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Knowledge Integration The Practice of Knowledge Management in Small and Medium Enterprises

:	tter, Jeroen Kraaijenbrink, Hans-Horst Schröder, Pons Wijnhov
:	Antonie Jetter, Jeroen Kraaijenbrink, Hans-Horst Schröder, Fons Wijnhoven
:	Knowledge Integration The Practice of Knowledge Management in Small and Medium Enterprises
:	Physica-Verlag
:	9783790816815
:	1
:	CHF 85.30
:

:	Management
:	English

:	204
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

The ability to manage knowledge is relevant for millions of small and medium sized enterprises (SMEs) that operate in high-tech environments. They strongly depend on external knowledge about customers, technologies, and competitors because, as opposed to large companies, they have limited internal knowledge resources and little power to control their business environments. Present KM literature, however, mainly focuses on large companies and therefore does not explain, how SMEs, for example, can successfully apply groupware, data mining, semantic networks, and knowledge maps. This book addresses this problem by introducing the concept of knowledge integration (KI) that places emphasis on the identification, acquisition and use of external knowledge. Drawing from this theoretical basis, the book presents concepts and instruments specifically designed for SMEs, as well as examples of their implementation and use in practice.

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".

	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	83
	83	83
	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 Kn