external and internal issues that are relevant to its purpose and that affect its ability to achieve the intended outcome(s) of its KMS ISO 30401 § 4.2 Understanding the needs and expectations of interested parties The organization shall determine the stakeholders (interested parties) that are relevant to the KMS (structured in terms of business and organizational performance, rather than knowledge management needs). To be compliant with these requirements, the organization must set a KM Framework that must address a certain number of subjects, as shown, for example, in the following items, see Ermine (2018). 4.2.1 The Objectives Top management must formulate the expectations for the company and participants in order to develop a strategic vision of knowledge and concentrate on the central question of knowledge. The objectives expressed can concern the following points:

• • • • • • • • • •

Creating a common culture for sharing knowledge; Identifying the needs of new knowledge and the best acquisition strategies; Identifying key knowledge; Constructing and maintaining the company memory; Contributing to the effectiveness of the activities and methods based on knowledge; Promoting learning (means of acquiring knowledge) by relevant systems; Organizing knowledge sharing; Ensuring creativity; Constructing knowledge networks inside and outside of the organization; Constructing a KM network within the organization.

Top management must support KM initiatives and communicate with all levels of the organization without leaving any space for ambiguity; the entire company must know that KM is an essential objective. 4.2.2 Responsibilities and Roles The successful implementation of a KM program depends on well-defined roles within the company. In many cases, these roles can be assigned to existing personnel. In large organizations, dedicated positions must be created, especially for the knowledge supervisor and knowledge managers.

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The highest-level role is that of the Board of Directors or a KM director committee at this level. The Board of Directors ensures alignment with the goals and objectives of the organization. They meet regularly or at important points in the program. The Chief Knowledge Officer (CKO) is responsible for establishing the KM strategy. This person identifies the appropriate resources to elaborate, implement, monitor, and assess the KM plan. The CKO reports directly to the Board of Directors. In large organizations, KM program managers called Knowledge Managers can be appointed in addition to the CKO. They are responsible for the daily management of the KM program in their unit. Knowledge Manager is a leading role, with the strategic responsibility of promoting KM, determining and allocating resources, and implementing, assessing, and improving the program continually. A Knowledge Manager can also facilitate an internal KM community of practice. In every organizational unit, a KM coordinator can be appointed. It is his responsibility to ensure that KM activities are implemented in the unit in question. All employees are responsible for KM activities in their respective work area, in particular to maintain a culture of knowledge sharing and capitalize on the knowledge they produce. These responsibilities must be part of their work plans and job descriptions, in line with the KM strategy and the company’s KM plan.

4.2.3 Resources There are three types of resources:

• • •

Resources dedicated to cross-disciplinary KM projects. They are attributed to the CKO for specific projects (company knowledge server, deployment of KM methods, knowledge communities, pilot projects). Resources allocated directly to the units or specific teams for specific KM projects, included in the company’s KM plan. Units distribute them to KM projects directly related to these units. Resources to support general KM projects (software development, educational engineering, information retrieval, documentary support).

4.2.4 Internal Communication As it is always the case for a strategic project, communication is essential. It must accompany a progressive development of KM by the company, starting from pilot projects, quick wins, identifying motivated units, and gradually disseminating messages (through presentations, training, seminars) that can focus on topics such as:

• • • •

The advantages of sharing, capitalization; Modifying personal attitudes; Recognizing the value of knowledge; Involving people in the knowledge process.

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4.2.5 Connections between KM and Other Company Issues Very quickly, KM, which is an encompassing issue, must be coordinated with several other elements already present in the company, and this can cause a certain number of problems. KM has strong connections with other issues, and it is important to show how those issues can benefit from the contribution of KM. Here are a few examples.

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Human resource plans

• • • • • • •

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Risk assessment (knowledge risks)

• • •

• •

•

Knowledge loss; Knowledge gaps; Knowledge clash.

Organizational units and operational processes

• •

Input for a provisional plan for jobs, in particular when new activities are integrated into the company; Input for training units and/or corporate university; Input for professional tracks; Specific KM criteria in individual assessments (sharing, capitalization, transfer); Information and communication technology systems; Integrating KM tools in the global information system (knowledge servers, knowledge modeling tools).

•

Defining operational knowledge to be managed directly by the operational unit; Integrating knowledge into professional processes.

Documentation and archives

• • •

Accessing information and documentary resources; Role of archiving in KM processes; Creating books, doctrines, guides.

Company development plan

• •

Integrating KM in the process framework; Indicators and reports from top management.

National context

• • • •

Integrating KM into national standards; Potentially changing policy directives; Institutional cooperation; Roles of different parties involved.

International context

• •

Integrating KM into international standards; Integrating international KM networks.

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4.2.6 Other Subjects of Interest to Consider Implementing a KM project on a large scale requires a cultural shift that can change certain well-established practices in a company. Some subjects that were previously not related to KM may require particular attention. Here are a few examples. 4.2.6.1 Intellectual Property. Contracts with employees can be modified to distinguish between “knowledge holders” and “knowledge owners.” To facilitate knowledge sharing between knowledge actors, the elicitation of expert knowledge, etc., the status of knowledge can be clarified by different means of intellectual property: licenses for knowledge use, copyright, “Creative Commons,” etc. 4.2.6.2 Information Security. New ways to collect, disseminate, and share knowledge are used in KM projects: recording interviews, films, sharing via networks, digital data collection, etc., and this can cause new problems regarding information security. These problems must be identified and discussed as soon as possible and preferably before the KM projects begin. 4.2.6.3 Respect for Private Life. KM encourages the expression of personal opinions and free discussions. However, utmost confidentiality must be maintained in this context.

4.3 Requirement 4.3: Identify the Critical Knowledge Domains As emphasized in previous chapters, the Knowledge Capital of an organization constitutes its most strategic assets. Managing this capital necessitates an audit of the knowledge resources, guided by the strategy defining the missions of the organization. As per the requirement 4.3 of the ISO 30401, the first step involving strategic analysis of Knowledge Capital aims to identify the critical knowledge domains of the organization and the adequate actions to reduce their criticality. ISO 30401 § 4.3 Determining the scope of the knowledge management system The organization shall determine the range and applicability of the KMS to establish its scope. Within this scope, and with respect to the organizational purpose, the organization shall identify, evaluate and prioritize the knowledge domains which have the greatest value to the organization and its interested parties, and to which the KMS should be applied. For instance, Chapter 3 in § 2: “Strategic assessment of the Knowledge Capital” provides a complete methodology seeks to identify, evaluate, and prioritize strategic and critical knowledge of the organization. This is then an adequate tool to be compliant with the requirement 4.3 of the ISO standard.

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4.4 Requirement 4.4: Implement an Effective and Holistic KMS The Clause 4.4 deals with implementation, maintenance, and continuous improvement of a KM System in the organization. It states that: ISO 30401 § 4.4 Knowledge management system The organization shall establish, implement, maintain and continually improve a KMS (strategy, processes, and their interactions), […] for the implementation of an effective and holistic KMS within the organization.

4.4.1 Implementing a KMS As discussed in Chapter 4, the “Virtuous KM Cycle” provides a complete methodology for implementing a KMS. The cycle supports the progress of the implementation of KM processes of the organization. The Knowledge Capital is organized, preserved, disseminated, and updated after each cycle is complete. That is how, the cycles are perpetuated. The progress of KMS implementation is checked in the classical PDCA (plan–do–check–act) method or Deming wheel.

4.4.2 Supervising a KM System Maintaining the KM System in a sustainable way requires its monitoring with a view for managing change requests according to the company needs. The key elements include a KM roadmap, broken down into KM phases and action plan as provided in the Virtuous Knowledge Cycle. The KM roadmap describes:

• • •

The allocation of necessary resources, The decision-making process, The reviewing process, to apply indicators to carry-on.

Reviews ascertain necessary improvements to be made in the KM System. It could be about KM training courses, extending best practices, developing new tools, or improving existing tools, new knowledge to capture with knowledge owner appointment. A review is carried out annually as per “KM System Decision Review” (described in the decision process). A set of KM indicators constitute a necessary tool for supervision and reviews of the KM System. It involves defining the right indicators to be put in place. However, the definition and implementation of indicators as an integral part of the KM approach meet the requirements of § 4.4 of ISO 30401. Implementation of indicators imparts better organization and governance of the KM System, thus guaranteeing rationality in the KM approach and the actions undertaken. Indicators can be classified in different categories and use them when it is needed. For instance:

•

Qualification of the Knowledge Capital (coverage rate of knowledge domains, level of formalization of knowledge domains, rate of critical knowledge

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managed by the KMS, the added value of the different knowledge domains managed by the KMS, etc.); Organization of the Knowledge Capital in terms of management and structuring (number and type of description of knowledge domains, integration of the defined structures of knowledge in the architecture of the information system, definition and assignment of roles concerning formalization, validation, and utilization of knowledge, sharing of the rules of formalization, validation, utilization of knowledge, etc.); Sharing of the Knowledge Capital in terms of accessibility and sharing methods (consultation and access rate for a database, a knowledge book, a knowledge repository etc., level of accessibility to knowledge, readability of the interface, number of clicks to reach the desired knowledge, average search time, level of user satisfaction in terms of access to knowledge and sharing, etc.); Evolution of the content of the Knowledge Capital (rate of revised/updated knowledge, rate of new knowledge, rate of knowledge, reused, readapted, rate of knowledge “Stored”/Archived, etc.); Actors’ satisfaction, involvement, cohesion, etc. (satisfaction rate of the different actors (contributors, users), level of contribution, level/volume of interactions collected: questions, feedback, etc., the quality level of the contributions, level of consistency between the contributions and the level of need (expressed or not) of users, etc.); Control of the KM System (achievement rate of the planned benchmarking actions, rate of achievement of the planned progress actions, rate of the reviews of the KMS dashboard, the evolution of the deployment rate of the KMS on knowledge domains); Application of KM and measurement of results or contribution to results (rate of KM activities taken into account in the processes, “reuse” the rate in operational activities, measurement level of value-added KM in the projects etc.).

4.5 Requirements 4.4.1 and 4.4.2: Knowledge Processes Knowledge processes are the core of the architecture of a KMS. Setting up a sustainable KM System relies on a set of processes that are well integrated with the company’s processes. KM processes are part of a company’s baseline processes. They are, in effect, support processes, but some of them are operational processes. For example, at the beginning of a design process, usually, a first task consists in gathering required knowledge, mainly in terms of reuse of models, concepts, methods, best practices, etc. This is a part of the “Share” process. At the end of this design process, there is a task of complementing the Knowledge Capital or at least giving the required information to do that. This is part of the “Capitalize” (capitalization) process, which encompasses the tasks of capturing and formalizing expert’s tacit knowledge before his retirement.No real separation between the different processes is discernible. For instance, when people, working in a design process, share knowledge to do their work, they also create and learn new knowledge.

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The requirements 4.4.1 and 4.4.2 deals with knowledge processes ISO 30401 § 4.4.1 Knowledge development The organization shall demonstrate that the KMS covers the following activities:

• • • •

Acquiring new knowledge Applying current knowledge Retaining current knowledge Handling outdated or invalid knowledge

ISO 30401 § 4.4.2 Knowledge conveyance and transformation The organizational knowledge management system shall include activities and behaviours, supporting all different types of knowledge flows, through systematic activities and behaviours, supporting the KMS objectives and covering the prioritized knowledge domains

• • • •

Human interaction: exchange and co-creation of knowledge Representation (demonstrating, recording, documenting and/or codifying) Combination (synthesis, curating, formalizing, structuring or classifying of codified knowledge, making the knowledge accessible and findable) Internalization and learning (reviewing, assessing and absorbing knowledge; incorporating it into practice)

The set of ISO compliant knowledge processes implemented in an organization depend upon the culture, the history, the nature of the organization. Each organization must decide the processes, adequate to fulfill the KM objectives. Some processes may exist already and may only need further development and improvement, whereas others may have to be implemented from scratch. Chapter 4, with the concept of Knowledge Process Wheel (§ 1.3), or with the more detailed Virtuous KM Cycle (§ 1.4 and following), gives a lot of possibilities to implement knowledge processes. Chapter 5, with the Daisy Model, gives a larger view of KM processes, including classical organizational processes.

4.6 Conclusion on ISO 30401 As per the above requirements of ISO 30401, it may be concluded that a KMS, to be compliant, must:

• • •

Rely on a correct definition of a KM framework in the organization, Contain the strategic and critical knowledge of the company, Implement processes in terms of: – – – –

Knowledge Knowledge Knowledge Knowledge

codification sharing search evolution.

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The alignment of the KMS with the corporate strategy, The construction of a repository of precise and chosen processes to build and maintain the KMS, The inclusion of the KMS into an integrated management system.

Considering the ISO 30401 certification, the development of a standardized KMS in the organizations is now a real challenge.

5. Conclusion International standardization of KM through the ISO 30401 certification is a historical step in the development of KMS. That way both the standards developed by ISO and IAEA are pioneering contributions. However, it may soon find a spurt into a lot of specific standards, to be developed for different domains of a KMS in different organizations. It is, therefore, high time to think of integrating ISO compliant KM, with the integrated management system of the companies. KM will then become an essential activity in the daily routines of the organizations.

References Club Gestion des Connaissances. (2017). KM handbook (version 20/05/2017). Retrieved from https://www.clubgc-km.fr/articles/35120-le-km-handbook-du-clubgc Ermine, J.-L. (2018). Knowledge management: The creative loop. London: Wiley and Iste Ed. International Atomic Energy Agency. (2016a). Governmental, legal and regulatory framework for safety (rev.1), IAEA Safety Standards Series No. GSR-Part 1. IAEA, Vienna. International Atomic Energy Agency. (2016b). Leadership and management for safety, IAEA Safety Standards SeriesNo. GSR-Part 2. IAEA, Vienna. International Atomic Energy Agency. (2018). Organization, management and staffing of the regulatory body for safety, IAEA Safety Standards Series No. GSG-12. IAEA, Vienna. International Atomic Energy Agency. (2018). Functions and processes of the regulatory body for safety, IAEA Safety Standards Series No. GSG-13. IAEA, Vienna. International Atomic Energy Agency. (2018). Establishing the infrastructure for radiation safety, IAEA Safety Standards Series No. SSG-44. IAEA, Vienna. International Standard ISO 30401:2018(E). International Standard ISO/FDIS 9001:2015(E). The declaration of John de Grosbois accessed by the IAEA public website https:// www.iaea.org/ All the citations of the IAEA or ISO standards are adapted from the different corresponding documents available from AIEA or ISO (https://www.iaea.org/, https://www.iso.org/standards.html)

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Chapter 9

Artificial Intelligence and Knowledge Management 1. Introduction By the turn of the twenty-first century, two terms namely information management and computer documentation were well established in their theoretical as well as practical connotations. Both of them served as tools for creating and managing the knowledge base of knowledge management systems (KMSs). Application of Artificial intelligence (AI) to KM provided the additional potential for the organizations to capitalize, share, and create knowledge, through a new relationship between the “people” and “information and communication systems.” During the last two decades, KM has been implemented in organizations steadily, for it effectively responds to the fundamental problems, now compounding with the phenomena of globalization, population aging, and digitalization. However, the technology offered by AI though helps transformation of intangible knowledge assets of the organizations into tangible resources; it is yet to become an integral part of KMSs at a large scale. “Knowledge” is the hub for both AI and KM. Expert systems acquire and accumulate both tacit and explicit knowledge from whatever means and use systematic rules in its processing and application for value addition and decisionmaking. The intimate connection of the two has given rise to cognitive computing that uses computerized models that simulate human brain processing by involving “deep learning and self-learning” neural networks. This requires reliable online knowledge bases that are now available abundantly, because of the plenty of easily accessible and workable knowledge base software. Business enterprises are setting up online knowledge bases so that their employees could update the database as well as themselves on daily basis to enable them to capture, find, and use knowledge to increase productivity and efficiency. Consequently, both KM and AI are gaining momentum. Integration of the two takes the organizations to the highest pedestal. In the process, the problem of unstructured data and structured data (big data) available abundantly has to be dealt with using cognitive computing. Most of the sophisticated KM software keep track with the organizational knowledge but unable to deliver as the knowledge base gets too vast. It is at this stage cognitive computing and AI can play a role. More recently, the significance Knowledge Management Systems, 171–185 Copyright © 2021 Shabahat Husain and Jean-Louis Ermine Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-80117-348-320210009

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of systematic analysis of data and text and the ability to mine large quantity of data, information, and knowledge to gain competitive advantage are being increasingly realized. As the volume of structured and unstructured data (big data) continues to rise unabated, chances of discovering new knowledge from the big data are mounting by leaps and bounds. At this juncture, cognitive computing while playing its role in knowledge extraction from big data ensures interconnectivity between AI and KM. Some of the KM tasks that can be handled by AI are

• • • • •

Foretell customer’s choice Extract and transfer data from email and Android phones into a database of records Extract terms from business contracts Update CRM (customer relationship management) system that can help analyze interactions of past, present, and future customers Provide customer service or reply to staff queries

A combination of KM and AI congregates information in a knowledge base to help organizations’ staff to find out the right information and respond to the customers in real time. It thus boosts the capacity of the staff rather than replace it for the benefit of the organization. A “KMS” joins together explicit and tacit knowledge in the contextualized handling throughout the organization using ICT and/or elements of AI. Broadly speaking, as KMS facilitates the staff of an organization to improve understanding, collaboration, and synchronization, it is described as a four-process framework consisting of gathering, organizing, refining, and disseminating. The technologies required to build upon the existing KMS platform are a web server, network, KM portals, visitor tracking, and many other requirements, web-based discussion forums, blogs, wikis, expert systems, etc. In the modern era, the KMS plays a crucial role in the provision of access to the knowledge of what is required. All organizations, therefore, must have KMS to sustain their work and decision-making power by accessing not only published output but also the individuals’ experiences.

2. Components of Knowledge Management Systems Irma Becerra-Fernandez and Rajiv Sabherwal (2015) have identified the following components of a KMS:

2.1 Knowledge Application Systems A knowledge application system (KAS) utilizes organizational knowledge exploited by the individuals without its tangible acquisition. The process of integration of organizational knowledge into its products and/or services is knowledge application. In the process, knowledge is acquired from within or outside of the

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organization, while the staffs who have acquired knowledge confirm the areas to be taken into account for processing further. The role of metadata at this stage is to ascertain the methods best suited for the acquisition of knowledge and practices followed. A KAS helps individuals in the acquisition of knowledge that has already been secured from different people in a controlled manner. KAS depends upon direction and routines that are supported by both mechanisms and technologies. The mechanisms assisting the “Direction” to such systems include help desks, support centers, and hierarchical relationships, whereas those facilitating “Routines” are the policies of the organization, work practices, and principles/ protocols. Both the “Direction” and “Routines” are also facilitated by the technologies namely advisor system, decision support system, fault diagnosis systems, help desk, and expert systems. All such systems work in a way a mechanical engineer or any technical person, for that matter, tries to solve a technical problem at hand by fault diagnosis and using decision support system or any other means for troubleshooting. It, therefore, follows that knowledge application process is facilitated by the application of mechanisms and technologies through direction and routines either within a given organization or in between other organizations. AI-enabled technologies facilitate KASs that in turn assist KM processes in an organization. Two intelligent technologies mostly used in KMSs are

2.1.1 Rule-based Expert Systems KAS is an amalgamation of rule-based systems (RBS) and case-based reasoning (CBR) (Becerra-Fernandez & Sabherwal, 2015). In RBS, a set of rules or heuristics define a knowledge domain, whereas in CBR, an expert is either unable to understand the domain completely or maybe nonexistent or the domain covers the entire organization, instead of limited individuals. When the traditional algorithmic ability of computers cannot be used for problem-solving, AI provides computers with the ability to represent and manipulate symbols to work out the solutions, as a human mind does. Knowledge and intelligence are closely interwoven. The human mind can manipulate modern AI systems, which are based on the understanding that intelligence and knowledge are tightly intertwined. While knowledge is associated with the cognitive symbols that human mind can manipulate, human intelligence enables learning and communication for problem-solving. Thus new knowledge is acquired through intelligence. The KASs (AI-based systems) provide ways and means of managing knowledge comprising of its application, capturing, sharing, and discovery. In such systems, domain knowledge is represented by rules and models for the storage, manipulation, and interpretation of knowledge/information. A knowledge engineer in collaboration with a subject expert brings out organizational and individual knowledge and represents it in computer-usable form. Despite some limitations (like the number of rules required to represent the domain), many systems have been successfully developed using a rule-based approach for the storage, manipulation, and interpretation of knowledge/information.

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2.1.2 Case-based Reasoning Systems Many AI-based KASs, developed on CBR methodology, are based on the dynamic memory model proposed by Schank in 1982, which works the same way as the human brain acts to solve the current problems by recalling the past problems/cases and their solutions, so that the old solutions may either be adopted for current problem solving or a new one may be generated, tested, and ultimately added to the case library for future use. CBR systems have some benefits over RBS, especially when

• • •

The attributes of the cases are too many to frame rules; The expert, who fully understands the domain, is not available; The knowledge is spread over the whole organization, rather than one or two individuals.

Development of a KAS involves six steps that form a case method cycle, as given hereunder (Kitano, 1993; Kitano & Shimazu, 1996): (1) (2) (3) (4) (5) (6)

The system development process, Case library development process, System operation process, Database mining process, Management process, Knowledge transfer process

CBR systems also have their limitations, such as the following:

• • •

They are task-specific; They face security problem (when cases have sensitive information); They may affect the speed and cost of the system, for the case library must represent a sufficiently large number of cases for the later processes to be followed effectively. The same may affect system’s speed as well as the cost of computing and searching.

2.2 Knowledge Capture Systems These systems are meant to preserve and formalize knowledge. Both tacit and explicit forms of organizational and individual’s knowledge are drawn out and stored in knowledge capture systems (KCSs) by using methodologies, machines, or technologies so that it can be shared and utilized by others within or across the organizations. Storytelling and folklore have been used in ancient civilizations for the transmission and preservation of traditions and culture in the past and present generations. The reservoir of organizational knowledge lies among the working staff, relieved workers, experts, competitors, customers, suppliers, and even prior employers of the organization’s new employees.

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As provided in Nonaka–Takeuchi SECI Model in Chapter 3, KCSs are based upon the methodologies and technologies that make possible knowledge management process of externalization (knowledge conversion from implicit to explicit type) and internalization (knowledge conversion from explicit to implicit type). Means are available in traditional KMSs for both types of conversions. Storytelling, interview, and lecture methods are good enough for knowledge conversion from implicit to explicit type, while as observation or practice-based learning and person-to-person meetings may result in knowledge conversion from explicit to implicit type. In the modern KCSs, the process of externalization is made possible through knowledge engineering involving the use of CBR systems, whereas the process of internalization is invoked through computer-assisted simulations, which promote individual learning as well. Storytelling, as a means of externalization, is used in many reputed organizations such as 3M Corporation and IBM. However, the articulation of stories should be truthful, influential, and effectual. Phoel (2006) underlies eight steps to powerful storytelling as follows: (1) (2) (3) (4) (5) (6) (7) (8)

Have a clear purpose. Identify an example of successful change. Tell the truth. Say who, what, and when. Trim detail. Underscore the cost of failure. End on a positive note. Invite your audience to dream.

The process of externalization is facilitated by such techniques like storytelling and interview which have been studied at length and applied in the design and development of expert systems by holding interactive sessions between the knowledge engineer and domain expert. Such systems are though computerreadable, but may not fully serve the ultimate purpose of KCSs of capturing, storing, and knowledge sharing. Thus intelligent technologies can help solve these problems by Knowledge modeling tools, known as “concept maps” or “C maps.” Concept maps are the representation of organized knowledge, structured hierarchically. Each unit, called semantic unit, consists of two concepts enclosed in boxes or circles, connected by a link (word), as shown in Fig. 9.1. There are concept maps in different domains of knowledge, wherein the concepts may have an associative type of relationship, called “crosslinks” that give rise to what is known as “semantic network.” Thus the knowledge domain of the expert is represented in the form of concept maps that can facilitate the development of KCS by the help of CmapTools (Cañas et al., 2004). These tools are the browsers based on concept maps. Such tools help navigate the users through clusters of associative networks or semantic networks finding the way through logical linkages.

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C R O

Knowledge Acquisition System KAS

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Observation

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Deep Learning

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Knowledge Discovery DB

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Assisted by Lesson learned sys

Expertise locator sys

Assisted by Computer assisted simulation

Community of practice

Knowledge Modelling Semantic Network

Fig. 9.1.

Concept Map of Artificial Intelligence–Based Knowledge Management.

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It can thus be summarized that (1) Knowledge engineer and domain expert interact to elicit knowledge. (2) Domain knowledge of the expert is organized into hierarchical structures by developing concept maps. (3) The associative relationship of the concepts of various domains forms clusters or semantic networks in a multimedia system. (4) CmapTools assist the users to browse through the conceptual model to develop a broad view of a particular expert’s domain of knowledge.

2.3 Knowledge Sharing Systems They are meant to organize and distribute knowledge. KM in an organization deals with both types of knowledge viz. explicit and tacit. A knowledge sharing system (KSS) aims to be of assistance to the users to share both types of knowledge. As the nature of the explicit knowledge differs from that of implicit knowledge, different methods and means are adopted in designing the KSS, as detailed hereunder:

2.3.1 Explicit Knowledge Sharing Systems These systems are generally of two types, namely lessons learned systems and expertise locator systems. 2.3.1.1 Lessons Learned Systems. A meticulous working of any organization involves commissioning of certain projects as and when required. After achieving the desired goal, the projects are formally decommissioned. At this stage, followup actions are identified, benefits obtained are reviewed, and project evaluation is done, to enlist good and bad experiences, along with the steps for improvements of future projects. The last phase of project management is what is called as lessons learned, which can be achieved by employing the lessons learned tools. Organizations may develop their lessons learned templates that consist of necessary fields/ themes to improve future projects of the organization. In any case, the last phase of the KM project takes care of all sorts of organizational knowledge (explicit, implicit, and embedded), acquired from the project to maximize its sharing (http://wiki.doingprojects.org/index.php/Lessons_learned_-_a_tool_for_sharing_ knowledge_in_ project_management. Accessed on 06-08-2020). ´ Weber, Aha, Muñoz-Avila, and Breslow (2000) describe that the lessons learned process in an organization typically involves the following five generic steps: (1) (2) (3) (4) (5)

Step Step Step Step Step

1: 2: 3: 4: 5:

Collecting (record) Validating Storing (and categorize) Disseminating (communicate) Reuse

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Step 1 involves the collection of knowledge or lessons, which are validated for establishing relevance and making recommendations in Step 2. In Step 3, lessons learned are categorized and stored in various ways (e.g., intranet/extranet, content/ document management systems). Step 4 deals with the dissemination of knowledge and sharing of lessons learned that are finally reused in other projects in Step 5. Once the lessons learned are documented and stored, organizations must not forget them, which will otherwise amount to wastage of money and manpower. Therefore, to improve efficiency, the sharing of the lessons learned must be ensured. After all, the efficiency of KMS in an organization is a function of the lessons documented and those used, as given below: KMS Efficiency ¼

Lessons Documented Lessons Used

2.3.1.2 Expertise Locator Systems. The intellectual capital of an organization needs to be identified. The expertise locator systems are knowledge repositories wherein knowledge competencies of the organization are cataloged so that the expertise may be tapped whenever required. Expertise locator KMS depends upon the staff of the organization who feed their self-assessed competencies to be searched for specific tasks later. This technique of filing self-assessment by the employees of the organization may result in overassessment and biased self-reporting. Expert’s profiles can be maintained by applying data mining techniques that facilitate the extraction of new knowledge from web-related data, which is accessed efficiently by applying IR techniques. That is why some expertise locator KMS like that of the National Aeronautics and Space Administration (NASA) makes use of these methods (Irma Becerra-Fernandez & Juan Rodriguez, 2001).

2.3.2 Implicit Knowledge Sharing Systems The intellectual capital of the organization consists of its intellectual assets, comprised of both explicit and tacit knowledge. Tacit knowledge is always at a risk of being lost resulted from the departure of its employees either for better job opportunities or because of their retirement. The implicit KSSs support tacit knowledge shared by communities of practice (CoPs). Communities are groups of people who join together to share and learn from one another and who are held together by a common interest in a body of knowledge. Communities come together either face-to-face or virtually and are driven by a desire and need to share problems, experiences, insights, templates, tools, and best practices. (McDermott, 2000). In short, CoPs provide one of the best platforms for sharing tacit knowledge to enrich the intellectual capital of the organization. It is for the top management to provide suitable ways and means for the creation and development of CoPs. System admin plays a yeoman’s job in the formation and functioning of CoPs, for which certain organizations embark upon framing a set of rules for dialogue and

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knowledge sharing, such as Personal Identification and Good Conduct of the participants, as well as Responsibility of posted contents and their fair use, etc. The most significant purpose of CoPs is to trace and apply the existing organizational knowledge. Knowledge repositories furnish a common virtual workplace wherein not only the new knowledge of the organization is stored and systematized but also presentations, tools, and other valuable materials are downloaded from it.

2.4 Knowledge Discovery Systems These systems also create new knowledge. This is done either through socialization with other knowledgeable persons/experts or by finding certain patterns in the documented knowledge. It, therefore, follows that while the former deals with the tacit knowledge, the latter is a source of explicit knowledge. Apprenticeships, employee rotation, conference discussions, poster sessions, and brainstorming are some of the methods of creation of new knowledge through the process of externalization. Organizations interested in the new knowledge create opportunities for such socialization of its employees. The brainstorming sessions consist of a facilitator and also innovators. A facilitator is a person who controls discussion, while innovators are the persons involved in providing a solution to the problem. Top management’s role is crucial in the creation of new explicit knowledge by collaborative problem-solving, joint decision-making, and collaborative creation of documents. New knowledge may be discovered from databases through a process called as knowledge discovery in database (KDD), which is nothing but a data mining technique that works upon data assortment, data cleansing, integrating prior knowledge on datasets, and elucidating precise solutions from the observed results. During the last decade, KDD processes have grown tremendously to include semantic query optimization, knowledge acquisition for expert systems, and information theory, so that mining of low-level data may dig out high-level knowledge. In any case, the KDD process converts data into information and information into the intellectual capital of the business enterprise. It may be said in conclusion that the days of information filtering through the predetermined rules-based software are over, as their output was also of predetermined nature that hardly served the needs of the hour. AI now deals with complex pattern recognition problems through “deep learning.” Such applications used by Google recently in detecting faces from a set of some 37,000 images achieved an 82% accuracy. Google now offers its AI cloud services, which claims to double the income of the companies that fully absorb this technology by 2030. Their prepackaged solutions will solve most of the business problems. Google Cloud AI provides benefits of machine learning within reach across the organization. Bitrix24, a knowledge base software, connects with 7 million organizations from 187 countries. A template can create a knowledge base with customization facility. For displaying important company information or news to new team members, a company knowledge base can give access permission to everyone. It

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provides readymade knowledge base templates, online document storage, social knowledge sharing, etc.

3. The Companies Empowering Intelligent Knowledge Management A recent Mckinsey Global Survey has revealed 25% rise per year in the use of AI in standard business processes as it has yielded increased revenue in which intelligent tools and services have been playing a significant role for enabling speed, insight, and accuracy. Consequently, AI and cognitive computing technologies have been adopted by KM vendors. A list of the organizations that provide business houses with the necessary hardware, software, and knowledge to manage effectively is given as follows (Wells, 2020):

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•

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ABBYY – A global leader in Digital IQ for the enterprise, ABBYY is creating a new class of AI technologies that provides the digital workforce with the skills to understand enterprise content and processes. Accenture – Applied intelligence is how Accenture implements AI in business – with an approach that embeds AI, automation, and analytics at the core of the enterprise to eliminate silos, create more agile and adaptive processes, and enable better decision-making. Access Innovations – With its patented software, Data Harmony, Access Innovations offers a reliable, human-supervised AI platform that uses more than 20 types of algorithms to help users efficiently find the information they need – when they need it. To learn more, read Heather Kotula, VP Marketing and Communications’ AI Trailblazer Insight. Adobe – AI, machine learning, and deep learning are powering Adobe’s products and services to help solve problems in content understanding; recommendations and personalization; search and information retrieval; prediction and journey analysis; content segmentation, organization, editing and generation; and more. Alation – With behavioral and linguistic intelligence technologies, collaboration capabilities, and open interfaces, Alation, a pioneer in the data catalog market, provides a platform for a range of metadata management applications by combining machine learning and human insight. Alfresco – The Alfresco Digital Business Platform offers open, secure content services – including the ability to enrich content and gain insights with AI and machine learning services – to help users unlock value from their most important business information. alexandrya – Powered by cutting-edge AI, alexandrya overcomes the challenge of finding internal files and information with natural language processing that enables full-text search in Microsoft Office programs such as PowerPoint, Excel, Word, and PDF files. Amazon Web Services – Because machine learning solutions require a range of supporting technology, Amazon Web Services allows customers to choose

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from a broad set of services that match their business needs – from pretrained AI services to fully managed, comprehensive machine learning solutions. ASG Technologies – ASG, a provider of solutions to capture, manage, and govern information assets, offers the Data Intelligence metadata management solution featuring a trust model that enables data consumers to understand data’s fit and value for both human and AI/ machine learning–driven analytics. Automation Anywhere – A leader in robotic process automation, Automation Anywhere helps customers automate end-to-end business processes with software bots – digital workers that perform repetitive and manual tasks, resulting in productivity gains, optimized customer experience, and more engaged employees. BA Insight – Providing a software portfolio that solves internal enterprise search problems, BA Insight uses a best-of-breed, “plug-and-play” strategy that enables organizations to change search engines or AI technologies without the cost and disruptions that “lift-and-shift” strategies can cause. Bizagi – With a platform for intelligent process automation, Bizagi connects people, applications, robots, and information to foster collaboration between business and IT, enabling faster adoption and success. Bold360 by LogMeIn – Helping customer support teams to meet evolving customer expectations, LogMeIn provides the Bold360 AI-powered customer engagement suite, which allows agents to see real-time customer activity so they are better informed and have visibility into key metrics directly in their workspaces. CallMiner – Providing the Eureka engagement analytics platform, CallMiner leverages AI and machine learning to transform the voice of customers and agents into operational intelligence at scale with automated performance and sentiment scoring, topic discovery, and trend presentation. Capacity – Bringing the power of AI to work for the business, Capacity offers a knowledge-sharing platform that intelligently captures and curates the tacit knowledge of an organization for instant access by all users to the knowledge of its most experienced veterans. Conga – Conga, a leader in digital document transformation, has combined with Apttus, a provider of quote-to-cash solutions, to create a new entity operating under the Conga brand with offerings that span configure-pricequote, contract lifecycle management, document generation, process automation, and e-signature. Deloitte – Helping to harness the power of “with,” Deloitte allows organizations to identify unique advantages through AI and analytics so they can move faster with greater precision, pinpoint truths that improve decision-making, and create beneficial connections with customers. eGain – With a comprehensive suite of applications to help clients deliver memorable, digital-first customer experiences in an omnichannel world, eGain delivers customer engagement solutions that help enable digital transformation for leading brands – powered by virtual assistance, AI, knowledge, and analytics. To learn more, read Anand Subramaniam, SVP WW Marketing’s AI Trailblazer Insight.

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Enterprise Knowledge – A service firm that integrates knowledge management, information management, information technology, and agile approaches to deliver comprehensive solutions, Enterprise Knowledge creates tailored, practical, and results-oriented platforms that enable customers to adapt to changing needs. To learn more, read Zach Wahl, Founder and CEO’s AI Trailblazer Insight. e-Spirit – e-Spirit offers the FirstSpirit Digital Experience Platform (DXP), which includes a hybrid CMS, AI-driven personalization, and omnichannel marketing capabilities, to enable marketers to deliver personalized and synchronized content across all channels. Franz – An early innovator in AI and a leading supplier of semantic graph database technology with expert knowledge in developing and deploying knowledge graph solutions, the foundation for which lies in the facets of semantic technology provided by its AllegroGraph and Allegro CL platforms. To learn more, read Jans Aasman, CEO’s AI Trailblazer Insight. Hitachi Vantara – The digital infrastructure and solutions subsidiary of Hitachi, Ltd., Hitachi Vantara works with customers, applying industrial and digital capabilities to data and applications to help them develop new revenue streams, unlock competitive advantages, lower costs, enhance customer experiences, and deliver value. Indico – Allowing enterprises to deploy AI to unstructured content challenges while eliminating many of the common barriers to adoption, Indico, a provider of intelligent process automation solutions, helps turn the process into profit by automating manual, labor-intensive, document-based workflows. Kyndi – Leveraging novel techniques to address a long-entrenched enterprise problem – building AI capability into applications – Kyndi provides Reading Automation AI software that transforms the way product teams and developers integrate AI into their internal and customers applications. Lucidworks – Providing the Lucidworks Fusion Platform, Lucidworks lets customers deploy AI-powered data discovery and search applications in a cloud-native architecture and interact with those applications by leveraging existing machine learning models and workflows or create and deploy new models using popular tools. To learn more, read Vivek Sriram, Chief Product Officer’s AI Trailblazer Insight. Luminoso – Turning unstructured text data into business-critical insights, Luminoso uses a commonsense AI approach to understanding language to empower organizations to discover, interpret, and act on what people are telling them. M-Files – Providing an intelligent information management platform, M-Files uses AI technologies in its Intelligent Metadata Layer to break down silos by delivering an in-context experience for accessing and leveraging information that resides in any system and repository. Microsoft – With the recent launch of Project Cortex, a new service in Microsoft 365 that uses advanced AI to deliver insights and expertise in apps that users work with every day, Microsoft harnesses collective knowledge to empower people and teams.

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Mindbreeze – Mindbreeze is a provider of appliances and cloud services for enterprise search, applied AI, and knowledge management that utilizes an insight engine that offers a consolidated view of corporate knowledge, regardless of where and how the data is stored. Neo4j – A provider of graph database technology, Neo4j drives innovation and competitive advantage with a relationships-first approach that enables applications to tackle connected data challenges posed by AI, fraud detection, realtime recommendations, and master data. Nuance Communications – With decades of domain and AI expertise, Nuance delivers solutions that understand, analyze, and respond to people – amplifying human intelligence to increase productivity and security. Nuxeo – From making the power of AI predictions accessible to nontechnical users to making full, cross-departmental workflows that work, Nuxeo, the developer of a cloud-native content services platform, helps people realize new value from digital information. Observe.AI – With its Voice AI Platform, which leverages the latest speech and natural language processing technologies, Observe. AI helps organizations quality-check calls, ensure compliance, and offer targeted coaching. Omega3c – Omega3c helps companies to automate their customer service across all channels through AI, speech, and language platform and chatbot solutions to improve repetitive or low-value processes and provide answers more quickly. One Network Enterprises – Powered by NEO, a machine learning and intelligent agent technology, the One Network intelligent business platform for autonomous supply chain management includes modular industry solutions for multiparty business that help organizations lower costs, improve service levels, and run more efficiently. Ontotext – Provides agile enterprise data management that enables organizations to connect data in reusable knowledge graphs, accumulate data preparation efforts by describing and linking data for further analytics, and apply better governance using a graph and semantic technology stack. SAS – From machine learning, computer vision, and natural language processing to forecasting and optimization, SAS AI technologies support diverse environments and scale to meet changing business needs and provide organizations with more intelligent, automated solutions. SDL – A provider of content creation, translation, and delivery, SDL offers Hai, its linguistic AI, which enables users to quickly understand and generate content for worldwide audiences and forms the backbone of SDL Machine Translation. Semantic Web Company – A provider of graph-based metadata, search, and analytic solutions, Semantic Web Company helps customers manage corporate knowledge models, extract useful knowledge from big datasets, and integrate both structured and unstructured data to recommend strategies for organizing information at scale. To learn more, read Andreas Blumauer, founder and CEO’s AI Trailblazer Insight.

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ServiceNow – ServiceNow, a digital workflow company that makes work better for people, recently introduced the Now Platform Orlando release, featuring Now Intelligence, a new set of AI and analytics capabilities. Signal AI – Signal AI is an AI-powered BI and media monitoring company that provides organizations with insights into digital, print, and broadcast media, news, and regulatory data and leverages machine learning to track the competitive landscape and changes to regulations and monitor reputations. Sinequa – Sinequa provides a cognitive search and analytics platform for Global 2000 companies and government agencies that combines search with advanced natural language processing, machine learning, and deep learning algorithms to extract insight from data for users in their work context. Smartlogic – Smartlogic’s Semaphore is a semantic AI platform that enables organizations to reveal and extract knowledge from their digital data ecosystem to yield business insight, enriching enterprise information with context and meaning, extracting critical facts and relationships, and harmonizing disparate information. Starmind – Offering an AI platform that unlocks employees’ collective intelligence and expertise, Starmind supercharges productivity, innovation, and career development to identify subject matter experts across organizations, access undocumented knowledge, and automatically create skill profiles in real-time. To learn more, read Marc Vontobel, Founder and CTO’s AI Trailblazer Insight. TTEC – A technology and services company, TTEC provides automation, AI, and RPA solutions that help drive service costs down by empowering customer self-service and augmenting the associate experience for many of the world’s most iconic brands. UiPath – UiPath is leading the “automation-first” era – enabling robots to learn new skills through AI and machine learning by offering a hyperautomation platform that combines a robotic process automation solution with a suite of capabilities UJET – UJET is propelling customer experience into the digital age by empowering support organizations with tools and technology to create intelligent workflows, make data actionable, and design a modern business model. Unvired – Unvired enables businesses to work smarter by turning AI-enabled conversations with enterprise systems into actions through the creation of a bots platform called Chyme, on top of which digital assistants for help desk, sales, customer service, and procurement have been built. Verint – With a focus on customer engagement optimization and cyber intelligence, Verint is a global leader in actionable intelligence solutions that enable decision-makers to anticipate, respond, and take action with more informed and timely decisions. Virtusa – A global provider of digital business strategy, digital engineering, and IT services and solutions, Virtusa delivers AI-led digital transformation solutions that reinforce human efficiency and creativity with immediate and practical benefits.

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References Becerra-Fernandez, I., & Rodriguez, J. (2001). Web data mining techniques for expertise-locator knowledge management systems. Fourteenth International Florida Artificial Intelligence Research Society Conference. (FLAIRS) Proceedings, 2001. Becerra-Fernandez, I., & Sabherwal, R. (2015). Knowledge management: Systems and processes (2nd ed.). New York, NY: Routledge. Cañas, A., Hill, G., Carff, R., Suri, N., Lott, J., & Eskridge, T. (2004). CmapTools: A knowledge modeling and sharing environment. In A. J. Cañas, J. D. Novak, & F. M. Gonzalez (Eds.), Concept maps: Theory, methodology, technology: Proceedings of the first international conference on concept mapping (Vol. I, pp. 125–133). Pamplona: Universidad Publica de Navarra. Kitano, K. (1993). Challenges for massive parallelism. Proceeding of the 13th Annual Conference on Artificial Intelligence (IJCAI-93), Chabery, France (pp. 813–834). Kitano, H., & Shimazu, H. (1996). The experience-sharing architecture: A case study in corporate-wide case-based software quality control. In D. Leake (Ed.), Case-based reasoning: Experiences, lessons, and future directions (pp. 235–268). Menlo Park, CA: AAAI Press. McDermott, R. (2000). Community development as a natural step. Knowledge Management Review, 3(5), 16–19. Phoel, C. M. (2006). Leading words: How to use stories to change minds and ignite action. Harvard Management Communication Letter, 3–5. Schank, R. (1982). Dynamic memory: A theory of learning in computers and people. New York, NY: Cambridge University Press. ´ H., & Breslow, L. A. (2000, October). An Weber, R., Aha, D. W., Muñoz-Avila, intelligent lessons learned process. In International symposium on methodologies for intelligent systems (pp. 358–367). Berlin, Heidelberg: Springer. doi:10.1007/3-54039963-1_38 Wells, J. (2020). KMWorld AI50: The companies empowering intelligent knowledge management. KM World, July. Retrieved from https://www.kmworld.com/Articles/ ReadArticle.aspx?ArticleID5141554. Accessed on August 08, 2020.

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Chapter 10

Evaluation of Knowledge Management System 1. Introduction From a managerial perspective, Knowledge Management (KM) today is a growing issue in both public and private enterprises, which aims to formalize and transfer specific knowledge and know-how in the organization, with the purpose of capitalizing and operating this knowledge to increase organizational performance. Nonetheless, the tasks involved are challenging, for example: Criteria for Selection of organizations for Knowledge Management, Management of Communities of practice, Tactics to be used for KM resulting innovations. In the preceding chapters, tools and techniques forming integral parts of a knowledge management system (KMS) have been discussed at length. Over the years, many KM models have appeared to guide managers in the implementation of a KMS. However, no single KM model is found to be suitable for different organizations because divergent estimation of knowledge management processes implemented and validated in real-world settings have resulted in different structures. Consequently to have homogenous practices and a common framework to assist the implementation of KMS in organizations, International Organization for Standardization (ISO) has published ISO 30401 standard in 2018. The rationale of the standard is to provide a basic framework to assist organizations in the development of a KMS which can effectively promote valuecreation through knowledge. It, therefore, confirms the availability of KM tools and techniques on one hand and ISO standards for setting a KMS in organizations on the other. However, the effectiveness of the KMS of an organization should be evaluated incessantly to help in the identification of the areas for improvement and ultimately helping in the complete realization of goals more effectively. The evaluation process embarks upon collection and analysis of information about core components of KMS, such as Organizational Knowledge Base, Knowledge workers, KM processes, KM Tools and outcomes. To ensure the efficiency of a given KMS, it is important to periodically evaluate its KM activities. The conducted evaluation will reveal the success percentage of any system. To find the strengths and weaknesses of the system and manage risks, an organization can select and follow a given standard and claim to be compliant. It should perform an internal audit as a part of KMS to make improvements. Knowledge Management Systems, 187–202 Copyright © 2021 Shabahat Husain and Jean-Louis Ermine Published under exclusive licence by Emerald Publishing Limited doi:10.1108/978-1-80117-348-320210010

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Adoption of a certain standard, like ISO, shows that the KMS is ISO compliant and may go ahead for certification.

2. Benefits of Certification An ISO-certified organization has the following benefits:

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It can claim and show to the customers and other concerned parties that their system is ISO compliant. It reduces the chances of customers’ level of auditing because of independent certification. It fulfills the emerging demand of certain organizations that their suppliers should comply with ISO standards.

3. Selection of a Certification Body For improving the efficiency and effectiveness of operations, an organization can implement certain standards, like QMS (ISO 9001), without seeking certification. The decision for an independent audit of the KMS to the relevant standard is to be taken by top management on business grounds, so as to meet customers’ preferences, to motivate its staff, to ensure a particular standard in bringing out quality products and services. In the same context, the selection of a reputable certification body is as important as the evaluation itself. This is because no law of land prevents anyone to set up a company, the “certification body” that awards certificates. The credibility of such a company should be ensured. A certification body must be accredited or officially approved to perform audit and issue certificates in certain business areas by a national level accreditation body, which in turn is a member of International Accreditation Forum (www.iaf.nu). It may, however, be accredited to ISO/IEC 17021 Conformity assessment – Requirements. ISO/IEC is a joint technical committee of the ISO and the International Electrotechnical Commission (IEC). The ISO/IEC 17021 is a conformity assessment – requirements for bodies that conduct audit and issue certification of management systems. Its latest version was published on 2020-04-17. However, different countries have different accreditation policies. In some accreditation is an obligation, while in others it is a matter of choice. Even some nonaccredited certification bodies may enjoy a good reputation in a specific area at the national level. While evaluating certification bodies, the selection criterion should be the standard of the certification company and not the cost of auditing.

4. Successful Evaluation The evaluation of a KMS should be well thought of and meticulously planned, which entails enough time, necessary resources, and required expertise to carry out the evaluation process. A good evaluation program is customized as per the

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requirements of the KMS under reference. It is built upon the available corpus of organizational knowledge and resources. The process of evaluation is participatory, meaning thereby, that all stakeholders be involved in the course of action. A truthful evaluation should bring out the strengths and weaknesses of the system. While focusing upon the strengths of the system, the evaluation process helps to turn the weaknesses into strengths by putting the resources to better use. A quality system of evaluation with quality design, effective data collection methods, and data analysis give accurate results. A good system of evaluation should be a model for others to conduct evaluation along the same lines, to achieve similar results.

5. Quality Management System (ISO 9001) Any organization, whether public or private, may adopt an integrated approach for better customer satisfaction as well as to improve the efficacy of the system. For improving the efficiency and effectiveness of operations, an organization can implement certain standards like Quality Management System (ISO 9001) without seeking certification. The standard prescribes a set of requirements that the organization has to pursue to apply a ‘quality management system’ involving all the integral parts of the company for proficient delivery of quality products and services. ISO 9001 is a recognized system used in the companies of all sizes and turnovers. The standard is revised from time to time, and the year of revision is made part of ISO 9001, like ISO 9001:2015, which is the most recent version that came out in 2015. Once an organization has fully implemented the ISO 9001, it can get the same verified through an independent agency, so that an “ISO 9001 certification” may be issued to the organization.

6. Benefits of Implementing ISO 9001 A company with ISO 9001 certification will always have an edge over its competitors without ISO certification. Making use of customers’ awareness of the quality benefits, the companies may use the ISO certification as a marketing tool. A vast number of companies are precisely doing the same as a part of their marketing strategy. Consequently, certain companies try to get the certification as quickly as possible because this is sometimes a mandatory clause of supply order. ISO-certified companies are not only a matter of preference for the customers but also an overriding condition. The trend is becoming a norm in the modern-day world, wherein qualities of the products are never compromised. Therefore, sooner the ISO 9001 certification, better it will be for marketing the products. As discussed in chapters 7 and 8, the ISO standard was designed and developed to improve the company’s procedures and operations, the certification vis-`a-vis marketing issues came up later. Companies with ISO 9001 quality management system have some operational and marketing benefits over their competitors, such as superior competence of manufacture and delivery of the products and services;

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better protection of the staff with health and industrial hazards, effective use of organization’s resources resulting in larger pecuniary benefits.

6.1 ISO 9001 Certification Process ISO 9001 certification being a fantastic marketing tool, “over one million companies and organizations in over 170 countries” are accredited to it. For most of the companies want to implement the said standard with minimum input and maximum output, the “Business-friendly tools for ISO 9001” provide the following five steps for certification: 6.1.1 Step 1: Preparation Good planning is the backbone of any success story. Sound planning is indispensable for the implementation of ISO 9001 in enterprises of any size. The enduring success of ISO depends upon how well the company has prepared for certification. Size of the companies is also important in the initial steps of ISO implementation, as the approaches related to small-, medium-, and large-scale industries are dissimilar. For instance, the level of documentation and leadership requirements in the enterprises of various sizes will be different to meet the organizational goals. To set the ball rolling and also to oversee the procedures adopted for certification, a lead person or a project leader is appointed. In a medium-size organization, this person may be a consultant appointed from outside, whereas in a small-size company he may be internal staff, generally from quality assurance group. Outsourcing an ISO consultant will entail considerations of time taken and finances involved in the completion of the project. However, ISO 9001 templates and certification kit are already available to assist the internal staff to fulfill the stated goal. In addition to that, due weightage is given to the Lead Person, Top Management, and employees as per ISO 9001:2015 standard. 6.1.2 Step 2: Documentation Documentation about the Policies of the organizations as well as Standard Operating Procedures (SOPs) must follow the technical requirements, provided in ISO 9001:2015 standard. Such documents must reflect the company’s complete needs under the present circumstances. The documentation must be made ready by a person, well familiar with the organization’s operations and culture. In some organizations, more emphasis is attached to the processes than on documentation. However, new processes introduced as a result of the ISO 9001 certification process need proper documentation, as also the procedures, forms, and work instructions that provide added value to the products and services. 6.1.3 Step 3: Implementation When all the documentation is ready with modified and new procedures, the staff has to implement them, for which they have to develop familiarity at each step

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and adjust their working accordingly. The more user-friendly the new processes are, the easier it is for the staff to implement them. To make the quality management system workable each staff of the organization has to understand new processes and adjust themselves in the new working environment created due to the adoption of ISO 9001:2015 standards. As per the standard, the documented instructions inform of texts, flowcharts, pictures, or videos required to be explained to the employees to add value to the business. Here the art of writing instructions as SOPs plays a significant role by stating the minutest details necessary for standardizing or describing a process. Work instructions, as well as training of employees, go together. Once the work instructions have been implemented as per ISO 9001, they may be reviewed to find out improvements in work efficiency. 6.1.4 Step 4: Internal Audit Company’s internal audit means inspections to check the compliance of ISO 9001. The audit is conducted by one or more staff of the company, trained in ISO auditing, or may even be outsourced. Such audits are conducted not only at the time of implementation of ISO standard but also later on frequent intervals. The new ISO 9001 procedures and work instructions prepared and executed during documentation and implementation stages are verified through internal auditing. 6.1.5 Step 5: Certification The verification of the implementation of ISO 9001 procedures should follow the ISO certification process by a third party. It entails a visit of ISO 9001 auditor to undertake a site audit of the company, about two months after the internal audit has been conducted so that sufficient data are available on record. Some companies offer certification services, but the one accredited by a national certification body is picked up for the purpose. It sends the auditors to perform a certification audit and then issues an ISO 9001 certificate. Due preparation is, however, necessary before the arrival of auditors. Up-to-date documents consisting of records after the ISO implementation is kept ready, while the staff is prepared mentally to answer the typical audit queries. The staff may be provided with a short multimedia video and/or certification kits to prepare themselves for the audit. Having achieved the ISO 9001 quality standard, the companies may capitalize by publicizing the certification through whatever means necessary. However, due credit of certification is given to the staff. The process of the audit should be repeated at least once or twice a year.

7. Knowledge Management in the ISO 9001:2015 Standard ISO, the largest standardization body in the world is a nongovernmental organization representing a network of national institutes from 165 countries. It aims

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to produce international standards, called ISO standards, implementable in the industrial and commercial organizations leading to the coveted seal of conformity to international standards. The ISO 9001:2015 version had included a paragraph devoted to KM (ISO DIS 9001 § 7.1.6) since 2015. The requirements of this version are well known by KM practitioners as follows:

• • •

Identify the required knowledge necessary for business processes and conformity for products and services Maintain and disseminate the knowledge Identify how to acquire or access additional required knowledge

The 2015 version of the ISO standard, lays the foundations for the KM processes that must be implemented in companies. Notes 1 and 2 of the said standard outlines the concept of internal Knowledge Capital as well as the link with the existing external capital in a rudimentary way. Neither the information was distinguished from the knowledge nor was the concept of tacit knowledge discussed. In any case the introduction of a paragraph on “Organizational Knowledge” in the international standard is a historic step for the recognition and implementation of KM in companies. As detailed in Chapter 8, the ISO 30401 standards published in 2018 is an important milestone in the history of KM, for it is a general standard of KM applicable in all types of organization. The standard is dedicated to the definition and the implementation of a KMS. Taking a holistic view it can be integrated into a global management system, as defined by different ISO standards. The objectives provided in the introductive part are as follows:

• • •

“The purpose of this ISO standard is to support organizations to develop a management system that effectively promotes and enables value-creation through knowledge. A knowledge management system aims to contribute to the achievement of a company’s strategic objectives through the preservation, dissemination, sharing and evolution of its Knowledge Capital. The purpose of the standard is to set rigorous principles and requirements for knowledge management.”

Recommendations provided in chapter 4 of the ISO 30401 standard are not only mandatory for the implementation of the standard but also for the purpose of evaluation of the KMS to be compliant with the given ISO requirements. These are as follows:

• •

4.2 Requirements 4.1 and 4.2: Dwell upon setting a KM framework 4.3 Requirement 4.3: Identify the critical knowledge domains

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4.4 Requirement 4.4: Implement an effective and holistic KMS 4.5 Requirements 4.4.1 and 4.4.2: Knowledge processes 4.6 Conclusion on ISO 30401

As per the above requirements, it may be concluded that a KMS, to be compliant, must:

• • •

Rely on a correct definition of a KM framework in the organization, Contain the strategic and critical knowledge of the company, Implement processes in terms of:

– – – –

Knowledge Knowledge Knowledge Knowledge

codification sharing search evolution

8. Salient Features of Evaluation of KMS Based upon the above requirements, the following PPT highlights the essential elements to be looked into for the purpose of evaluation of a KMS:

Is your system a Knowledge Management System compliant with the KM standards ? Jean-Louis ERMINE

© Jean-Louis Ermine 2019 1

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• A brief history of KM standards

• Requirements for a KMS (ISO 30401)

© Jean-Louis Ermine 2019 2

The KM standardization, a work in progress

2015 : 2017 : ISO 9001:2015, 7.1.6 Integration of KM in the Organizational international standards Knowledge of the nuclear domain (Started in 2012 ?)

(Started in 2002)

2017 : Operational KM repository by the “Club Gestion des Connaissances “ in France

2018 : ISO 30401 Knowledge Management Systems

(Started in 1999)

(Started in 2015)

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The IAEA Repository

© Jean-Louis Ermine 2019 4

The French KM Repository OBJECTIVE Provide an operational repository for: Plan Implement Maintain A Knowledge Management System Focused on the practical application of relevant KM processes that manage the company's Knowledge Capital Based on the concepts and tools developed for almost 20 years by the French KM club (Club Gestion des Connaissances) Compatible with ISO standards

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ISO 30401 Knowledge Management Systems — Requirements The purpose of this ISO standard is to support organizations to develop a management system that effectively promotes and enables value-creation through knowledge. A knowledge management system aims to contribute to the achievement of a company's strategic objectives through the preservation, dissemination, sharing and evolution of its knowledge capital. The purpose of the standard is to set rigorous principles and requirements for knowledge management. The standard was published in November 2018.

© Jean-Louis Ermine 2019 6

• A brief history of KM standards

• Requirements for a KMS (ISO 30401)

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Set a KM Framework § 4.1 Understanding the organiza on and its context The organiza on shall determine external and internal issues that are relevant to its purpose and that affect its ability to achieve the intended outcome(s) of its KMS

§ 4.2 Understanding the needs and expecta ons of interested par es The organiza on shall determine the stakeholders (interested par es) that are relevant to the KMS (structured in terms of business and organiza onal performance, rather than knowledge management needs)

§ 3 From KM strategy to KM implementa on 3.1 Who are the Stakeholders 3.2 Establishing a KM framework 3.3 Evalua on of the Knowledge Maturity of a company KM Framework Knowledge Maturity assessment © Jean-Louis Ermine 2019 8

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Knowledge processes § 4.4.2 Knowledge conveyance and transformation The organizational knowledge management system shall include activities and behaviours, supporting all different types of knowledge flows, through systematic activities and behaviours, supporting the KMS objectives and covering the prioritized knowledge domains a) Human Interaction: exchange and co-creation of knowledge b) Representation (demonstrating, recording, documenting and/or codifying) c) Combination (Synthesis, curating, formalizing, structuring or classifying of codified knowledge, making the knowledge accessible and findable) d) Internalization and learning (reviewing, assessing and absorbing knowledge; incorporating it into practice)

§ 4.4.1 Knowledge development The organization shall demonstrate that the KMS covers the following activities : a) Acquiring new knowledge b) Applying current knowledge c) Retaining current knowledge d) Handling outdated or invalid knowledge

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Knowledge processes § 5 Implementing the KM plan 5.1 Knowledge organisation 5.2 Knowledge codification 5.3 Knowledge sharing 5.4 Knowledge search 5.5 Knowledge creation and innovation

Knowledge Based Documents Knowledge Books Community Maturity Assessment Knowledge Transfer Processes A Knowledge Based Innovation Process Innovation Maturity Assessment

© Jean-Louis Ermine 2019 12

ISO 9001:2015 § 7.1.6 Organizational knowledge

• The organization shall determine the knowledge necessary for the operation of its processes and to achieve conformity of products and services. • This knowledge shall be maintained and be made available to the extent necessary. • When addressing changing needs and trends, the organization shall consider its current knowledge and determine how to acquire or access any necessary additional knowledge and required updates. © Jean-Louis Ermine 2019 13

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Questions for your KMS

Do you have defined the correct KM framework of your system?

Does your system contain the strategic and critical knowledge of the company? What are the processes implemented in your system in terms of : Knowledge codification Knowledge sharing Knowledge search Knowledge evolution?

© Jean-Louis Ermine 2019 14

Benefits of ISO 30401

Align your KMS with the corporate strategy

Build a repository of precise and chosen processes to build and maintain your KMS Include your KMS into a Integrated Management System Challenge for the ISO certification (ISO 30401 certification is coming soon !)

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ISO 30401 : A PDCA virtuous approach

§ 4.1 Understanding the organiza on and its context § 4.2 Understanding the needs and expecta ons of interested par es § 4.3 Determining the scope of the knowledge management system § 4.4 Knowledge management system § 4.5 Knowledge Management culture

Con nuous Improvement

Support (§7) Opera ons (§8)

Planning (§6)

Leadership (§5)

Performance evalua on (§9)

Improvement (§10)

© Jean-Louis Ermine 2019 16

Personal advertising

© Jean-Louis Ermine 2019 17

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© Jean-Louis Ermine 2019 18

Index Absorption, 44 Abstraction, 44 Academic institutions, 21–22 Ambiguity, 146 Artificial intelligence (AI) case-based reasoning systems, 174 deep learning, 171 explicit knowledge sharing systems, 177–178 implicit knowledge sharing systems, 178–179 intelligent knowledge management, 180–184 knowledge, 171 knowledge application system, 172–174 knowledge capture systems, 174–177 knowledge discovery systems, 179–180 knowledge sharing systems, 177–179 rule-based expert systems, 173 self-learning, 171 Boisot I-Space KM model, 43–44 Capitalization process, 11 Knowledge Book, 83 realization of, 81–83 scoping, 81 Case-based reasoning systems, 174 Certification, 188 body selection, 188 Certified knowledge manager (CKM), 18 Choo Sense-making KM model, 52–56 decision-making, 54 knowledge creation, 54

organizational knowing cycle, 54 sense-making, 52–54 Cognitive processes, 51 Collective learning, 109–111 Combination, 50 Competence management, 112–114 Compound Annual Growth Rate (CAGR), 36 Computerised environment for human learning (CEHL), 141–142 Confidence building, 146 Corporate intranets, 40 Creativity, 87 Critical capacities assessment, 69–71 Critical knowledge domains, 165 assessment, 71–73 Customer relationship management (CRM), 118–119 Daisy model capitalization and sharing process, 93–97 capitalizing/sharing knowledge, 92 creativity process, 105–107 distortion, 100–101 environment, 92 hypothesis, 98 identification, 101 intelligence, 99 interaction process, 97–104 inventiveness process, 107 knowledge-based innovation process, 105–107 knowledge capital, 104–105 knowledge conversion cycle, 93–97 knowledge creation process, 99, 102, 104, 107 knowledge management issues, interaction process, 103–104

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Index

knowledge resource, 98 learning/creating knowledge, 92 learning process, 108–117 projection, 99–100 relevant feedback, 101 representation, 102 water cycle metaphor, 96–97 Data, information, knowledge, and competence (DIKW), 62–63 Decision-making, 54 Deep learning, 171 Diffusion, 44 Dissemination, 89 Double-loop learning, 111 E-learning, 135–142 E-PLearn, 141–142 European Foundation for Quality Management (EFQM), 45 components, 45 Experts creativity, 88 Explicit knowledge sharing systems, 177–178 Externalization, 49–50 Guesswork, 146 Homo sapiens, 1 Humanitarian assistance/disaster relief (HA/DR), 15–16 Human resource plans, 164 Human resources management, 111–114 Husain–Ermine AI-KM Model, 56–58 Implicit knowledge sharing systems, 178–179 Individual learning, 108–109 Information and communication technology (ICT), 7–8, 17 Information and Knowledge Society (KS), 7–11 Information security, 165 Innovative knowledge, 87–89 Intellectual property, 165

Intelligent knowledge management, 180–184 Internal audit, 191 Internal communication, 163 Internalization, 50 International organization, 148–150 International Organization for Standardization (ISO) critical knowledge domains, 165 human resource plans, 164 information security, 165 intellectual property, 165 internal communication, 163 ISO 30401, 161, 168–169 knowledge management at the regulatory level, 159–161 knowledge management system, 161 knowledge processes, 167–168 nuclear knowledge management, 157–159 objectives, 162 organizational knowledge, 156 resources, 163 responsibilities, 162–163 roles, 162–163 safety standards, 159 Inventiveness, 87–89 ISO 30401, 161, 168–169 ISO 9000 and knowledge transfers, 150 ISO 30401:2018 knowledge management systems, 150–152 ISO 9001:2015 Standard certification, 190–191 documentation, 190 implementation, 190–191 internal audit, 191 preparation, 190 Japan Advanced Institute of Science and Technology (JAIST), 21 Knowledge codification, 64 crash, 11 creation, 64

Index discovery systems, 179–180 dissemination, 64 domains assessment, 128–130 drilling, 87 economy, 10 elicitation, 133–134 evaluation, 64 identification, 64 Knowledge access, creation, and transfer (K-ACT) model, 19 Knowledge application system (KAS), 172–174 Knowledge-based innovation (KBI) creativity, 87 dissemination, 89 experts creativity, 88 innovative knowledge, 87–89 inventiveness, 87–89 knowledge drilling, 87 prospective elements collective coconstruction, 88 tangible intellectual capital, 88 Knowledge Book, 83 e-learning, 135–142 IMS learning design, 135–137 MASK/IMS–LD, 137–142 Knowledge Book in electronic form (eKBook), 134–135 Knowledge capitalization, 11 Knowledge capital strategic assessment action planning, 74–76 critical capacities assessment, 69–71 critical knowledge domains assessment, 71–73 strategic alignment, 74–76 Knowledge capture systems (KCSs), 174–177 Knowledge framework for knowledge society, 8–9 Knowledge management (KM), 10–11, 61, 143 application, 37–38 Boisot I-Space KM model, 43–44 capturing, 37 certified knowledge manager, 18

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Compound Annual Growth Rate, 36 conceptual development, 14–15 data, information, knowledge, and wisdom, 2–3 definition, 34–35 development, 17 discovery, 37 education, 21–22 explicit knowledge, 5tacit knowledge vs., 6 generation, 2 health care, 22–23 Homo sapiens, 1 information and communication technology, 7–8, 17 Information and Knowledge Society, 7–11 Knowledge access, creation, and transfer model, 19 knowledge framework for knowledge society, 8–9 knowledge-related concepts, 9–11 knowledge society to knowledge management, 9 law firms, 23–24 Minnesota department of transportation, 35–36 models, 42, 58 police, 24–25 processes, 35–38 sharing, 37 social learning cycle, 44 stages, 44 tacit knowledge, 5–6 total quality management, 19 types of, 4–6 universe of knowledge, 3–4 Knowledge Management Assessment Tool (KMAT), 14 Knowledge management system (KMS), 142–143, 150–151, 161 applications, 39–40 building, 38–41 certification, 188

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Index

certification body selection, 188 corporate intranets, 40 development, 15–16 evaluation, 188–189, 193 examples, 40–41 ISO 9001:2015 Standard, 189, 191, 193 library and information science, 20–21 modules, 38–39 National Library Board, 19–21 nonprofit-making organizations, 19–25 profit-making organizations, 25–27 Quality Management System, 189 Knowledge process knowledge codification, 64 knowledge creation, 64 knowledge dissemination, 64 knowledge evaluation, 64 knowledge identification, 64 knowledge process wheel, 64 knowledge sharing, 64 Knowledge pyramid, 62–63 Knowledge sharing, 11, 64 capitalization, 11 systems, 177–179 Knowledge society (KS), 10 knowledge management to, 61 knowledge value chain, 61–63 Knowledge strategic assessment, 132 Knowledge transfer expert/novice comodeling, 85 knowledge book direct transfer, 85 knowledge server/knowledge portal, 86 learning system, 86 socialization, transfer process based on, 85 transfer devices, 84–86 transfer process, 83–84 Knowledge value chain (KVC), 61–63 data, information, knowledge, and competence (DIKW), 62–63 knowledge pyramid, 62–63

management, 63 transformation chain, 63 Law firms, 23–24 Learning process collective learning, 109–111 competence management, 112–114 double-loop learning, 111 human resources management, 111–114 individual learning, 108–109 recruitment, 115–117 reflexive learning, 111 single-loop learning, 110–111 training, 115, 117 Learning system, 86 Library and information science (LIS), 20–21 MASK/IMS–LD computerised environment for human learning, 141–142 E-PLearn, 141–142 evaluation quiz, 141 general scenarios identifying, 137 principal activities model scenarios, 137–138 reservoir engineering, 138–141 Method for analyzing and structuring knowledge (MASK), 67–69 Microsoft SharePoint software, 22 Minnesota department of transportation (MnDOT), 35–36 National Library Board (NLB), 19–21 Nonaka–Takeuchi SECI model, 48–51 Nonprofit-making organizations, 19–25 Nuclear knowledge management, 157–159 Organizational knowing cycle, 54 Organizational knowledge, 156

Index Organizational Leadership Questionnaire (OLQ), 14 Petroleum Engineering and Development Department (PED), 126 Knowledge domains assessment, 128–130 Knowledge strategic assessment, 132 strategic alignment, 131 strategic capacity analysis, 128 strategic know-how, 127–128 Police, 24–25 Problem-solving, 44 Productivity enhancement, 146–147 Product standardization, 147 Profit-making organizations, 25–27 Prospective elements collective coconstruction, 88 Quality assurance, 146 Recruitment, 115–117 Reflexive learning, 111 Reservoir engineering, 126–127, 138, 141 Results-Approaches-Deploy-AssessRefine (RADAR), 47–48 Roos model, 51–52 Rule-based expert systems, 173 Rule-based systems (RBS), 173 Scanning, 44 Selection process customer relationship management, 118–119 usage-centered selection, 119–121 Self-learning, 171 Sense-making, 52–54 Single-loop learning, 110–111 Socialization, 49 Socialization, Externalization, Combination, and Internalization (SECI), 48 Social learning cycle (SLC), 44

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Sociotechnical design, 120 Socio-Technical Knowledge Processing System, 56–58 Sonatrach, national oil company of Algeria Petroleum Engineering and Development Department, 126 reservoir engineering, 126–127 strategic assessment of knowledge, 127–132 thinking/acting, 126 Standardization benefits, 145–147 consumers, 148 international organization, 148–150 ISO 9000 and knowledge transfers, 150 ISO 30401:2018 knowledge management systems, 150–152 organizations, 148 product standardization, 147 purpose, 147 steps, 148 systems analysis, 145 technology, 148 Total Quality Management, 150 Strategic alignment, 74, 76, 131 Strategic assessment of knowledge, 127–132 Strategic capacity analysis, 128 Strategic know-how, 127–128 Systems analysis, 145 Tacit knowledge activity model, 80 capitalization process, 81–83 concept model, 80 evolution model, 81 history model, 81 Knowledge Book in electronic form, 134–135 knowledge books, 77–83 knowledge elicitation, 133–134 knowledge modeling, 78–81

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Index

phenomena model, 79 task model, 81 Tangible intellectual capital, 88 Technical design, 120 Top level of safety standards, 159 Total quality management (TQM), 19, 21, 150 Training, 115, 117 Transformation chain, 63 Usage-centered selection, 119–121

KM plan elaboration, 65–66 knowledge capital, 67 strategic assessment, 65–66 knowledge management processes implementation, 67 knowledge resources organization, 66–67 method for analyzing and structuring knowledge, 67–69 Von Krogh model, 51–52

Virtuous KM cycle, 64–67

Web-based KMSs (WBKMSs), 22

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