Knowledge Integration (eBook)

Preface 5
Table of Contents 7
1 Knowledge Management: More than a Buzzword 12
1.1 Introduction 12
1.2 The Relevance of Knowledge Management for High- tech Small and Medium Sized Firms 13
1.3 Knowledge Management – What Is It About? 14
1.3.1 Knowledge Management versus Competence Management 14
1.3.2 Approaches to Knowledge Management 14
1.3.3 Levels of Knowledge Management 16
1.4 What Aspects Are Related to Knowledge? 17
1.4.1 Content in Knowledge Identification and Acquisition Processes 18
1.4.2 Utilization of Knowledge in Contexts 20
1.4.3 Knowledge Flows 20
1.4.4 Knowledge Media 21
1.5 The Knowledge Integration Context 23
1.6 Outline of this Book 24
References 26
2 Knowledge Integration by SMEs – Framework 28
2.1 Introduction 28
2.2 High-tech SMEs: Characteristics and Differences 29
2.3 Types and Sources of Knowledge 30
2.4 KI Processes and Activities 33
2.5 KI Problems and Solutions 36
2.6 Summary and Conclusions 38
References 38
3 Knowledge Integration by SMEs - Practice 40
3.1 Introduction 40
3.2 Analysing KI in SMEs: Research Framework 40
3.3 Research Method 42
3.4 Results 43
3.4.1 NPD Process 44
3.4.2 Sources 44
3.4.3 KI Process 46
3.4.4 Problems 47
3.4.5 Solutions 48
3.4.6 Match 49
3.5 Differences between SMEs 50
3.6 Conclusions and Implications 52
References 54
Appendix: Questionnaire 54
4 Organizing the Toolbox - Typology and Alignment of KI Solutions 57
4.1 Introduction 57
4.2 Definitions and Principles of the Typology 58
4.3 Typology of KI Tools and Techniques 60
4.3.1 Activities for Latent Knowledge 61
4.3.2 Activities for Explicit Knowledge 62
4.3.3 Activities for Tacit Knowledge 68
4.3.4 Motivating Activities 68
4.4 Knowledge Integration Strategies 69
4.5 SME Suitability 72
4.6 Conclusions 72
References 74
5 Elicitation – Extracting Knowledge from Experts 75
5.1 Motivation and Introduction 75
5.2 A Psychological Perspective on Knowledge Elicitation 75
5.2.1 Theoretical Background 75
5.2.2 Relevance for Knowledge Management 78
5.3 Elicitation in Practice 79
5.3.1 Identification of Experts 79
5.3.2 Activation and Capture of Knowledge 80
5.3.3 Knowledge Interpretation and Documentation 81
5.4 Implementation Experience 82
5.4.1 Identification of Experts at CEROBEAR 83
5.4.2 Activation and Capture: Free Association & Episodic Interviews
5.4.3 Interpretation and Documentation: Building an Ontology 84
5.5 Discussion and Conclusions 85
References 85
6 Codification – Knowledge Maps 87
6.1 Introduction 87
6.2 Knowledge Codification and Knowledge Maps 87
6.3 Types of Knowledge Maps 89
6.3.1 Hierarchical or Radial Knowledge Structure Maps: Concept Maps and Mind Maps 90
6.3.2 Networked Knowledge Structure Maps: Causal Maps 91
6.3.3 Knowledge Source Maps 92
6.3.4 Knowledge Flow Maps 93
6.4 Case Study: Knowledge Maps to Improve NPD 95
6.4.1 Process Assessment 95
6.4.2 Improved Processes: AIXTRON’s Knowledge Application Map 97
6.5 Discussion and Conclusion 98
References 99
7 Detection – Electronic Knowledge Retrieval 102
7.1 Introduction 102
7.2 IR Systems for Knowledge Detection 102
7.2.1 Traditional IR Search Methods 103
7.2.2 Information Retrieval and the WWW 104
7.2.3 New Impulses in IR Systems 105
7.3 Implementation at a High-tech SME 107
7.3.1 The High-tech SME: CEROBEAR 107
7.3.2 Focus: Development of a Customer- Specific Ontology 108
7.3.3 Results and Evaluation 109
7.4 Discussion and Conclusion 110
References 111
8 Assessment – Making Sense of It All 112
8.1 Introduction 112
8.2 What Is Knowledge Assessment? 113
8.3 Critical Analysis of Assessment Practices 114
8.3.1 Theoretical Background and Practical Framework 114
8.3.2 Alignment of Available Practices 115
8.4 The Decision-Validity-Tracking (DVT) Method 116
8.5 Lessons Learned from the Implementation at Optibase 121
8.6 Conclusions 123
References 124
9 Transfer - Knowledge Transfer in Networks 126
9.1 Introduction 126
9.2 Theory on Knowledge Transfer in NPD Processes 126
9.2.1 The Character of Knowledge and Networks in Transfer Processes 127
9.2.3 Some Consequences of Cognitive Distance for Networking of Small Firms 128
9.3 The WAP Project, an Example of Knowledge Transfer in a Network 130
9.3.1 Context of the Project 130
9.3.3 Knowledge Transfer Mechanisms 132
9.4 Conclusions 135
References 136
10 Motivating – Incentive Systems for Knowledge Provision 138
10.1 Introduction 138
10.2 Design Areas of Incentive Systems for Knowledge Provision 139
10.2.1 Definition of Knowledge Goals 139
10.2.2 Definition of the Application Area 140
10.2.3 Definition of Incentive Tools 140
10.2.4 Measurement and Evaluation of Employee Performance 141
10.3 Implementation of Incentive Systems 141
10.3.1 Analysis of the Status-quo 141
10.3.2 Concept Development and Elaboration 143
10.3.3 System Introduction 145
10.3.4 System Checking 145
10.4 Case Study at HEAD Acoustics 146
10.4.1 HEAD Acoustics and the Focus of the Project 146
10.4.2 Results 147
10.5 Summary and Conclusion 151
References 151
11 Supporting Knowledge Integration at SMEs – The KINX Portal 154
11.1 Introduction 154
11.2 Information Services and Scope of the KINX Portal 156
11.3 Knowledge Integration Portal Description 157
11.3.1 The KINX Portal Public Area 159
11.3.2 The Private Area 161
11.3.3 The Administration Area 166
11.4 Portal Development Process 167
11.5 Conclusions and Discussion 168
References 169
12 Supporting Knowledge Integration at SMEs – Policies 172
12.1 Introduction 172
12.2 Reasons for Supporting KI in SMEs 172
12.3 Profiles of KI Support Measures for SMEs 173
12.4 Usage of Selected KI Support Measures 178
12.5 The Effectiveness of KI Support Measures 179
12.6 Summary and Recommendations 183
References 184
13 Wrapping It All Up - Past, Present and Future of Knowledge Integration 186
13.1 Introduction 186
13.2 Evaluation of KI - What Does It Promise and Does It Keep What It Promises? 187
13.2.1 The Theoretical Perspective 188
13.2.2 The Empirical Perspective 190
13.2.3 The Tools Perspective 192
13.3 The Further Development of KI Requirements and Opportunities for Improvement 196
13.3.1 Conceptual Improvements 196
13.3.2 Instrumental Improvements 197
13.4 Outlook - The Future of KI 199
References 201
Biographical Information about the Authors 204
List of Authors’ Addresses 208
Index 212

5 Elicitation – Extracting Knowledge from Experts (S.64)

Antonie Jetter

Chair for Business Administration with focus on Technology and Innovation Management, RWTH Aachen University, Germany, jetter@tim.rwth-aachen.de

5.1 Motivation and Introduction

The activity of elicitation – the explication of unarticulated latent knowledge that the knowledge owner might not even be fully aware of – is an important first step for many knowledge activities, such as codification and transfer of knowledge. Elicitation requires that people are conscious of and successfully express their knowledge and that their expressions are adequately represented and interpreted.

Cognitive psychologists have long been interested in learning and have therefore developed methods to research what people know (knowledge contents), how their knowledge is organized in the human brain (knowledge structures) and how content and structure change in the course of time. Though many of the research methods they use have been adopted in other areas (e.g., marketing, managerial cognition, expert system design), they are still relatively unknown in the field of knowledge management (KM).

Furthermore, some elicitation methods that have originated in psychology are applied in KM with very little consideration for their theoretical background and application domains. Consequently, the knowledge that is captured in KM practice is sometimes only an insufficient representation of expert knowledge. This chapter will briefly discuss the psychological perspective on knowledge elicitation, and its value for knowledge management (Sect. 5.2), before it presents elicitation methods for three distinct steps in the elicitation process (identification of experts, activation and capture; interpretation and documentation) in Sect. 5.3.

In Sect. 5.4 it will then present a case study of a high-tech SME that has applied the elicitation techniques of episodic interviews and free word association for building ontologies for knowledge search and retrieval.

5.2 A Psychological Perspective on Knowledge Elicitation

5.2.1 Theoretical Background

Many researchers in cognitive psychology are primarily interested in the structures of knowledge in the human brain. It is widely accepted that the brain follows the principle of cognitive economy and organizes related knowledge content in struc- tures that can be easily accessed and processed as an entity. Elicitation results (e.g., the speed and order of a test person’s statements) are used to infer these structures [9].

Models of knowledge structures vary greatly. One very influential idea of knowledge organization, e.g., grounds on the notion that de-contextualized knowledge about facts – so-called semantic knowledge (e.g., historical data, the members of the European Union, the differentiating characteristics of mammals) – is organized in network structures. These knowledge structures consist of verbal concepts and propositions about them and are usually represented through graphs, with concepts being the nodes and relations being the edges. The sentences "A tree is a plant", "Plants need sunlight", "Oaks are trees" for example, contain four concepts (tree, plant, sunlight, oaks) that are linked through the relations "is a", "need", and "are".