Organising Knowledge: Methods and Case Studies

Organising Knowledge Methods and Case Studies Edited by Johannes Gadner, Renate Buber and Lyn Richards Organising Kn...

Author: Johannes Gadner | Renate Buber | Lyn Richards

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Organising Knowledge Methods and Case Studies

Edited by

Johannes Gadner, Renate Buber and Lyn Richards

Organising Knowledge

Also by the same editors Applying Quantitative Methods to Marketing Management Research

Organising Knowledge Methods and Case Studies Edited by

Johannes Gadner Renate Buber and

Lyn Richards

Selection and editorial matter © Johannes Gadner, Renate Buber and Lyn Richards 2004 Individual chapters © the contributors 2004 All rights reserved. No reproduction, copy or transmission of this publication may be made without written permission. No paragraph of this publication may be reproduced, copied or transmitted save with written permission or in accordance with the provisions of the Copyright, Designs and Patents Act 1988, or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, 90 Tottenham Court Road, London W1T 4LP. Any person who does any unauthorised act in relation to this publication may be liable to criminal prosecution and civil claims for damages. The authors have asserted their rights to be identified as the authors of this work in accordance with the Copyright, Designs and Patents Act 1988. First published 2004 by PALGRAVE MACMILLAN Houndmills, Basingstoke, Hampshire RG21 6XS and 175 Fifth Avenue, New York, N.Y. 10010 Companies and representatives throughout the world PALGRAVE MACMILLAN is the global academic imprint of the Palgrave Macmillan division of St. Martin’s Press, LLC and of Palgrave Macmillan Ltd. Macmillan® is a registered trademark in the United States, United Kingdom and other countries. Palgrave is a registered trademark in the European Union and other countries. ISBN 1–4039–1661–6 This book is printed on paper suitable for recycling and made from fully managed and sustained forest sources. A catalogue record for this book is available from the British Library. Library of Congress Cataloging-in-Publication Data Organising knowledge: methods and case studies / editors, Johannes Gadner, Renate Buber, Lyn Richards. p. cm. Includes bibliographical references and index. ISBN 1-4039-1661-6 (cloth) 1. Knowledge, Theory of. 2. Knowledge management. 3. Gabek (Computer file) I. Gadner, Johannes. II. Buber, Renate, 1954- III. Richard, Lyn. BD175.0595 2003 001-dc22 2003056407 10 9 8 7 6 5 4 3 2 1 13 12 11 10 09 08 07 06 05 04 Printed and bound in Great Britain by Antony Rowe Ltd, Chippenham and Eastbourne

Contents Preface

vii

Notes on the Contributors

xii

Acknowledgements

xvi

Part I 1

2

Theoretical and Methodological Issues

Structures and Diversity in Everyday Knowledge: From Reality to Cognition, Knowledge and Back Markus Franz Peschl Situatedness and Artefacts in Expert Knowledge and Activity Hanna Risku

3

The Human Foundation of Knowledge Management Thomas Bernhard Seiler

4

Conceptual Knowledge Organisation and the Problem of Understanding Johannes Gadner and Doris Ohnesorge

5

6

Gestalt-Based Linguistics and the GABEK Method: What Cognitive Science has to Say on ‘Space Logic’ in Everyday Thought Michael Kimmel Managing Complexity: Knowledge Organisation and Qualitative Software Lyn Richards

Part II 7

3

28 43

60

75

93

Case Studies

Consumers’ Knowledge of Controlled Ventilation Systems Johannes Gadner and Renate Buber v

113

vi Contents

8

9

10

11

12

13

The Process of Web Personalisation: A Framework to Determine Appropriate Personalisation Systems Sonja Grabner-Kräuter

125

Evaluating a Computer-Based Marketing Course by Means of Focus Groups and Interviews Regina Höld

138

The Job Image of Young Sales Assistants: Introspection and Young Consumers’ Assessment Renate Buber

153

Venture Brazil: Studying Austro-Brazilian Joint Ventures by Means of GABEK and WinRelan Jürgen Mühlbacher and Jochen Penker

168

Austrian Furniture Retailers’ Perspective on the Austrian Design Sector Renate Buber and Marion Friedmann

180

Influences of Organisational Learning on Organisational Performance and Success Roland Yeo

193

Index

215

Preface Knowledge forms the basis of all human behaviour in general and management activities in particular. Although the identification, acquisition, organisation and storage of knowledge, as well as the efficient use of knowledge, are not new in the scientific and economic worlds, the systematic development and use of methods and tools to organise and manage knowledge constitute a new and increasingly important aspect of academic research and management practice (McDonald et al., 1997; Stewart, 1997; von Müller, 2000; Wille, 1999a; ReimannRothmeier and McElroy, 2002; Mandl, 2000). In management practice knowledge organisation mostly involves the use of databases and the development of data-mining tools, but in academic research the search for adequate definitions of knowledge, knowledge organisation and knowledge management is ongoing (Schreyögg and Geiger, 2003). This book aims to help bridge the gap between scientific research on knowledge organisation and the economic use of knowledge management applications by providing both a theoretical discussion of issues in knowledge organisation and knowledge management, and several case studies focusing on knowledge-based problem situations in management and the application of qualitative methods to solve them. To cover the wide range of research perspectives and practical applications (themes, approaches, methods, levels of generalisability), knowledge is defined here as actively processed networks of cognitive maps based on the experiences and intrinsic capacities of individuals as well as social/cultural standards, patterns and schemes for organising perceptive information on environmental effects and regulating individual behaviour within social systems (Oeser and Seitelberger, 1995, pp. 203–6; Shore, 1996, pp. 311–15). This definition is critically examined and elaborated on in order to point out the problems of systematically and theoretically oriented research in knowledge organisation and management. Following Schreyögg and Geiger (2003, pp. 12–13), the kind of knowledge dealt with here is characterised by three factors. First, knowledge is propositional, that is, it is directly bound to communication and has to be negotiated in discourses. Second, every proposition explicitly or implicitly claims validity. This means that knowledge demands reasons.

vii

viii Preface

Third, it is not sufficient just to propose reasons for qualifying propositions as knowledge – these reasons must be recognised within the specific context being researched. Thus knowledge in this context refers to all sorts of conceptual knowledge, that is, knowledge properties that are available from verbal expression and as text. In this book, various methods and tools that are used to organise and manage verbal data – that is, propositional knowledge – are described theoretically and their practicability in various contexts is demonstrated. In addition the terms knowledge organisation and knowledge management are discussed. The core reason for knowledge organisation is to ease access to knowledge and to make complex knowledge properties more intelligible (Wille, 1999a,b; Kiel and Rost, 2003). Knowledge organisation can be defined as a precondition for knowledge management which focuses on the systematic and strategic use of knowledge in economic situations (McElroy, 2002). In short, knowledge has to be organised systematically in order to manage and use it efficiantly. This book is a result of the ‘First International Workshop on Qualitative Research in Marketing and Management’ organised by the Department of Retailing and Marketing at the Vienna University of Economics and Business Administration (VUEBA), QSR International (Qualitative Solutions in Research), Melbourne, and the Institute for Knowledge Organisation (IWO–Institut für Wissens organisation), Vienna–which took place at VUEBA 9–11 April 2002. The IWO was responsible for organising the third day of the workshop which dealt with qualitative methods for knowledge organisation and their application to marketing and management research and practice-the outcome % is presented in the 13 chapters of this book. The book is divided into two parts. Part I contains six approaches to various issues in knowledge organisation and knowledge management. In Chapter 1 Markus F. Peschl defines different sorts of knowledge from an interdisciplinary perspective. He describes various approaches to and definitions of knowledge, as well as their cognitive scientific backgrounds. The use of knowledge properties in several scientific and practical contexts is also addressed. From a conceptual point of view, a model of everyday and expert knowledge is developed. In Chapter 2 Hanna Risku critically examines situatedness and artefacts in expert knowledge and activity. She starts from the point where everyday knowledge is transformed into expert knowledge by creating flexible reference models based on recurring regularities or patterns. These general reference models distinguish experts from novices who use superficial characteristics to categorise problems, relying on explicit, visible keywords.

Preface

ix

In Chapter 3 Thomas Bernhard Seiler analyses the nature and fundamental conditions of knowledge as prerequisites for knowledge organisation and management. The analysis reveals a contrast between objectified knowledge and its idiosyncratic origin, and it is shown that assessment of the knowledge that is implicit in the structure of an organisation and in the behaviour of its members requires qualitative research methods. In Chapter 4 Johannes Gadner and Doris Ohnesorge focus on methodological aspects of the use of conceptual structures for the organisation and systematisation of knowledge and the problem of understanding. After a general and theoretical overview of Gordon Pask’s notion of protolanguage, two computer-supported qualitative methods of knowledge organisation based on this concept are described. In Chapter 5 Michael Kimmel discusses recent findings in cognitive linguistics and their relevance for the computer-supported process of gestalt building using the GABEK method. While the method’s underlying assumption about the condensation and spatial grouping of knowledge into gestalt clusters is confirmed, cognitive approaches point to the method’s shortcomings and the constraints on automated gestalt building in on-line cognition. In Chapter 6 Lyn Richards discusses the problem of managing complexity. She argues that all (software-assisted) qualitative research requires the organisation of knowledge that is provided a priori, discovered during enquiry and derived from search and scrutiny. She describes a number of software packages and various ways of organising, processing and representing knowledge in form of conceptual data. Part II presents case studies of the organisation and management of knowledge. In Chapter 7 Johannes Gadner and Renate Buber, concentrate on consumers’ knowledge of and attitudes towards energy-efficient buildings with controlled ventilation systems. From their daily experiences of their living and working environments, consumers know about certain aspects of the products and technologies they use. The authors discuss how the knowledge and experiences of inhabitants of energyefficient buildings could be used to design targeted marketing strategies. In Chapter 8, Sonja Grabner-Kräuter focuses on web-usage mining, which is defined as the discovery of knowledge about user access patterns from web servers. It comprises the systematic organisation of general access pattern tracking and customised usage tracking. The chapter provides an overview of recommendation systems and analyses the main factors that have to be considered when determining whether a system is adequate. It is shown that the success of personalisation

x Preface

systems and strategies depends on the complexity of the core products’ attributes, the degree of variation between customers’ needs and the degree of variation in customers’ life-time values. In Chapter 9 Regina Höld evaluates a computer-based marketing course by means of focus groups and interviews. The aim is to evaluate how students acquire knowledge by working with on-line course material. The principles of data collection via focus groups and interviews are introduced, and data collection and data analysis in a pilot project on the evaluation of a marketing course for first-term students is described. Students’ knowledge is organised and managed by means of QSR N6 and the resulting improvements of the course material are described. In Chapter 10 Renate Buber analyses the image of jobs in low-price clothing shops from both the customers’ and the employees’ perspective. Using the GABEK method and systematising customers’ and sales assistants’ experiences with the buying process, she gains insights into sales assistants’ job image. A number of measures to improve the image of the job, improve sales assistants’ job satisfaction and consequently to enhance business performance are recommended. In Chapter 11 Jürgen Mühlbacher and Jochen Penker investigate key success factors in Austro-Brazilian joint ventures, based on the knowledge and individual experiences of Austrian and Brazilian managers. The use of the GABEK method to gain an understanding of complex joint-venture systems is described, focusing on the process of coding verbal data. Austrian furniture retailers’ perspective on local design is investigated by Renate Buber and Marion Friedmann in Chapter 12. The authors relate how experts in furniture retailing evaluated the image of design in Austria, the problems they identified and the measures they suggested to solve these problems. Based on the experts’ comments, the authors point out the need to foster design consciousness among the Austrian people. In Chapter 13 Roland Yeo investigates the influence of organisational learning on organisational performance and success. In-depth interviews were conducted in four Singaporean organisations in order to explore issues pertaining to the developmental process of learning, the stages of learning, the factors that influence learning and the relationship between learning and performance. The data was analysed using qualitative content analysis techniques.

Preface

xi

References Kiel, E. and Rost, F. (2003) Einführung in die Wissensorganisation (Würzburg: Ergon). McDonald, C., Pun, W. K. and Weckert, J. (1997) ‘Knowledge management for the applied sciences’, in B. R. Gaines and R. Uthurusamy (eds), Artificial Intelligence in Knowledge Management (Stanford, CA: AAAI Press), pp. 110–13. McElroy, M. W. (2002) The New Knowledge Management: Complexity, Learning, and Sustainable Innovation (London: Butterworth-Heinemann). Oeser, E. and Seitelberger, F. (1995) Gehirn, Bewusstsein und Erkenntnis (Darmstadt: Wissenschaftliche Buchgesellschaft). Reimann-Rothmeier, G. and Mandl, H. (2000) ‘Wissensmanagement im Unternehmen’, in C. Maar, H. U. Obrist and E. Pöppel (eds), Weltwissen – Wissenswelt: Das globale Netzt von Text und Bild (Cologne: DuMont), pp. 271–82. Schreyögg, G. and Geiger, D. (2003) ‘Wenn alles Wissen ist, ist Wissen am Ende nichts?!’, Die Betriebswirtschaft, vol. 63, no. 1, pp. 7–22. Shore, B. (1996) Culture in Mind: Cognition, Culture, and the Problem of Meaning (Oxford: Oxford University Press). Stewart, T. A. (1997) Intellectual Capital: The New Wealth of Organizations (New York: Currency Doubleday). von Müller, A. A. C. (2000) ‘Das Erzeugen, Speichern und Nutzen von Wissen als Schlüsselqualifikationen der Zukunft’, in C. Maar, H. U. Obrist and E. Pöppel (eds), Weltwissen – Wissenswelt: Das globale Netzt von Text und Bild (Cologne: DuMont), pp. 262–8. Wille, R. (1999a) ‘Conceptual landscapes of knowledge: a pragmatic paradigm for knowledge processing’, in W. Gaul and H. Locarek-Junge (eds), Classification in the Information Age (Heidelberg: Springer), pp. 344–56. Wille, R. (1999b) ‘Menschengerechte Wissensverarbeitung’, in P. Bittner and J. Woinowsky (eds), Mensch – Informatisierung – Gesellschaft (Münster: LIT), pp. 87–104.

Notes on the Contributors Renate Buber is Assistant Professor in marketing at the Vienna University of Economics and Business Administration (VUEBA). She holds a doctoral degree in marketing and an MBA in business pedagogy from VUEBA, as well as a diploma in psychology from the University of Vienna. In January 2003 she was visiting professor at the Institute for International Studies, Rhamkhamhaeng University, Bangkok, Thailand, and since then has served as a teacher of PhD candidates in marketing. Besides a strong interest in qualitative research methodologies, her major research and teaching areas are consumer behaviour, sales management and not-for-profit management. At present she is heading a two-year research project on consumer behaviour in the recreation areas of shopping malls, funded by the Austrian National Bank. Marion Friedmann studied business administration (tourism and leisure, retailing and marketing) at the Vienna University of Economics and Business Administration. During her studies she worked as an interviewer in several market research projects. Her special interests are culture and the arts, including – music, photography and creativity in general. Since 2002 she has worked as a marketer for ARENA, a youth and cultural centre in Vienna that stages concerts, open-air cinema in summer, theatre productions, art performances and so on. Johannes Gadner studied philosophy, anthropology and psychology at the University of Vienna, the Free University Berlin and University College London (UCL). In 1997–98 he worked as a research assistant at UCL and the Anna Freud Centre in London. In 1999 he won a PhD in philosophy from the University of Vienna. From 1999 to 2001 he worked as a researcher in the Department of Philosophy, Leopold Franzens University, Innsbruck. Since 2001, as one of the founders, he has worked as a researcher at and secretary of the Institute of Knowledge Organisation (IWO – Institut für Wissensorganisation). He is also a secretary of the Club of the Green Party in the Austrian Parliament, which enables him to communicate to the public the relevance of science and research to society. His main research interests are knowledge organisation, cognitive science, epistemology and hermeneutics. xii


xiii

Sonja Grabner-Kräuter is Associate Professor of Marketing and International Management at the University of Klagenfurt, Austria, where she teaches international marketing, Internet marketing and export finance. Her current research interests are Internet marketing and business ethics. She has written a book on corporate ethics and published articles in numerous journals. Regina Höld designs learning materials for retailing and marketing courses and is a member of the e-learning project group at the Vienna University of Economics and Business Administration (VUEBA). She worked as an assistant teacher at the Vienna Business School during the winter of 2002, and will soon finish her studies in business administration at VUEBA, with majors in business pedagogy and education, plus retailing and marketing. The subject of her diploma thesis was the qualitative evaluation of computer-based learning in marketing. Michael Kimmel earned an MA in political sciences in Vienna. After that he turned to the field of cognitive anthropology and cognitive linguistics, and his PhD thesis focused on imagery and metaphor in culture. Other fields of interest are the theory of knowledge organisation and interdisciplinarity in the cognitive sciences. He has worked as an assistant at the Vienna University of Economics and as a researcher at the Institute of Advanced Studies in Vienna. The topics of his publications include new social movements, the theory of cultural comparison, cultural imagery, and the cognitive impact of narratives. He is currently, a freelance researcher in the field of cognitive metaphor theory and divides his time between Vienna and Budapest. Jürgen Mühlbacher is a University Assistant at the Vienna University of Economics and Business Administration, where he previously studied business administration. After working as a controller in the field of investor relations, in 1998 he returned to VUEBA as a lecturer and research assistant and in 2001 he received PhD. He has taught a number of MBA and MAS courses, and his current research focuses on management development and cultural aspects of knowledge management. Doris Ohnesorge is a graduate of Leopold Franzens University, Innsbruck, Austria. During her studies in business administration she specialised in cross-cultural management. She wrote her diploma thesis on cross-cultural management and interactions and transactions between Austrian and Thai managers. She spent one year in an international programme at Chulalongkorn University, Bangkok, Thailand, organised by Michigan State University. During her studies in Thailand

xiv Notes on the Contributors

she conducted empirical research on cross-cultural management. She has work experience in the field of accounting and controlling, and has been involved in a number of projects. She has attended conferences on qualitative and cross-cultural research in Europe, Australia, the United States and various Asian countries. Jochen Matthias Penker began his studies in international business at the University of Innsbruck in 1994. Between 1996 and 1997 he studied and worked at the University of Northern Iowa. In 1999 he was awarded a scholarship by the Italian Ministry for Foreign Affairs and conducted a research study at the Università degli Studi di Roma/La Sapienza. In 2000 he received a master’s degree from the University of Innsbruck and commenced his doctoral studies at the Vienna University of Economics and Business Administration. Since 2002 he has worked for the Austrian Federal Ministry for Economic Affairs and Labour. His research interests are success factor research, international cooperation, joint ventures, cross-cultural research and international trade. Markus Franz Peschl is Associate Professor at the Department of Philosophy of Science and Social Studies of Science, University of Vienna. His academic interests are cognition and cognitive modelling, cognitive science and parallel distributed processing, the philosophical and epistemological foundations of cognitive science and cognitive modelling, knowledge representation in natural and artificial cognitive systems, the philosophy of science, constructivism and neurophilosophy (the application of concepts from cognitive science to the philosophy of science). Lyn Richards is Director of Research Services at QSR (Qualitative Solutions in Research) International, Melbourne, Australia. As a sociologist she taught qualitative methods at the undergraduate and graduate levels, and wrote four books based on qualitative family research projects. These led to the development, with computer scientist Tom Richards, of the NUD*IST research project (on ways of handling nonnumerical, unstructured data by indexing, searching and theorising). The resulting software is now in its sixth version and has been joined by a partner software package, NVivo. Lyn is the author of Using NVivo in Qualitative Research, Using N6 in Qualitative Research (with Pat Bazeley), The NVivo Qualitative Project Book and Readme First for a User’s Guide to Qualitative Analysis (with Jan Morse). She has taught qualitative methodology and the use of qualitative software to some three thousand researchers in fourteen countries.


xv

Hanna Risku is Head of the Centre for Knowledge and Information Management and Deputy Head of the Department for Telecommunication, Information and Media at Danube University Krems, Austria. She studied translation studies at the Universities of Tampere (Finland) and Vienna. In 1996 she received a PhD from the University of Vienna. She has worked as lecturer at the Universities of Granada (Spain), Skövde (Sweden), Vienna and Tampere. She researches and publishes on the cognitive foundations of intercultural communication, translation and knowledge management and the role of information technology in translation and terminology management. Thomas Bernhard Seiler studied philosophy and theology at the University of Fribourg, Switzerland. After graduating he studied psychology in Fribourg, Paris and Berlin, where he received his PhD in 1966. In 1971 he became a Professor at the Free University Berlin, and in 1976 moved to the University of Darmstadt, Germany, where he remained until his retirement in 1993. His empirical and theoretical research focused on cognition and he had a special interest in Piaget’s work. Recently he has worked in the field of philosophical and psychological theories of concepts and meaning. Roland Yeo is a Lecturer at Temasek Polytechnic in Singapore. He has been involved in research into and the teaching of communication management, including the fields of technical, business and organisational communication at Temasek Engineering School. He is a member of the core teaching team for creative problem solving, a cognitive thinking subject. He is also active in managing creative thinking projects and has been instrumental in the development of problem-based learning initiatives at the Engineering School. His research interests include organisational learning, intangible performance management and strategic change processes. He has published papers in a number of journals.

Acknowledgements This book is the outcome of a long-standing collaboration between the three editors. It started out as brainstorming on a train-ride somewhere in Austria, continued with a thrilling conference in Vienna, and ended reading and discussing the ever so many interesting papers. It was an extraordinary experience meeting all the contributors as well as our audiences, who generated the discussions and the momentum to go ahead with the book. The presentations, discussions and informal conversations about qualitative methods for knowledge organisation and their application to marketing management research laid the foundation-stone for this book. Keith Povey and his team’s efforts to review and make positive suggestions elegantly put the finishing touches to it.

Vienna and Melbourne

JOHANNES GADNER RENATE BUBER LYN RICHARDS

Part I Theoretical and Methodological Issues

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1 Structures and Diversity in Everyday Knowledge: From Reality to Cognition, Knowledge and Back Markus Franz Peschl

Introduction The notion of knowledge is omnipresent in the field of knowledge management and knowledge organisation. This chapter begins with an epistemological reflection on the systems involved in the processes of representing, transferring and managing knowledge. It will be shown that knowledge is fundamentally tied to the process of cognition. By implication, knowledge has to be understood as a highly dynamic process emerging from the interaction between a cognitive system, its natural environment and its non-natural environment (for example artefacts, symbols and so on). Based on these epistemological insights, a framework will be developed to offer some orientation in the ‘jungle’ of notions and concepts of knowledge. The various dimensions of knowledge (such as local-distributed, representational-situated, mapping construction and so on) will be discussed, together with their relevance to the field of knowledge management. Investigation of the object of knowledge reveals that technological and information processing approaches to knowledge management and to our everyday knowledge cover only a small fraction of what knowledge actually comprises.

Reality, cognition, knowledge The fascination of knowledge, knowledge management and knowledge organisation A huge number of papers, books and Internet sites on the topics of knowledge management and knowledge organisation have appeared 3

4 Structures and Diversity in Everyday Knowledge

during the past 15 years (for example Nonaka and Takeuchi, 1995; Davenport and Prusak, 1998; Bukowitz and Williams, 1999; Liebowitz, 1999). During this period the subject has received growing attention not only in the field of computer science but also in the more practical domains of economic applications and business. Almost all large businesses have introduced some kind of knowledge management initiative (more or less successfully). Moreover knowledge management and knowledge organisation have been studied and developed far beyond the classical boundaries of scientific disciplines. Computer science only covers the technological aspects and provides the software and hardware. In order to cover the field of knowledge management adequately, methods and concepts from psychology, economy, sociology, group therapy, philosophy, epistemology, cognitive science, artificial intelligence and so on are at least as important as the techniques and tools offered by computer science. Hence knowledge management and knowledge organisation have become interdisciplinary undertakings that run from empirical findings and methods (such as interviewing techniques) to theoretical considerations (for example, about mental models) and practical business applications. What is it that makes this field so attractive for such a variety of disciplines and applications? One possible answer seems to be the discovery of the concept, the role and the (in many cases hidden) wealth of knowledge. Of course the concept of knowledge is not at all new. Rather it is possibly one of the oldest in our history of thinking – simply because thinking without knowledge is impossible. In the days of the ancient Greeks, questions about knowledge were central to philosophy and gave birth to epistemology – the study of the nature of knowledge, its development and dynamics1 (Churchland, 1979, 1988; Kornblith, 1993; Brook and Stainton, 2000). The study of knowledge remained in the philosophical domain until the arrival of cognitive science, artificial intelligence and knowledge management. The concept of knowledge has become increasingly important as the economic focus has shifted from material resources to the knowledge and intellectual assets of organisations and their employees. This has been accompanied by a growing demand for knowledge-based products, ‘brain workers’ and knowledge-based and knowledge-processing computer software. While this indicates an initial shift from the ‘real’ to the ‘virtual’, the insight that knowledge has a value and that both knowledge and its value are ‘more real’ than was originally assumed, the ontological status of knowledge has shifted back from the virtual to the real. From an epistemological perspective, the question we are confronted

Markus Franz Peschl 5

with concerns the efficiency and/or application of knowledge in reality, that is, the process of transforming knowledge into concrete actions with real effects. However is this the only purpose of looking at knowledge? Is there more to the concept of knowledge than merely understanding it as a means of increasing efficiency and profits? What is knowledge on a more fundamental level? What could a more philosophical perspective offer to the field of knowledge management and knowledge organisation?

Domains of knowledge In order to answer these questions it is necessary to take a closer look at the basic situations in which knowledge is developed, represented, used and transferred. Several domains of knowledge are discussed in the following subsections.

Reality, the environment and cognitive systems2 The domain of reality covers the whole environment. It is not limited to material entities but includes all dimensions of reality. Hence ‘hidden processes’ such as those behind the molecular structure of physical entities, together with cultural artefacts, language, other cognitive systems and mental events are all part of reality or the environment. The goal of every cognitive system is to acquire knowledge about this environment. It should be made clear, however, that reality per se cannot be reached in principle; depending on the epistemological position it is possible to approximate reality (Popper, 1934) or obtain only negative feedback from reality (von Glasersfeld, 1984, 1995; Schmidt, 1987, 1992; Peschl, 1994). In contrast to large parts of the environment, cognitive systems (including human beings) are living systems. Maturana and Varela (1980) stress that there is a mutual dependency between the process of cognition and being a living system. In order to exist and to stay alive, cognitive processes are necessary for orientation, planning, finding food, obtaining energy and so on. On the other hand cognitive processes are not completely detached from the material substratum. Living matter is necessary to keep these processes going since neural activity is based on living neural tissue. The cognitive system acts as the substratum for the processes of cognition and life. The outcome of the process of cognition is twofold. First, cognitive processes lead to the generation of behaviour, which can be observed by an external observer. The second outcome concerns the internal domain of the cognitive system: knowledge is generated during the


course of processing stimuli, adapting to the environment and controlling bodily and external dynamics.

The relation between reality and the cognitive system In order to relate the domains of reality and the cognitive system it is necessary to establish some kind of link between these two domains. In cognitive systems this link is realised in the form of the sensory system (Martin, 1991) and motor systems. The sensory system is responsible for transforming environmental states and state changes into internal (that is, representational) signals. This transformation is referred to as the process of transduction. On the output side, motor systems are responsible for transforming representational signals such as ‘plans’ and ‘knowledge’ into motor behaviour that has a direct physical effect on the internal and external environments. The relation between reality and the cognitive system also raises questions about the epistemological relation. What is the representational link between these two domains? Is it a relation of mapping, of construction or of fitting? Philosophy, epistemology and the philosophy of science are mainly concerned with these questions.

Knowledge and knowledge representation It is this domain that is of principal interest in this chapter, but its role cannot be understood if it is not embedded in the context of the other domains. In a way the realm of knowledge is the ‘missing link’ between the input and output of the organism. In most cognitive systems the representational mechanisms – usually the neural structure – are highly complex and non-linear. Neural structures are responsible for holding knowledge,3 and thus for transforming input into output in a nonlinear manner. What we as observers normally refer to as knowledge is – from the perspective of cognitive systems and cognitive (neuro)science – nothing but a complex transformation function that establishes a link between the organism’s internal state and history, its sensory input and its motor output. This sounds relatively simple, but it is perhaps the most complex subject encountered in the modern natural sciences. From the perspective of knowledge management, this domain is especially interesting as it is the area we have to search for a more profound understanding of what knowledge is and how it can be organised in the context of the other domains.


The circular relationship between knowledge and reality The domains discussed above are linked together in a circular fashion. The cognitive system and its representational structure or knowledge cannot be understood properly if they are treated in isolation from the environment or as a static system. Rather they have to be seen as a part of a circular feedback process: the generated behavioural output influences – mediated by environmental structures – parts of the sensory input. The sensory input influences – mediated by the non-linear representation system – the motor output. Abstractly speaking, two feedback loops are involved, each interacting with the other and trying to achieve a state of equilibrium or homeostasis. From a biological perspective, the internal loop is responsible for keeping the cognitive system alive and coupled to the environmental dynamics in a stable manner via the external loop. Epistemologically speaking, this process can be interpreted as trying to achieve an epistemological equilibrium between the internal knowledge structures (embodying behavioural strategies) and external environmental constraints and perturbations (Maturana and Varela, 1980).

Internal and external knowledge So far knowledge has been characterised as something that can be found only inside a cognitive system. If the notion of knowledge is interpreted in an extreme manner, this is true because knowledge is radically tied to cognitive processes. It is the cognitive system that knows, applies, understands and constructs knowledge. This perspective is counterintuitive as one would normally assume that, for instance, a book, a CDROM, a linguistic artefact or a web page represents a piece of knowledge. However, looking more closely one can easily see that these artefacts are only patterns in space and time that have no meaning. It is only when they are interpreted by a cognitive system that they achieve a ‘knowledge effect’, triggering representational states inside the cognitive system that can be interpreted as knowledge transfer and knowledge acquisition via these artefacts. Of course it is not possible to determine what is really understood by the perceiving cognitive system. Interpretation is always determined by the ontogenetic and phylogenetic history and current internal state of the perceiving system (von Glasersfeld, 1983; Roth, 1991). Knowledge is always situated in the internal domain. ‘External knowledge’ – that is, symbolic artefacts – is a meaningless construct. External knowledge entities are only material and energetic patterns that carry


the possibility of being interpreted as meaningful knowledge structures if the perceiving cognitive system is disposed so to do. One has to keep in mind that representational artefacts such as written (or spoken) text, images, tools, pieces of art or architecture and so on are the result of purposeful behavioural output and motor action. In other words, parts of the cognitive system’s knowledge are externalised via the motor system and are ‘materialised’ in the structure of the environment by changing the environment’s state(s). These artefacts form the cultural background of our world, or more precisely are physical structures that carry potential meaning. Even though external knowledge is not knowledge in the strict sense, it is clear that it plays an extremely important part in any cultural process as well as in any kind of knowledge management or knowledge organisation. In sum, external knowledge is an intrinsically meaningless vehicle whose purpose is to trigger representational processes inside the individual cognitive system. Hence the environment acts as a source of potential knowledge. Symbolic or linguistic artefacts – that is written text, iconic representations, representations using certain codes and so on – consitute a special subclass of artefacts as they have been ‘designed’ and externalised to serve a referential function: a shift in the object of knowledge has occurred in symbolic artefacts. It is not so much the exterior appearance or form of, for instance, a word or symbol that is of relevance, but the phenomenon, object or event to which it refers. Symbolic artefacts are the foundation for all cultural processes, and they constitute an alternative vehicle for passing knowledge from one cognitive system or one generation to another. In this sense symbolic artefacts are an extragenetic channel for knowledge transfer. Symbolic artefacts can be used for the automatic generation of other symbolic artefacts based on the (automated) manipulation of matter as a carrier of the symbolic artefacts. This is the epistemological foundation for any kind of computer (Turing, 1936, 1950). Symbolic artefacts such as computer programs represent an algorithm for manipulating existing symbolic artefacts to generate new symbolic artefacts, and thus new knowledge.

Concluding remarks on the ontological domains in knowledge management and knowledge organisation If we wish to obtain a realistic perspective on knowledge and its organisation or management we have to realise that studying cognitive processes, and more specifically their ability to represent and create knowledge, is essential both to understanding the processes involved in


knowledge management and to establishing and designing a successful and efficient knowledge management strategy. Strictly speaking, knowledge is always radically tied to cognitive processes. Such a perspective has several implications for our understanding of knowledge in organisational, scientific, cultural and everyday contexts. First, knowledge is not a static body of facts, information or representations; rather knowledge has to be understood as a highly dynamic process whose substratum and roots lie in the processes and dynamics of cognition. Second, knowledge is not to be found ‘out there’; artefacts are only carriers of potential knowledge whose meaning has to be constructed by an individual cognitive system and its neural mechanisms in a process of interacting with natural entities and artefacts. Third, in a strict sense knowledge is always ‘private’ or internal and has a highly subjective character. This applies to the process of representation as well as to the generation of knowledge. This perspective has far reaching consequences for almost every domain in which knowledge is involved (von Foerster, 1973; Maturana and Varela, 1980; Schmidt, 1987; Varela et al., Peschl, 1994; von Glasersfeld, 1995). Fourth, knowledge is not the result of an isomorphic or homomorphic mapping process. The nervous system acts as a device for the construction of representational structures. These structures are system dependent and their epistemological relation to reality is a relationship of functional fitness (von Glasersfeld, 1984, 1995). The goal of knowledge is not to represent environmental structures, events or phenomena in the representational substratum as a map or passive projection of reality. Rather the representational structures themselves embody the knowledge responsible for generating functionally fitting behaviour (Clark, 1999, 2001; Bechtel and Abrahamsen, 2001). Finally, one of the central problems in the field of knowledge management is the question of how knowledge is transferred and spread in an organisation. The constructivist position requires that the classical idea of knowledge being transferred from one brain to another has to be abandoned. Knowledge is not a ‘thing’ that can be extracted from one cognitive system and fed into another. This model has to be replaced by a concept of interaction, mutual adaptation and the establishment of a consensual domain between the participating systems (Maturana and Varela, 1980). That is why the process of knowledge transfer and learning has to take place in a highly interactive setting in which all the systems involved are continuously obliged to adapt mutually to each other (for example in discussions, in a cultural or scientific discourse


and so on) in order to establish a domain of minimal (semantic) consensus. Having identified the systems, structures, and relations involved in the processes of representing, creating, generating and transferring knowledge, we shall now consider the notion of knowledge and its diversity.

Perspectives on knowledge Nonaka and Takeuchi (1995, p. 58 f.) define knowledge as follows: First, knowledge, unlike information, is about beliefs and commitment. Knowledge is a function of a particular stance, perspective, or intention. Second, knowledge, unlike information, is about action. It is always knowledge ‘to some end’. And third, knowledge, like information, is about meaning. It is context-specific and relational. In our theory of organisational knowledge creation, we adopt the traditional definition of knowledge as ‘justified true belief’. … While traditional epistemology emphasises the absolute, static, and nonhuman nature of knowledge, typically expressed in propositions and formal logic, we consider knowledge as a dynamic human process of justifying personal belief toward the ‘truth’. … This understanding emphasises that knowledge is essentially related to human action.

The nature of knowledge Epistemology is traditionally concerned with the problem of how to decide which beliefs or classes of belief meet the standard for knowledge (Nonaka and Takeuchi, 1995, p. 21 f.; Brook and Stainton, 2000). The notion of knowledge has to be revised in several ways if we are to achieve a better understanding of knowledge in the context of our original question about knowledge management, knowledge organisation and knowledge representation, and its embeddedness in cognitive as well as social and cultural dynamics.

‘Aboutness’ and intentionality Both our everyday experience and theoretical considerations in the fields of philosophy, psychology and knowledge management show that knowledge is not a static, monolithic and homogeneous phenomenon. The diversity of perspectives on knowledge seems to be almost as great as the diversity of objects and phenomena in our world. If there is one concept that can unify this wide range of approaches to


understanding knowledge it is the concept of intentionality (Harman, 1998), which underlies most of the notions of knowledge. Ontologically speaking, knowledge is always ‘ontologically remote’ with respect to the actual and concrete reality. Nevertheless these domains are related to each other. This relationship can be characterised (with a lot of caution) as a kind of referential link in a very first approach. Knowledge can be said ‘to be about’ something. So the remainder of this chapter will focus on the following questions: In which sense(s) can knowledge be said ‘to be about’ something? What is that ‘something’? What is/are (the) possible object(s) of knowledge? What are the epistemological relations ( between reality and knowledge) and substrata or carriers of knowledge?

Knowledge and behaviour In most cases knowledge per se is not directly visible or measurable. What can be observed are behavioural actions that are more or less adequate in a certain environmental context. From an evolutionary or a constructivist perspective (von Glasersfeld, 1991, 1995), adequate behaviour can be defined as ‘functionally fitting behaviour’ in terms of fitting in with the environmental and internal factors that affect the organism’s survival. Adequate behaviour points to a source that can be identified as knowledge and has the following characteristics: it is (1) responsible for generating these behavioural dynamics, and (2) seems to maintain some relation to the environmental structures; otherwise it would not be possible to generate behaviour that took account of and respected environmental constraints.

The dimensions of knowledge These are several dimensions along which knowledge can be categorised.

Knowing and ‘to be able to’ Knowledge is the premise for any kind of ability. Thus a distinction has to be made between ‘knowing’ and ‘to be able to’. In order to be able to externalise behaviour, to engage in an (internal or external) action, to make a decision or to show competence in an area, it is necessary to possess the knowledge upon which these abilities are based. What an external observer would interpret as a skill or competence is actually an expression of the cognitive system’s internal knowledge, just as any kind of motor action or behavioural dynamics is the result of internal representational dynamics.


These categories – that is, knowledge versus skill and behaviour – must not be confused: knowing is an internal category whereas ‘to be able to’, in terms of an individual’s skills, are an external observer’s categories. In the case of abilities or competencies the observer deals with concrete behaviours, while in the case of knowledge we are more in the realm of the premises of abilities, and therefore in the realm of potential behaviours. Hence in order to achieve a better understanding of abilities or skills it is necessary to go back to the roots of knowledge and its structure.

Procedural knowledge versus declarative knowledge This categorisation concerns the classical distinction between ‘knowing that’ and ‘knowing how’. Declarative knowledge has a rather static character and comprises factual knowledge that can be registered and represented in a declarative manner, for example facts about a phenomenon, and a complex scene is analysed by fragmenting it into a more or less structured or hierarchical list or network of representational entities (symbols, certain states of a system and so on). Much of what has traditionally been thought of as knowledge has been this kind of declarative knowledge. With the advent of almost unlimited access to such knowledge via the Internet or libraries, the focus has shifted to alternative ways of knowing, especially of ‘knowing where’. Procedural knowledge concerns procedures, strategies and dynamic processes. Unlike declarative knowledge, it is very difficult or almost impossible to make an explicit list of facts for procedural phenomena. Due to its procedural nature, procedural knowledge resists fragmentation and reduction into separate, modular and linguistically explicit entities. It does not involve explicit factual representations and has a tendency towards behavioural action and efficient knowledge. To ‘know where’ relates to the domain of organisational knowledge, social knowledge and knowledge about how to acquire, organise and manage knowledge in terms of ‘metaknowledge’. Nowadays this type of knowledge is particularly interesting and important as it is more valuable and efficient to find reliable and well-structured sources of knowledge than actually to ‘know’. To ‘know where’ involves both declarative and procedural knowledge.

Implicit or tacit knowledge versus explicit knowledge Polanyi (1966) introduced the concept of tacit knowledge (as opposed to explicit knowledge). Tacit or implicit knowledge is highly contextspecific. It concerns behavioural and thinking patterns and in most cases it cannot be formulated in natural language. It seems that this kind of knowledge defies almost any attempt at formalisation,


verbalisation or systematisation. It cannot be made explicit, either in everyday language terms or in some formal system. Hence tacit knowledge is personal, strictly subjective and inaccessible. It can only be observed indirectly via behavioural dynamics, such as manual or intellectual skills. Unlike explicit knowledge, it cannot be expressed in natural language, not even by the possessors of the skills or knowledge in question. Tacit knowledge develops and is created through direct physical experiences with the structures, dynamics and constraints of the environment. No language or explicit reflective procedures are necessary for these processes. Classical examples are the process of learning how to ride a bicycle and the development of manual skills. Explicit knowledge, on the other hand, can be expressed in natural language and formalised. In a way it is like the tip of the iceberg that comprises the whole body of knowledge. This makes explicit knowledge publicly accessible and transmittable, and thus highly attractive for any kind of knowledge organisation, knowledge management, knowledge transfer and learning. Despite its implicitness, Polanyi (1966) shows that tacit knowledge is important for most everyday procedures and for many areas in the expert domain (Winograd and Flores, 1986; Hoffmann, 1998; Nilsson, 1998). In many areas, expert knowledge has the character of tacit knowledge and has developed over years of experience. Of course the implicit–explicit distinction cannot be isolated from the procedural– declarative distinction. Procedural knowledge tends to be implicit, experience-based and efficient as well as action-oriented, whereas declarative knowledge assumes that the entities of representation (in both the representational and the environmental domain) are explicit. The distinction between implicit and explicit knowledge is the subject of most efforts in the field of knowledge management. The whole process of knowledge elicitation is directed towards the question of how implicit and tacit knowledge can be made explicit, and therefore transferable and manageable (Nonaka and Takeuchi, 1995; Liebowitz, 1999; Probst et al., 1999; Procter, 2002). From the iceberg analogy it is clear that there is unity in the body of knowledge: both the tacit and the explicit constituents are necessary and mutually dependent on each other. Hence the implicit–explicit distinction is not a separation, but a distinction in the sense of an intellectual means of better understanding the dimensions of knowledge.

Episodic knowledge versus semantic knowledge Episodic and semantic knowledge could be seen as a subtype of declarative knowledge in that they are fact oriented, represent certain explicit


aspects of the world, are accessible via introspection and can be formulated and communicated in natural language. The distinction between them is classically illustrated by the difference between ‘remembering’ (for episodic knowledge) and ‘knowing’ (for semantic knowledge): Episodic remembering takes the form of ‘mental travel through subjective time’, accompanied by a special kind of awareness (‘autonoetic’, or self-knowing, awareness). Semantic knowing takes the form of thinking about what there is or was, or could be in the world; it is accompanied by another kind of awareness (‘noetic’, or knowing awareness). Language is frequently involved in both episodic and semantic memory, but it need not be. (Wilson and Keil, 1999, p. 278) Episodic knowledge is highly contextual and also includes an affective element. It is holistic, emotionally rich and one re-experiences a whole scene or situation. Despite its factual character, episodic knowledge has implicit components. Semantic knowledge, on the other hand, is rather neutral and forms the basis of episodic knowledge, meaning that there can be no episodic knowledge without semantic knowledge (but the reverse does not apply).

Background, contextual, unconscious and inaccessible knowledge versus conscious and accessible knowledge A slightly different distinction is introduced when one investigates the structure of knowledge from the perspective of accessibility. From our own experience and from Freud’s psychoanalytic approach, it is clear that large parts of our knowledge are not directly accessible by conscious operations. These parts mostly concern contextual, situational aspects of the world, such as default assumptions, stable conditions, background knowledge and so on, or hidden psychological aspects of our inner world. Of course these inaccessible components are closely related to tacit knowledge; but are they inaccessible because they are tacit, or vice versa (Dienes and Perner, 1999)? In either case, there has to be some kind of knowledge about these aspects of the external and internal world; otherwise the cognitive system could not behave in an adequate manner by taking all these contextual or unconscious aspects into account.

Distributed knowledge versus local knowledge In both epistemology and cognitive science there has been a long debate on how knowledge is represented in cognitive or neural systems. Two opposing positions have developed in the course of this


debate: distributed versus local representation. This distinction is elated to the disagreement about how knowledge is organised and represented in the representational substratum. The classical idea of knowledge is based on the assumption that there is a one-to-one relationship between the entity to be represented (the repraesentandum) and the representing entity (the repraesentans). In the local(ist) point of view, one entity or phenomenon is represented by a single representational unit, for example a symbol or a single activity in a neurone (Barlow, 1972). Besides the many philosophical and practical problems of this pproach, local-style representation is highly implausible from the perspective of neuroscience. The distributed knowledge standpoint, on the other hand, is based on the assumption that knowledge is represented in a distributed manner in the representational substratum. The connectionist approach and neuroscience have developed this concept both theoretically and empirically (Hinton et al., 1986; Rumelhart et al., 1986; Rumelhart and McClelland, 1986; Smolensky, 1988; Elman, 1991; Bechtel and Abrahamsen, 2001). The ‘distributed movement’ has set off a longlasting and fundamental debate in the fields of philosophy of the mind and cognitive (neuro)science (Fodor and Pylyshin, 1988; Clark, 1989; Bechtel and Graham, 1998; Bechtel and Abrahamsen, 2001, pp. 156 ff.) In the field of (artificial) neural networks, distributed representation refers to the assumptions that the repraesentandum is represented by a pattern of activation (or connection weights or attractors) that spreads throughout the neural substratum. All representational entities such as neurones participate in the representation of a particular chunk or unit of knowledge; at the same time a single unit or neurone participates in the representation of a large number of knowledge chunks. From the perspective of knowledge, the idea of a one-to-one or an n-to-one relationship between repraesentandum and repraesentans has to be abandoned in the distributed paradigm. In the field of connectionism and cognitive science, a wide variety of notions of ‘distributedness’ have been developed in recent years (Gelder, 1992, provides a detailed analysis of the different forms of distribution in knowledge and knowledge representation). The key issue in the controversy between distributed and local representation is the question of what is the ‘currency’ of knowledge: with the advent of highly successful and both theoretically and empirically sound models of cognition that employed the concept of distributed knowledge representation, the long tradition of a homomorphic/isomorphic mapping


of knowledge was seriously put into question. Until that time the symbolic/linguistic and mental imagery/pictorial (Kosslyn, 1990, 1994) approaches had been more or less the only candidates for the ‘stuff’ that knowledge is made of. Distributed representation has introduced a completely new alternative and has become a new category and serious applicant for the ‘currency’ of knowledge.

Rule-based knowledge versus ‘spreading activation’ dynamics In the domain of knowledge and knowledge representation, rules are used in order to represent states and state transitions, and thereby to depict the dynamics of a system. Rules combine procedural knowledge in the derivation part and declarative knowledge in the premises and conclusions. Rules represent knowledge on dynamics in the environment by establishing a link between conditions – that is, a certain state in the environment (the premise) – and conclusions, that is, the successor state in the environment if the conditions or premise(s) are satisfied. This is clearly a local form of representation. In contrast to rule-governed dynamics, neural systems follow the principle of ‘spreading activation’ (McLeod et al., 1998; Bechtel and Abrahamsen, 2001). Patterns of neural activation spread through the natural or artificial neural network and thereby develop a representational/knowledge dynamics. This dynamics of spreading is governed by the current configuration of the synaptic architecture. This concept of knowledge dynamics is closely related to the notions of distributed representation and tacit knowledge. Apart from its theoretical and empirical plausibility, it turns out that spreading activation systems can simulate rule-based systems (Elman, 1991). Hence this approach seems to be more general than purely rule-based knowledge.

Actualised knowledge versus potential knowledge There exist at least two states of knowledge; (1) actualised knowledge, which is consciously or unconsciously present at a certain moment in time (for example one thinks of a house) and whose substratum consists of patterns of activation in the neural system; and (2) potential knowledge, which can be actualised or derived but is not explicitly present at the moment (for example one is capable of thinking of a house, but does not necessarily think of it at that moment). The latter state of knowledge is associated with the neural architecture or body structure. It is waiting to be actualised by, for instance, gene expression, learning


processes or the spread of activation triggered by internal and external stimuli and the phylogenetic and ontogenetic history of the organism.

Representational knowledge versus embodied, situated and embedded knowledge The classical representational approach is based on the assumption that knowledge is about a certain event, phenomenon or factor in the environment. This view of knowledge is closely related to the local and explicit format of knowledge. Furthermore the approach implies that knowledge is abstract, propositional, detached from reality (Harnad, 1990; Cangelosi and Parisi, 2002), individual and restricted to a single cognitive system, as well as general and rather static (with respect to semantics). Situated approaches, on the other hand, claim that knowledge is interactive, embodied, context-dependent, embedded via sensory and motor systems, social and dynamic. There is a continuous ‘codevelopment’, a flow and exchange of information and signals, and a mutual dependency and perturbation between the cognitive system’s knowledge and its natural and artificial environment. The situated perspective on knowledge takes these assumptions seriously. This has resulted in alternative ways of representing knowledge about the world; namely by actively interacting with the world and by taking the world itself as an active source of knowledge and knowledge representation. Parts of knowledge and operations on knowledge are ‘outsourced’ back to the world by means of artefacts (Brooks, 1991). Knowledge is not restricted to the cognitive system, but is a process that develops dynamically in the interaction between the cognitive system’s representational structure and the world of natural entities and artefacts. Finally, the situated approach is closely related to the dynamic systems approach to cognition and knowledge (Thelen and Smith, 1994; Port and Gelder, 1995; Gelder, 1998; Ward, 2002) rooted in cybernetics (Wiener, 1948; Ashby, 1964) and the theory of dynamic systems. In this approach, cognitive systems are understood as dynamically evolving. Such systems are represented by a set of differential equations and generate more or less intelligent behaviour. Knowledge cannot be found in explicit variables, but is implicitly hidden in the relations between them that are expressed by the differential equations. Hence neural systems are a special case of dynamic systems. It is clear that such a view of knowledge has far-reaching implications not only for our understanding of knowledge but also for any kind of knowledge management, knowledge transfer and learning and teaching processes.


Representational strategies versus behavioural strategies If the considerations on situated knowledge are combined with the constructivist approach and the concept of distributed representation from (computational) neuroscience, an alternative dimension of knowledge is suggested: knowledge cannot be isolated in a single representational structure but is embodied throughout the neural and body architecture. The classical idea of mapping environmental structures into representational structures has to be abandoned in favour of the following concept: the neural (and/or body and/or genetic) structures do not hold knowledge about the environment (in the classical representational sense), but act as mechanisms to generate adequate behaviour (Varela et al., 1991; Peschl, 1994, 1997; Clark, 1999). Hence the concept and constraint of ‘aboutness’ has become less strict and has been replaced by the criterion of functional fitness; any (representational) mechanism that is capable of generating functionally fitting and adequate behaviour can be accepted as holding adequate knowledge about the world. This means that the neural architecture is less about representing knowledge about the world and more about how to deal and interact successfully with the world. In this sense it is about ‘strategic’ and behavioural knowledge.

Epistemological relationships: mapping, adaptation, construction Another dimension along which one can distinguish knowledge concerns the question of which kind of epistemological relationship is realised between the domains of reality and knowledge. Roughly speaking, four possible positions can be identified on a spectrum that ranges from naïve realism to solipsism: ●

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Mapping: knowledge is the result of a homomorphic or isomorphic process of passively mapping reality. Adaptation: knowledge is the result of processes of adaptation that have occurred in the course of the organism’s phylogenetic and ontogenetic history. Construction: active processes of construction develop representational structures and mechanisms in such a way that they are capable of successfully cooperating with the environmental and internal constraints; knowledge has a rather active character. Solipsism: knowledge is the result of pure speculation and imagination; it is completely dissociated from reality. Although logically irrefutable, this position is not really of relevance for our question about the dimensions of knowledge.


Depending on the position adopted with respect to the epistemological relationship, the understanding and handling of knowledge will differ vastly. A moderate constructivist perspective on both reality and its constraints, as well as the active and constructive character of the cognitive system and the resulting knowledge, has turned out to be an adequate position in the context of knowledge management processes.

Ways of knowing reality The reality- and object-oriented approach to knowledge Apart from the dimensions of knowledge developed so far, an alternative way of looking at knowledge can be suggested: the ‘object-oriented approach to knowledge’. What is meant by ‘object-orientedness’? Knowledge always has an aspect of reality as its point of reference. The objectoriented approach to knowledge focuses on the following question: what is the object of a particular knowledge structure or knowledge process? That is, to what aspect of reality does a particular knowledge structure or knowledge process refer? The following taxonomy of knowledge is a first step towards establishing a framework for orientation.

Data and facts In general the notions of data and facts are synonymous. They are the most primitive representations of knowledge extracted and acquired from reality. Both notions etymologically reveal important aspects of this type of knowledge and knowledge in general. The Latin root of the word data is the verb dare, to give. As ‘data’ is the plural passive form of the past participle, ‘data’ are those which are given (in reality). ‘Fact’, on the other hand, has its roots in the verb facere, to make. Thus ‘fact’ refers to things that are (actively) made or produced. One can easily see the dichotomy between the two; a dichotomy that can be found on every level of knowledge and in every process of knowledge representation. It is the tension between receptiveness and openness on the one hand, and autonomy, construction and activity on the other hand. This is one of the fundamental problems in the process of representing knowledge about the world, and all the difficulties of knowledge acquisition, knowledge representation, perception and theory-ladenness in science and everyday knowledge are implicitly present (Peschl, 1999, 2001). This tension is just another way of expressing the spectrum of epistemological positions between naïve realism and solipsism.


Data and facts are the very first and ‘raw’ representations of the world. They are the result of direct interactions with the environment, either via the sensory system (Martin, 1991) or via scientific gauges. As an example, think of a number on a voltmeter – just the number without the unit of measurement. The objects of data and facts are quantitative values of a certain (physical) aspect, dimension or modality of an environmental structure or entity. Data are neither neutral nor ‘given’. In every registration or sensory process both an activity and receptivity are involved. Admittedly the resulting knowledge, is rather poor and far from a comprehensive and coherent representation of the world. It is an unordered collection of quantitative values that indirectly refer to states in the environment at certain points in space and time. Nevertheless data and facts are the basis upon which all the other levels of knowledge are built. This applies both to everyday and to scientific knowledge.

Information The definitions of information are as rich and numerous as theories on the nature of the world, cognition, biology and so on (Shannon, 1949; Oeser, 1976; Rasmussen, 1991; Bischof, 1998; Zeigler et al., 2000). For the purpose of our question about the classification of knowledge, information can be understood as data and facts that are put into a (theoretical or semantic) context. In a way, ‘quality’ is reintroduced to the quantitative values of data and facts by putting them into the context of a theoretical or semantic framework, for example by adding the unit of measurement to the quantitative value. Of course this does not bring back the original quality of the environmental event or phenomenon. In a way information has a ‘pseudoqualitative quantitative’ character. Despite this reintroduction of qualitative aspects, information is still far from constituting knowledge (in a more strict sense). At best information is unordered knowledge or a representation of the world without any assessment or judgement. That is exactly what is typically found on the Internet.

Scientific theories Scientific theories try to deal with the problem of organising unordered and unrelated information. By actively and inductively relating information, extracting and constructing regularities, and operationalising and formalising these regularities, theories discover and introduce order and relationships into the unordered body of information (Oeser, 1976). What is the object of a theory? Theories primarily aim to establish an understanding of the function(ing), the ‘how’ of an observed


phenomenon. This is achieved by inductively establishing relationships and regularities between data/information. The resulting knowledge on a phenomenon deals with order in space and time, tendencies, spatiotemporal changes, dynamics, functional factors and the validity of the theories themselves (at least to a certain degree). Formally speaking, scientific theories could be reduced to rule-based knowledge. Almost every theory can be transformed into a complex set of rules of the form ‘If reality is in state x and this reality is triggered by event e, then reality will change into state y (with a certain probability)’. Hence such knowledge is extremely well suited to making predictions, which is one of the main goals of natural science, and to actively and purposefully manipulating specific aspects of reality, which is the goal of every technology. It is evident that this kind of knowledge is aimed at its own application in reality rather than at understanding aspects of reality. At best scientific theories provide a kind of superficial pseudounderstanding by trying to make us believe that, if one has understood the functioning, the ‘how’, of a phenomenon, one has also understood the ‘what’, the meaning, the finality and so on of this phenomenon. This thinking is deeply rooted in theories from the natural sciences and dominated by a positivistic perspective on the world. Philosophically speaking, this perspective reduces the world to the material and efficient cause (causa efficiens and causa materialis) and to the realm of scientific theories. Finally, even though scientific theories are said to represent the summit of human knowledge, it turns out that they are rather superficial and remain at the material surface of the object of interest. For efficient predictions and the manipulation and control of environmental phenomena this might be sufficient, but for a deep understanding it far from satisfies our thinking and intellect. What has been said about scientific theories also applies to everyday knowledge. Within the everyday domain, theories often appear as ‘recipe knowledge’.

Knowledge in the proper sense With this kind of knowledge we are entering a new dimension of reality. This can be illustrated by a common experience. When thinking about a problem or phenomenon, one might reach a point of deeper understanding that goes far beyond understanding the mere functioning of a phenomenon – it seems as though one has started to understand the phenomenon ‘from within’; a kind of ‘intellectual intimacy’ is established between the object and the person’s thinking.


This must not be misunderstood as purely intuitive knowledge; rather it is highly qualitative intellectual knowledge that has as its object the ‘what’, the meaning, the intelligible form, the finality, the substance (␱␯␴␫␣, the being, the nature, the causes and the principles in a philosophical sense) of a phenomenon. The resulting knowledge is fragile, has to be rediscovered every time and is difficult to verbalise. It enables our thinking to penetrate reality and discover its metaphysical foundations. Only at this level one can speak of knowledge in a proper and stricter sense. Up to this level, knowledge remains rather superficial and quantitative. By entering into this dimension of reality, a new quality – the quality of ‘meaning’ – opens up in the domain of knowledge. It is only at this level that scientific theories receive their meaning, their finality and their context, and it is at this level that cultural, social, linguistic and most everyday processes are embedded. Many of the problems encountered in knowledge management, knowledge transfer and education have their foundation in the neglect of this dimension of knowledge and human thinking. The so-called ‘knowledge society’4 is – in most cases – a ‘recipe-application society’, located on a lower level of knowledge. It is more convenient and politically safer to remain outside the realm of knowledge in its proper or stricter sense.

Wisdom Pushing the level of knowledge and thinking to the extreme leads to a new domain of knowledge – wisdom. This is not some esoteric and obscure form of knowledge, but concerns the outermost questions that can be reached by our (philosophical) thinking. At the centre of such inquiries stands the quest for the sources and foundations of our being and the being of the world. Whether this will lead to the philosophical discovery of a Creator depends on the ideological orientation and intellectual honesty of the person conducting the inquiry. Philosophically speaking, wisdom deals with fundamental questions and leads to even deeper insights into the world, such as dependency on a Creator (on the level of being) and so on. Such considerations eventually lead to what is understood as ‘wisdom’ or ‘wise advice’, gained by accumulating knowledge of life and experience.

Mystical knowledge Contrary to all the other forms of knowledge, mystical knowledge does not depend solely on the efforts of those who acquire it. It is a welldocumented phenomenon observed across most cultures that humans


have experiences – visions, intuitions and so on – that cannot be explained by classical science or philosophy. Whatever the source of this knowledge, it not only seems to be the product of the person concerned, but also includes an element of ‘being given’. The extent to which this type of knowledge plays a role in the everyday life of ordinary people has yet to be determined. However, it should be mentioned for the sake of completeness.

Conclusions Despite the diversity of types, domains and levels of knowledge, it should be borne in mind that we experience knowledge as a unity. Types, domains and levels emerge as a result of analysis. It is not claimed that the distinctions of knowledge presented in this chapter give a complete picture of knowledge – the complexity of reality and the consequent complexity of knowledge and of the cognitive processes responsible for generating it make completeness impossible. Rather the aim has been to develop a framework to deal with the problems and questions that occur in the context of knowledge management and knowledge organisation. In conclusion, it has to be stressed that knowledge is not a static body or thing, but a dynamic process; it cannot and should not be studied as an isolated phenomenon, rather it is intrinsically linked to cognitive processes and their representational capacities. Moreover knowledge is (physically and culturally) embedded in its environment, hence it can be understood adequately only if it is studied in the context of its carriers (cognitive and neural processes) and its interaction with the environment; knowledge acts as a kind of mediator between these two domains. Finally, knowledge goes far beyond knowing the functionality of a phenomenon; there is a deeper dimension behind the exterior behavioural dynamics of knowledge, its material substratum and its functionality. It is the meaning, the ‘what’ of a phenomenon that is at its heart.

Notes 1. ‘Epistemology is the philosophical sub-area that focuses on the nature, extent, and origin of human knowledge. Traditionally, epistemologists address three central questions: (1) What is knowledge? (2) What can we know? (3) How is knowledge acquired?’ (Brook and Stainton, 2000, p. 1). 2. Reality and environment are used synonymously; a reality refers to a concrete object, entity and so on in the environment.

24 Structures and Diversity in Everyday Knowledge 3.

4.

In neural systems it is not possible to isolate representational entities such as symbols or frames; rather the neural architecture itself represents the knowledge in the sense of being responsible for generating behaviour that fits the environmental structures and ensures the organism’s survival (Peschl, 1997; Clark, 1999, 2001; Pfeifer and Scheier, 1999; Bechtel and Abrahamsen, 2001). See for instance the strategic goal set for Europe in 2010 at the Lisbon European Council in March 2000 (http://europa.eu.int/comm/employment_ social/knowledge_society/).

References Ashby, R. W. (1964) An Introduction to Cybernetics (London: Methuen). Barlow, B. (1972) ‘Single units and sensation: a neuron doctrine for perceptual physiology’, Perception, vol. 1, no. 1, pp. 371–94. Bechtel, W. and Abrahamsen, A. (2001) Connectionism and the Mind. Parallel Processing, Dynamics, and Evolution in Networks (Malden, Mass., and Oxford: Blackwell). Bechtel, W. and Graham, G. (eds), (1998) A Companion to Cognitive Science (Oxford: Blackwell). Bischof, N. (1998) Struktur und Bedeutung. Eine Einführung in die Systemtheorie für Psychologen (Bern: Hans Huber). Brook, A. and Stainton, R. J. (2000) Knowledge and Mind. A Philosophical Introduction (Cambridge, Mass.: MIT Press). Brooks, R. A. (1991) ‘Intelligence without representation’, Artificial Intelligence, vol. 47, pp. 139–59. Bukowitz, W. R. and Williams, R. L. (1999) The Knowledge Management Fieldbook (London: Prentice-Hall). Cangelosi, A. and Parisi, D. (eds) (2002) Simulating the Evolution of Language (Berlin and New York: Springer). Churchland, P. M. (1979) Scientific Realism and the Plasticity of Mind (Cambridge and New York: Cambridge University Press). Churchland, P. M. (1988) Matter and Consciousness. A Contemporary Introduction to the Philosophy of Mind (Cambridge, Mass.: MIT Press). Clark, A. (1989) Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing (Cambridge, Mass.: MIT Press). Clark, A. (1999) ‘An embodied cognitive science?’, Trends in Cognitive Sciences, vol. 3, no. 9, pp. 345–51. Clark, A. (2001) Mindware. An Introduction to the Philosophy of Cognitive Science (New York: Oxford University Press). Davenport, T. H. and Prusak, L. (1998) Working Knowledge. How Organizations Manage What They Know (Boston: Harvard Business School Press). Dienes, Z. and Perner, J. (1999) ‘A theory of implicit and explicit knowledge’, Behavioral and Brain Sciences, vol. 22, no. 5, pp. 735–55. Elman, J. L. (1991) ‘Distributed representation, simple recurrent networks, and grammatical structure’, Machine Learning, vol. 7, nos 2/3, pp. 195–225. Fodor, J. A. and Pylyshin, Z. W. (1998) ‘Connectionism and cognitive architecture: a critical analysis’, Cognition, vol. 28, pp. 3–71.

Markus Franz Peschl 25 Gelder, T. J. v. (1992) ‘Defining “distributed representation” ’, Connection Science, vol. 4, nos 3/4, pp. 75–191. Gelder, T. J. v. (1998) ‘The dynamical hypothesis in cognitive science’, Behavioral and Brain Sciences, vol. 21, pp. 1–14. Harman, G. (1998) ‘Intentionality’, in W. Bechtel and G. Graham (eds), A Companion to Cognitive Science (Oxford: Blackwell). Harnad, S. (1990) ‘The symbol grounding problem’, Physica, vol. 42, pp. 335–46. Hinton, G. E., McClelland J. L. and Rumelhart D. E. (1986) ‘Distributed representations’, in D. E. Rumelhart and J. L. McClelland (eds), Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Foundations (Cambridge, Mass.: MIT Press), pp. 77–109. Hoffmann, A. G. (1998) Paradigms of Artificial Intelligence: A Methodological and Computational Analysis (Berlin and New York: Springer). Kornblith, H. (ed.) (1993) Naturalizing Epistemology (Cambridge, Mass.: MIT Press). Kosslyn, S. M. (1990) ‘Mental imagery’, in D. N. Osherson and H. Lasnik (eds), An Invitation to Cognitive Science (Cambridge, Mass.: MIT Press), pp. 73–97. Kosslyn, S. M. (1994) Image and Brain. The Resolution of the Imagery Debate (Cambridge, Mass.: MIT Press). Liebowitz, J. (1999) Knowledge Management Handbook (London and New York: Routledge & Kegan Paul). Martin, J. H. (1991) ‘Coding and processing of sensory information’, in E. R. Kandel, J. H. Schwartz and T. M. Jessel (eds), Principles of Neural Science (New York: Elsevier), pp. 329–40. Maturana, H. R. and Varela, F. J. (eds) (1980) Autopoiesis and Cognition: The Realization of the Living (Dordrecht and Boston: Reidel). McLeod, P. M., Plunkett, K. and Rolls, E. T. (1998) Introduction to Connectionist Modelling of Cognitive Processes (Oxford and New York: Oxford University Press). Nilsson, N. J. (1998) Artificial Intelligence: A New Synthesis (San Mateo, CA: Morgan Kaufman). Nonaka, I. and Takeuchi, H. (1995) The Knowledge Creating Company. How Japanese Companies Manage the Dynamics of Innovation (Oxford: Oxford University Press). Oeser, E. (1976) Wissenschaft und Information: systematische Grundlagen einer Theorie der Wissenschaftsentwicklung (Vienna and Munich: Oldenburg). Peschl, M. F. (1994) Repräsentation und Konstruktion. Kognitions- und neuroinformatische Konzepte als Grundlage einer naturalisierten Epistemologie und Wissenschaftstheorie (Braunschweig and Wiesbaden: Vieweg). Peschl, M. F. (1997) ‘The Representational Relation Between Environmental Structures and Neural Systems: Autonomy and Environmental Dependency in Neural Knowledge Representation’, Nonlinear Dynamics, Psychology, and Life Sciences, vol. 1, no. 2, pp. 99–121. Peschl, M. F. (1999) ‘The development of scientific theories and their embodiment in the representational activities of cognitive systems. Neural representation spaces, theory spaces and paradigmatic shifts’, in P. v. Loocke (ed.), The Nature of Concepts (London: Routledge & Kegan Paul), pp. 184–214. Peschl, M. F. (2001) ‘Constructivism, cognition, and science. An investigation of its links and possible shortcomings’, Foundations of Science, vol. 6, no. 1, pp. 125–61.

26 Structures and Diversity in Everyday Knowledge Pfeifer, R. and Scheier, C. (1999) Understanding Intelligence (Cambridge, Mass.: MIT Press). Polanyi, M. (1966) The Tacit Dimension (Garden City, NY: Doubleday). Popper, K. R. (1934) The Logic of Scientific Discovery (New York: Basic Books). Port, R. and. Gelder, T. J. v (eds) (1995) Mind as Motion: Explorations in the Dynamics of Cognition (Cambridge, Mass.: MIT Press). Probst, G., Raub, S. and Romhardt, K. (1999) Wissen managen. Wie Unternehmen ihre wertvollste Ressource optimal nutzen (Frankfurt/M: Gabler). Procter, R. W. (2002) ‘Knowledge elicitation methods and their major advantages and disadvantages. Communications of the ACM Tables’, http://www. psych.purdue.edu/proctor/ACM_tables.pdf. Rasmussen, S. (1991) ‘Aspects of Information, Life, Reality, and Physics’, in C. G. Langton, C. Taylor, J. D. Farmer and S. Rasmussen (eds), Artificial Life II (Redwood City, CA: Addison-Wesley), pp. 767–73. Roth, G. (1991) ‘Die Konstitution von Bedeutung im Gehirn’, in S. J. Schmidt (ed.), Gedächtnis. Probleme und Perspektiven der interdisziplinären Gedächtnisforschung (Frankfurt/M: Suhrkamp), pp. 360–70. Rumelhart, D. E., Hinton, G. E. and McClelland, J. L. (1986) ‘A general framework for parallel distributed processing’, in D. E. Rumelhart and J. L. McClelland (eds), Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Foundations (Cambridge, Mass.: MIT Press), pp. 45–76. Rumelhart, D. E. and McClelland, J. L. (1986) ‘On learning the past tenses of English verbs’, in J. L. McClelland and D. E. Rumelhart (eds), Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Psychological and Biological Models (Cambridge, Mass.: MIT Press), pp. 216–71. Schmidt, S. J. (ed.) (1987) Der Diskurs des Radikalen Konstruktivismus (Frankfurt/ M: Suhrkamp). Schmidt, S. J. (ed.) (1992) Kognition und Gesellschaft. Der Diskurs des Radikalen Konstruktivismus 2 (Frankfurt/M: Suhrkamp). Shannon, C. E. (1949) The Mathematical Theory of Communication (Urbana, Ill.: University of Urbana Press). Smolensky, P. (1988) ‘On the proper treatment of connectionism’, Behavioral and Brain Sciences, vol. 11, pp. 1–74. Thelen, E. and Smith, L. B. (1994) A Dynamic Systems Approach to the Development of Cognition and Action (Cambridge, Mass.: MIT Press). Turing, A. (1936) ‘On computable numbers, with an application to the entscheidungsproblem’, Proceedings of the London Mathematic Society, vol. 42, pp. 230–65. Turing, A. (1950) ‘Computing machinery and intelligence’, Mind, vol. 59, no. 236, pp. 433–60. Varela, F. J., Thompson, E. and Rosch, E. (1991) The Embodied Mind: Cognitive Science and Human Experience (Cambridge, Mass.: MIT Press). von Foerster, H. (1973) ‘On constructing a reality’, in P. Watzlawick (ed.), The Invented Reality (New York: Norton), pp. 41–61. von Glasersfeld, E. (1983) ‘On the concept of interpretation’, Poetics, vol. 12, no. 3, pp. 254–74. von Glasersfeld, E. (1984) ‘An introduction to radical constructivism’, in P. Watzlawick (ed.), The Invented Reality (New York: Norton), pp. 17–40.

Markus Franz Peschl 27 von Glasersfeld, E. (1991) ‘Knowing without metaphysics. Aspects of the radical constructivist position’, in F. Steier (ed.), Research and Reflexivity (London and Newbury Park, CA: Sage), pp. 12–29. von Glasersfeld, E. (1995) Radical Constructivism: A Way of Knowing and Learning (London: Falmer). Ward, L. M. (2002) Dynamical Cognitive Science (Cambridge, Mass.: MIT Press). Wiener, N. (1948) Cybernetics: Control and Communication in the Animal and the Machine (New York: Wiley). Wilson, R. A. and Keil, F. C. (eds) (1999) The MIT Encyclopedia of the Cognitive Sciences (Cambridge, Mass.: MIT Press). Winograd, T. and Flores, F. (1986) Understanding Computers and Cognition: A New Foundation for Design (Norwood, NJ: Ablex). Zeigler, B. P., Praehofer, H. and Kim, T. G. (2000) Theory of Modeling and Simulation. Integrating Discrete Event and Continuous Complex Dynamic Systems (San Diego, CA: Academic Press).

2 Situatedness and Artefacts in Expert Knowledge and Activity Hanna Risku

Introduction This chapter discusses expert knowledge and activity, that is, the specific cognitive aspect of expert competency. Particular emphasis is placed on two concepts in modern cognitive science, namely situatedness and artefacts. The chapter begins by discussing some of the findings of the problem-solving studies of the 1980s and 1990s that were further developed by the approach of situated, embodied cognition. The theoretical discussion is illustrated with practical examples from the field of professional translation as intercultural text production. Any explanation of the term ‘expert activity’ will necessarily remain incomplete until the origins of the facts in question have been fully understood. This didactically profitable view of cognitive processes also enables a more appropriate theoretical differentiation than is possible by simply listing the characteristics of expert knowledge. There are abilities that can be developed through training that reflect professional experience but are not a sign of expert competency. Typical examples are the application of learned analysis models, adherence to specific rules in research and the use of learned factors to justify a solution. Characteristic of these abilities is compliance with rules learned either explicitly during education or implicitly in our professional lives. Regardless of the economic and systematic importance of applying methods for an action, we contend that the application of methods alone does not constitute the essence of expert competency. Normally non-experts are unaware of methods and novices devote much time and effort to following rules. Experts, on the other hand, have already familiarised themselves with relevant methods, defined or 28

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adapted them to suit their own fields and combined them with their own experience. In this way experts create flexible models that sensitise them to different types of communication and problems and enable them better to cooperate with their partners. Experts work with abstract schemas (referred to as Superzeichen by Dörner, 1982, p. 62) or complexity-reducing system gestalts that facilitate the perception of known structures (Chi et al., 1981). These schemas are the result of an increase in reflexivity, which is also closely linked to the degree of familiarity with the action. The creation of a variety of flexible patterns as by-products of interactivity (recursivity) is one of the basic concepts in the connectionist models of the brain. This will now be discussed in more detail, along with several other characteristics of expert knowledge.

Reflexivity and interactivity The prominent characteristics of expert knowledge are highly developed forms of those attributes of human thought that allow us to communicate and conduct scientific research, namely a high degree of reflexivity and interactivity. However reflection should not be confused with the mere verbalisation of a process: it is a general feedback principle that should in no way be understood as internal, verbalised dialogue (Bereiter and Scardamalia, 1987, p. 301). Consequently reflexive yet non-verbalisable problem-solving processes are not a contradiction in terms. The reflexivity phenomenon is a familiar concept in research into written composition: not only are expressions selected to meet communication requirements, the requirements themselves are also influenced by the expressions used. Bereiter and Scardamalia (ibid.) stress the interactive ‘linking operations’ between the content and rhetorical aspects of expert text composition; and not just in the familiar (to novices) direction from content to rhetorical structure, but also bidirectionally through mutual feedback. It would be wrong to expect that – following a thorough analysis – all that would remain to be dealt with would be the actual wording of the text. Indeed aspects of a text that require further analysis only become apparent during the wording phase. A coherent text cannot be constructed from prefabricated parts. On the contrary it requires constant supplementary research and refinement, whereby the target situation poses its own problems and the emerging text raises its own questions.

30 Expert Knowledge and Activity

Generalisability and flexibility According to Chi et al. (1981) novices make use of superficial characteristics to categorise problems, and in the process rely on explicit, visible keywords. Experts, on the other hand, keep their reference models as general as possible, using the post-editing phase to search for regularities or patterns that will enable them to create analogies in the future. This finding is supported by the observation that experts generally take longer than novices to categorise problems. There is a good reason for discussing generalisability with flexibility. Cognitive abstraction, which helps us to act as experts, has nothing to do with the situation-independent inductive ability to create explicit axioms from experience, or with the static deduction of existing axioms in individual actions. On the contrary, action schemas always have a particular perspective: they are created to handle a specific situation and attain specific goals. As a result generalisation and flexibility go hand in hand. The results obtained by Putz-Osterloh (1987) illustrate the high degree of flexibility and abstraction in expert knowledge. Putz-Osterloh (1987) used ecological and economic research scenarios to examine the influence of expertise on how we handle unfamiliar systems. Individual novices and business experts were all given the tasks of managing a small business (a tailor’s shop) and an ecological project (humanitarian aid in a developing country). In the first scenario – the tailor’s shop – the experts achieved better results on management, cross-departmental strategy and system knowledge (here ‘strategy’ refers to procedural knowledge and was observed; ‘system knowledge’ refers to declarative knowledge and was deduced from statements made by the experts). Although in the second scenario – the management of an ecological project in the African bush – the experts did not achieve significantly better controlling results than the novices and their information-gathering strategies did not differ significantly, they still produced better results than the novices in all other aspects. They also seemed to process the available knowledge in a different manner from the novices, and as a result produced better hypotheses, planned more effectively and carried out superior system analysis. Putz-Osterloh (1987) holds that these differences were due to the experts having a more general (declarative) system knowledge and heuristic (procedural) strategies for reference, and to the fact that they processed information on a higher level of abstraction than did the novices.

Transformability Transformability is closely linked to generalisability. Nonetheless it is important to underline the advanced form of transformability that is

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evident in expert action. Both experts and novices use keywords, but only experts reorganise them into coherent representations. In other words experts carry out a qualitative analysis of the meaning of keywords. To complete activated schemas and modify them to fit the situation, what is perceived must also be transformed (Chi et al., 1981, pp. 145, 149). An example taken from translation as an expert activity is the use of idioms (for example in journalistic texts). A possible ‘transport strategy’ based solely on meaning is to consult a dictionary for equivalent or similar sounding expressions in the target language. If this fails to produce results the translator will be left with two alternatives: to attempt a literal translation or to create a semantic equivalent in the target language. A transformational strategy, on the other hand, will allow the translator to use expressions to construct a coherent picture of the communication situation and its design, for example by incorporating additional knowledge on the author’s background and role, assessing the relevance of the expressive form used for text production, recognising the expression as a particular stylistic device and evaluating its coherence with other expressive forms in the same text. Hence just a few words can open up manifold levels of interpretation.

Procedurality Expert knowledge is anything but a canon of rules, propositions and symbols taken from a textbook. The way in which we deal with knowledge in the form of declarative and procedural reflection is far more important than ‘cramming rules’. Not only can the particular be generalised and the abstract be substantiated, but also the symbolicdeclarative can be operationalised and the operative-procedural can be symbolised. The more interlinked the knowledge, the more useful it becomes in real problem solving (Vester, 1980, p. 475). According to Chi et al. (1981, p. 137), procedural knowledge on the applicability of action strategies appears to be essential for problemsolving competency. Experts are also able to offer potential solutions to a problem, or more precisely, potential solutions form an integral part of expert schemas (ibid., p. 140). As a result experts can compare possible results with goals prior to applying their methods and therefore carry out functional evaluations.

Verbalisability One characteristic of expert knowledge is the dissonance between performance and the ability to verbalise. Explicit knowledge about our


personal problem-solving strategies does not automatically guarantee a better problem-solving ability. On the contrary, test subjects with poorer results are typically more adept at putting things into words than those who master a problem (Berry and Broadbent, 1987). Berry and Broadbent (1988) and Karnas and Cleeremans (1987) prove that practice does not increase our verbalisable knowledge of a system. Tacit, nonverbalisable knowledge is generated primarily from the subdominant effects of a complex system. The more complex the system, the more tacit knowledge will be generated. Chi et al. (1981) rightly criticise the common dismissal of tacit knowledge as ‘instinctive’, which incorrectly suggests that emotions might be secondary or even undesirable. Is it therefore logical to conclude that we can really only expect verbal reasoning and analysis from novices and that we should apply the work of experts without questioning? On the contrary: whilst experts might not be able to formulate their action principles in explicit rules, they verbalise more than novices, but in another field. Indeed Chi et al. (ibid.) demonstrate that experts constantly question their level of knowledge and control their own actions more consciously. This is confirmed by Schwarz (1992, p. 129): empirical studies showing that improvements in the use of strategies correlate with increased awareness of the state of our own memory suggest that we develop primarily metacognitive abilities in strategy acquisition. Externalisation and evaluation of our own actions (or metacognition) is thus one of the developmental principles of expert competence. However this is not the ability to create linguistic representations, but rather the ability to use language to create declarative knowledge and reflect on our own actions. Verbalisation as a cognitive competence is not only useful for developing our own strategies, it also forms a decisive social component of expert work. In negotiations with authorities, consumers and clients, experts are responsible for establishing needs and for trying to present a common understanding of their interpretation of the problem. This is only possible through verbalisation, which serves to inform on selected strategies, goals and/or possible alternative actions. According to Broadbent et al. (1986), verbalisability is not an absolute ideal, but the form of knowledge that is most applicable to the situation. Verbalisable knowledge is required for cooperation not only with consumers and clients, but also with other experts. For example translation teams have to be coordinated and any layout requests explained. When translators employ the services of specialists (for example documentalists) they must be able to verbalise their own requirements as clients.

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Problems with verbalisation raise questions about practical and scientific methodologies. Many of the results cited in problem-solving research were obtained from empirical studies using the thinking-aloud (TA) method, in which subjects are requested to verbalise their current thoughts. The only concrete results to which the researcher has direct access are those obtained from the visible problem-solving process; any other information must be obtained from the analysis of the TA protocols. However the profitability of this method is relativised by the fact that knowledge in general (Munz, 1993, p. 57) and expert knowledge in particular cannot be adequately verbalised. Only the subjects’ retrospective interpretations of their own actions can be recorded and put into words. Since the interview situation will also inevitably influence this interpretation, the protocols available to the researcher bear little resemblance to the actual process that occurred in the subjects’ brains. Furthermore the researcher must also interpret the subjects’ self-interpretations, thus further reinforcing the constructiveness of scientific (and empirical) description. If we appreciate how constructive the material obtained from protocol studies can be and do not confuse these results with thinking itself, then this method offers a legitimate means of building heuristic hypotheses. Legitimate, but limited: Börsch (1986) notes that automatic verbalisation processes are not accessible and that a heavy cognitive burden will slow down or interrupt verbalisation. Nonetheless protocol studies are an excellent method of researching how both novices and experts talk about action and knowledge – the comments they make can often bring to light some interesting differences in the way in which knowledge is presented.

Identification and motivation The characteristics of expert action discussed above emphasise the significance of reflection and experience: knowledge is inherent in success or failure, goals, methods and solutions. Holland (1992) and Quinn (1992) also emphasise the influence of self-definition on the part of the actor and identification with the problem. Novices do not feel self-determined; they follow rules laid down by others. They become more independent as their knowledge takes on form and their competency increases through reflection and experience. However for a long time they must base their actions and judgements on other people’s rules. Experts are emotionally involved in the problem-solving process, take responsibility for results and see problems


not as a collection of individual elements but holistically as opportunities, risks and threats. Independence, responsibility and emotional involvement also increase motivation, and ultimately the readiness to initiate action instead of simply following orders. Interdisciplinary expert cooperation is complicated – and therein lies its fascination – by the fact that each individual participant is a member of a different subject culture and has his or her own view of the situation in question. Like intercultural communication, interdisciplinary cooperation brings together different worlds and perceptions. The fact that experts focus on a single discipline and their own individual roles allows them to identify, classify and resolve problems in a professional manner. In doing so they make judgements: what is fundamental to the current situation? What can be ignored? What is the best way to organise the work? Experts are trained (and have learned) to recognise trustworthy sources and tasks that do or do not bring prestige. In this way they develop an identity as representatives of their field of competency. Building a professional identity is a process of establishing borders. Professional identity arises not only from the positive definition of what people want to be, but also from the negative definition of what they are not and what they do not want to be. As they gain expertise and their work becomes more personal and individual (Spiro, 1982; Risku, 1998, p. 114) they begin to identify themselves with their tasks and roles. In his classic (but still valid) work, Dahrendorf (1958/1960) describes the role as a bundle of normative expectations that belong to a social position (Holz-Mänttäri, 1984, pp. 40 ff.; Pöchhacker, 1994, pp. 71 ff.; for the expert role see Venkula, 1987/1994; Hütter and Perger, 1994; Risku, 1998, pp. 90 ff.). This can be the simultaneous roles of father, translator and regular supplier – roles are made up of different levels (Pöchhacker, 1994, pp. 72 f.) and can also change with the situation. The job title alone is not significant. In many professions, responsibilities and thus roles differ. Every profession has managers, assistants, servers and operators (Risku and Freihoff, 2000). In the context of translating, role distribution is defined in cooperation with the translators. Clients deliver information to the translators but cannot define their role: in the case of expert work and tasks, only specialists can determine the appropriate method of action, and simply attributing a role does not automatically imply behaviour that will conform to expectations. The formulation of (and reasoning behind) role distribution is one of the main tasks of professional translators, as

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described by Holz-Mänttäri (1984, 2001). When translating, things can become much more difficult if the client has not been made properly aware of all the requirements. As consumers, clients, users and recipients (Holz-Mänttäri, 1984) they are not aware of what they are ordering or what will be delivered, and are often let down by translators who have failed to define their relevant roles.

The situated, embodied cognition view Since the birth and establishment of cognitive science in the 1960s, each dominant paradigm has brought its own metaphors for the workings of the mind and the nature of the human being. Depending on the cognitive approach adopted, human beings have been described – explicitly and implicitly – as information processors, bookkeepers, accountants, telephones, computers and other ‘symbol manipulators’; they have also been described as researchers or archaeologists who try to recognise patterns in their material. In this way they could be compared with neural networks and other systems based on dynamic recognition through connectionist networks. In the situated and embodied cognition approach, human beings are considered as partners, players, artists, architects or other creative beings dependent upon their physical and psychological possibilities (that is, bodies and situations). The concepts of embodiment and situatedness have been established over the past 20 years by the situated, embodied cognition approach. Researchers in a number of disciplines have contributed to this new paradigm for explaining cognition, and although to some extent they have worked independently of each other they have arrived at surprisingly similar conclusions. The disciplines in question include developmental psychology, man–machine communication, artificial intelligence, situated robotics and artificial life (Suchman, 1987; Thelen and Smith, 1994; Steels and Brooks, 1995; Hendriks-Jansen, 1996; Clancey, 1997; Clark, 1997). Situatedness and embodiment are currently changing the focus of cognitive scientific research in important ways. No longer do we simply ask what actually goes on in the human brain; rather we have widened the scope of the question to include the whole human being and his or her individual history and environment.

Background: symbol manipulation and connectionism In the 1980s the cognitive approach of connectionism had already changed our view of human behaviour, and consequently of expert


activity. Cognition was no longer seen simply as a linguistic activity in which symbols were manipulated according to fixed rules. Cognition and translation had proved to be quite different from the simple code switching seen in telecommunications and data processing (telephones and computers). Semantic networks and other well-ordered, well-defined systems reached their limits when faced with real-life language. Indeed human cognition and translation proved to be far more flexible and constructive than had been assumed prior to the birth of connectionism. The brain was no longer seen simply as a database, but as a dynamic, holistic network that was able to create patterns of activation – memories were rich with all manner of scenes, tones, smells, tastes, motions and emotions. As a result semantic property systems were substituted by experience-based, non-linguistic, emotionally loaded examples, schemes or prototypes (Rosch, 1973).

Situated, embodied cognition While the connectionist revolution brought about radical rethinking it maintained some of the main visions of the symbol manipulation approach: the primary focus was placed on the individual, and in particular on the dynamic workings of the human brain. Here we can still note the influence of language theories which list units of knowledge such as characteristics, propositions, symbols, scripts, the ‘nodes’ of semantic networks and the relatively fixed schemes of the early connectionist approaches. In this respect they are consistent with those theories which describe expert competence as the application of learned actions. The next radical changes in the visions and terminology of cognitive science were brought about by situated, embodied cognition. Here the central concern was not the brain itself but the fact that it allowed us to interact with the environment, artefacts and other human beings. Situated cognition not only examined individuals and their previous knowledge and skills, it also looked at their interaction with artefacts (such as language) and their social environment. The new catch phrase was ‘embodiment’. Brooks (1995) started afresh by creating autonomous agents who were supposed to get on in the world on their own. In doing so he had to renounce two of the basic assumptions made by cognitive science at the time: that the brain stores information about the environment by means of mental representations and that the brain controls intelligent action.

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Both connectionists and situationists see perception and cognition as constructive, selective, context-dependent, non-linguistic, unpredictable pattern construction. However according to the situated, embodied cognition approach, the importance lies neither in the scripts nor in the more or less flexible schemes stored in the long-term memory that supposedly control our behaviour in the form of instructions for action. We are not intelligent because we use the knowledge that resides in our brain (here the situation would only complement our prior knowledge). Action is a contextual activity, appropriate navigation in a specific environment. If situated, embodied cognition were to be taken seriously in describing expert activity, a new role would have to be given to prototypes, cultural norms and conventions: these are initially hypotheses, but are always adaptable to the specifics of the anticipated situation. Whilst we do not fill our preconceived schemes with the contents of the specific situation, we rely on it as a fundamental source and as the indispensable resource when looking for meaning, trying to make sense and acting. Schemas and concepts do not determine action, they only enable us to devise vaguely formulated plans and explain our actions afterwards. Action and meaning, on the other hand, happen in the here and now. Schemas allow us to make best use of the resources at our disposal at a given moment and to interpret the situation and its verbalised components in a way that makes sense. Therefore it is the situation as such that primarily allows us to master complex tasks. Thus the abilities of the expert do not come into play when slavishly applying a previously formulated or learned plan; they manifest themselves in the continuous formulation of new hypotheses (Suchman, 1987). According to situated, embodied cognition, we learn to break up the behavioural process into discrete actions (each with a clearly definable beginning and end) and to categorise the world into static objects in a specific social environment and culture (Hendriks-Jansen, 1996). This behaviour has both an organisational and a coordinative purpose. There are specific reasons for this ‘illusion’ of clearly defined actions and intentions. It is an indispensable simplification that allows us to coordinate groups and deal with complex systems (ibid.). It enables us to explain everyday events and understand others. However explanations of intent and verbal expressions do not represent our inner world of reality. They are cultural artefacts, one part of our environment, and we fall back on them, consciously and subconsciously, when we think, behave and perceive. This has important implications for the power of culture as cooperatively learned and artefact-mediated behaviour.


Much of the complexity is supported by external structures (or is ‘scaffolded’: Clark, 1997) through management systems, learned procedures and explicit strategies. The claim that thinking does not imply an operation on syntax by means of symbols alone was one of the main arguments of the connectionists. But it was situated, embodied cognition that first questioned the traditionally omnipotent role of the brain and the minor role of the body and the environment. According to situated action, one of the main reasons for our intelligence is that we delegate knowledge to our environment and motions. Thus we reduce the need to store it, search for it and process it in the brain. The principle of cognitive economy manifests itself in many different ways. We design supermarkets and offices to suit their specific purpose, we place an empty packet of coffee outside our front door in order to remind us to buy some more, and we use specific forms of discourse in our conversations (for example when we go for a job interview or participate in scientific discussions). Rooms, organisations, forms of discourse and actions that serve other actions become tools in the wider sense of the word (Hendriks-Jansen, 1996). They put a framework around our actions and understanding; in other words they are cultural scaffolding. External aids such as maps, sheets of music, grammars and scientific models are all used as scaffolding: they are teaching, learning and organisational aids (‘plan as resource’). Those who are unfamiliar with a certain expert activity need decontextualised rules since they do not know the context and have not yet developed an ‘interactively emergent organisation of behaviour’ – a Merkwelt (ibid.; Sudnow, 1978). This is where the explicit rules of GOFAI (good old-fashioned artificial intelligence) come into play, if only as an aid for the subsequent learning phase. As novices become more experienced and involved, they no longer follow the decontextualised rules they started off with. In the end the expert activity begins to follow its own implicit rules (Jääskeläinen, 1993, pp. 116 f.). Highly competent professionals have already gone through the rule-governed phase of following learned procedures and can now rely on their reflexive, theory-and experiencebased intuition (Risku, 1998). The scaffolding needed for the learning process is removed and replaced with the flexibility and freedom gained in the process of shaping a Merkwelt. Experts show their professionalism in one job after another, but each job remains unique. They find it difficult to describe these activities.

Hanna Risku 39

Expert activity typically means managing the relations between any number of documents and materials – statistical data, correspondence, specifications, first drafts, former jobs for the same client and so on. These can be verbal, paper-based or electronic, or often a combination of all three. All these individual objects relate to each other in complex ways and often have to be processed simultaneously. Thus the individual history and the present environment, together with its artefacts, form an integral part of the process of expert knowledge and activity. Due to the major role played by the environment, any attempts to explain expert activity by describing processes in the brain or an individual alone are bound to fail. Complex systems that are capable of expert activity include people, their specific social and physical environments and all their cultural artefacts. We often only have access to certain competences in a specific situation, that is, within the context of a particular activity and the environment in which it is carried out. Our understanding is constructed differently to suit the goals we have set ourselves and the roles we play. It changes in structure and perspective, but it is not stored statically in the brain to be recalled at any time.

Methodological consequences Empirical research turns into the study of processes and dynamics of particular situations: research no longer solely describes types and statistics, and microscopic analysis allows it to show the underlying changes throughout the process. The specific activities observed depend on the complete range of characteristics that make up the context in question and the personal histories of the people involved. As a result the observed sequence can only assume the quality of a snapshot. However it is possible to formulate hypotheses based on general patterns of activity, as demonstrated by Goodwin (1997), who studied strategies for establishing foreground and background (focusing), and HendriksJansen (1996), who studied object-oriented parent–child dialogues when introducing children to a culture-specific way of thinking. A qualitative analysis of expert action that does not rely on predefined abstract categories and coding allows us to concentrate on the specific content and perspective of the persons observed. We always observe a number of systems at the same time, systems that affect and change each other and are themselves subject to a constant process of change – each cognitive activity involves at least two systems


‘simultaneously shaping each other’s change’ (van Gelder, 1998, p. 622). Accordingly input–output relationships become illegitimate simplifications. Any efforts to find the one determinant or the central control mechanism are bound to fail. Whilst knowledge-based representations allow us to model achievements, they do not allow us to model internal processes. They are legitimate as the diagnoses of a distanced observer. However if we use generalised descriptions as tools to shape the real world and support the work of the individual, they can actually help us to coordinate groups and deal with complex systems. One distinction of the situated approach is that work activities are understood as socially organised and situated in specific but dynamic contexts. They cannot be unfolded into preset sequences or tasks; they constantly adjust themselves to the work of others and to their changing settings. One consequence of taking this approach seriously is the perception that the social organisation of work does not pre-exist in any precise or detailed way but is constituted ‘in the doing’ by practitioners (Suchman, 1987). This means paying close attention to the ‘communities of practice’ in which work is accomplished and whose members have appropriate competencies in terms of skills, experience and knowledge of both the setting and each other’s work. In other words it means paying appropriate attention to the ‘actor networks’ in which complex work activities are negotiated (Wagner, 1997). Cognitive and social skills are closely interrelated. By assuming the role of the expert we develop the ability to cope with the challenges this role imposes. Thus professional identity and competence are associated not only with the individual but also with social or other processes that involve a strong social element. Empirical research into the social and spatial characteristics of working environments will be necessary to recognise fully and understand the cultural and developmental aspects of expert knowledge. This type of research must begin with the actual work practices of experts and their cooperation partners. Consequently the main research methods will have to include ethnographic studies of multidisciplinary communications in teams. To conclude, it seems evident that the emphasis of the current cognitive scientific discussion on situatedness and artefacts will require new concepts in the theoretical description of expert knowledge and activity. In addition, today we are witnessing a wider view of knowledge as an object and activity as a social and cultural phenomenon, with all the associated methodological consequences.

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References Bereiter, C. and Scardamalia, M. (1987) The Psychology of Written Composition (Hillsdale: Lawrence Erlbaum). Berry, D. C. and Broadbent, D. E. (1987) ‘The combination of explicit and implicit learning processes in task control’, Psychological Research, vol. 49, pp. 7–15. Berry, D. C. and Broadbent, D. E. (1988) ‘Interactive tasks and the implicit–explicit distinction’, British Journal of Psychology, vol. 79, pp. 251–72. Börsch, S. (1986) ‘Introspective Methods in Research on Interlingual and Intercultural Communication’, in J. House and S. Blum-Kulka (eds), Interlingual and Intercultural Communication. Discourse and Cognition in Translation and Second Language Acquisition Studies (Tübingen: Narr), pp. 195–210. Broadbent, D. E., FitzGerald, P. and Broadbent, M. H. P. (1986) ‘Implicit and explicit knowledge in the control of complex systems’, British Journal of Psychology, vol. 77, pp. 33–50. Brooks, R. (1995) ‘Intelligence without reason’, in L. Steels and R. Brooks (eds), The Artificial Life Route to Artificial Intelligence. Building Embodied, Situated Agents (Hillsdale: Lawrence Erlbaum), pp. 25–81. Chi, M., Feltovich, P. and Glaser, R. (1981) ‘Categorization and representation of psychic problems by experts and novices’, Cognitive Science, vol. 5, pp. 121–52. Clancey, W. J. (1997) Situated Cognition. On Human Knowledge and Computer Representations (Cambridge: Cambridge University Press). Clark, A. (1997) Being There. Putting Brain, Body, and World Together Again (Cambridge, Mass.: MIT Press). Dahrendorf, R. (1958/1960) Homo Sociologicus. Ein Versuch zur Geschichte, Bedeutung und Kritik der Kategorie der sozialen Rolle (Opladen: Westdeutscher Verlag). Dörner, D. (1982) Die Logik des Mißlingens. Strategisches Denken in komplexen Situationen (Hamburg: Rowohlt). Gelder, T. van (1998) ‘The Dynamical Hypothesis in Cognitive Science’, Behavioral and Brain Sciences, vol. 21, no. 5, pp. 616–28. Goodwin, C. (1997) ‘The Blackness of Black: Colour Categories as Situated Practice’, in L. B. Resnick, R. Säljö, C. Pontecorvo and B. Burge (eds), Discourse, Tools, and Reasoning. Essays on Situated Cognition, Computer and System Sciences 160 (Berlin: Springer), pp. 111–40. Hendriks-Jansen, H. (1996) Catching Ourselves in the Act. Situated Activity, Interactive Emergence, Evolution, and Human Thought (Cambridge, Mass.: MIT Press). Holland, D. (1992) ‘How cultural systems become desire: a case study of American romance’, in R. D’Andrade and C. Strauss (eds), Human Motives and Cultural Models (Cambridge: Cambridge University Press), pp. 59–61. Holz-Mänttäri, J. (1984) Translatorisches Handeln. Theorie und Methode (Helsinki: Suomalainen Tiedeakatemia). Holz-Mänttäri, J. (2001) ‘Skopos und Freiheit im translatorischen Handeln’, TextconText, vol. 15, no. 2, pp. 181–96. Hütter, A. and Perger, J. (1994) Expertenwissen und Verwaltung (Innsbruck: Landesumweltamt).

42 Expert Knowledge and Activity Jääskeläinen, R. (1993) ‘Investigating Translation Strategies’, in S. TirkkonenCondit and J. Laffling (eds), Recent Trends in Empirical Translation Research (Joensuu: Joensuun yliopisto), pp. 99–120. Karnas, G. and Cleeremans, A. (1987) ‘Implicit processing in control tasks: Some simulation results’, (paper presented at the First European Meeting on Cognitive Science Approaches to Process Control, Marcoussis, France, 19–20 October). Munz, P. (1993) Philosophical Darwinism: On the Origin of Knowledge by Means of Natural Selection (London: Routledge). Pöchhacker, F. (1994) Simultandolmetschen als komplexes Handeln (Tübingen: Narr). Putz-Osterloh, W. (1987) ‘Gibt es Experten für komplexe Probleme?’, Zeitschrift für Psychologie, vol. 195, pp. 63–84. Quinn, N. (1992) ‘The motivational force of self-understanding: evidence from wives’ inner conflicts’, in R. D’Andrade and C. Strauss (eds), Human Motives and Cultural Models (Cambridge: Cambridge University Press), pp. 90–126. Risku, H. (1998) Translatorische Kompetenz. Kognitive Grundlagen des Übersetzens als Expertentätigkeit (Tübingen: Stauffenburg). Risku, H. (2002) ‘Situatedness in Translation Studies’, Cognitive Systems Research, vol. 3, no. 3, pp. 523–33. Risku, H. and Freihoff, R. (2000) ‘Kooperative Textgestaltung im translatorischen Handlungsrahmen’, in A. Chesterman, N. G. San Salvador and Y. Gambier (eds), Translation in Context. Selected contributions from the EST Congress, Granada 1998 (Amsterdam: John Benjamins), pp. 49–59. Rosch, E. (1973) ‘Natural Categories’, Cognitive Psychology, vol. 4, pp. 328–50. Schwarz, M. (1992) Einführung in die Kognitive Linguistik (Tübingen: Francke). Spiro, M. E. (1982) ‘Collective Representations and Mental Representations in Religious Symbol Systems’, in J. Maquet (ed.), On Symbols in Anthropology: Essays in Honor of Harry Hoijer (Malibu: Udena), pp. 45–72. Steels, L. and Brooks, R. (eds) (1945) The Artificial Life Route to Artificial Intelligence. Building Embodied, Situated Agents (Hillsdale: Lawrence Earlbaum). Suchman, L. (1987) Plans and Situated Actions. The Problem of Human–Machine Communication (Cambridge: Cambridge University Press). Sudnow, D. (1978) Ways of the Hand: The Organisation of Improvised Conduct (London: Routledge and Kegan Paul). Thelen, E. and Smith, L. B. (1994) A Dynamics Systems Approach to the Development of Cognition and Action (Cambridge, Mass.: MIT Press). Venkula, J. (1987/1994) ‘Miksi tietoyhteiskunta ei tule?’, in J. Venkula (ed.), Tiedon suhde toimintaan (Helsinki: Yliopistopaino), pp. 47–69. Vester, F. (1980) Neuland des Denkens. Vom technokratischen zum kybernetischen Zeitalter (Stuttgart: Deutsche Verlags-Anstalt). Wagner, I. (1997) Esprit Project DESARTE. First Phase Final Report (Vienna: Technical University).

3 The Human Foundation of Knowledge Management Thomas Bernhard Seiler

Effective knowledge organisation and management is now considered to be a major precondition for successful corporate administration and management. The concept of knowledge organisation includes all the processes and activities needed to optimise the knowledge needed for a company to perform efficiently. It implies that a large amount of implicit knowledge is contained in the organisational structures and the production processes of the company, in the rules of interaction between different persons and groups, and especially in the successful performance of highly qualified employees. One main purpose of knowledge management is to identify the entire body of such knowledge and to make it accessible to all the members of an organisation. This chapter investigates the nature and fundamental conditions of the knowledge with which knowledge management has to deal. It is claimed that here personal knowledge should be distinguished from objectified knowledge and that objectified knowledge or information originates from the attachment of personal knowledge to material signs. From the virtual nature of such knowledge it follows that its meaning is only accessible to those persons who dispose of the idiosyncratic knowledge structures in question. This fundamental condition does not prevent conventional knowledge or information from offering important advantages: besides serving as a useful and necessary adjunct to human thinking, it can be processed by formal, mechanised procedures. However the formalised processing of knowledge not only generates advantages, it can also prove dangerous. Following an analysis of the nature and conditions of knowledge, important consequences for the practice of knowledge management in business administration and production are presented. The main focus will be on a truly human form of knowledge management that requires 43

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continual interpretation and control through personal but mutually shared insights.

Knowledge management as corporate self-reflection Knowledge management now enjoys considerable prestige (Weggemann, 1999). Its usefulness and prestige may be due to the fact that knowledge management is a form of corporate self-reflection. What is true for individuals can also be true for a community or corporation. Just as individuals would not reach high intellectual standards without self-reflexive cognition, a company’s self-consciousness and impact is strongly influenced by effective knowledge management (Davenport and Prusak, 1998, pp. 195 ff.) A company that reflects on its goals, resources and activities not only acquires more power, but also its activities become more realistic and effective. Therefore success may depend on revealing, expanding and effectively using the whole body of knowledge of which the company implicitly disposes. Only a small proportion of a company’s knowledge is explicitly known – much more is hidden in its structures, activities and processes and in the skills and activities of its employees (Maar, 2000, p. 16). This tacit knowledge is of vital importance to the success of the company and an important task of knowledge management is to make it explicit and available to the members of the company (Stewart, 1998). Knowledge management provides companies with strength and effectiveness (Voß and Gutenschwager, 2001), and we would expect it to be a common practice in corporate organisations. However the fact that people recognise and speak about the necessity of knowledge management does not necessarily mean that they put it into practice (Davenport and Prusak, 1998; Probst et al., 1999). In this chapter, we shall not tackle the practical and political questions but shall focus on fundamental problems with the knowledge dealt with in knowledge management. We cannot understand what knowledge management is and what activities it demands if we do not consider the nature of the knowledge it investigates and manages. Hence we first have to consider the concept of the knowledge. In doing so we distinguish between two fundamental forms of knowledge, idiosyncratic knowledge and objectified knowledge. Knowledge management has to do with both of them.

Thomas Bernhard Seiler

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The concept of knowledge The word ‘knowledge’ has many meanings. It is usually defined as the verbalised product of everyday or scientific thinking, but this definition omits essential aspects. Verbalisation is not essential, for knowledge and cognition are not the preserve of the brain alone – they are a fundamental property of all the activities of living beings. For survival, living beings respond to objects in their immediate environment. Unless these actions take account of properties of the objects and the situation, success cannot be guaranteed. Taking these properties into account is a cognitive act and gives a cognitive nature to actions. According to the epistemology of Jean Piaget (1936, 1937, 1945, 1947), cognition is intrinsic to all human activities and is based on their fundamental interactive character (Bickhard, 2000). Such activities acquire a dispositional character through condensation and repetition, and give rise to knowledge structures. These are the founding framework of the human thinking and knowing that we call knowledge. Since the activities of any living being are essentially individual, knowledge is essentially idiosyncratic. Living beings act to satisfy their own needs. Thus the knowledge originating in their acts and implied in their cognitive structures is strictly their own possession. We call this idiosyncratic knowledge and define it as knowledge that individuals dispose of in an active or a passive way. Idiosyncratic knowledge is the primary and original kind of knowledge, but its content is not exclusively based on individuals’ own experiences.

Idiosyncratic knowledge Cognition is essentially interactive (Piaget, 1936, 1945; Campbell et al., 2002). While acting in response to the environment, the structures on which the activity is based adapt to the environmental conditions and at the same time interact and combine. In this way new structures emerge and eventually qualitatively different structures and levels of thinking are constructed. Simplifying the matter by emphasising the main qualitative differences, we can distinguish between knowledge that is exclusively realised from actions performed and conceptual knowledge (Campbell et al., 2002). It is the great achievement of ontogenetic development that these new and qualitatively different forms of knowing and thinking have evolved. To understand their nature and function, two important facts have to be considered. First, these levels are neither exclusive nor hierarchically organised. When the structures


of a new level are built, the former ones do not vanish. Therefore structures of all levels coexist. Second, some new structures are built by transforming older ones. Let us now consider some important kinds of knowledge.

Knowledge implicitly contained in actions performed This kind of knowledge is restricted to performed actions, though it is not of a purely sensomotoric nature. Performed knowledge consists of very common actions or skills that enable individuals to perform adequately in complex situations. They combine complex human actions with internal representations and the intuitions that guide and control them. Whereas sensory motor knowledge does not presuppose any internal representations or intuitions and consists of externally performed actions guided by immediate sensory perceptions, performed knowledge presupposes an individual who disposes of internal intuitions and imaginations. Images and intuitive knowledge are internal (not photographical) representations of objects, events and situations the individual has previously constructed by complex sensory motor activities. By a process of internalisation their activation is made independent of the presence of objects (Piaget, 1945). The individual who acts in this way does not have explicit or conceptual knowledge of the external or internal conditions. Nevertheless it would be unreasonable to dismiss the idea that there is a cognitive aspect to such a performance. Consider the case of workers who control a machine or direct the course of a production process by means of a complex system of activities that are perfectly adapted to each other as well as to the properties of the machine, the material and the situation. Although these workers may not be able to name the parts of the machine, the material or the situation that prevails during their actions, an enormous amount of knowledge about these is implied in their actions, even if they are not able to conceptualise precisely what they are doing or what hints and so on they are reacting to. Nobody will deny that such knowledge is of vital importance in all human activities. It could even be claimed that most human knowledge is of this implicit kind, or what Polanyi (1973, 1983) has called tacit knowledge. Explicit conceptual and verbal knowledge account for the smallest proportion of human knowledge and – as we shall see later – often do not adequately represent and reconstruct the knowledge that is implicit in our actions. The same is true of the social activities of corporate employees. When they interact with others, instruct other individuals, direct a group, control a machine or work on a product,


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their actions and instructions contain a huge amount of knowledge that they are often unaware of having or are unable to explain verbally, but there is no doubt that such knowledge is of great importance to the success of a company.

Conceptual knowledge Conceptual knowledge is often considered to be knowledge par excellence. It is commonly defined as knowledge that is or can be expressed through language, but this definition is insufficient and ambiguous. The essence of conceptual knowledge is preverbal, does not necessarily presuppose verbalisation and only parts of it can be verbalised. Conceptual knowledge emerges out of radical transformations of purely acted knowledge, images and intuitions. These transformations make conceptual knowledge independent not only of present objects and situations, but in principle also of perceptible properties and images. Conceptual knowledge consists of concepts that construct their meaning by distinctive characteristics and by their relation to other concepts. Therefore they always build complex systems; a concept never exists in isolation. It is of fundamental importance to recognise that concepts function not just as descriptions but also as normative principles (Cassirer, 1993; Brandom, 1994; Seiler, 2001a). Notwithstanding this normative character, concepts have a generative potential in all actualisations and applications. Another property that is ascribed to conceptual knowledge is a fundamental abstractness, meaning that concepts are conceived of constituting their objects by rather abstract features and relations. But we must apply two restrictions: abstraction is common to all kinds of knowledge, and knowledge is abstract but not produced through an empirical abstraction. Although knowledge disposes of an inherent phenomenal consciousness, neither reflexive consciousness nor verbalisation are necessary conditions for idiosyncratic knowledge. To know that we know and what we know supposes later transformation and development by reconstructing our implicit knowledge through concepts. Concepts and conceptual thinking are the basis of reflexive consciousness. By using them, individuals are able to reconstruct some of their experiences and actions, thereby becoming consciously aware of them and able to communicate them to others. There is some evidence that reflexive reconstructions are not possible without verbal support. For the same reasons our conscious self is a late ontogenetic construction (Bickhard, 2000; Campbell et al., 2002; Seiler, 2003).


Properties and conditions of idiosyncratic knowledge The forms of knowledge dealt with so far are idiosyncratic or personal in nature. Although idiosyncratic knowledge is not necessarily subjective, it is personal in essence. As we shall see, such knowledge is primary and original, and it is presupposed by information.

Idiosyncratic knowledge is not observable Personal knowledge presupposes a personal inner experience that is not accessible from outside or open to direct observation. This is a fundamental condition with which knowledge management has to deal, and it is the main reason why the identification of idiosyncratic forms of knowledge is difficult and demands special methods. However this assertion is not universally accepted. There are many who prefer to conceive of idiosyncratic knowledge as open behaviour. If this were true the task of knowledge management would be much simpler. To address this conception we shall consider the following cases. The first case consists of purely performed knowledge, as described above. Such knowledge is taken as given when individuals perform an action in such a way that they achieve their goal but do not consciously know how they are performing it and why the action is successful. But the knowledge implied is never restricted to pure actions; it always includes intuitive aspects and is controlled by imaginative or conceptual hints. Thus when observing actions by individuals in ordinary situations we can see what they are doing but we are unable to decide why they are so acting and on what hints they are supporting their actions. In such cases there is some intuitive knowledge about the goal of the actions and how the individuals must perform to achieve it, even though they do not conceptually reflect the goal or analyse the course of their performance. The second case is when individuals consciously and clearly know why and how they are acting, which factors and hints they are reacting to, and are able to verbally translate their knowledge. If these individuals do not tell us why they are acting and what they are thinking, pure observation will never enable us to reveal the individuals’ motives and reasons for their action. What we observe are external and concrete aspects of the object and the situation the individuals are acting upon. We can never be sure whether these aspects are decisive for their performance or whether other hints are being taken into account. Moreover actions are always embedded in a complex system of goals, ideas and motives that are important to the individuals concerned but mere observation cannot reveal.


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In the third case individuals perform perfectly and reach the goal of the action without hesitation, but have wrong ideas about the logic of their action. There is considerable evidence from developmental psychology that children are able to perform difficult actions in highly pertinent and stringent ways even though their conceptual explication of the situation is totally wrong. There can be no doubt that this is also common among adults. Conceptual knowledge is not a simple translation of knowledge acted out. Rather it presupposes a creative reconstruction.

Idiosyncratic knowledge has an emotional quality Another fundamental aspect of idiosyncratic knowledge – its emotional quality – is often overlooked in research into knowledge. Idiosyncratic knowledge is not purely rational. By virtue of its emotional essence every act of thinking and judging is accompanied by affective experiences and is determined by intrinsic values and interests (Piaget, 1937, 1947). The emotional load and intrinsic values of the knowledge to be identified and interpreted have positive and negative implications for knowledge management. Knowledge or experience with a negative connotation, for instance, tends to resist more conscious reflection. Let us consider the special case of individuals who feel positively or negatively about their work and the company for which they work. They are often positively or negatively motivated to put their knowledge at the company’s disposal (Weggemann, 1999, pp. 24 f., 237 ff.). More crucially, if individuals are aware of the importance of their personal knowledge to the company and realise that their own position in the company depends on it, they may not be willing to divulge it for fear of losing their position and their power.

Idiosyncratic knowledge has two fundamental sources Idiosyncratic knowledge is neither innate nor born from purely private and subjective experiences, nor is it purely and simply the effect of social pressure and indoctrination. In terms of Piaget’s genetic epistemology all personal knowledge is produced by a personal construction. This construction has two strongly interdependent sources, that is, a continuous, adaptive reconstruction of inner and outer personal experiences, and a lifelong discourse with the surrounding sociocultural world.


Objectified and conventional knowledge Idiosyncratic knowledge has been described above as the original form of knowledge on which human acting and thinking is based, and it has been maintained that one of the most important aspects of knowledge management and knowledge investigation consists of identifying the idiosyncratic knowledge of corporate employees, and particularly that held by highly qualified employees. However knowledge management involves not only idiosyncratic knowledge but also – and in almost all situations – conventional knowledge or information.

The nature and function of objectified knowledge Objectified knowledge consists of idiosyncratic knowledge attached to material signs. It results from the fact that intelligent individuals are able to attribute fragments of their knowledge to signs. Thus knowledge acquires another status – it is externalised and its meaning is attributed to these signs. Two essential conditions are inherent in this process. First, objectification is not a material act; its nature is cognitive because knowing subjects attribute a meaning to a material object through an act of signification. Second, the material sign – whether it consists of an action, a figurative symbol or a verbal expression – does not contain the meaning by itself. It only refers to the object meant. This relation of meaning exists by virtue of semiotic attribution, that is, the cognitive content of the sign – its meaning – is in a virtual state. Only the individual brain asserts meaning to it. In other words meaning requires a semiotic attribution by the speaker and semantic interpretation and comprehension on the part of the listener. A main form of objectified knowledge is verbal knowledge, that is, knowledge attributed to language. The same applies to language that neither discloses its conceptual content by itself nor can be taken as an essential condition for knowledge. The inverse is true: conceptual knowledge is a precondition to understanding the meaning of language (Seiler, 2001a). Objectified knowledge has many functions for the individual, as already mentioned. For instance it is a kind of external memory and a condition for self-consciousness. But its main function is communication. Idiosyncratic knowledge cannot be shared with other people unless it is objectified and attributed to a sign. Idiosyncratic knowledge acquires a new status through the process of objectification: it changes into collective property.


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Objectified knowledge has to be conventionalised In the process of communication, knowledge is not only objectified but also conventionalised, meaning that both the knowledge and the sign to which it is attributed require mutual agreement. Without standardisation or unification, communication and understanding are not possible. However standardisation does not require an explicit agreement rather it is often realised through use and exchange. By this means idiosyncratic knowledge is socialised and turned into the collective property of culture and society. As collective property, conventional knowledge is submitted to continuous exchanges between society’s members and to scientific discourse – it does not remain unchanged, but is subject to development. Objectified and normalised (standardised) knowledge is usually called information.

Conditions and properties of conventional knowledge or information As research on knowledge always has to do with conventional knowledge – that is, information and language – it is important to look at some of its conditions and properties a little more closely.

Conventional knowledge is of a virtual nature The meaning of objectified knowledge is based on a semiotic link. For this reason its meaning – the knowledge implied – is in a virtual state. It could also be said that knowledge is implicit in the signs by which it is objectified and that information is dead or mute if it is not reactualised by an interpretation by an intelligent being. This is also true of conventional verbal knowledge. However the situation is paradoxical insofar as listeners cannot interpret the information without knowing its meaning, and without interpreting it they cannot know its meaning. How, then, is it possible to acquire new meanings? The answer is that individuals construct meaning hypotheses by relating the sign to the objects and actions of the situation in which it is used and by simultaneously searching the knowledge base. Thus they develop new cultural and scientific knowledge, as expressed in texts and discourses.

Most verbal signs have multiple meanings Most words not only have several different habitual meanings, but speakers can also give them new metonymic or metaphoric meanings (Lakoff, 1986). The fact that all verbal expressions have multiple


meanings makes interpretation especially difficult. Their meanings can only be uncovered if we consider the entire verbal and situational context. It is often claimed that knowledge contained in verbal instructions and texts is explicit. However this claim is rather questionable. Verbal expressions and written texts exclusively convey implicit conventional knowledge that has to be made explicit through interpretation. The task of interpreting verbal information is complicated for two main reasons. First, the interpretation an individual gives to a piece of information or a verbal expression seldom completely corresponds to its conventional meaning or to the meaning given to it by its author. There is no interpretation that is not based on idiosyncratic knowledge, which by its very nature has its own specific perspectives and emotional value. However individuals are able to distinguish their own idiosyncratic knowledge and meaning from the conventional meaning of a word or a sentence. This is the consequence of the fact that humans are able to develop different conceptual systems that they can subsequently interrelate. The second difficulty arises from a problem that is often overlooked: the conventional meaning of most words and sentences assumes different levels. In other words we can understand them on a very superficial level by knowing only what object or situation is meant, or we can penetrate them more deeply and discern important aspects, conditions and relations.

Interpretation is an open-ended process This third condition is a direct consequence of the first two. Conventional knowledge is not known unless individuals understand it by semantic interpretation and comprehension. But for the reasons given above, in most cases the interpretation of expressions and texts does not consist of a single act but is a never ending process. Moreover it has consequences for the interpreter because such processes entail the modification of his or her idiosyncratic knowledge.

Knowledge management What has been said about the concept of knowledge so far calls for specific principles of knowledge management that concern its content, spirit, activities and methods. Only knowledge management that is based on such principles is truly human.


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The hermeneutic character of human knowledge management Knowledge management involves exploring and extracting the multiple forms of idiosyncratic knowledge held by employees and has to gather and order knowledge that is objectified in corporate structures and processes. Both of these tasks require multiple hermeneutic interpretations and decisions. It is up to the knowledge researchers and managers to assess the meaning of the information collected and the usefulness of the corporate processes, as well as their results. Knowledge management that is based exclusively on formal principles and automatic procedures is fundamentally flawed. For these reasons, proper human knowledge management is hermeneutic in nature and makes great demands. Therefore only individuals who are well acquainted with the corporate processes and their conditions can decide on the appropriateness and usefulness of knowledge management. To optimise their interpretations, knowledge managers not only have to possess a wide range of knowledge about the organisation of the company and its activities and goals, but also have to use appropriate research methods. Proper human knowledge management should go beyond economic factors and objectives. From a truly human point of view the goal of knowledge management ought not be restricted to enhancing the economic success of the company, it should also foster the well-being of employees and consider the whole of society. The situation is made more intricate and even paradoxical in that knowledge managers not only have to interpret the actions they observe, the organisational structures and information given in verbal form, but also have to objectify their subjective interpretations anew in language. Unless the interpretation is expressed in a verbal or written form it cannot be delivered to or be dealt with by other persons. Thus objectification plays a role in knowledge management. The final task of knowledge management involves multiple rearrangements of the knowledge collected and objectified. First, the entire knowledge base has to be arranged in such a way that all users can easily find what they need. Second, as not all users need the same knowledge and many of them are not interested in acquiring knowledge that they do not need for their corporate tasks, they must be allowed to restrict their demand to what they consider is necessary and useful. However it could be profitable to give them the opportunity to learn about things they do not need to know but would like to know, as such information could enhance their motivation. Third, the information to be offered to employees has to be adapted to the level of understanding of the users.


Knowledge management is a joint activity As knowledge management is performed on behalf of a community it is not a solitary but a joint activity. As it is usual for more than one person to be charged with knowledge management, the activities of those involved require continuous coordination and discourse. Furthermore they have to adapt their activities to the interests and needs of the entire corporate community. Interactions and discourses between those who engage in knowledge management are needed to provide a broader basis to their research activities. They are especially important in respect of giving objectivity to their interpretations and objectifications. If they do not discuss their subjective interpretations and descriptions they will never achieve an acceptable level of objectivity.

Knowledge management is not formalistic Knowledge management owes at least part of its success to the availability of computer and information technologies. Complex procedures are used to order the information gathered and to lay it down in a data bank or intranet, from where it can be retrieved in any form and combination that the users require (Eppler, 1997). As presentation of the information collected is an essential part of knowledge management, such technologies are of great importance. However the processing and storing of information by means of electronic devices involves intrinsic dangers as well as offering huge advantages.

Advantages A great advantage of information technology is its ability to store unlimited quantities of data. Whereas human memory is limited and always exerts a transformational effect on the information stored (the information recalled is never identical to its state before storage), electronic memory systems take in as much data as required and without distorting or misrepresenting them in any way (Shapiro, 1997). Moreover data are retrievable in numerous combinations, each of which can prompt thinking on new solutions, and can even be used to support creative thinking. While there are many external memory stores to support human thinking, for example encyclopaedias, electronic memory stores outstrip them all in power and speed.

Limitations and dangers A problem with all kinds of formalisation is that they increase the distance between the data and their meaning. Extracting meaning from


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ordinary language demands considerable cognitive and linguistic (verbal) competence. Interpretation is especially difficult if texts do not describe the verbal and situational context in which they originated. In the case of formalised and digitalised information the interpretation requires an additional step. As the logic of formal processes is substantiated in algorithmic principles that do not warrant by themselves a correct and useful application in knowledge management, the individual knowledge research has to decide about their appropriateness and usefulness (Wille, 1987).

Human knowledge management is a kind of research activity Knowledge management is not only work on a company’s behalf but also a kind of research, and as with all research the truth and reliability of the results depend on the methods used. There are many commonly accepted tools for identifying, generating and processing implicit and explicit knowledge, and these need not be discussed here. But the situation is different for hermeneutic knowledge management. The tools and methods adapted for this task are less well known and seldom used.

The methods and tools adapted for hermeneutic knowledge management are of a qualitative nature One task of knowledge management is to reveal knowledge that is embedded in corporate structures and activities and in the performances and cognitive structures of employees. Most of the knowledge to be identified and made explicit is either personal or of a concrete and singular nature, for which standard quantitative measures are of no use. Instead qualitative methods are required to reveal and investigate the idiosyncratic experiences and thoughts of individual, and to assess their perspectives, capacities and skills. Qualitative methods are unparalleled in their capacity to catch idiosyncratic knowledge, extract meanings and evaluate feelings and values. A qualitative investigation presupposes consciousness and selfreflection on the part of the subjects and uses open questions that the subjects have to answer on the basis of their explicit knowledge (Seiler and Wannenmacher, 1983). The next step is to extract the meaning of the data obtained and corroborated it by means of sensible analysis. There are different techniques to help with this difficult task.

Qualitative methods of identifying personal knowledge Standardised questionnaires are not suitable for uncovering personal knowledge. Only semistructured interviews with open-ended questions give individuals the opportunity to externalise their thoughts and


feelings. Other useful methods are Kohlberg’s (1984) dilemmas, or stories on which the individuals are asked to comment, and Zelger’s (2000) GABEK method. The GABEK method is useful for gathering individuals’ evaluative answers as well as for detecting general trends and criteria. In many respects it is similar to ‘Anforderungsanalyse’ (Andelfinger, 1997), where a collective perspective is obtained on a common task carried out by a number of individuals. All factors, conditions and requirements that are relevant to the task and the situation have to be taken into account. The participants negotiate the proposed topics and formulate a joint solution for the tasks with arguments all can agree upon.

Formal methods of interpreting and condensing verbal data What we find by means of qualitative instruments are the verbal expressions of individuals. A scientific approach ought not to stop at this level of analysis. By means of interpretations, researchers can reconstruct the main line of thinking of individuals and compare with or contrast it to the thinking of others. There are a number of formal methods and sophisticated programs to assist with this difficult task, including ‘hermeneutically reducing and paraphrasing’ expression in texts (Sieloff, 1995) and tools for ‘categorical analysis’ (Seiler, 2001b, 2001c).

Methods of analysing the results Although descriptive statistical presentations can be useful in some circumstances, a more useful instrument is ‘formal concept analysis’ (Wille, 1987; Ganter and Wille, 1999). This complex method allows multiple relations in the data to be ordered and visualised without modifying their original state or transforming them into problematic numbers.

Methods of uncovering intuitive knowledge that is implicit in the performance of skills This task is of special interest to knowledge managers and also presents special difficulties. As persons who act intuitively are generally unaware of the decisive aspects of their performance and the multiple decisions made when acting they are not able to verbalise their actions, and therefore neither standardised questionnaires nor semistructured interviews are useful means of gathering their implicit knowledge. Simple observation is of no help either, so the persons concerned need guidance and hints if they are to conceptualise the circumstances and effects of their


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actions. The use of probing questions that are continually adapted to the situation and to the person’s answers can be a good strategy, but it often has little effect in the first interview so subsequent interviews are needed.

Problems connected with qualitative methods Most of the qualitative methods mentioned above presuppose conscious introspection on the part of the individuals being studied. Conscious introspection is not automatic and involves a difficult reconstruction by means of an explicit theory. Such theories are often very deficient, and knowledge management researchers using qualitative methods have to be aware of this and find ways of circumventing it. Another problem is that qualitative investigations and data analyses involve continuous interpretation. This is not only difficult but also requires great effort and ample time and money.

Concluding remarks Knowledge management is often associated with another fashionable notion: the idea of corporate identity and corporate culture. I believe that corporate culture is an antecedent of and corporate identity is both a goal and an effect of proper human knowledge management. If a positive atmosphere reigns in the company and its managers and employees are willing to cooperate, knowledge management can be introduced and prove useful not only in respect of the economic and financial success of the company but also in fostering a collective identity, mutual understanding and cooperation among its employees.

References Andelfinger U. (1997) Diskursive Anforderungsanalyse (Frankfurt: Peter Lang, 1997). Bickhard, M. H. (2000) ‘Emergence’, in P. B. Andersen, C. Emmeche, N. O. Finnemann and P. V. Christiansen (eds), Downward Causation (Aarhus, Denmark: University of Aarhus Press, pp. 322–48. Brandom, R. B. (1994) Making It Explicit. Reasoning, Representing, and Discursive Commitment (Cambridge, Mass.: Harvard University Press). Campbell, R. L., Christopher, J. C. and Bickhard, M. H. (2002) ‘Self and Values. An Interactivist Foundation for Moral Development’, Theory and Psychology, vol. 12, no. 6, pp. 795–823. Cassirer, E. (1993) ‘Zur Theorie des Begriffs. Bemerkungen zu dem Aufsatz von G. Heymans’, in R. A. Bast (ed.), E. Cassirer: Erkenntnis, Begriff, Kultur – Aufsätze Cassirers aus der Zeit von 1913–1939 (Hamburg: Meiner), pp. 34–55.

58 The Human Foundation of Knowledge Management Davenport, T. H. and Prusak, L. (1998) Wenn ihr Unternehmen wüsste, was es weiß … Das Praxisbuch zum Wissensmanagement (Landsberg: Moderne Industrie). Eppler, M. J. (1997) ‘Praktische Instrumente des Wissensmanagements – Wissenskarten: Führer durch den Wissensdschungel’, Gablers Magazin, vol. 8, pp. 10–13. Ganter, B. and Wille, R. (1999) Formal Concept Analysis. Mathematical Foundations (Berlin and New York: Springer). Kohlberg, L. (1984) ‘The Meaning and Measurement of Moral Development’, in L. Kohlberg (ed.), Essays on Moral Development. II: The Psychology of Moral Development (San Francisco: Harper & Row), pp. 395–425. Lakoff, G. (1986) Women, fire, and dangerous things. What categories tell us about the nature of thought (Chicago, Ill.: University of Chicago Press). Maar, C. (2000) ‘Envisioning knowledge: Die Wissensgesellschaft von morgen’, in C. Maar, H. C. Obrist and E. Pöppel (eds), Weltwissen – Wissenswelt (Cologne: DuMont), pp. 11–19. Piaget, J. (1936) La naissance de l’intelligence chez l’enfant (Neuchâtel: Delachaux & Niestlé). Piaget, J. (1937) La construction du réel chez l’enfant (Neuchâtel: Delachaux & Niestlé). Piaget, J. (1945) La formation du symbole chez l’enfant (Neuchâtel: Delachaux & Niestlé). Piaget, J. (1947) La psychologie de l’intelligence (Paris: Presses Universitaires de France). Polanyi, M. (1973) Personal Knowledge: Towards a Post-Critical Philosophy (London: Routledge). Polanyi, M. (1983) The Tacit Dimension (Gloucester: Peter Smith). Probst, G., Raub, S. and Romhardt, K. (1999) Wissen managen: wie Unternehmen ihre wertvollste Ressource optimal nutzen (Wiesbaden: Gabler). Seiler, T. B. (2001a) Begreifen und Verstehen. Ein Buch über Begriffe und Bedeutungen (Darmstadt: Allgemeine Wissenschaft). Seiler, T. B. (2001b) ‘Entwicklung als Strukturgenese’, in S. Hoppe-Graff and A. Rümmele (eds), Entwicklung als Strukturgenese (Hamburg: Kovac), pp. 15–122. Seiler, T. B. (2001c) ‘Strukturgenetische Begriffsforschung am Beispiel des Arbeitsbegriffs’, sozialer sinn: Zeitschrift für hermeneutische Sozialforschung, vol. 1, pp. 83–122. Seiler, T. B. (2003) ‘Die Genese des Bewusstseins: Emergenz oder naturgesetzlicher Verlauf?’, in I. Hosp, P. Mulser and W. Schnedelseker (eds), Entwicklung des Universums und des Menschen (Herdeck: GCA-Verlag) Seiler, T. B. and Wannenmacher, W. (1983) ‘How can we assess meaning and investigate meaning development. Theoretical and methodological considerations from an epistemological point of view’, in T. B. Seiler and W. Wannenmacher (eds), Concept Development and the Development of Word Meaning (Berlin: Springer), pp. 320–39. Shapiro, Y. (1997) ‘The Consciousness Hype: What Do We Want Explained?’, Theory and Psychology, vol. 7, no. 6, pp. 837–56. Sieloff, U. (1995) Natur- und Arbeitsideale von Erwachsenen. Ein komponentenorientierter Ansatz zur texthermeneutischen Rekonstruktion alltäglicher moralischer Willensvorstellungen (unpublished dissertation, Darmstadt: Technische Universität).


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Stewart, T. A. (1998) Der vierte Produktionsfaktor: Wachstum und Wettbewerbsfähigkeit durch Wissensmanagement (Munich and Vienna: Carl Hanser). Voß, S. and Gutenschwager, K. (2001) Informationsmanagement (Berlin and New York: Springer). Weggemann, M. (1999) Wissensmanagement: Der richtige Umgang mit der wichtigsten Unternehmensressource (Bonn: MITP). Wille, R. (1987) ‘Bedeutungen von Begriffsverbänden’, in B. Ganter, R. Wille and K. E. Wolff (eds), Beiträge zur Begriffsanalyse (Mannheim: B.I.Wissenschaftsverlag), pp. 161–211. Zelger, J. (2000) ‘Twelve Steps of GABEKWinRelan. A Procedure for Qualitative Opinion Research, Knowledge Organization and Systems Development’, in R. Buber and J. Zelger (eds), GABEK II: Zur qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 205–20.

4 Conceptual Knowledge Organisation and the Problem of Understanding Johannes Gadner and Doris Ohnesorge

Introduction Knowledge has been defined as processed networks of cognitive maps based on the experiences and intrinsic capacities of the individual, as well as social/cultural standards, patterns and schemes that organise perceptive information on environmental effects and regulate individual behaviour within social systems (Oeser and Seitelberger, 1995, pp. 103, 203 ff., 219 f.; Shore, 1996, pp. 311 ff.). In this context, knowledge refers to all sorts of conceptual knowledge, that is, knowledge properties that are available for verbal expression and as text (Zelger, 1999a). When dealing with complex knowledge systems we are confronted with a vast amount of conceptual data or texts. Based on two empirical examples, this chapter will demonstrate how the computer-supported GABEK method can be used to process and structure conceptual knowledge in order to foster understanding according to Pask’s (1976) conversation theory and Gadamer’s (1993) hermeneutical approach. The first example focuses on processes of knowledge organisation and the generation of conceptual network graphs. The second example relates to the process of building gestalten trees and generating hierarchically structured conceptual networks (Zelger, 1999b, 1999c). The question of how GABEK can facilitate understanding in complex social situations and intercultural environments will also be addressed.

What are GABEK and WinRelan? GABEK (GAnzheitliche BEwältigung von Komplexität, Holistic Processing of Complexity, © Zelger, 1990–2003) is designed to assist the organisation, processing and representation of knowledge. Based on the 60

Johannes Gadner and Doris Ohnesorge 61

natural language processing of verbal data, it is a qualitative research approach to knowledge organisation that facilitates the integration of different knowledge properties in order to build up a complex picture of the investigated knowledge system. GABEK and the related software, WinRelan (Windows Relational Analysis, © Zelger, 1990–2003) facilitate the connection and integration of the detailed, experiencebased knowledge of many individuals within organisations, institutions and societal groups. The method is based on the theory of linguistic gestalten developed by Zelger (1999b), which elaborates on Stumpf’s (1939) gestalt theory, a model of cognitive knowledge processing and organisation (Sergin, 1992; Zelger, 2000a) and Pask’s (1976) concept of protolanguage. The following section describes how knowledge can be represented in conceptual networks by systematically applying the concept of protolanguage (or protologic) to conceptual knowledge that is available as text.

The representation of knowledge by conceptual networks A given set of conceptual knowledge, as defined above (resulting from conversations, interviews and so on), functions as an unstructured and unordered natural language database. For example 20 interviews of about 20 minutes in length result in approximately 100 pages of text (A4, spacing 1.5). The text is defined into thematically coherent text units (Zelger, 1999b, pp. 51 f.) and entered into the file cards of the WinRelan indexing system (Aa1–Aa9, Ab1–Ab9, Ba1, and so on), with each card containing a single text unit. In order systematically to process the vast amount of conceptual data it is necessary to reduce its complexity. To do this, GABEK applies Pask’s (1976) notion of protolanguage and the idea behind its computer-implementable formalisation, THOUGHTSTICKER, to the process of coding text. Pask (1976, p. 225 ff.) maintains that complex theories and fields of knowledge can be represented in a very simple protolinguistic and protological way by excluding grammatical and logical elements and reducing the content to single concepts. THOUGHTSTICKER is the process of condensing and representing knowledge by key concepts (Pask, 1979; Pask and de Zeeuw, 1992). In the GABEK coding process the user identifies and marks key concepts that represent the text in every file card (Buber and Kraler, 2000, pp. 117 f.) Synonyms and homonyms are eliminated. This can be demonstrated with an example from a study on consumers’ knowledge and experience of and attitudes towards energy-efficient buildings with

62 Knowledge Organisation and Understanding Saving energy Advantage Windows closed Winter

Controlled ventilation

Comfortable Ag6

Room atmosphere Fresh air supply

Bc5

Constant temperature Heating system

EEB Figure 4.1 Text file cards Ag6 and Bc5 connected by five nodal concepts

controlled ventilation systems (Rohracher et al., 2001). The text unit stored on file card Ag6 – ‘Energy efficient buildings (EEB) have one big advantage. They produce a comfortable room atmosphere and fresh air without opening the window. This saves energy, especially during winter’ – contains the following concepts: ‘EEB’, ‘advantage’, ‘comfortable’, ‘room atmosphere’, ‘fresh air supply’, ‘windows closed’, ‘saving energy’, ‘winter’. These key concepts function as nodes for the conceptual connection of the text unit in file card Ag6 with other text units that contain the same key concepts. Figure 4.1 shows the connection between file cards Ag6 and Bc5 – ‘What do I associate with energy efficient buildings? Well, first of all, saving energy. Then, thanks to the controlled ventilation system I have a comfortable room atmosphere with constant temperature and fresh air. And I do not need another heating system’ – via four nodal concepts: ‘saving energy’, ‘comfortable’, ‘room atmosphere’, ‘fresh air supply’ and ‘EEB’. The nodes of a complex conceptual network are determined by the key concepts on every file card in the indexing system. Sentences are represented as sets of concepts, text units are represented as simple conceptual networks and the conceptual database is represented as a complex conceptual network (Figure 4.2). Quine (1980) defines knowledge systems as complex networks of individual concepts and propositions whose relevance increases with the number of their connections. This obviously makes things very complex. A core facet of GABEK is the discovery of meaningful relationships between apparently divergent statements and concepts, that is, the discovery of hidden order in apparent chaos by means of network graphs.


Figure 4.2 A conceptual network based on clusters of texts connected by key concepts Source: Zelger (1999b), p. 52, reproduced with the permission of StudienVerlag, Innsbruck, Vienna and Munich.

Knowledge organisation by conceptual network graphs A network graph is a tool to identify basic structures within the conceptual network by eliminating redundant information. It helps to organise and filter networked concepts that can be used to explain and understand specific conceptual associations and meanings. This is illustrated in Figure 4.3 using data from a study on intercultural (Austrian and Thai) management (Ohnesorge, 2002). The graph shows the interviewed Austrians’ conceptual associations of the key concept ‘meeting’ (in terms of a business meeting). Every key concept in the conceptual network is connected to other key concepts. By means of WinRelan it is possible to identify the connections by navigating through the network. The researcher can further explore any concept and its connections in the networks until no further connection exists. When interpreting the graph, the meaning of ‘meeting’ for the interviewed Austrians becomes

64 Knowledge Organisation and Understanding

Business Company

Work

Product

Meeting

Negotiation

Employee

Experience Communication

Manager

Phone Boss

Figure 4.3 Network graph of the key concept ‘meeting’

evident. Eventually, based on data from several interviews, the meaning of a concept can be defined at a higher level. When using Pask’s notion of THOUGHTSTICKER, an individual concept is best represented as an entity with multiple profiles derived from many different sources. ‘The key notion of THOUGHTSTICKER is that what is represented is shared meaning. This gives rise to the notion that the meaning of concepts cannot be represented by their location with respect to their subsets and supersets. To an important extent, they receive their meaning from the sum total of all the perspectives that contribute to it’ (Pask and Gregory, 1986, p. 221). This is exactly what the network graph represents. It attempts to define a concept as the repertoire of ways in which it is understood by different people. The meaning of ‘meeting’ in our example is constructed by the individual associations made by the Austrian executives and managers. Consequently what is represented is the shared meaning held by the participants in the study, facilitating our understanding of the meaning of concepts used by a specific social or cultural group.

The organisation of knowledge by gestalten trees Another way of understanding complex conceptual networks is to generate a hierarchically structured gestalten tree. Evidence from cognitive neurobiology has shown that human perception is strongly


context-dependent. We perceive the external world not via isolated perceptual data but from holistic gestalt patterns (Roth, 1995, pp. 237 ff.). The same applies to understanding language. We do not perceive and understand isolated words. Instead, context-dependent related verbal complexes are interpreted as meaningful wholes (Pask, 1980, pp. 357 ff.; Gregory, 1986, pp. 176 f.; von Glasersfeld, 1996, p. 228; Strauss and Quinn, 1999, pp. 42 f.). In order to understand a concept we need relative ‘hierarchies of concepts where the place of each concept is fixed in relation to the others’ (Gregory, 1986, p. 175). In line with Stumpf’s (1939) gestalt theory of perception, Zelger (1999b, 1999c) proposes a linguistic structure, facilitating a systematic, hierarchic grouping of unordered conceptual data that he calls a ‘linguistic gestalt’ – an abstract entity that consists of a conceptual network of nodal concepts in different sentences. A linguistic gestalt is formed according to seven syntactic and semantic rules: the rules of formal connectivity, formal variety, consistency, pragmatic applicability, formal distance, semantic implications and selective representation (Zelger, 1999b, pp. 54 ff.; Zelger and Gadner, 2000, pp. 4 ff.) It is based on several syntactically and semantically related partial structures, that is, text units. The criterion that distinguishes a specific linguistic gestalt from the rest of the conceptual network is the specific set of interrelations of nodal concepts in the connected sentences. In order to identify and understand meaningful relations between isolated statements we shall briefly look at the conditions of and procedures to obtain linguistic gestalten in conceptual networks. To form linguistic gestalten from unordered conceptual data, a group of texts connected by nodal key concepts (as shown in Figure 4.1) is isolated from the conceptual knowledge network. Contradictory or redundant texts within a group and texts that do not contain knowledge that is relevant to the situation in question are eliminated. Meaningful and thematically coherent texts are processed according to the rules. Figures 4.4 and 4.5 illustrate how the key nodal concepts that connect a group of four texts are used to formulate a selective representation of these texts. The selective representation is based on the content of the key concepts. The whole conceptual knowledge system is organised by applying these procedures, resulting in a set of linguistic gestalten containing new texts in the form of selective representations. Then the same rules are applied to the set of linguistic gestalten to obtain linguistic gestalten of progressively higher orders, that is, ‘linguistic hypergestalten’, and so on. The bottom-up process of gestalt building is shown in Figure 4.6.


C2

T1

C1

T2

C3

C4

C5

C6 T3

T4 C7 C8

Figure 4.4 Text group with four texts (T1–T4) connected by eight key concepts (C1–C8) C2 C1

C8

C3

Selective representation

C7

C4

C5 C6

Figure 4.5 The nodal key concepts form the basis of the selective representation of the text group

The process is repeated until no further higher-order gestalten can be organised. The products of this analysis are several hierarchically structured linguistic gestalten, the sum of which is called the ‘gestalten tree’ (Figure 4.7). The gestalten tree is a hierarchically organised, self-similar conceptual knowledge structure (Zelger, 1999c). Formal self-similarity is given as all the syntactic rules hold at all levels of the gestalten tree. Semantic self-similarity holds because all the concepts and the meaning given at the highest level are also present in the more complex details in the lower ones (Zelger, 1999c; Zelger and Gadner, 2000, p. 7). The texts at the highest level of the gestalten tree are of higher generalisability and relevance. Obviously this coincides with a loss of detailed

Johannes Gadner and Doris Ohnesorge 67 Summary

HHG1

HG...

G...

G5

HG3

G6

HG4

G7

HHG2

HG5

G8

G9

HHG3

HG6

G10

HG...

G11

G12

G13

G...

A... A07 A08 A09 B01 B02 B03 B04 B05 B06 B07 B08 B09 C... C06 C07 C08 C09 D01 D... D09 D...

Figure 4.6 The bottom-up process of gestalt building

information. But every level of the gestalten tree can be verified by navigating through the texts of the next level down, through to the original answers on the lowest level of the conceptual knowledge structure. Figure 4.7 shows a gestalten tree based on the above mentioned study on consumers’ knowledge about energy-efficient buildings. In this case the hyperhypergestalt is the highest level of generalisation. In terms of Pask’s notion of THOUGHTSTICKER, generalisations are public concepts – that is, concepts that are common to a group of individuals, a society or a culture – that organise other (relatively) lower-order public concepts in terms of their similarities and differences (Pask and Gregory, 1986, pp. 206, 220). Applying this view to the gestalten tree, it can be defined as the maximal context of gestalten of the highest order organising lower-order gestalten in terms of similarities and differences. The gestalten tree is a general knowledge structure within a complex

68 Knowledge Organisation and Understanding Gestalt Hypergestalt (H)

Gestalt Gestalt Gestalt

Hyperhypergestalt (HH)

Hypergestalt (H)

Gestalt Gestalt Gestalt Gestalt

Hypergestalt (H)

Gestalt Gestalt Gestalt Gestalt

Hypergestalt (H)

Gestalt

Figure 4.7 Structure of a gestalten tree

conceptual network that consists of unstructured and unordered natural language data. It organises groups of topics and public concepts into coherent structures that designate agreements and disagreements among interviewees. Although Pask and Gregory (ibid., p. 210) maintain that exclusively hierarchical representations of knowledge produce restricted views on subject matter ‘obtained by focusing on one topic and its possible derivation from others’, the hierarchical organisation of concepts and topics in gestalten trees can be helpful in constructing and defining relevance for a given data set in a knowledge system. In gestalten trees, relevance is defined by the number of connections of concepts and text units in agreement with the syntactic and semantic rules (Zelger and Gadner, 2000, pp. 7 f.). Therefore the hierarchical structure of a gestalten tree represents the relevance of concepts and topics within a conceptual knowledge system. This does not mean that the topics and knowledge properties contained in lower-order gestalten are not of interest. Rather the hierarchical structure facilitates the identification of topics and thematic trends in the knowledge system in question by providing a systematically structured, general overview (Gadner and Zelger, 2000, pp. 236 f.).

Knowledge organisation and the problem of understanding Structuring and organising conceptual knowledge by means of GABEK reduces redundant and complex information, making it easier to


understand complex social and cultural situations. When dealing with knowledge the problem of understanding is crucial especially when different social and cultural groups are involved who lack a common language (Gadamer, 1993, p. 184), resulting in tension and misunderstandings. In Gadamer’s (1993) hermeneutical approach, understanding refers to artistic practices or ‘the art of interpretation’. It is a linguistic process to construct and convey a common world view based on a common ground for communication (Gadamer, 1997, p. 75). Knowledge organisation by means of GABEK, as described in this chapter, is a way of finding a common language for the different groups involved in a specific situation. In fact one of the central purposes of knowledge organisation is to promote understanding (Born, 2001, pp. 530 ff.). But how do we understand and learn from the data? In order to understand understanding, von Foerster (1970) proposes a synthesis of individual cognitive and social interactive processes. Understanding is an individual cognitive process of constructing meaning to gain a coherent and consistent mental representation of internal and external perceptions (von Glasersfeld, 1996, pp. 221 ff., 230 f.). It is essentially based on a dialectic process of interaction between known and unknown, familiar and unfamiliar (Schäfter, 1991, pp. 12 ff.; Geertz, 1994, pp. 20 ff., 289 ff; Gadner, 1997). This is true for a conversation between two individuals and for any (linguistic) interaction between an individual and his or her environment (von Glasersfeld, 1996, pp. 214 f.). Based on the view of cognition as an intersubjective conversational process, Pask et al. (1973) define understanding as essentially social. According to Pask’s (1976) conversation theory, the complexity of knowledge means that it is necessary to organise it if we are to understand each other. Conversation theory deals with the processes by which we come to understand or misunderstand each other’s views, and with the dynamics of incorporating different perspectives into the structure of one’s own understanding (Pask and Gregory, 1986, p. 205). In this view, ‘a topic T is understood by a participant if and only if T is explained and if T can also be derived from other topics in the conversational domain, i.e. a derivation is an explanation or systematic justification of an explanation’ (Pask, 1976, p. 4). Both the explanation and the derivation of a topic is only possible if an individual knows the rules and norms of a language embedded in a specific language game (Wittgenstein, 1993, paragraph 199). In this context, Polanyi (1973, pp. 18 ff.) proposes the concept of ‘personal knowledge’, which he defines as an action that requires skills


and personal judgments of perceived situations. We … [learn] to speak of things in a certain language, in which there are names for various kinds of objects, names by which objects can be classified, making such distinctions as between past and present, living and dead, healthy and sick, and thousands of others. … The curious thing is that we have no clear knowledge of what our presuppositions are and when we try to formulate them they appear quite unconvincing. (Ibid., p. 59) In order to understand the meaning of concepts and actions, background experience plus social and cultural knowledge is required (Seiler, 2000, p. 432). Understanding and knowledge are fundamentally linked in an infinite recursive process (von Foerster, 1970, pp. 8 f.; 1973, pp. 39 ff.). Concepts and conceptual knowledge properties can be conceived of as collections of self-organised processes. These are organisationally closed in that they are stable and autonomous, and yet they are informationally open to each other in that they help to give meaning to one another, which is the condition for understanding. In turn the process of understanding is the process of knowledge acquisition. In other words understanding is fundamentally based on specific (implicit and explicit) knowledge of linguistic and social habits, and knowledge is achieved through the interactive processes of understanding that are referred to as learning (Ruschmann, 1999, pp. 259 f.). Understanding and learning have to do with the process of meaning construction, which may be defined as ‘a Piagetian “assimilation” process whereby people employ old cognitive models as resources for making sense out of novel experiences’ (Shore, 1996, p. 319). As pointed out by Seiler (2000, p. 434), a concept is not restricted to one meaning, but is often very complex and full of variety. In general the speakers of a language are not consciously aware of the variety of meanings of the concepts they use (Aitchison, 1994, pp. 42 ff.). Rather they are subject to ‘tacit knowing’, meaning that ‘we can know more than we can tell’ (Polanyi, 1983, p. 4). The meaning of a concept might be more than the person who uses it will ever know (Wittgenstein, 1993, paragraph 560),1 if it means anything at all (Polanyi, 1973, pp. 250 ff.). If we are not aware of all the meanings of a concept, how can we fully understand it? One answer is that we understand through context. The meaning of a concept derives not only from specific words but also from the context in which it is used (Wittgenstein, 1993, paragraph 43).


Concepts do not stand alone; they are embedded in complex networks that are joined together by syntactic and grammatical rules (Seiler, 2000, p. 435). If we are to understand conceptual knowledge systems they have to be organised in such a way as to provide a holistic overview and a meaningful context (Zelger, 2000a, pp. 60 ff.) The network graph facilitates understanding by organising concepts on the basis of their meaning to different people. The gestalten tree organises isolated conceptual knowledge properties (text units) into context-dependent conceptual complexes (gestalten) that are understood as meaningful wholes. Both methodological tools foster understanding by organising knowledge and constructing meaning.

Conclusion GABEK and WinRelan are designed to organise knowledge by generating conceptual network graphs and hierarchically structured gestalten trees. By using GABEK as a qualitative method for knowledge organisation, it is possible to explain and understand facts, ways of argumentation and patterns of behaviour, as well as the connections and concepts that lie behind the conceptual knowledge of societal groups. The increasing internationalisation of organisations and their networks and the growing complexity of decision-making processes mean that mutual understanding in the global context is becoming more and more important. Qualitative methods of knowledge organisation are important in overcoming these problems and developing solutions based on the knowledge of the people involved. GABEK and its software application WinRelan facilitate the connection and integration of the detailed, experience-based knowledge of the many individuals who make up organisations, institutions or societal groups. The purpose of the method is to manage complexity and derive a holistic understanding of social situations.

Note 1. ‘Die Bedeutung des Wortes ist das, was die Erklärung der Bedeutung erklärt. D.h.: willst du den Gebrauch des Worts “Bedeutung” verstehen, so sieh nach, was man “Erklärung der Bedeutung” nennt.’ (‘The meaning of a word is what the explanation of the meaning explains, i.e., if you want to understand the use of the word “meaning” you have to look up the “explanation of the meaning” ’ (Wittgenstein, 1993, paragraph 560).


References Aitchison, J. (1994) Words in the mind: An introduction to the mental lexicon (Oxford and Cambridge: Blackwell). Born, R. (2001) ‘Was kann und soll es bedeuten, in Zusammenhang mit Wissensmanagement und Cognitive Science von “Wissen” zu sprechen?’, in R. Born and O. Neumaier (eds), Philosophie-Wissenschaft-Wirtschaft: Miteinander denken – voneinander lernen (Vienna: öbv&hpt), pp. 528–33. Buber, R. and Kraler, C. (2000) ‘How GABEK and WinRelan support qualitative research’, in R. Buber and J. Zelger (eds), GABEK II: Zur Qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 111–37. Gadamer, H. G. (1993) Hermeneutik II: Wahrheit und Methode (Tübingen: J. C. B. Mohr and Paul Siebeck). Gadamer, H. G. (1997) ‘Zur Hermeneutik: Sprache und Verstehen’, in H.G. Gadamer and J. Gronding (eds), Gadamer Lesebuch (Tübingen: J. C. B. Mohr and Paul Siebeck), pp. 75–80. Gadner, J. (1997) ‘Der Fall Kaser: Über Psychotherapie, fremde Kultur und das Problem des Verstehens’, Der Schlern. Zeitschrift für Südtiroler Landeskunde, vol. 71, no. 3, pp. 539–44. Gadner, J. and Zelger, J. (2000) ‘Organizational Development by GABEK. A Case Study’, in R. Buber and J. Zelger (eds), GABEK II: Zur Qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 233–58. Geertz, C. (1994) Dichte Beschreibung: Beiträge zum Verstehen kultureller Systeme (Frankfurt: Suhrkamp). Gregory, D. (1986) ‘Philosophy and practice in knowledge representation’, in J. Zeidner (ed.), Human Productivity Enhancement: Organizations, Personnel, and Decision Making, vol. 2 (Westport, Conn.: Praeger Publishers), pp. 165–203. Oeser, E. and Seitelberger, F. (1995) Gehirn, Bewusstsein und Erkenntnis (Darmstadt: Wissenschaftliche Buchgesellschaft). Ohnesorge, D. (2002) Inter-Cultural Management: Interactions and Transactions between Austrian and Thai Companies (Innsbruck: Diplomarbeit, University of Innsbruck). Pask, G. (1976) Conversation Theory (Amsterdam: Elsevier). Pask, G. (1979) A protolanguage – Lp: The THOUGTHSTICKER language (Richmond: System Research). Pask, G. (1980) ‘Developments in conversation theory’, International Journal of Man–Machine Studies, vol. 17, no. 1, pp. 357–411. Pask, G. and de Zeeuw, G. (1992) Interactions of Actors, Theory and Some Implications (Amsterdam: Elsevier). Pask, G. and Gregory, D. (1986) ‘Conversational Systems’, in J. Zeidner (ed.), Human Productivity Enhancement: Organizations, Personnel, and Decision Making, vol. 2 (Westport, Conn.: Praeger Publishers), pp. 204–35. Pask, G., Scott, B. C. and Kallikourdis, D. (1973) ‘A theory of conversations and individuals’, International Journal of Man–Machine Studies, vol. 5, no. 2, pp. 443–566. Polanyi, M. (1973) Personal Knowledge: Towards a Post-Critical Philosophy (London: Routledge). Polanyi, M. (1983) The Tacit Dimension (Gloucester: Peter Smith).

Johannes Gadner and Doris Ohnesorge 73 Quine, W.V.O. (1980) From a logical point of view: Nine logico-philosophical essays (Cambridge, Mass.: Harvard University Press). Rohracher, H., Kukovetz, B., Ornetzeder, M., Zelger, T., Enzensberger, G., Gadner, J., Zelger, J. and Buber, R. (2001) Akzeptanzverbesserung von NiedrigenergiehausKomponenten als wechselseitiger Lernprozess von Herstellern und AnwenderInnen (Graz, Innsbruck and Vienna: Bundesministerium für Verkehr, Innovation und Technologie). Roth, G. (1995) Das Gehirn und seine Wirklichkeit. Kognitive Neurobiologie und ihre philosophischen Konsequenzen (Frankfurt: Suhrkamp). Ruschmann, E. (1999) ‘Philosophische Hermeneutik als Rekonstruktion mentaler Prozesse’, in J. Zelger and M. Maier (eds), GABEK: Verarbeitung und Darstellung von Wissen (Innsbruck and Vienna: StudienVerlag), pp. 259–70. Schäfter, O. (1991) ‘Modi des Fremderlebens: Deutungsmuster im Umgang mit Fremdheit’, in O. Schäfter (ed.), Das Fremde: Erfahrungsmöglichkeiten zwischen Faszination und Bedrohung (Opladen: Westdeutscher Verlag), pp. 11–42. Seiler, T. B. (2000) ‘Wer sagt uns, was die Worte bedeuten?’, in R. Buber and J. Zelger (eds), GABEK II: Zur Qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 429–46. Sergin, V. (1992) ‘A global model of human mentality’, in R. Trappl (ed.), Cybernetics and Systems Research (Vienna, Singapore, New Jersey, London, and Hong Kong: World Scientific), pp. 882–90. Shore, B. (1996) Culture in Mind: Cognition, Culture, and the Problem of Meaning (Oxford: Oxford University Press). Strauss, C. and Quinn, N. (1999) A cognitive theory of cultural meaning (Cambridge: Cambridge University Press). Stumpf, C. (1939) Erkenntnislehre (Leipzig: J. A. Barth). von Foerster, H. (1970) ‘Understanding understanding’, Methodologica, vol. 7, pp. 7–22. von Foerster, H. (1973) ‘On constructing a reality’, in W. D. Keidel (ed.), Environmental Design Research (Stroudberg: Dowden, Hutchinson and Ross), pp. 35–46. von Glasersfeld, E. (1996) Radikaler Konstruktivismus: Ideen, Ergebnisse, Probleme (Frankfurt: Suhrkamp). Wittgenstein, L. (1993) Philosophische Untersuchungen (Frankfurt: Suhrkamp). Zelger, J. (1999a) ‘GABEK: A method for the integration of expert knowledge and everyday knowledge’, in D. J. De Tombe and E. Stuhler (eds), Complex Problem Solving. Methodological Support for Societal Policy Making (Munich and Mering: Hampp), pp. 20–45. Zelger, J. (1999b) ‘Wissensorganisation durch sprachliche Gestaltbildung im Verfahren GABEK’, in J. Zelger and M. Maier (eds), GABEK: Wissensverarbeitung und Wissensdarstellung (Innsbruck, Vienna and Munich: StudienVerlag), pp. 41–87. Zelger, J. (1999c) ‘Gestaltenbäume als fraktale linguistische Strukturen’, in W. Löffler and E. Runggaldier (eds), Vielfalt und Konvergenz der Philosophie (Vienna: Hölder-Pichler-Tempsky), pp. 116–22. Zelger, J. (2000a) ‘Parallele und serielle Wissensverarbeitung: Die Simulation von Gesprächen durch GABEK’, in R. Buber and J. Zelger (eds), GABEK II: Zur Qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 60–5.

74 Knowledge Organisation and Understanding Zelger, J. (2000b) ‘Twelve Steps of GABEKWinRelan: A Procedure for Qualitative Opinion Research, Knowledge Organization and Systems Development’, in R. Buber and J. Zelger (eds), GABEK II: Zur Qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 205–20. Zelger, J. and Gadner, J. (2000) ‘Knowledge organization by procedures of natural language processing. A case study using the method GABEK’, in G. Stumme (ed.), Working with Conceptual Structures (Aachen: Shaker), pp. 1–15.

5 Gestalt-Based Linguistics and the GABEK Method: What Cognitive Science Has to Say on ‘Space Logic’ in Everyday Thought Michael Kimmel

Introduction Cognitive linguistics confirms Zelger’s (1999, 2000) basic proposition about the condensation and spatial grouping of knowledge into gestalt clusters. The cognitive linguistic approach to conceptual and linguistic gestalten opens a vista on further facets of real-life cognition that might be incorporated into GABEK. Various cognitive findings point to constraints on automated gestalt building and caution us to consider top-down and bottom-up processes of thematic integration in combination. Suggestions for method development in GABEK include taking cognitive dilemmas more seriously and analysing clashing gestalten as integrated tropes. GABEK is a social science approach to the reconstruction of theme structures in discourse, and is employed to uncover patterns and logical relations that the analyst’s eye is wont to miss. Although it is above all an interpretive tool, it produces more than mere analytical artefacts. As Zelger (1999) emphasises, GABEK emulates natural cognition and locks into principles of human thought based on gestalt psychology. The avowed goals are to approximate actors’ intentionality in what ethnographers would call an ‘emic’ perspective, and to model as closely as possible what really takes place in the minds of interviewees. To what degree this claim is borne out is the subject of the present investigation. GABEK does not stand alone with its major hypothesis on gestalt cognition in discourse. While Zelger (2000) cites some evidence from general cognitive science to bolster his claims, this chapter draws attention to an extensive body of evidence from the burgeoning field of 75

76 Gestalt-Based Linguistics and the GABEK Method

cognitive linguistics, where gestalt cognition is a theoretical mainstay. The chapter will take stock of key theoretical aspects that converge with the claims made for GABEK and, on this basis, suggest new features that it could incorporate. It will also point out the controversial nature of some of GABEK’s assumptions and indicate where more empirical corroboration is needed lest we jump to premature conclusions. How can practice-oriented disciplines benefit from cognitive methods? In the social sciences, cognitive methods have sharpened our notion of knowledge as comprehension, encoding, storage and recall, shown us that explicit, conscious or rule-based knowledge is much less important than we like to think, and differentiated the cognitive strategies of experts and laypersons. They have also redirected paradigms, for example by advancing our perspectives on non-Aristotelian categorisation, perception as an imaginative process, embodied knowledge, subsymbolic knowledge models, distributed cognition or ‘structural coupling’ as way of understanding that knowledge is as much in the world as in our heads. Overall the striking feature that commends the disciplines subsumed as ‘cognitive sciences’ is their robust interdisciplinarity (Bechtel and Graham, 1998, pp. 95 f.). The cognitive project is driven by the search for ‘convergent evidence’ across independent methods (Lakoff and Johnson, 1999). We can never ‘look into heads’ directly, so that it is upon this convergence that claims of cognitive reality must rest. In this general spirit, this chapter focuses on convergent pointers from the study of language and mental imagery that are complementary to the GABEK approach to discourse. GABEK attempts to reconstruct the hierarchical scaffolding of knowledge in discourse. Its computer application WinRelan is an aid for coding interview material into clusters, which are then mapped into a hierarchy of gestalten, hypergestalten and hyperhypergestalten to simulate the progressively embedded levels of theme compression. The reconstruction of themes proceeds by grouping clusters through generative rules derived from gestalt psychology. The resultant gestalten tree is a powerful analytical tool to visualise ‘master maps’ of discourses. As well as offering expository expediency, Zelger (2000) proposes that hypergestalt clusters depict how interviewees actually represent themes. Rephrased in cognitive terms, GABEK and real-life thought are claimed to share the following key features: ●

The organisation of cognitive material through gestalt representations at multiple parallel and mutually embedded levels of specificity and schematicity.

Michael Kimmel 77 ●

●

●

●

The automated compression of everyday experience into higher-level gestalten. Theme finding by focal search in gestalt arrays (that is, the profiling of a certain detail), eliciting connected concepts by focal spread. Key concepts and lexical items function as associative pegs for discourse and as points of entry. Logical relations of discourse are superficial features and are grasped before content (deep structure) is.

Aspects of gestalt theory in cognitive linguistics Cognitive linguistics is a confluent of categorisation studies, metaphor theory, force dynamics, cognitive grammar and frame semantics (Ungerer and Schmidt, 1996), recently augmented by the theory of online discourse shifts between ‘mental spaces’ (Fauconnier and Turner, 2002). To varying degrees of explicitness all these strands assume that cognition is analogous to what we perceive1 and that it involves mental imagery operating on a gradient between sketchy ‘image schemas’ and ‘rich images’. Image schemas are especially significant cognitive tools: grounded in spatial logic, they are frequently stored and accessed as conceptual gestalten. Developmentally, image schemas derive from the kinaesthetic experiences our bodies undergo when negotiating space. Their skeletal nature allows the metaphorical use of image schemas to make sense of non-physical and abstract concepts, to structure analogy and to provide the basic building blocks of complex conceptual models.2 GABEK is not only consistent with this fundamental theory of gestalten, many aspects of it also dovetail with specific mechanisms posited by cognitive linguistics to explain the cognition of words and phrases. A tour d’horizon will show that these concepts both mirror and ground the discourse level in GABEK.

Categorisation and centre–periphery As indicators of a conceptual node’s importance, GABEK maps draw on centrality (that is, many convergent pointers) versus peripheral positioning in the field. A further measure is the incidence of multiple overlaps between concepts versus the relative isolation of a theme within the whole. Cognitive linguistics confirms the role of centrality, chaining and overlap in everyday cognition. The cognitive linguistics model is based on Rosch’s (1978) research, which shows that natural categories in folk models are different from criteria-based categories in logic


because they are structured by prototypes, that is, the examples that are most typical of a category.3 In a path-breaking work, Lakoff (1987, pp. 91 ff., 283) claims that such prototype effects and complex categories are processed as spatial gestalten. Categories with prototypes are not structured as containers as in mathematics, but as ‘radial’ categories, that is, they are internally structured by a chaining principle and by centre–periphery logic. Prototypical features such as ‘bad faith’ reside at the centre of the category (by virtue of centre–periphery logic, central is salient and important), while the less prototypical members are placed at the fuzzy outer reaches. Figure 5.1 depicts category prototypes as squares.

Prototype models and family resemblances as overlaps and chains Lakoff (1987, pp. 74 ff.) demonstrates that the English concept of ‘mother’ has no single definition – that is, necessary and sufficient attributes – that captures all the nuances of how the word is used. Rather it encompasses a genetic model, a nurturance model, a marital model (the father’s wife) and a genealogical model (the closest female ancestor). The different models are used for different senses of ‘mother’. Thus the proverb ‘necessity is the mother of invention’ draws on the birth model, while ‘he wants his girlfriend to mother him’ is derived from the nurturance model. With aprototypical ‘mothers’ (stepmother, surrogate mother, adoptive mother, foster mother, biological mother, donor mother and so on) these models do not go together, so we use them selectively. The concept of a ‘real mother’, in which all these criteria coincide, is situated where the submodels overlap, constituting the category prototype. Alternatively, categories that lack a single prototype can be held together by a chain of ‘family resemblances’ (Wittgenstein, 1953). For example

Figure 5.1 Prototypicality as a centre–periphery image

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the meanings of ‘game’ are like a thread whose strength comes not from one long fibre, but from innumerable tiny fibres that overlap locally.

Frames and profiling as figure–ground operations Fillmore’s (1977) frame semantics describes networks in long-term memory that encode the propositional knowledge chunks that are typically found in a single context. The model attempts to show how individual expressions correspond to selective activations from a network. For example the concept ‘buy’ involves a representation of the entities ‘buyer’, ‘seller’, ‘goods’ and ‘money’. Note that these same basic elements circumscribe a wider commercial event frame encompassing not only ‘buy’ but also ‘sell’, ‘charge’ and ‘pay’. Now, what differentiates the four verbs is simply a change of perspective within the same overall frame, either between a customer focus and a seller focus or between a goods focus and a money focus. Frames account for the complementarity of clause patterns in frame-related verbs. Taking the idea further, several approaches in cognitive linguistics represent contextually linked concepts as spatial (or analogue) arrays. Accordingly, as people think or talk they move between conceptual configurations in the same way as they move in physical space. They construe situations from different vantage points (Taylor and MacLaury, 1995). Any mental construal is like orchestrating a theatre scene with actors and props in front of a backdrop, directing the spotlights, zooming in and switching the viewpoint. The notion that explains how we express any single thought is called ‘profiling’ (technically a mental figure–ground operation), which is similar to the alternating spotlights in a frame. Langacker (1987), the founder of space grammar or cognitive grammar, argues that nouns correspond to the profiling of regions in this interactive mental model, while verbs and prepositions signal the profiling of relations. In general, on-line cognition highlights the presently actualised features of a broader model. At the same time, any momentary profile is only meaningful against the sum total of backgrounded elements and a large number of alternative configurations. In the example of making a Greek salad, first the concepts ‘board’, ‘knife’ and ‘chopping’ plus ‘tomatoes’, ‘onions’, ‘cucumbers’ and ‘peppers’ are foregrounded together, followed by ‘adding olives and cheese’ and the concepts ‘bowl’, ‘seasoning’ and ‘mixing’, and finally ‘plates’, ‘bread’ and ‘serving’ move into focus (Ungerer and Schmidt, 1996, pp. 195 ff.). In speech we invariably find viewing arrangements (Langacker, 1990). Every utterance is bound to a particular mental perspective, either subjective or objective. We frequently shift between these, for example


through switches from active to passive voice. Furthermore the speaking subject can be on stage in her or his own mind or move off stage if the mental focus narrows on the action.

Zoom, link and scope GABEK claims that the activation of concepts operates by what Zelger (2000, p. 53) calls focal spread. A lexical item brought up in a conversation can function as a point of entry into a conceptual web and is then negotiated from there. Zelger also speaks of a superficial or initial level of understanding a new concept and a deeper level into which one can then enter. Cognitive linguistics and schema theory suggest two major ways in which new but related concepts are activated: by following ‘links’ and/or by broadening the mental ‘viewing scope’. Linking has been addressed above, but the notion of scope requires some explanation. According to Langacker’s (1987) cognitive grammar, a specific level of cognitive processing is somewhat like the degree of granularity of photographs and selecting a level is like using a zoom lens. To start with, some concepts are more fine-grained than others at a given level (that is, they feature more details). If we ‘zoom out’, the mental picture becomes less fine-grained but a broader range of concepts moves into focus.

Action chains as force schemas Interpreting GABEK findings rests inter alia on analysing causal connections between the items in a gestalt by looking at their directionality, strength and evaluative loading. Cognitive linguistics assumes that directional links are what create causality in the mind in the first place. According to Langacker (1990, p. 219), a domain or cognitive model allows many paths along which the constituent regions can be connected, but in a given sentence only some are selected as action chains. He suggests that we represent the causal relations between the constituent concepts as a force chain that passes on a kinaesthetic impetus, much like billiard balls. An action chain can thus be inserted into a frame by superimposing force ‘arrows’ (Figure 5.2).

Research on high-level gestalten and the ‘spatialisation of form’ hypothesis A major claim about gestalt cognition is that cognitive contents are routinely compressed to generate a global representation (Zelger, 2000). It is undisputed that the ability to represent an idea’s gist or a story’s

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Figure 5.2 A network with an action chain as an impetus schema

main theme makes for both speedy recall and narrative economy. Compressions bring our ability to hold together and juggle one or two dozen conceptual spaces down to what Fauconnier and Turner (2002) call a ‘human scale’. Compressions thus make on-line complexity manageable and inferential links noticeable. The generation of macrostructures, for example wrapping up the gist of a story into a unique mental representation, has been extensively discussed in propositional discourse comprehension models (van Dijk and Kintsch, 1983; Kintsch, 1998). Here compression proceeds through successive stages of feature selection and integration based on propositional overlap. In the alternative space-logic approach, the compression of complex events or themes is seen as the construction of a global gestalt image, involving the alignment of the participant mental objects in a quasi spatial mental landscape, selecting outline features by ‘profiling’ aspects of entrenched mental models and ‘zooming out’ to see their global referential relations. Less salient aspects of knowledge items either recede into the background or become too fine-grained to notice in this global view. Yet this view on macrostructures still lacks explicit empirical justification. Prevalence, scope, memory and selection constraints, the role of top-down structures such as everyday action scripts, schemas or discourse genres, as well as the role of ‘invisible’ discourse context and world knowledge are far from settled. Cognitive linguists have suggested that macrostructures sometimes crystallise around dominant image schemas: the drama Macbeth features recurrent ‘container’ schemas (Freeman, 1995), the category of emotion concepts is governed by ‘force’ images (Kövecses, 2000) and ‘othering’ in racist ideology is organised by an ‘up-down’ structure (Dirven et al., 2001).4


A connected cognitive approach affords interesting clues as to how gestalten organise thought. Image schemas not only occur in sentences and higher levels of discourse content, but some also assist its interpretation by way of ‘default tools’, that is, entrenched expectational schemas that tell us how to organise incoming data and sequence our own output. Lakoff (1987, p. 283) proposes that the mechanisms used for organising and integrating conceptual material, event and causal schemas, category builders, attribute bundles, and so on are imagistic and spatial in nature. He dubs this the ‘spatialisation of form hypothesis’. The basic idea is ancient, as noted by Sadoski and Paivio (2001): the deliberate use of spatial structuring devices for memorisation is unmistakable in ancient rhetoric such as Quintilian’s (c. 35–96 AD) and elaborate medieval systems such as Ramón Llull’s. Mnemotechnics use the ‘method of loci’, thinking of the mind as a palace or storehouse, through whose rooms we wander to inspect memories. The more recent technique of ‘mindmapping’ charts thematic relations in a brainstorming process as branch structures on a blackboard. Computer-assisted visualisation methods such as GABEK sketch out entire discourse fields. What is exciting about Lakoff’s (1987) work is that he raises the question of whether our (non-deliberative) cognitive unconscious autonomously uses spatialised ‘mindmaps’ too. Despite the paucity of data, Lakoff’s hypothesis is supported by arguments (see Kimmel, 2002a) from the study of grammar and selective brain impairments (Deane, 1996), and embedding hierarchies between cultural schemas, for example general events, all emotions and the specific emotion of anger (Kövecses, 2000).

Gestalt embedding: how form and content schemas fold into one One of the prime assets of the Ockhamian parsimony of gestalt theory is that it allows for continuity in format between concepts and conceptual architecture. Consider the key tenet of schema theory in cognitive psychology that schematic and more detailed gestalten coexist in the mind at multiple embedded levels of abstraction. If indeed the forms in which the mind organises knowledge are images, then the many polysemous meanings of a lexical item or concepts connected in a larger model can be inserted into a single organising gestalt. This idea can be illustrated by the concept cluster underlying the English preposition ‘over’, expanding on Dewell (1994). Let us assume that semantic content and formal cognition are both based on imagery. Then images that

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stand for lexical items snugly fit into gestalt spaces structured by ‘container’, ‘link’, ‘opposition’, ‘centre–periphery’and so on. Linguistic evidence shows that there are more than 100 different senses of ‘over’, which can be grouped into subclusters. The (historically) most basic and cognitively most prototypical sense of ‘over’ evokes a mental arc schema. Other, less prototypical, schemas have developed out of this and profile the initial, middle or end part of the arc, flatten it out, apply the movement to swivelling objects, modify it into three-dimensional, planar, repetitive, space-covering senses and so on. To explain the organisation of these individual schemas into a larger gestalt of the category ‘over’ we can use spatial elements. The arc schema is placed at the centre of the cluster, denoting prototypicality, from which the derivations spread out via chaining. The prototypical arc stands in a contrastive relation with related mental prototypes, such as ‘above’. Within the ‘over’ cluster itself, some of the derivatives are second-order outcomes of a mere ‘family resemblance’ with the prototype. For example the planar covering sense of ‘over’ in ‘put the hands over the eyes’ no longer has an immediate resemblance to the arc schema, but the two are linked via the intermediate schema underlying expressions such as ‘drape the sheets over’, which retain arc-like features while also being three dimensional (this is the common feature with the planar sense). Figure 5.3 depicts the internal derivation chains and the opposition to the external ‘above’ schema. The spatial format for ordering the individual mental pictures depicted in Figure 5.3 is not merely expository. Rather it is claimed that we actually organise the various conceptual elements through spatial chaining, clustering, opposition, proximity and distance, that is, through embedding in tool-like master models.

Other gestalt-based features of thought and discourse – a brief round-up The strong points of GABEK as a cognitive model lie in its ability to depict clustering, connection and distance relations in order to find discourse nodes and key concepts and to map thematic integration. At the same time, from a cognitive linguistics perspective GABEK has a rather restrictive focus on two or three mental gestalt processes that reflect a simple set of coding and grouping rules (Zelger, 1999, pp. 54–8). It employs the ‘link’ and ‘node’ schemas together to explain how concepts are connected at a local level and how entire arrays are connected into higher-level gestalten. It also uses the ‘zoom in’ transformation to

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ABOVE SCHEMA

Stretch the rope over the yard

The sun rises over the mountains

Hit over the head The plane is flying over

OVER SCHEMA Live over the bridge

Fall over the cliff

The plane is over Baltimore Drape the sheets over

Walk over the field Pour over

Walk all over

Run over Hands over the eyes Put the ring over the finger

Profiled containers: central and independent variants Normal containers: subvariants Broken-lined containers: semiindependent variants Normal lines: intra-group links Arrows: split-off links Broken-lined double arrows: internal opposition relations normal double arrows:external

Figure 5.3 Imagistic category members embedded in an imagistic structuring ‘tool’ Source: Kimmel (2002a), p. 352.

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explain how detailed information is gradually recruited when one starts with a superficial (that is, ‘granular’) understanding, and the ‘zoom out’ to explain how the less detailed features of an array are dropped when wandering to a higher gestalt level with the mental focus. At present a much richer field of gestalt-based discourse organisation is being discussed by linguists, including iconographic reference frames for ideology that use plus/minus grids (for example, for in-group versus out-group) (Hawkins, 2001), hierarchy schemas and ‘belief granters’, which enable preconditions as force images (Johnson, 1987), schemas of causality as a linear force impetus and of events as spaces structured by force chains (Talmy, 1988), narrative or ritual structure schemas as balance, cycle, centredness/high-spot or accumulation (Turner, 1996; Kimmel, 2002a), schemas that organise logical relations in small-scale discourse as mental spaces (for example factual, belief, hypothetical) (Fauconnier and Turner, 2002), changes of mental perspective (Langacker, 1990; Taylor and MacLaury, 1995), foreground–background inversions (Grundy and Jiang, 2001), deliberate gestalt switches in paradoxes (Kimmel, 2002a) and purposeful inversion schemas between two gestalten as irony (Turner, 1996; Kimmel, 2002b). In short there are a number of features of gestalt cognition that GABEK’s current generative rules cannot tease from the data, especially iconicity of the linguistic medium, the on-line dynamics of discourse, tropes and background ideology. As will be shown next, GABEK maps tend to present an overly static, post hoc and global picture of discourse.

Cultural thoughtscapes top-down and bottom-up: themes, scenarios and metaphors If we map out discourse themes as a textbase topped by progressively higher levels of compression that go up to hyperhypergestalten, this invites an obvious question: which level directs the thought and speech of people? In this section it will be cautioned that cultural ‘thoughtscapes’ are multilayered and involve bottom-up (phrase level) as well as top-down (discourse schematic) constraints simultaneously. How these intermesh becomes evident in a case study by Quinn (1991). Based on interviews with American couples on their concept of marriage, Quinn argues for the existence of an underlying integrative level of discourse that she calls a cultural schema. It consists of eight themes: ‘lastingness’ (‘It was stuck together pretty good’), ‘mutual benefit’ (‘That was really something that we got out of the marriage’), ‘sharedness’ (‘We’re together in this’), ‘compatibility’ (‘The best thing


about Bill is that he fits me so well’), ‘effort’ (‘She works harder at our marriage than I do’), ‘difficulties’ (‘That was one of the hard barriers to get over’), ‘success or failure’ (‘The marriage may be doomed’) and ‘risk’ (‘There are so many odds against marriage’). At the same time the metaphorical images used by the interviewees reveal that these do not coincide with the eight themes, but cross-cut them. It is not uncommon for speakers to switch between ‘linkage’, ‘entity’, ‘force’ and ‘process’ images within as little as one sentence when they elaborate on a theme such as ‘sharedness’. Quinn (ibid.) concludes that lower-level cultural metaphors (such as ‘marriage is a shared journey’ based on ‘force’ and ‘process’) are driven by the extended cultural schema circumscribed by the themes represented in the marriage domain. While the point about an integrative level is well taken, it does not follow, as Quinn claims, that local metaphors are merely expository and not cognitive. It is too simple to say that the integrated high-level schema exclusively drives the way in which people reason about a topic. Small-scale metaphors (for example ‘institutions are containers’, ‘purposes are goals’), subscenarios (marriage discourse appropriates the scenarios for anger, arguments, love and so on) and general conceptual models (emotion, person, contract and so on) are autonomous conceptual entities in the American cultural repertory that populate a huge number of other contexts. Consequently their internal structure helps to shape in a bottom-up fashion how something is said and whether two ideas can be linked, for example with reference to their ontology of states versus processes. If there is anything the squabble about the relative weight of individual metaphors and superordinate cultural schemas (Quinn, 1991; Gibbs, 1994; Kövecses, 1999) demonstrates, it is the general pitfalls of reifying notions. While Quinn reads her data as circumscribed by eight internally complex themes (sharing, lastingness, mutual benefit and so on) and the single propositional background schema that organises these, Kövecses (1999) claims that all the themes are already encompassed by the single metaphor ‘marriage is unity’ and its logical entailments. Generally speaking the problem here is as follows. Depending on one’s interpretive ingenuity, it is frequently possible to come up with a highlevel formula. However the mere plausibility of these interpretations does not automatically imply their cognitive reality as conceptual pegs in the minds of interviewees. This has several implications for GABEK gestalten. First, in Quinn’s (1991) hierarchical cognitive architecture, themes are somewhat analogous to GABEK hypergestalten and the overarching cultural schema to

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hyperhypergestalten, while metaphors are situated somewhere between basic texts and gestalten. Second, Quinn’s data confirms the complex interrelations of the subthemes demonstrated by GABEK gestalten trees, as reflected in metaphors cross-cutting themes and themes assembled out of many metaphors. The data does not point to a linear condensation of conceptual material, such that higher levels are simple compressed versions of lower levels, but to a complex interweaving with cultural background knowledge. Third, the reliance on generic formulas at the expense of lower levels is beset by epistemological problems of reification. In principle GABEK is salutary here since it embraces as a core feature the reconstructability of lower levels and represents multiple levels in parallel. However the risky supposition remains that interviewees are in essence conceptually driven by the hyperhypergestalten, by a core idea, and generate the rest of their discourse out of this.

Interpreting hypergestalten: automatic compression How far up do gestalten extend in real thought? GABEK uses the construction of hyperhypergestalten for expository purposes to allow a ‘one-shot’ view of theme ramifications in discourse. This is perfectly sound and a considerable heuristic asset. Yet Zelger (2000) rather prevaricates about the extent to which hypergestalten are mentally real. Psycholinguistics provides some critical guidelines for estimating the weight we should accord to compressed formulas. On the one hand, summarising statements of themes are given a special and salient status in event memory (Guindon and Kintsch, 1984) and thematic abstraction may involve ‘adages that succinctly capture conflicts, planning failures, solutions, and resolutions’ (Graesser et al., 2002, p. 26). On the other hand GABEK, as defined by Zelger (2000, pp. 42 ff.), subscribes to a theory of on-line thought processes that is at fault in assuming that our minds churn out condensed hypergestalten automatically, routinely and instantaneously. Caution is in order. First, when expectations about the context are absent or attention and interest is low, complex gestalten may remain sketchy and incomplete (Kintsch, 1998). Second, above a certain level of complexity gestalten are very difficult to construct, due to memory and attention constraints. Cook’s (1990) study of how people imagine music shows that high-level gestalten of an entire piece of music, such as a tonal arc, are only accessible to experts who have undergone long training and use tools such as musical scores. Only at the scale of tones can a layperson recognise a gestalt within a few seconds. Zelger (2000,


pp. 41 ff.) clearly overestimates the capacity of the cognitive unconscious, when he characterises it as ‘virtually inexhaustible’. In sum, the hypothesis of automatic condensation into high-level gestalten must be discarded for all but familiar or simple data inputs. Overreliance on hypergestalten can incur further costs when interpreting data. Problems result from an analytic bias against the ephemerity and asystematicity of human knowledge systems, as well as the neglect of episodic knowledge. First of all, how people actually draw inferences often cannot be adequately explained from an abstractionoriented perspective: episodic memories take a core function in narrative and reasoning, which are lost in the compression perspective. For example individual episodic memories enter into general accounts of history (Bloch, 1998, p. 126). Based on a comparative study of Orissa in India and the United States, Shweder (1991, pp. 186 ff.) argues that certain cultural styles of argument are fundamentally misrepresented if they are cast in abstract notions because they employ prototypical stories in a ‘let me tell you a story’ mode of moral reasoning. Moreover the durability of representations may be considerably overstated by hypergestalt formulas, as a high processuality bias and a reluctance to accord permanence to fixed core concepts are typical of some cultural discourses (Cannell, 1999). The near-ubiquitous noun bias in methods of analysing discourse might considerably distort other cultural thought styles. Finally, it is questionable whether a high-level representation is needed at all for comprehending discourse (McCoon and Ratcliff, 1992). Judgments may not rely on stable models, but on ad hoc ‘adductive reasoning’ as the situation requires (Boyer, 1994).

Mapping cognitive dissonance, real dilemmas and irony It has been noted that GABEK hypergestalten are well suited for analysing convergent themes from a mass of interviews for the purposes of coherence building (Zelger, 2000, p. 70), for implementing social or organisational policies (Pothas and De Wet, 1999), and as a tool for systematising discussions and conflict resolution. This thematic convergence brings us to a final tricky facet of hypergestalt-based descriptions. Systematicity and logical integration are principally requirements of self-reflexive explanations and may play a lesser role in how people act in everyday contexts. Billig et al. (1988) demonstrate that dilemmas are an inherent feature of belief systems and describe how people come to terms with them. Cognitive anthropology shows that well-placed counter intuitiveness, such as in dragon or ghost stories, and calculated

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clashes with regard to a knowledge background may increase the relevance of a belief (Boyer, 1994; Sperber, 1996). Strauss and Quinn’s Cognitive Theory of Cultural Meaning (1997) is at pains to show that cognitive integration is not the whole story. Centripetal and centrifugal factors coexist in every cognitive system in all of the following parameters: motivational force and durability in the individual, historical durability, ‘thematic’ core notions and sharedness. Interviews with working-class Americans deprived of job opportunities and prospects have shown that the ideological belief in the land of opportunity and a strong personal resentment of experienced injustices can coexist. Cognitive dissonances of this sort occur because knowledge ‘bits’ are stored in different mental sections. Changes in tone, body language and so on tell us when interviewees are shifting between dissonant knowledge spaces – a leap that can happen in mid-sentence and to which qualitative methods should pay close attention. Hence giving conceptual fragmentation its due is a core challenge when developing methods of text or discourse analysis. In relation to GABEK, a possible shortcoming lies in the systematisation and linear condensation principle (Zelger, 2000, p. 69). Whenever the data give rise to conspicuously disconnected hypergestalten, this is an important finding in itself. Occasionally, incompatible hypergestalten may be connected in a hidden trope of what we could loosely call ‘irony’. Without sensitivity to pragmatic discourse cues, levelling clashing features in lower-level gestalten carries the risk of cancelling out effects that might be essential. We are aware of such ironic effects from small-scale discourse. For example Turner (1996, pp. 64–7) speaks of an ‘ironic tension between the image schemas’ (schematic gestalten) when analysing a scene in Shakespeare’s King John. The powerful king, sensing his impending decline, commands the messenger bearing ill news (who is probably kneeling before him) ‘pour down thy weather’, thus ironically likening him, a mere subject, to powers beyond the king – the powers of nature and fate. According to Turner the scene involves a blend of two inverse ‘up-down’ predications. One way to find irony is to look for second-level systematicity in first-level clashes. If local textual features recurrently contrast ‘up’ and ‘down’ schemas, this clash indicates an irony hypergestalt. GABEK could be effective in diagnosing large-scale irony by honing its tools of conceptual clustering. Going beyond the simple criterion of theme overlap, my proposal is for the GABEK coding to be supplemented with the notion of ‘integrated oppositions’. If, for example, a gestalten tree


turns out to have two conspicuously isolated gestalt clusters, we should test these for implicit connections, for example systematic psychological displacement. Methods of finding integrated oppositions should thus be capable of highlighting deliberately polar evaluations in two hypergestalten with reference to a hidden unifying theme.

Notes 1. The influential cognitive psychologist Barsalou calls these ‘perceptual symbol systems’ (Barsalou et al., 1999). 2. It will be shown that gestalten can run the gamut from local to global. The term thus spans slightly different senses: (1) a mental image of a word or sentence, and (2) a ‘one-shot’ representation of a complex discourse field, especially concerning the overlap of propositions. In GABEK the latter idea occupies centre stage. 3. We perceive a sparrow as a typical bird, while a penguin appears to be at the very fringe of the category. A hammer is the quickest to come to mind when subjects are prompted to name a tool, a chair is most readily associated with ‘furniture’ and a violin with ‘instruments’. Sweetser’s (1987) analysis of the English category of ‘lie’ shows that in judgments about what makes a typical lie the most prototypical trait is ‘bad faith’, while ‘intent to deceive’ and ‘factual falsehood’ are less prototypical. 4. Note however, that while compressed gestalten are essential for grasping symbolism, ‘megametaphors’ and themes, their build-up requires repeated, cascading cues and metaphorical priming in order to take effect (Kimmel, 2002b).

References Barsalou, L. and Solomon, K. O. and Wu, L.(1999) ‘Perceptual Simulation in Conceptual Tasks’, in M. K. Hiraga, C. Sinha and S. Wilcox (eds), Cultural, Typological, and Psychological Issues in Cognitive Linguistics (Philadelphia and Amsterdam: John Benjamins), pp. 209–28. Bechtel, W. and Graham, G. (eds) (1998) A Companion to Cognitive Science (Oxford: Oxford University Press). Billig, M., Condor, S., Edwards, D., Gane, M., Middleton, D. and Radley, A. (1988) Ideological Dilemmas (London: Sage). Bloch, M.(1998) How We Think They Think. Anthropological Approaches to Cognition, Memory and Literacy (Boulder, CO: Westview Press). Boyer, P. (1994) The Naturalness of Religious Ideas (Cambridge: Cambridge University Press). Cannell, F. (1999) Power and Intimacy in the Christian Philippines (Cambridge: Cambridge University Press). Cook, N. (1990) Music, Imagination and Culture (Oxford and New York: Oxford University Press). Deane, P. D. (1996) Grammar in Mind and Brain: Explorations in Cognitive Syntax (Berlin and New York: Mouton de Gruyter).

Michael Kimmel 91 Dewell, R. (1994) ‘Over Again: Image Schema Transformations in Semantic Analysis’, Cognitive Linguistics, vol. 5, no. 4, pp. 351–80. Dirven, R., Frank, R. and Ilie, C. (eds) (2001) Language and Ideology, Vol. II: Descriptive Cognitive Approaches (Philadelphia and Amsterdam: John Benjamins). Fauconnier, G. and Turner, M. (2002) The Way We Think. Conceptual Blending and the Mind’s Hidden Complexities (New York: Basic Books). Fillmore, C. (1977) ‘The Need for a Frame Semantics in Linguistics’, in H. Karlgren (ed.), Statistical Methods in Linguistics (Kronberg: Scriptor), pp. 5–29. Freeman, D. (1995) ‘ “Catch[ing] the Nearest Way”: Macbeth and Cognitive Metaphor’, Journal of Pragmatics, vol. 24, pp. 689–708. Gibbs, R. W. (1994) The Poetics of Mind. Figurative Thought, Language, and Understanding (Cambridge: Cambridge University Press). Graesser, A. C., Pomeroy, V. and Craig, S. D.(2002) ‘Psychological and Computational Research on Theme Comprehension’, in M. Louwerse and W. van Peer (eds), Thematics. Interdisciplinary Studies (Philadelphia and Amsterdam: John Benjamins), pp. 19–34. Grundy, P. and Jiang, Y. (2001) ‘Ideological Ground and Relevant Interpretation in a Cognitive Semantics’, in R. Dirven, B. Hawkins and E. Sandikcioglu (eds), Language and Ideology (Philadelphia and Amsterdam: John Benjamins), pp. 107–40. Guindon, R. and Kintsch, W. (1984) ‘Priming Macropropositions: Evidence for the Primacy of Macropropositions in Memory’, Journal of Verbal Learning and Verbal Behavior, vol. 23, pp. 508–18. Hawkins, B. (2001) ‘Ideology, Metaphor and Iconographic Reference’, in R. Dirven, R. Frank and C. Ilie (eds), Language and Ideology, vol. II (Philadelphia and Amsterdam: John Benjamins), pp. 27–50. Johnson, M. (1987) The Body in the Mind. The Bodily Basis of Meaning, Imagination, and Reason (Chicago, Ill.: Chicago University Press). Kimmel, M. (2002a) Metaphor, Imagery, and Culture (Vienna: University of Vienna). Kimmel, M. (2002b) ‘Penetrating Into the Heart of Darkness. How Imageschematic Macrostructures Shape Plot and Megametaphor’ unpublished working paper, Vienna. Kintsch, W. (1998) Comprehension (Cambridge: Cambridge University Press). Kövecses, Z. (1999) ‘Metaphor. Does it Constitute or Reflect Cultural Models?’, in R. Gibbs and G. Steen (eds), Metaphor in Cognitive Linguistics (Philadelphia and Amsterdam: John Benjamins), pp. 167–88. Kövecses, Z. (2000) Metaphor and Emotion. Language, Culture, Body in Human Feeling (Cambridge: Cambridge University Press). Lakoff, G. (1987) Women, Fire and Dangerous Things. What Categories Reveal About the Mind (Chicago, Ill.: Chicago University Press). Lakoff, G. and Johnson, M. (1999) Philosophy in the Flesh (New York: Basic Books). Langacker, R. (1987) Foundations of Cognitive Grammar, vol. 1 (Stanford, CA: Stanford University Press). Langacker, R. (1990) ‘Subjectification’, Cognitive Linguistics, vol. 1, no. 1, pp. 5–38. McCoon, R. and Ratcliff, R. (1992) ‘Inference during Reading’, Psychological Review, vol. 99, pp. 440–66.

92 Gestalt-Based Linguistics and the GABEK Method Pothas, A.-M. and De Wet, A.(1999) ‘Möglichkeiten der Zusammenarbeit in einer Konfliktsituation’, in J. Zelger and M. Maier (eds), GABEK. Verarbeitung und Darstellung von Wissen (Innsbruck, Vienna and Munich: StudienVerlag), pp. 138–51. Quinn, N. (1991) ‘The Cultural Basis of Metaphor’, in J. Fernandez (ed.), Beyond Metaphor. The Theory of Tropes in Anthropology (Stanford, CA: Stanford University Press), pp. 56–93. Rosch, E. (1978) ‘Principles of Categorization’, in E. Rosch and B. Lloyd (eds), Cognition and Categorization (Hillsdale, NJ: Lawrence Erlbaum), pp. 27–48. Sadoski, M. and Paivio, A. (2001) Imagery and text. A dual coding theory of reading and writing (Mahwah, NJ: Lawrence Erlbaum). Shweder, R. (1991) Thinking Through Cultures (Cambridge, Mass.: Harvard University Press). Sperber, D. (1996) Explaining Culture (Oxford: Blackwell). Strauss, C. and Quinn, N. (1997) A Cognitive Theory of Cultural Meaning (Cambridge: Cambridge University Press). Sweetser, E. (1987) ‘The Definition of Lie: An Examination of the Folk Models Underlying a Semantic Prototype’, in D. Holland and N. Quinn (eds), Cultural Models in Language and Thought (Cambridge: Cambridge University Press), pp. 43–66. Talmy, L. (1988) ‘Force Dynamics in Language and Cognition’, Cognitive Science, vol. 12, pp. 49–100. Taylor, J. R. and MacLaury, R. E. (eds) (1995) Language and the Cognitive Construal of the World (Berlin and New York: de Gruyter). Turner, M. (1996) The Literary Mind (Oxford: Oxford University Press). Ungerer, F. and Schmid, H.-J. (1996) An Introduction to Cognitive Linguistics (London and New York: Longman). Van Dijk, T. and Kintsch, W. (1983) Strategies of Discourse Comprehension (New York: Academic Press). Wittgenstein, L. (1953) Philosophical Investigations (Oxford: Blackwell). Zelger, J. (1999) ‘Wissensorganisation durch sprachliche Gestaltbildung im qualitativen Verfahren GABEK’, in J. Zelger and M. Maier (eds), GABEK. Verarbeitung und Darstellung von Wissen (Innsbruck, Vienna and Munich: StudienVerlag), pp. 41–87. Zelger, J. (2000) ‘Parallele und serielle Wissensverarbeitung: Die Simulation von Gesprächen durch GABEK’, in R. Buber and J. Zelger (eds), GABEK II. Zur qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 31–91.

6 Managing Complexity: Knowledge Organisation and Qualitative Software Lyn Richards

Introduction Qualitative research techniques are used when there is a need for a new understanding of a situation. To achieve an understanding of complex situations, the challenge faced by the researcher is to manage that complexity. This chapter shows that all qualitative research requires knowledge organisation. Managing the overwhelming detail of data and putting it into context requires sophisticated storage and access methods, which can assist a project of any size to achieve a better, more rigorous outcome. Software designed for that purpose is useful in qualitative research of any scale. And a toolkit for qualitative analysis is also a toolkit for organising many types of knowledge – what is known a priori and what is discovered during the enquiry, as well as the knowledge derived from search and scrutiny. Researcher and manager share the task of bringing these together in order to reach an understanding of a situation, an issue or a problem. Why should a software program to assist qualitative research be viewed as a toolkit for knowledge organisation? For many qualitative researchers the terms seem incompatible – qualitative work is about intuition, exploration, theorising and discovering, not about managing or organising what is known. But the task is to account well for complex data, and the challenge is to manage that complexity. Qualitative research techniques are used where there is a need for a new understanding of a situation. How is a problem perceived by the people concerned? Why is an innovation unsuccessful or a message rejected? When the goal is to learn from people’s behaviour or the meanings they 93

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place on events, the researcher seeking to learn from the data must avoid pre-emptively reducing it to numbers or quick summaries or quotes. The detail is retained until it is understood and the context is retrieved in order to understand. So these data are complex in form (varied, unpredictably sourced and multilayered) and come in many shapes and sizes. Analysis is similarly complex, and never merely a matter of gathering and summarising everything that is available on a topic. Enter computers as complexity managers. Qualitative software offers researchers a tool to achieve a better understanding. If rich descriptions of individual cases will do, researchers may not need computer tools. Small, neat projects allow rapid access to remembered quotations and attractively illustrated reports. But most qualitative research is not like that. For projects of any scale, researchers have to do justice to complex material, and knowledge management is the first and necessary task. At the extreme, in an international or cross-site project the researcher must thoroughly explore complex texts and non-textual data at both the micro and the macro level. Managing such overwhelming detail and seeing it in context requires sophisticated storage and access methods. And those methods can assist a project of any size to achieve a better, more rigorous outcome. The development of qualitative software has brought an increasing emphasis on the management of different sources of knowledge as software tools have matured. This chapter tracks the progress of software tools for which the present author was part of the development team: the NUD*IST software, and later NVivo.

Developing tools for qualitative computing The story of qualitative software is a short one in evolutionary terms. The first generation of qualitative programs became available in the late 1980s. The first international conference on qualitative computing was held in 1989 (Fielding and Lee, 1991, 1998), and the first on a particular program (NUD*IST) followed ten years later. Today qualitative software is widely used and taken for granted. In a remarkably short time computer techniques not only were accepted but became expected, and computerisation contributed significantly to the spread and influence of qualitative research (Weitzman and Miles, 1995). In their 1994 edition of a 1984 textbook that made mention of computing Miles and Huberman (1994, pp. 43–4) declared that ‘the researcher who does not use software beyond a word processor will be hampered in comparison to those who do’. Their enthusiasm was understandable,

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and the advantages of qualitative software for any qualitative project are evident. Even the simplest of software packages offer support to researchers that is lacking in manual methods, including the ability to manage messy, unstructured data and information about it, to code and reliably retrieve data, to search the text and to ask questions about patterns of coding. But software can offer much more in terms of qualitative tools. For up-to-date information on the range of qualitative software the only source is the Internet. For full details of the claims made of programs and free demonstrations it is necessary to go to the website of the software developer in question; links can be found at http://www.soc. surrey.ac.uk/caqdas/. QSR maintains references and on-line sources, as well as information on the current versions of the software discussed here: see http://www.qsrinternational.com/.

What were we trying to do? The NUD*IST research project commenced at La Trobe University (Melbourne) in 1979, when software was designed by Tom Richards, a computer scientist, to support my research in sociology. The project was regarded as maverick so we were allowed to adopt a crazy acronym (NUD*IST stands for ‘Non-numerical Unstructured Data Indexing Searching and Theorizing’; its latest versions are known simply as N5 and N6). The stimulus was a project that required a longitudinal, community-study research design to link qualitative and quantitative data in a complex comparison. Such data were virtually unmanageable by the conventional methods of filing and retrieval (T. and L. Richards, 1994a; Richards, 1990, 1998). The qualitative researcher’s challenge has a lot in common with that of any professional who faces knowledge organisation. Just as for a doctor, a personnel manager, a chef or a journalist, the problem is to investigate not just everything on one topic but the complex relationships between topics. Experience allows them to locate, and consider together, everything on a case (a patient, an employee, a recipe, a story), and careful management of the data allows them to access by topic all of the clinical records on a disease, the names of all employees who work part-time, all recipes for tuna or everything about a particular politician. But the ability to explore relationships across topics is often limited to recall of particular cases. Understanding patterns or processes in the interrelationship of topics requires another level of analysis. Like all such professionals, qualitative researchers usually need to manage knowledge not just by case or topic but by topic relations,


the ways in which different messages intertwine or overlap. Cases and topics have to be understood as changing and context-bound. So an understanding of a situation or process is never achieved by just one ‘cut’ of the data. Filing by topic is usually the first step, but it is never the last one. In the manual system I developed for handling data the emphasis was on managing data by topic. As categories were created, files were made for them, and as material on a category was located it was copied and the copy was filed appropriately. If a passage contained five categories, it was copied and filed five times. When I wished to write about a topic (say, attitudes towards part-time work) I took out the relevant file and spread out the contents, sorting them into piles in an attempt to find patterns, and making more piles of memos, a rag-bag of bits of paper and yellow stick-its. As each category was created I located it in an index system, using coloured cards to indicate level of category, subcategory and so on, for example, ideas about work/parttime work/preference for part-time work. It took me many years to learn that researchers all over the world had invented the same inadequate system! It was rarely documented in theses or books on methodology, since publicising such a primitive data access process would lower the prestige of qualitative work. Very few methodology texts even mentioned the need to manage data. Even now, few thoroughly describe the manual handling of data (for exceptions see Lofland and Lofland, 1984; Strauss, 1987; Miles and Huberman, 1994). Yet this ancient and undocumented method probably remains the most common way of managing complex qualitative data. Such methods, which we later termed ‘code and retrieve’, were never satisfactory. The researcher needs a filing system that is not only reliable and thorough but also ‘remembers’ and ‘talks’, with drawers that are linked, files that can lead one back to the original data to place extracts in context, contents that are flexible, can be merged or divided and grow with new discoveries; a system that provides the ability to store information on people and sites, to search iteratively and build on searches, and to store discoveries and ideas and access them when required. It was just such a system that we set out to design.

The first task: coding complex data When specialist programs were first developed for qualitative research they offered researchers the promise of efficiently organised knowledge by topic. Embattled by the inability to access data accurately (and the attendant low status of qualitative analysis), researchers welcomed what Renate Tesch (1990) termed ‘organising systems’. Many of the

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early software developers, including us, saw as core to qualitative research the coding of text for retrieval by topic (L. and T. Richards, 1994). The first specialist qualitative programs brought computing to the code-and-retrieve methods. All of them, even the most simple, to some extent freed researchers from clerical, idea-killing drudgery and inadequate access. Unlike filing cabinets, index cards and coloured pens, the computer provided the ability to code text rapidly and in multiple ways, thus pushing back the boundaries of limited storage capacity and memory. The software was able to link texts with large numbers of topics, and retrieve everything on a topic reliably and rapidly. With NUD*IST we did this by creating ‘nodes’ for topics. Coding was simply a matter of selecting the text to be coded and the node to code it at, then NUD*IST stored a reference to that text at the node. Ideas on the topic could be stored there too. Coding could be done automatically by searching the data for a word (such as ‘part-time’). Hence coding became very easy and the barriers to coding were all but removed, promising dramatic advances for the researcher: ●

●

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Coding was likely to be much more thorough, and therefore more relied on during analysis. If coding involved minimal effort, or was even automated, detailed coding would be encouraged. Much more descriptive coding would be possible, supporting pattern finding on, say, demographic variables. Far more material could be handled at a useful level of coding and access would be much easier, so coding was likely to be relied on during more stages of the analysis. Nodes, unlike filing cabinets, would not fill up since they would contain not copies of text but references to it.

NVivo took the fluidity of coding even further. Any characters selected could simply be ‘dragged’ onto the node. It took one click to make a node from a word in the text (‘in vivo’ coding in the grounded theory method). The goal was to make coding feel like thinking aloud. It also addressed another challenge in qualitative projects, the often large amount of ‘factual’, usually demographic, data to be stored. NUD*IST stored and used such data in the tree structure of nodes: given a hierarchy, the values of variables could be easily expressed. If you wished to store information on respondents’ religion, for example, you created nodes for religion/Catholic, religion/Protestant, religion/Muslim and so on and coded the documents at the appropriate nodes. Now you could search for the data on, say, guilt obtained from


interviews with Catholic respondents. The information was available to conduct thorough comparisons, by clearly identifying all the relevant factors, and to support even very large-scale linking of qualitative and quantitative databases (Bazeley, 2002). But for some the ability to store information was a deadly trap – it was possible to develop a massive node system this way, importing hundreds of variables from SPSS and creating many more hundreds of nodes (one for each value of each variable). In NVivo the storage of such information was simplified into an attributes system that was designed to separate the descriptive coding task from the theory-creating task. If the storage and use of such data was important to a project, it was necessary for it to be done very swiftly so that it would not detract from the theorising goal of qualitative research. The attributes of documents or nodes (and therefore of cases) could be handled rather like a spreadsheet and used in any search for or selection of data. The information on the attributes of a case was conveyed by coding, and patterns could be searched for instantly. As computer assisted coding was increasingly adopted by qualitative researches, not surprisingly one consequence was overreliance on coding – elsewhere I have argued that coding became the dominant paradigm of qualitative computing (L. Richards, 2002). Qualitative research, like all knowledge management, required the exploration of data, not merely the placement of data into topics. So coding had to be a means to an end, not an end to enquiry. Therefore the researcher was handed back control over the codes and the results of coding, and was invited to manage the codes and to keep thinking beyond coding.

Managing knowledge by managing codes From the earliest versions of NUD*IST the emphasis was not just on coding but also on managing what coding produced, namely a new sort of knowledge. The topics or categories created during coding represented what was already known about the data or was being explored. When researchers coded on paper they rarely managed topics. The topics stayed on the paper, coloured lines in marked-up text. If topics were recorded on cards, sometimes the emerging categories were managed in index systems. But these tended to be limited and rigid; flexibility always threatened efficiency. A central design principle in NUD*IST was that the topic or category should be seen and explored as an item in its own right. Upon seeing a new topic the researcher would create a node to store it, and perhaps

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references to texts on it or memos about ideas on it. The node would be a container for everything about that topic. So nodes would be much more than mark-ups of text or index cards. Being items apart from the text, nodes could be created, merged, shifted or deleted as understanding of that topic and its relation to other topics developed. The researcher would be able to organise and manage ideas, independently of the text to which they referred. The earliest versions of NUD*IST pioneered the possibility of arranging nodes in the familiar shape of hierarchical index systems. This was hardly a radical departure as creating hierarchical ‘trees’ of categories, subcategories and so on was how personnel managers arranged staff files or chefs arranged recipes. But the response to this facility clearly showed the resistance of traditional qualitative researchers to the idea that they were managing knowledge. Of course ideas could be created out of order, and as a researcher identified relationships they could be inserted into an index. Indeed many qualitative methods required this. With NUD*IST the researcher could create a more general ‘parent’ node when relationships appeared, and move the related subcategories that came under it. Yet the research debate focused on the perceived danger that researchers working with such managed index systems would think ‘top down’. The promised ability (not requirement!) to manage index systems far more flexibly than was possible with a card index system was interpreted by several commentators as forcing researchers to think hierarchically. When N4 introduced an optional free nodes area for categories that were not placed hierarchically, the entire problem was seen as resolved and the concern about ‘forcing’ hierarchical thinking has not been heard again since. Yet the new free nodes area made absolutely no difference to the method, indeed most experienced NUD*IST users had previously made a free nodes area in the index system for idiosyncratic categories. This story is instructive: qualitative researchers are highly conservative in their methods. In the years that followed, successive qualitative software packages developed support for hierarchical indexing and attracted no critical comment at all. Organising codes in hierarchical trees has remained an option with both N4 and NVivo, with both containing areas of the index system for nodes without structure (T. and L. Richards, 1995, 1997). Both toolkits allow the flexible altering of nodes – their definitions, locations and contents of coding. Playing with an index system can be highly informative in terms of identifying trends and topics of the data. A comparison of index systems can provide the basis for discussions


of related projects. Index system management offers considerable advantages: ●

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Coding is more accurate and reliable since categories are easier to locate, and therefore are more consistently explored and used. Projects commonly commence with many dimensions of the research problem, and researchers can express these clearly in nodes before coding begins. Learning from the data, researchers can often develop categories very rapidly. By managing them in index systems, researchers can see what is developing, what is missing and where the current concept fits in with the growing understanding. Teams are able to coordinate the coding categories so that similar questions can be asked in different sites or studies (or differences explored via the merging of projects). Researchers can view the development of concepts and understanding over time, monitoring how the index system grows or shrinks as things start to make sense.

NVivo has provided not only a free nodes area but also new ways of organising and managing complex concepts and documents. Sets of documents or nodes can be made for any temporary or longer-term purpose; researchers can search just this set or look for patterns of differences between sets of data or ideas. Sets and node systems are complementary methods of shaping data. Documents or nodes can be put in any number of sets (an alias is placed there) but a node usually has one logical place in an index tree. Sets, like heaps of paper on the living room floor, may be temporary and tentative, merely data housekeeping processes (but housekeeping is a large part of complexity management!). Or placing documents in sets may be a route to explanation, revealing shapes or patterns in the data. Placing nodes in sets can help to identify groups of concepts that recur together, or be a first step in sorting out the big story from the confusion. In a project with many researchers working in loosely related topic areas, researchers could make sets to gather nodes for discussion, or select one researcher’s indepth interviews for further analysis. They are very easily made and unmade – or finely crafted – depending on the researcher’s goals. In a multisite project, sets could be used to group together all concepts that are common to several sites, or to hold concepts that require discussion. They are fully integrated into all processes – coding with a set, filtering

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a set by codes or attributes of the documents or nodes, or asking questions about a set.

Asking qualitative questions: what the filing cabinet could not do Finding answers to questions about the relationship between ideas and data is the common goal of qualitative research and knowledge organisation, and the ability to engage in qualitative questioning was what made computers immediately appealing to researchers. Qualitative researchers do not usually want to retrieve everything about a topic. For example in a management project on work commitment, to retrieve everything coded according to traditional ideas of work would mean retrieving a large proportion of the data. Rather the task is to explore and ask questions about individuals’ ideas of work. When qualitative computing tools developed a search capacity (very unevenly) they were usually restricted to Boolean searches (‘and’, ‘or’, ‘not’). But it rapidly became clear that qualitative researchers did not require such logically tidy searches for much of their work. Rather they preferred to grope around questions like for example such as: ‘What does part-time work have to do with guilt?’ From the start the NUD*IST software provided a logically complete set of tools to ask questions of coding – boolean, proximity and special context searches. Weitzman and Miles (1995, p. 256) declared that NUD*IST (3rd revision) had ‘unparalleled power’ in respect of asking questions of coding. In N4 this power was increased and the researcher could restrict a search to a single document or coded text. Conversely in NVivo it was softened to support much more exploratory searching and questioning that was about finding a pattern, rather than searching for a logical combination of codes. Meanwhile the software addressed another issue. Qualitative research (and indeed almost all knowledge management) usually requires more than one question, so software had to facilitate iterative questioning. For example the answer to one question – such as ‘What do part-timers say about a worker’s needs?’ – might become part of another question: ‘If they see that workers need motivation, what do they say in that context about their own experience of paid work?’ With NUD*IST and NVivo this was done by the software automatically making a node for the result, and coding there all the text located by the search. The intersection of ‘motivation’ and ‘worker’s needs’ made a node that coded all text coded at both those earlier nodes. Now it could be asked what part-timers said in this context about their own experience.


This code-based facility for iterative searching became (and remains) one of the major means of helping researchers to ‘get up off’ the data. We dubbed this ‘system closure’ (T. and L. Richards, 1994b): Coding or retrieval seems to be basic to such procedures, but researchers also want to hold their growing nets or hierarchies of concepts, evidence links, groupings of ideas and so on, that make up the explanatory structure in an accessible way that will help them see where they have been and give access to the fine grains out of which we build the larger grains. The software system that would help with this would hold not just the data and tools for manipulating it, but also in some sense the growing analysis and explanation system … . [It] would treat the analysis/explanation material added to the database as more data alongside the original textual material … . Methodologically this is known as system closure: Results obtained about the system, analytic techniques used on the system, become part of the system. A hallmark of qualitative social science research (but not of physics) is that the data being researched in a project are closed over its own techniques and results. (Ibid., p. 449). The available searches combined coding, text search and information on the source of data in a wide range of questioning, making it possible to ask practically anything that could be expressed as characters in the text or coding at nodes. Results could be saved as another new node and used to frame the next search. Such a tool kit was perfect for some researchers and some projects, especially ones in which questions were easily asked in terms of codes, but qualitative questioning is often less precise and more tentative. NVivo introduced a search tool that integrated text and coding searches and guided the researcher by framing questions: ●

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‘What do I want to ask?’ Select the sort of search (from any of the text and index searches) and specify how it will be done. ‘Where do I want to look?’ Define the scope of the search by choosing to look at particular documents, material coded by a particular node or nodes or combinations of documents and coding, and then select the scope according to coding or attributes. Define the data you are exploring – ‘Which workers am I looking at here?’ ‘Are there any women?’ ‘What do I want to do with the results of a search?’ Choose whether the results are to be viewed or saved as a node or several nodes, or saved in other ways. Choose where they are placed in the index

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system and the context of the finds. View and visually explore the results for example a matrix of religion by family values will show patterns of coding and a mouse click will enable you to browse the text represented in that cell. Of course these new tools also had unintended effects! Extensive searching power was great if the coding was perfect and users were aware of the fluidity and tentativeness of the categorisation and tagging process that coding represented. Qualitative analysis was often more exploratory than exact, qualitative data could be messy and not easily approached with precise tools. For such data there was a double challenge: searching had to be made more accessible and manageable, and the programs had to facilitate thinking beyond coding and other means of linking.

‘Coding on’ and recontextualising – enter the live node browser It became increasingly evident that many researchers found it hard to view the new coding processes as representing tentative thinking, the formation of ideas. So coding had to be made much more flexible. Code-and-retrieve methods remained essentially about the passage of text they were coding and retrieving. And the ability to conduct rigorous retrieval and searching revealed researchers’ limitations in respect of what to do when coding and how well it was done. Responding to concern that coding narrowed analysis and prevented higher-level connecting, first N4 and then NVivo offered tools to avoid the decontextualising of data. Evidently researchers did not want to be faced with disembodied extracts from their data that they could only revisit one by one. For many important qualitative purposes they needed to see all the text in a category at the same time, to reflect on it as a whole, spread it to a wider context, summarise it and rethink it. NUD*IST 4 introduced interactive browsing of the results of coding-now a signature facility in all QSR software. It has required a substantial shift in approach. The software invites the researcher to browse the data, code and recode it, rethink the coding strategy, return to the data in different documents and revise it. The results of the search are viewed in the node browser, to expand and rethink the search, and eventually recode the data again. Retrieving the results of coding is no longer seen as an end in itself but rather as a step towards finer categorisation and rethinking in a process that we call ‘coding on’. Coding has become an ongoing process of idea generation and the management of ideas, as finer subcategories emerge or related categories are drawn together.


In the node browser, text segments can be seen and considered together, and therefore described or, better, queried and reviewed. The context is always available. The researcher can ask to go to its source or to broaden the extract, selecting exactly what context is coded. Viewing and rethinking of the coded material can (and usually does) result in more coding, more linking and more editing as the researcher moves closer to an understanding. This in turn supports ‘dimensionalising’ – extracting the elements of a concept and refining the various meanings people put on a process.

Beyond coding: editing, linking and modelling As the qualitative programs established code and retrieve as the central mode of handling qualitative data for this genre of software, a few commentators expressed concern about the purposes to which these tools might be put (T. and L. Richards, 1994b; Seidel and Kelle, 1995). Often the research task was not to retrieve everything about a topic, but to reflect, to get away from the data. The task was to see the whole story, not the idiosyncratic episode. Qualitative research normally required an in–out process, researchers had to find ways of zooming in as well as ways of achieving a wide-angle view. The process of comprehending was often a painful journey that progressed from examining piles of seemingly unrelated bits of knowledge to identifying something that was patterned, predictable and flowed (Morse, 1994, p. 30). The goal was to develop ‘the emerging theory so that the theory is applicable to other settings and to other populations to whom the research may be applied’ (ibid., p. 34). The data records had to be linked and projected to wider theoretical vantage points. That was hard to do if the records were static, cut up in the coding process and isolated from contexts beyond the research acts of which they were artefacts. With the design of NVivo the basic architecture of documents and nodes was retained. But now it became possible to ascribe attributes to documents and nodes and to manage them in sets. But the ways of ‘thinking up’ from the data were much more varied. The simplest change was to support the coding of documents in such a way that they were not reduced to relatively static plain text. Most of the early qualitative software assumed relatively homogeneous and unchanging data that was coded for retrieval according to topic. In order to manage the data in this way, data records were usually constrained to text. And despite the development of increasingly rich text processing, qualitative computing remained largely wedded to plain text. Moreover in order for the coding to remain accurate, all programs allowed little freedom to

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change that text. Many of the first-generation programs permitted no change once a document was in the program, so in this respect N4 was radical in enabling editing that did not invalidate coding after the document was in process (Richards, 1997). It became increasingly obvious that the challenge for qualitative data handling (as for all knowledge organisation) was to keep close to data that was continually growing and changing. Access was much harder if documents were allowed to shift as ideas formed and interpretation was challenged. Yet the content and shape of records had to change with the situation and the understanding. NVivo was designed to handle dynamic, compound documents that could be explored in linking and searching processes that relied only optionally on coding. Its design made coding only one way of connecting data and ideas. If data came in a ‘rich text’ format it could go straight into the project without stripping out the meanings expressed in font, colour and so on. Moreover we saw that the now familiar hypertext was unsuited to qualitative research. Hypertext that took one out and away from a document was not sufficient and there was a need for another way of linking. So NVivo provided hyperlinks to other data – without or within the project. The document could contain a direct link to an audio recording (taking the researcher to the sound of an anguished voice, or more pragmatically the section that was not transcribed). The profile could contain links to workplace photos or handwritten memos from a colleague. And all project documents, or nodes, could be linked to each other. NVivo also made it possible to draw and change, collect and layer visual representations of data and ideas in ‘live’ models linked to the data. Layered models could represent a growing understanding of the data or different interpretations. Researchers could link and group them visually, create and change styles of representation, set up layers and compare the model with other models. Documents, nodes and attributes were ‘live’ in the model and researchers could click on their icons to display their data or properties. A model could be saved and developed under a different name, so that a record remained of the project process. And the ideas that came from these processes? Strangely, ways of recording ideas were the Cinderella of qualitative computing. Computer-based ways of thinking and storing ideas (arguably the prime goal of qualitative researchers) lagged way behind the coding and storage of information. Yet in precomputer days very many researchers using various methodologies would have seen memos as the prime output of their work. When what was seen in the data was thought about,


reflected upon and recorded, the result was not coding at a category or the storage of information, but a memo. Memos in manual methodologies were often very disorganised and excruciatingly difficult to find and manage. Yellow stick-its fell off in the bus; scribbled notes were lost between other scribbled notes. Linking these precious records in the project, and linking them to the places in the data where the idea originated, seemed an obvious and fairly achievable goal for programs. First NUD*IST and then NVivo offered a range of ways to annotate text, even when it was already coded, and to store memos about documents or ideas. Nowadays, by means of NVivo annotations can simply be edited into the document, since editing and coding are freely combinable. Or an annotation can be placed ‘behind’ the text with a ‘DataBite’ hyperlink to it. Memos are at last full-status documents, textually rich, editable and codeable, and any number can be linked anywhere in the text, or to any document or node. Software tools for qualitative research or knowledge organisation and management are useful only if they fit the research goals. Software cannot construct understanding: all such tools require skilled craftspeople and wisdom about their use. The emphasis in qualitative research is on the discovery and construction of understanding, not the testing of established theories. Most researchers who know what they are looking for are not normally working qualitatively. The early software tools were better at storing occurrences of expected themes than assisting the emergence of new ones. Those tools enhanced the reliability and sophistication of codebased enquiry, the power tool of theory testing. Theory construction remained much more challenging to software designers (just as it was to researchers). The newly enhanced access strengthened the emphasis on coding, including the linking of qualitative and quantitative coding, and was well suited to many descriptive or pattern-finding methods and survey-based research. But these programs were less suited to field research and theory-construction techniques such as the grounded theory method, or to real-world situations where an understanding was expected to emerge from the data. The construction of theory required different processes and different ways of organising and managing knowledge.

Teamwork and collaboration A final common ground between qualitative research and knowledge organisation is the need to coordinate the work of different researchers and projects. The QSR software offers several means of synthesising the

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work of many researchers. With NUD*IST researchers can seamlessly merge individual projects using a partner software, QSR Merge, which fits together with N4, 5 or 6. It brings together the document and index systems into a new project that contains all the variety of the original projects. This can be a very powerful tool to strongly integrate teamwork. Planned appropriately, team projects can merge data from different sites, apply the same attributes and codes to all data, and use search tools to ask very specific comparative questions. If merging is to be meaningful a sufficient degree of commonality is required. The most successful applications of QSR Merge are by teams whose individual projects address the same research questions in different settings (so the structure of the projects will be very similar) and whose members plan their projects for later merging, agreeing, for example, on a core index system. The merging of projects offers a new sort of coordination. Whatever the choice of software or mode of teamwork, teams can work within a framework of software to develop categories, change definitions, explore what is coded there, code on and store systematic information on attributes, with very marked benefits for the progress of the projects. Moreover the teams are able to challenge each other’s discoveries or build cumulatively upon them. A separate program, Merge for NVivo, offers for a far more sophisticated merging process, including the ability to align projects prior to merging and to assess and change their various parts. Qualitative methodologies are inherently comparative – the creation of new ideas and exploration of their relations are powered by constant comparison. Merging offers different ways of managing complex comparisons, ways that are simply not possible manually. A number of researchers can design and share common ways of handling information and follow common lines of enquiry by: ●

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systematically storing information – projects can share coding and attributes, merging them as appropriate; managing growing ideas, identifying differences and comparing the ideas structure across projects; searching and pattern-finding – searches can compare projects and provide analysis across sites or contexts.

Conclusion This chapter has described only a tiny proportion of the tools offered by QSR software programs. The goal has not been to teach readers about


them, but to add to the discussion on research design issues and making the best use of the best tools. There is always a need for good design and planning. Complexity management and knowledge organisation in qualitative research (as in medicine, management, cooking and journalism), requires sensitivity to the special character and uses of the unruly and the surprising. During the evolution of the leading family of qualitative software tools there has been a tension between strengthening the management of ideas, concepts and data, and increasing flexibility. There has been a progression from linear processes to cyclical and iterative ones, and from directed searches to exploration. All qualitative research has two facets – creativity and organisation. But the latter is often a necessary condition for the former. Theory construction at the micro level will be richer if the project’s parts can be meaningfully linked. Teamwork is successful if agreement on the shaping of data and the use of coding is reached before the data builds up. Comparison is only useful if there is a common research design. In any research (and in any context – the clinic, the corporation, the kitchen or the newspaper office), be it qualitative or quantitative, the value of the outcome will depend on the quality of access to and understanding of what is known and discovered at each stage of analysis.

References Bazeley, P. (2002) ‘Computerized data analysis for mixed methods research’, in A. Tashakkori and C. Teddlie (eds), Handbook of Mixed Methods for the Social and Behavioural Sciences (Thousand Oaks, CA: Sage), pp. 385–422. Fielding, N. and Lee, R. (1991) Using Computers in Qualitative Research (London: Sage). Fielding, N. and Lee, R. (1998) Computer Analysis and Qualitative Research (London: Sage). Lofland, J. and Lofland, L. H. (1984) Analyzing Social Settings (Belmont, CA: Wadsworth). Miles, M. B. (1979) ‘Qualitative data as an attractive nuisance: The problem of analysis’, Administrative Science Quarterly, vol. 24, pp. 590–601. Miles, M. and Huberman, M. (1994) Qualitative Data Analysis: A Sourcebook of New Methods (Thousand Oaks, CA: Sage). Morse, J. M. (1994) ‘Emerging from the data?: the cognitive processes in qualitative inquiry’, in J. M. Morse (ed.), Critical Issues in Qualitative Research Methods (Thousand Oaks, CA: Sage), pp. 23–42. Richards, L. (1990) Nobody’s Home: Dreams and Realities in a New Suburb (Melbourne: Oxford University Press).

Lyn Richards 109 Richards, L. (1995) ‘Transition Work! Reflections on a Three-Year Women’s Health Project’, in R. W. Burgess (ed.), Computing and Qualitative Analysis (London: JAI Press), pp. 105–40. Richards, L. (1997) ‘User’s Mistake as Developer’s Challenge’, Qualitative Health Research, vol. 7, no. 3, pp. 406–16. Richards, L. (1998) ‘Closeness to Data: the Changing Goals of Qualitative Data Handling’, Qualitative Health Research, vol. 8, no. 3, pp. 319–28. Richards, L. (2002) ‘Qualitative computing – a methods revolution?’, International Journal of Social Research Methodology, vol. 5, no. 3, pp. 263–76. Richards, L. and Richards, T. (1994) ‘From Filing Cabinet to Computer’, in A. Bryman and R. G. Burgess (eds), Analyzing Qualitative Data (London: Routledge), pp. 146–72. Richards, T. (2002) ‘An intellectual history of NUD*IST and NVivo’, International Journal of Social Research Methodology, vol. 5, no. 3, pp. 199–214. Richards, T. and Richards, L. (1994a) ‘Creativity in Social Sciences: the computer enhancement of qualitative data analysis’, in T. Dartnall (ed.), Artificial Intelligence and Creativity: An Interdisciplinary Approach (Dordrecht: Kluwer), pp. 365–83. Richards, T. and Richards, L. (1994b) ‘Using Computers in Qualitative Research’, in N. K. Denzin and Y. S. Lincoln (eds), Handbook of Qualitative Research (Thousand Oaks, CA: Sage), pp. 445–62. Richards, T. and Richards, L. (1995) ‘Using Hierarchical Categories in Qualitative Data Analysis’, in U. Kelle (ed.), Computer-Aided Qualitative Data Analysis: Theory, Methods and Practice (London: Sage), pp. 62–8. Richards, T. and Richards, L. (1997) ‘Qualitative Software and Analysis Structures: Solving Problems or Creating Them?’, in R. Klar and O. Opitz (eds), Classification and Knowledge Organization (Freiburg: German Classification Society), pp. 390–8. Seidel, J. and Kelle, U. (1995) ‘Different Functions of Coding in the Analysis of Textual Data’, in U. Kelle (ed.), Computer Aided Qualitative Data Analysis: Theory, Methods and Practice (London: Sage), pp. 52–61. Strauss, A. L. (1987) Qualitative Analysis for Social Scientists (Cambridge: Cambridge University Press). Tesch, R. (1990) Qualitative Research: Analysis Types and Software Tools (Basingstoke: Falmer). Weitzman, E. and Miles, M. B. (1995) Computer Programs for Qualitative Data Analysis (Newbury Park, CA: Sage).


Part II Case Studies


7 Consumers’ Knowledge of Controlled Ventilation Systems Johannes Gadner and Renate Buber

Introduction When dealing with product development and innovative advancements in ecological technologies, as well as correlating business interests, there is a need to integrate expert and everyday knowledge in planning and decision-making strategies. From their experience of daily routines in their living and working environments, consumers know about specific aspects (standards, strengths, weaknesses and so on) of the products and technologies they use (Herstatt and von Hippel, 1992). In the case of energy-efficient buildings and ventilation technologies, the knowledge and experiences of the users of these products can be of help in future innovations and improvements (Bailetti and Litva, 1995; Rohracher and Tritthart, 1997). Moreover knowledge of consumers’ attitudes is of great value when defining target-group marketing strategies (market segmentation and market communication) (Kotler, 2000; Kroeber-Riel and Weinberg, 2003). The aim of this chapter is to investigate attitudes towards energyefficient buildings by presenting a case study of consumers’ knowledge of the pros and cons of energy-efficient buildings with controlled ventilation systems. The following questions will be addressed: ●

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Which factors are decisive in people’s decision to purchase energyefficient buildings with controlled ventilation systems? Which factors are obstacles to the purchase from the customers’ point of view? How can the findings – the consumers’ and the experts’ knowledge – support companies’ marketing planning? 113

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Besides being useful for marketing managers, the study’s findings can help to integrate customer and user interests into the process of improving existing ecological technologies. They could also advance our understanding of consumer motivation and behaviour. This is especially important in respect of a long-term strategy towards solving the complex problems involved in local and regional sustainable development. The findings of the study can be used to make technical improvements and to develop a general framework that can be adapted to individual companies’ marketing strategies.

Consumers’ attitudes towards energy-efficient building components Despite consumers’ product knowledge and its influence on consumer behaviour, as defined by Herstatt and von Hippel (1992), in the marketing and consumer behaviour literature attitudes are one of the most important constructs when describing and explaining consumer behaviour. Item batteries, questionnaires and quantitative models to measure attitudes still dominate applied research (Kroeber-Riel and Weinberg, 2003, pp. 196 ff.). For the research reported here a qualitative approach was chosen because there was a lack of knowledge on the motives, opinions and attitudes of users and non-users of energy-efficient buildings. The dominance of technical information in the marketing of the products demanded an exploratory approach to investigating individual consumers’ experiences with energy-efficient buildings that exceeded the technical dimension of consumers’ product knowledge. The main aim of the project – to obtain results that would enable implementation recommendations to be deduced – also played a part in the methodological decision to gain insights into consumers’ thoughts, emotions and arguments rather than to confirm hypotheses.1 Controlled ventilation systems in residential buildings are still uncommon and tend to be viewed sceptically by potential consumers. Very often this is due to the fact that people do not know the difference between a controlled ventilation system and air conditioning, and therefore tend to dwell on the negative aspects of air-conditioned buildings, such as the hermetically sealed and chilly environment. Despite this negative image and consumers’ lack of detailed information on the product, due to the increasing construction of low-energy buildings the market for controlled ventilation systems is growing. Consequently consumers are gaining experience of these systems and the various ways they can be integrated into heating systems. There are those who have considered

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buying a controlled ventilation system but eventually decided against it. A detailed analysis of the arguments of these people and the experiences of users of the systems, their reasons for installing them and their attitudes towards them provides a useful starting point for a more focused marketing concept for such systems.

Methodology The interviews The participants in the study were divided into two groups: lead users, as defined by Herstatt and von Hippel (1992, pp. 214 f.), and non-users. Each group had special experience with the products and therefore potentially relevant knowledge for future marketing strategies. Fortythree people were interviewed, of whom 23 were lead users (eight lived in one-family houses and 15 in apartments) and 20 were non-users who had been interested in buying a low-energy dwelling but had eventually decided against it. Both groups were interviewed about their specific experiences. The interviews were guided by a manual containing open-ended questions that would allow the interviewees to speak about the research issue in their own words and to talk about incidents and experiences of special relevance to them. This would enable us to learn as much as possible about the interviewees’ product knowledge, personal experiences, specific problems, needs and desires in relation to the product in particular and living issues in general (for the design of qualitative interviews see for example Rubin and Rubin, 1995, pp. 42 ff.). The open-ended questions would also help us to identify ‘tacit knowledge’ (Polanyi, 1969), that is, knowledge gained through and applied in practice but rarely communicated directly. Appointments for the interviews were made on the phone. The interviews lasted between 20 minutes and 1.5 hours and were conducted in the interviewees’ homes.

Data analysis The interviews were tape-recorded and then transcribed. The data were analysed using the GABEK method and its computer application WinRelan (Zelger, 2000; Buber and Kraler, 2000). Two principal features of WinRelan were used to analyse the data: network graphs and evaluation profiles. Network graphs provide an overview of topics (represented as concepts) that are related to each other in terms of their contents. It is possible to choose any topic from the verbal data and investigate its

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connections with other topics. Evaluation profiles reveal the positive and negative aspects of a product from the users’ point of view.

Results Network graphs The following network graphs summaries the lead users’ and non-users’ knowledge about the most important feature of energy-efficient buildings – the controlled ventilation system.

Lead users’ product knowledge Figure 7.1 shows the areas of product-related knowledge on controlled ventilation systems articulated by the occupants of one-family houses. The fact that controlled ventilation systems provided a constant supply of fresh air without having to open the windows was generally viewed as pleasant, particularly during the summer. Having such a system was seen as offering many advantages compared with living in a traditionally heated and ventilated building, including energy saving. Figure 7.2 shows that the occupants of apartments were more ambivalent about their controlled ventilation systems. They too found it pleasant to have fresh air without opening the windows, but the system was seen as too expensive in relation to what was saved on energy. There were also perceived problems with the control of the system, including the possibility of switching it off, and uncertainty about its basic functioning. Among this group of users there seemed to be a complete lack of information on the product in general.

Open windows Summer

Installation CVS

Fresh air

Pleasant Energy saving

Figure 7.1 Product-related knowledge of the occupants of one-family houses on controlled ventilation systems (CVS)


Ignorance

Open windows

Information

Switch off

Control CVS

Pleasant

Function

Fresh air

Expensive Heating costs

Figure 7.2 Product-related knowledge of the occupants of apartments on controlled ventilation systems (CVS)

Non-users’ attitudes towards controlled ventilation systems This group had doubts about controlled ventilation systems in terms of draught, noise, hygiene and the layout of the ducts. The fact that they found it very difficult to obtain comprehensive information added to their doubts. Such systems were also seen as being too expensive – as one interviewee put it, ‘Controlled ventilation systems simply do not pay.’ A few of the interviewees pointed out that it was cheaper to open the windows. All in all, controlled ventilation systems were evaluated very ambivalently. They might have benefits in terms of fresh air, energy saving and the reduction of outside noise and humidity, but they put the user under pressure and gave a feeling of dependency on technology.

Evaluation profiles The following subsections analyse the verbal data in order to obtain more detailed findings. It is important to note here that the interviewees all expressed their opinions freely and without being prompted. Knowledge and experience always lead to some sort of evaluation, and the data analysis revealed both implicit and explicit evaluations.

Lead users’ product evaluation Tables 7.1 to 7.4 provide detailed overviews of the arguments for and against controlled ventilation systems. As can be seen from Table 7.1 the occupants of one-family houses were very positive about the

118 Consumer’s Knowledge of Ventilation Systems Table 7.1 One-family households’ positive evaluations Subject

Evaluations

Room atmosphere

Good Improved Pleasant

Room temperature

Comfortably cool in summer Comfortably warm in winter Constant, steady Easy to control

Closed windows

No unpleasant smells from outside Permanently pleasant fresh air Promotes good sleep

Air quality

Good (even with guests in house) Improved Pleasant

Air quality in bedroom

Very good

Living quality

Comfortable Improved Pleasant

Smells (cooking, smoking, bedroom)

No extractor fans needed No problem Opening of windows not necessary for airing Unpleasant smells disappear quickly or do not become evident

Saving on electricity

Advantage

Energy consumption

Comfortable, i.e. saving energy is primarily seen as luxury, financial aspects are of secondary importance only Reduced

Financial considerations Living comfortably is more important Insects

Entry prevented (air filter)

Air exchange Price

Permanent Reasonable

system’s contribution to a positive and pleasant room atmosphere. This was ensured by the easily controlled constant temperature and the permanent supply of fresh air. In fact most of the positive evaluations were connected to comfort and quality of life. Interestingly the reduction of energy consumption seemed to be less important than is assumed by manufacturers, trading companies and marketers.


Table 7.2 shows the same people’s negative evaluations of the system. As can be seen, there were three main criticisms: (1) the system was too expensive, (2) there was too little information on the technical and other details of the system, and (3) the system was too noisy. Table 7.3 shows the positive evaluations made by the occupants of apartments. This group particularly appreciated the fact that open windows were not necessary for ventilation and fresh air, but this benefit was far outweighed by the list of criticisms (Table 7.4). The main complaints were to do with (1) individual regulation of the heating and Table 7.2 One-family households’ negative evaluations Subject

Evaluations

Cost of system

No knowledge/information Obstacles for distribution of product Relatively high, too expensive, very expensive Should be less

Information on the technology and on low energy consumption

Insufficient, not known Scepticism, suspicion is increased by insufficient information Should be more extensive

Functioning of product

Information should be improved and made public Not known

Advertising

Adapt for architects, builders, clients and building owners Should be focused on the demands of target groups Should be intensified

Architects

Should be better informed about costs Should offer and sell the product

Builders

Should offer and sell the product

Clients and building owners

Should be informed about controlled ventilation systems by architects and builders

Noise from air supply

Sometimes a problem

Permanent noise

A problem

Ventilators

Audible Develop noises

Materials (metal ducts)

Cheap

Financial factors

Impediment to installation Obstacle to the expansion of environmentally friendly buildings

Heat insulation

Expensive

120 Consumer’s Knowledge of Ventilation Systems Table 7.3 Apartment dwellers’ positive evaluation Subject

Evaluations

Ventilation during winter

Constant supply of warm fresh air Less need to ventilate by opening windows

Air supply in Bathroom, WC

Advantageous due to immediate reduction of smells

No need for window in bathroom

Useful

Humidity

No mould Reduced

Maintenance

Almost free of maintenance Service scarcely necessary

the inability to switch the system on or off, (2) the noisiness of the system and the spread of odours from other apartments, and (3) the lack of information on technical and other details. As can be seen from the above discussion, there were considerable differences between the views of the two groups of users. In total, 61.4 per cent of the positive views on the system were expressed by the occupants of one-family houses. In contrast 74.7 per cent of the negative views came from the apartment dwellers (Rohracher et al., 2001, p. 99 ff.).

Implications for the marketing of controlled ventilation systems Greater acceptance of controlled ventilation systems will depend on a large number of technical and personal factors that could easily be addressed by the manufacturers, including the necessity of considering individual customers’ needs. If these factors were addressed during the product development and marketing planning processes, acceptance of the systems and of sales could be increased. This section suggests ways of addressing the lack of concordance between consumers’ perceptions and evaluations of the product and companies’ marketing tactics, based on the findings of our study. All the evaluations relating to price and costs were negative. For manufacturers, retailers and other groups with an interest in increasing the distribution of the technology it is essential to compose positively worded cost–benefit statements that can be easily comprehended by

Johannes Gadner and Renate Buber 121 Table 7.4 Apartment dwellers’ negative evaluations Subject

Evaluations

Regulation/control of system

Improvement necessary Switching on/off not possible Unpleasant feeling of depending on technology

Room temperature

Not adjustable Problematic Too warm in bedroom

Noise

Unsatisfactory Disturbing

Smells from neighbours

A problem Due to faulty building work Reduces quality of life Unpleasant Very annoying

Information (technical)

Insufficient Needs to be more extensive No informal communication between tenants and landlords Rental contract does not contain information

Advertising

Should be intensified Should be focused on the demands of target groups

Cost of system

Lack of information Obstacle to distribution of product Obstacle to the expansion of environmentally friendly buildings

Quality of atmosphere

Very unpleasant for contact lense wearers Causes coughing in winter Humidifier necessary Very dry

Financial factors

Product does not pay Too expensive

Traditional heating

Costs the same as controlled ventilation system

potential buyers. These should include: ● ●

●

Statements on the costs and benefits for various target groups. Statements on the financial advantages of using a controlled ventilation system. Brochures explaining why consumers should invest in controlled ventilation systems rather than other systems or commodities.

122 Consumer’s Knowledge of Ventilation Systems ●

Uncoupling the perception of product from price.

With regard to the findings about the product itself, it is clear that there is a need to: ●

●

●

●

●

●

●

●

Explain that a controlled ventilation system can fulfil the basic need for heating. Redefine the characteristics of the product’s core functions in relation to the information consumers need. Offer alternative arrangements of the technical equipment that take aesthetic considerations into account. Explain the product’s symbolic values more clearly (for example environmental awareness, future trends, buying decisions, living as a leisure activity, cosiness, well-being), stress the ‘living quality’ in contrast to technological aspects. Solve the perceived hygiene problem by offering a cleaning service with the system or install the system without the geothermal heat exchange. Sell the benefits of the product (for example clean and fresh air, heating). Offer specially arranged solutions for the individual consumer’s needs and wishes. Develop a mark of quality (awarded by independent organisations).

Distribution and installation issues were also raised by the interviewees and the findings show that the following matters should be addressed: ●

●

●

Training should be provided to electricians and ventilation technicians in respect of product knowledge, information issues and communication. All persons and institutions involved in controlled ventilation systems should coordinate their activities and speak to the public with one voice. There is a need for show houses where the public and prospective purchasers can see the system at work.

With regard to communication and promotion, from a marketer’s point of view it is alarming how negatively the interviewees spoke of the communicational and promotional abilities of retailers, electricians, architects, builders and so on. The following list offers some practical measures for marketing managers to consider when planning their campaigns. ●

Increase the quality and amount of advertising and promotional features in the mass media.

Johannes Gadner and Renate Buber 123 ●

● ● ● ● ●

●

Accentuate factors such as consumer satisfaction and well-being, and socially and environmentally aware lifestyles. Intensify the use of personal selling and promotions. Plan public relations systematically focused on target groups. Include satisfied users as examples in promotional activities. Obtain the endorsement of well-known personalities. Provide architects, builders, suppliers and so on with informational material and special training courses on how to address the needs, wants and doubts of prospective customers. Redefine terms that denote draughts, ‘including put air in the system’, ‘draw air off the system’, ‘air stream’ and so on.

Summary In our study, lead users living in one-family houses mainly had a positive view of controlled ventilation systems. Non-technical aspects of the systems were generally viewed positively, including quality of life, good air quality, comfort and well-being. Non-users mostly spoke negatively about the systems, probably due to their lack of information on and experience of the product. In their opinion the technology would have a negative effect on primary human needs such as cosy living and good health. Such views were reflected in the opinions expressed by lead users who lived in apartments, but not in the opinions of those who lived in one-family houses. Marketing managers should start to consider the needs and wants of individual potential customers, and to take their subjective wishes, emotions and convictions into account. The results of this study were used to develop a follow-up project aiming to produce an information folder and to provide stakeholders – manufacturers, architects, trading companies, consumers – with product relevant knowledge.

Note 1. This research was part of the project reported in Rohracher et al. 2001.

References Bailetti, A. and Litva, P. (1995) ‘Integrating consumer requirements into product designs’, Journal of Product Innovation Management, vol. 12, no. 3, pp. 3–15. Buber, R. and Kraler, C. (2000) ‘How GABEK and WinRelan Support Qualitative Research’, in R. Buber and J. Zelger (eds), GABEK II. Zur Qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 111–37.

124 Consumer’s Knowledge of Ventilation Systems Herstatt, C. and von Hippel, E. (1992) ‘From Experience: Developing New Product Concepts Via the Lead User Method. A Case Study in a “Low-Tech” field’, Journal of Product Innovation Management, vol. 9, no. 3, pp. 213–21. Kotler, P. (2000) Marketing Management (Upper Saddle River, NJ: Prentice-Hall). Kroeber-Riel, W. and Weinberg, P. (2003) Konsumentenverhalten (Munich: Vahlen). Polanyi, M. (1969) Knowing and Being (Chicago, Ill.: University of Chicago Press). Rohracher, H., Kukovetz, B., Ornetzeder, M., Zelger, T., Enzenberger, G., Gadner, J., Zelger, J. and Buber, R. (2001) Endbericht zum Projekt Akzeptanzverbesserung von Niedrigenergiehaus-Komponenten als wechselseitiger Lernproze␤ von Herstellern und AnwenderInnen (Vienna: Bundesministerium für Verkehr Innovation und Technologie). Rohracher, H. and Tritthart, W. (1997) ‘Energy Efficiency: Regulation vs. Integration of Users’, in LandesEnergieVerein Steiermark (ed.), Energy Efficiency in Public and Municipal Buildings (Graz: LandesEnergieVerein Steiermark), pp. 123–30. Rubin, H. J. and Rubin, I. S. (1995) Qualitative Interviewing. The Art of Hearing Data (Thousand Oaks, London and New Dehli: Sage). Zelger, J. (2000) ‘Twelve Steps of GABEKWinRelan’, R. Buber and J. Zelger (eds), GABEK II: Zur Qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 205–20.

8 The Process of Web Personalisation: A Framework to Determine Appropriate Personalisation Systems Sonja Grabner-Kräuter

Introduction Personalised marketing is aimed at meeting customers’ needs in respect of information and product offerings, and thus at building positive longterm relationships between the firm and its customers. The customerspecific orientation of marketing activities requires extensive information on customers, including their preferences and behaviour. Individualised strategies and measures have been used for decades in traditional industrial marketing, based primarily on personal interactions between supplier and customer. Due to the development of new information and communication technologies, personalised or individualised marketing techniques can now be used even in mass markets. Because of its particular characteristics (interactivity, multimedia ability, the availability of information at any time and anywhere) the Internet is an especially suitable vehicle for personalisation strategies. Comprehensive information on customers can be gathered on-line and analysed easily at a reasonable cost. Based on a thorough interpretation of this information – combined with modular value-creation processes in business firms – customerspecific products and services offer a promising alternative for companies whose customers have tended to be rather anonymous. The intense competition among Internet-based businesses to acquire new customers and retain existing ones has made Web personalisation an essential part of e-commerce. Many companies see Web personalisation as an antidote to the ‘commoditisation’ that plagues their industries and as a magic formula that will help to reverse eroding customer loyalty (Rangaswamy and Balakrishnan, 2002). The opportunities 125

126 Determining Appropriate Web Personalisation Systems

and risks involved in personalised on-line marketing are discussed in the literature emanating from two main research branches: the customeroriented literature on one-to-one marketing and relationship marketing, and the more technology-oriented literature on the methods, support tools and applications of Web mining and Web personalisation, where questions to do with customer orientation and satisfaction play only a minor role. Because of their different foci, the findings of the two research camps are kept separate and are not usually taken up by researchers in the other field. This chapter attempts to connect the fundamental marketing principles of on-line personalisation with the prerequisites and opportunities of applying Web personalisation techniques and systems. In the following section the basic principles of personalised on-line marketing will be summarised. Personalisation initiatives require both quantitative and qualitative research efforts. In the early stage of personalisation strategy development, qualitative data analysis plays a dominant role because qualitative research methods such as in-depth interviews with individuals and focus groups are the best means of exploring and understanding potential consumers’ requirements and the added value that personalisation could offer them. However the focus here is on Web mining and thus on the quantitative side. Based on a description of the various Web mining categories the process of content and usage-based Web personalisation is analysed in the second section. This is followed by an overview of personalisation systems and the factors that have to be considered when deciding on an appropriate system.

Principles of personalised on-line marketing Personalised on-line marketing is the practice of tailoring information, services, products and/or promotions to an individual Web user. The customisation of Web content and services should result in user-specific recommendations based on the past behaviour of the user or other users with similar customer profiles (Vassiliou et al., 2002). Personalised online marketing follows the basic principles of marketing approaches that demand a strict focus on individual customers and their specific requirements. The customercentric view is reflected in marketing concepts such as one-to-one marketing (Peppers and Rogers, 1997) and customer relationship marketing or management (Eggert, 2001; Palmer, 2002). Viewed as a philosophy, personalised marketing shares with the traditional concept of marketing a focus on satisfying customers effectively and profitably. Similarly the common ground between relationship marketing and one-to-one marketing is in their focus on customer orientation, customer retention and customer dialogue (Grönroos,

Sonja Grabner-Kräuter

127

1994; Morgan and Hunt, 1994; Bagozzi, 1995; Peppers and Rogers, 1997; Parvatiyar and Sheth, 2000; Eggert and Fassott, 2001). Businesses have to obtain reliable information on their customers and build up a customer database that is suitable for treating customers on a relationship basis. The best way to identify personalised customer experiences is to establish individual learning relationships between the company and its customers (Figure 8.1). Hence personalisation strategies are concerned with the creation, development and enhancement of individual relationships with carefully targeted customers and customer groups (Payne, 2001). The customercentric focus combined with preferential treatment of selected customers should maximise the total customer life-time value, resulting in profitable customer relationships in the long term, both for the company and its customers. The development of modern information and communication technologies, and especially the Internet, has allowed the paradigm of individual customer relationships to be extended to situations in which there are either a few or many sellers and perhaps possibly millions of buyers. The Web is a powerful vehicle for direct and personalised communication between companies and their customers. It has multiplied the amount of available information and given firms the opportunity to respond effectively and efficiently to their customers’ needs. A user’s every movement constitutes a piece of marketing information that can be matched to numerous databases to yield infinite permutations of consumer profiles (Prabhaker, 2000). From a strategic management or marketing perspective, any personalisation initiative must begin with an understanding of what

Increased purchasing (repurchasing, cross selling, etc.) Relevant customer information Company

Loyalty

Learning relationship Personalised information, products, services, promotion, navigation, etc.

Added value

Figure 8.1 The marketing cycle of personalisation

Customer


personalisation means to the company. When developing and directing a company’s personalisation strategy, quantitative and qualitative approaches to marketing research should be combined. Qualitative methods such as the use of focus groups and in-depth interviews with individuals can provide useful information that can be used to assess how an organisation’s existing and prospective customers are likely to interact with the company through the Internet. Qualitative methods are a good means of discovering whether customers would be willing to buy on-line, what would compel them to do so, what they perceive as having added value for them, how they judge the quality and value of available products, and what are the ‘must have’/unacceptable features and characteristics of the product in question. The data collected will be an important element in the company’s overall Internet strategy as well as in its decision on whether and how to implement Web personalisation (Vassiliou et al., 2002). From a functional or instrumental view, Web personalisation can be defined as any action that tailors the Web experience to a particular user or set of users (Mobasher et al., 2000b). Web personalisation thus encompasses a continuum of techniques and technologies to anticipate visitors’ needs and provide them with a more valuable experience of a company’s Web site. Added value for the customer comes from the provision of personally relevant content, functionality and/or navigation. In fact Internet users may not notice any obvious signs of personalisation but will simply find that the site efficiently meets their needs (Chadsey, 2000). The customer’s experience can be something as casual as browsing a Web site, or as (economically more) significant as trading stocks or purchasing books or flight tickets. The actions of the on-line firm can range from simply making the presentation more pleasing, to anticipating the needs of a user and providing customised information, products and services. The principal tasks in Web personalisation include the modelling of Web objects (Web sites or products) and subjects (users), the categorisation of objects and subjects, matching between and across objects and/or subjects, and determination of the set of actions to be recommended for personalisation.

Web mining as a prerequisite for personalised on-line marketing Web mining categories Web mining can be broadly defined as the discovery and analysis of useful information from the World Wide Web (Cooley et al., 1997).


129

Similarly Kosala and Blockeel (2000) define it as the overall process of discovering potentially useful and previously unknown information or knowledge from the Web. In Web mining, data can be collected from the server side, the client side and proxy servers, or obtained from an organisation’s database, which contains business data and/or consolidated Web data (Srivastava et al., 2000). Web mining may be categorised into three areas of interest based on which part of the Web is being mined (Kosala and Blockeel, 2000): Web content mining, Web structure mining and Web usage mining (Figure 8.2). Web content mining consists of obtaining useful information from the Web contents, which can encompass a very broad range of textual, graphic, audio and video data, metadata and hyperlinks. The purpose of Web content mining is mainly to assist or improve information finding or filtering information to users, usually based on inferred or solicited user profiles. Web structure mining refers to the process of deducing information from the Web texture and links between references and referents. The aim is to discover the model underlying the link structures on the Web and thus to describe the organisation of the content. Intrapage structure information includes the arrangement of various HTML or XML tags within a given page (Srivastava et al., 2000). The most important kind of interpage structure information is hyperlinks connecting one page to another.

Web mining

Web content mining

The automatic process of extracting information from the content or description of documents gained from sources across the WWW • Web page content mining • Search result mining

Web structure mining

The process of deducing information from the WWW texture and links between references and referents • Hyperlink structure analysis (cross-linkage analysis) • Tracing web structure artefacts • Collection of information on a document's popularity, importance and richness

Figure 8.2 A taxonomy of Web mining Source: Adapted from Cooley et al., (1997) and Zaïane (1999).

Web usage mining

The process of discovering user browsing and access patterns from web servers • Preprocessing • Transaction identification • Pattern discovery tools • Pattern analysis tools


Web usage mining may be defined as the process of discovering user access patterns from Web servers. It comprises general access pattern tracking and customised usage tracking (Zaïane, 1999). The aim to make sense of the data generated by Web users’ sessions or behaviours, and the focus is on techniques to predict user behaviour when the user interacts with the Web. While Web content and structure mining directed at collecting ‘real’ or primary data available on the Web, Web usage mining involves the collection of secondary data derived from users’ interactions with the Web, such as IP addresses, page references, date and time of access and so on (Kosala and Blockeel, 2000). Web usage data include data from Web server access logs, proxy server logs, browser logs, cookies, user queries, registration data, user sessions or transactions, existing user profiles, bookmark data, mouse clicks and scrolls, and any other data that results from interactions. Before analysing the process of Web personalisation it should be emphasised that the distinctions between the above categories of Web mining are not clear-cut. In practice the three categories can be used in isolation or combined in an application, especially in the case of Web content and structure mining.

The process of content- and usage-based Web personalisation The overall process of content- and usage-based Web personalisation may be divided into two components (Mobasher et al., 2000a): an off-line component and an on-line one (Figure 8.3). The off-line process consists of data preparation and specific Web mining tasks, such as the discovery of usage profiles. In the on-line process the recommendation system considers the active server session in conjunction with the discovered patterns and profiles to provide personalised content. The effectiveness of personalisation actions can be enhanced by incorporating content profiles into the recommendation process, resulting in Web personalisation based on usage and content mining (Mobasher et al., 2000b). Data preparation consists of data cleaning, session identification, page-view identification, episode identification and support filtering.1 This results in aggregate structures that contain the preprocessed usage and content data to be used in the mining stage. The establishment of usage profiles is the result of a variety of data mining algorithms such as the discovery of association rules or sequential patterns, clustering, classification and dependency modelling. The critical step is the derivation of good-quality and useful (that is, actionable) aggregate profiles from these patterns (Mobasher et al., 2000b). It should be noted that there is an inherent conflict between the information needs of the


131

Figure 8.3 The process of Web personalisation Source: Adapted from Mobasher et al., 2000b.

analysts or site administrators, who require detailed usage data, and the privacy of Internet users, who want as little data as possible to be collected (Cooley et al., 1997).2 The revealed patterns are then used in the on-line process to provide personalised content to users based on their current navigational activity. The recommendation engine is the on-line component of a Web personalisation system. It controls the on-line process and considers the active server session of the Internet user in conjunction with the revealed patterns to provide personalised content. Again, a variety of techniques can be used to match the active user session with one or more of the usage profiles. The task of the recommendation or personalisation system is to provide a recommendation set for the current (active) user session, consisting of the objects (recommended links or products, text, banners, graphics and so on) that most closely match the current user profile (Mobasher et al., 2000a, p. 149). Matching the current user’s activity against aggregate usage profiles must happen quickly enough for the on-line consumer to perceive no delay.

Determining the adequacy of a personalisation system Because personalisation systems require a considerable investment, the question arises as to which situational factors determine the adequacy of a personalisation strategy and the choice of a personalisation system.


Key product attributes

Following Hanson (2000), four types of personalisation system (which can be used either separately or in combination) can be distinguished: rule-based systems, CASE systems, endorsement systems and collaborative filtering systems (Figure 8.4). The right choice of personalisation system mainly depends on the heterogeneity of customers’ needs, the nature of the product or service attributes, and the distribution of customers’ life-time value. In the face of uniform customer needs and quantifiable product attributes, a rule-based system may be the most effective choice for personalisation. Such systems observe the users’ behaviour and/or use customer databases to make educated guesses about which special offers, promotions and information should be presented to each visitor. Rule-based systems allow Web firms to specify rules based on, for example, user demographics or static profiles (collected through a registration process). This requires data and modelling expertise by the Web firm. The rules are used to select the content to be served to a particular on-line consumer (Mobasher et al., 2000a). Among the advantages of rule-based systems are the simplification of personalisation logistics for the site owner and the balance of differences between customers and between the profitability of product-lines without forcing the user to answer questions or fill in questionnaires (Hanson, 2000). Not being dependent on human input can also be an advantage in respect of objectivity, topicality and the avoidance of bias (Mobasher et al., 1999). The disadvantages are the high cost of sophisticated rule-based tools (for example BroadVision) and the fact that such tools are not yet available to small and medium-sized enterprises. Computer assisted self-explication (CASE) systems can handle more complicated customer and product spaces if the product attributes

Qualitative, complex

Endorsement

Collaborative filtering

Quantitative few

Rule-based

CASE

Uniform Highly differentiated Customer needs, product space Figure 8.4 Framework to determine an adequate personalisation system Source: Adapted from Hanson (2000).


133

are limited and quantifiable. These systems are based on the idea that products and services are composed of a number of attributes that the consumer can identify and evaluate. CASE systems therefore require users to be asked about their preferences. The challenge is to ask them in a way that encourages accurate, truthful and useful answers. The users are asked about unacceptable as well as ‘must have’ features and characteristics. The result of user evaluation is a small number of highly ranked alternatives. CASE systems work best when a relatively small number of well-understood attributes and features are to be evaluated and consumers can therefore easily outline their tastes and desires, with the occasional explication from and assistance of the system (Hanson, 2000). Endorsement systems work best when customers’ needs are similar but judging the quality and explaining the value of available products is difficult. In buying processes where product evaluation is based mainly on experience or credence qualities it is important for the consumer to obtain reliable information on the on-line retailer – his or her competence, reliability and honesty (Grabner-Kräuter, 2002). In rather complicated situations (such as when choosing one of several alternative treatment options for a health problem) the best personalisation service may simply be an endorsement that will help to simplify the choice for the consumer. Endorsement systems can also connect users with preferred local (service) providers. The functionality of such systems depends on the existence of certified, accredited suppliers and a guarantee that the required degree of quality has been achieved (Hanson, 2000). Surveys, expert panels or other measures of quality must be available from the databases that are mined in the off-line process of Web personalisation. Collaborative filtering systems predict a person’s likely interest in certain information or items by matching that person’s recorded interests with those of a community of people and sharing ratings between like-minded persons (nearest neighbours) (Herlocker et al., 2000; Mobasher et al., 2000a). Each user of a collaborative filter system rates items or information in order to establish a profile of interests. The system then matches that user with people with similar interests or tastes, and the ratings provided by these people are used to generate recommendations for the user. The choice of recommendations is based on the assumption that this particular user will value those things which the like-minded people have enjoyed. Some of the highest-profile Web sites, such as Amazon.com, Cdnow.com and MovieFinder.com, have successfully used collaborative filtering systems.


Collaborative filtering systems are useful for products whose attributes are qualitative and/or complex. However the customer’s perceived risk should not be too high – while computed recommendations are often adequate or correct, occasionally they can be very wrong. One of the reasons why recommendations are sometimes unsatisfactory is that the computations of collaborative filtering systems are often based on extremely sparse and incomplete data. Moreover small sample population can result in lower-quality recommendations. Reaching a critical mass of users is a challenging managerial task – users must be encouraged to spend time on completing data forms and making trade-offs before expecting accurate results. Another factor that sometimes leads to low-quality recommendations is that collaborative filtering systems are stochastic processes that compute predictions based on models that are heuristic approximations of human processes (Herlocker et al., 2000). In addition collaborative filtering systems favour a static environment as they are based on the assumption that behavioural trends derived from past sessions will continue in the future. To determine which personalisation system is appropriate, at least three factors have to be considered: the nature of the key attributes of the product or service, the variation in customers’ needs, and the variation in customers’ lifetime values. These factors are incorporated into a flowchart, where each step involves a question whose answer will determine whether a product, service, market or customer base is suited to a particular personalisation approach (Figure 8.5). If customers’ lifetime values and needs do not vary significantly, personalisation is

yes

Q1 no

Q2

yes

Q3

yes

Collaborative filtering

no

no

CASE

Q2

Endorsement

Q3 yes

no Do not personalise

yes no Rulesbased

Q1: Do customers’ lifetime values vary significantly? Q2: Do customers’ needs vary significantly? Q3: Are the product attributes qualitative or complex?

Figure 8.5 Flowchart to determine a suitable personalisation approach Source: Adapted from Hanson (2000).


135

unlikely to be profitable and it is recommended not to personalise. If customers’ lifetime values vary significantly but their needs are similar and the products’ attributes are quantifiable and not complex, a rule-based approach will allow transparent customisation. If the product needs of customers are similar but the products’ attributes are qualitative or complex, an endorsement system that will simplify the customers’ choice will work best. If the customers’ lifetime values and needs vary significantly the right choice of personalisation system will depend on the nature of the key product attributes: if the product attributes are limited and quantifiable a CASE-based approach can help customers to find their preferred product or service combination by means of structured questions. If the customer and product space settings are too complicated to be able to make personalised recommendations based on formal models, a collaborative filtering system can offer educated, word of mouth choices.

Conclusion On-line personalisation offers many opportunities for individualised communication with Internet users as well as for suggesting individualised goods and services to potential customers. To derive benefit both for the company and its customers, personalisation strategies have to be based on a thorough exploration of customers’ needs and wants and on a comprehensive understanding of what personalisation means to the company. The value of personalisation increases as the opportunities for differentiation, customisation and catering to individual tastes increase. However, despite software firms’ and Web consultants’ euphoria about the technological potential of personalisation tools, their actual benefit to the customer should not be overestimated (Grabner-Kräuter and Lessiak, 2001). Compared with traditional catalogue sales, Web personalisation systems do offer advantages, but in many situations personal sales and customer support remain superior to suggestions and offers that are created automatically by e-commerce recommender systems. The analysis in this chapter has shown that the adequacy of personalisation systems and the success of personalisation strategies depend on the complexity of the core product attributes, the heterogeneity of customers’ needs and the variation in customers’ lifetime values. If a company is active in a mass market where customers’ lifetime values do not vary significantly and differentiation is neither possible nor desirable, Web personalisation is unlikely to be profitable. Another relevant factor is the willingness of on-line consumers’ to provide personal data


and information on their preferences to Web companies. Consumers will only be willing to fill in questionnaires truthfully and provide sensitive information to on-line retailers if personalisation will bring them added value and individual benefits. The combination of Web usage mining and content mining can increase the usefulness and accuracy of personalised on-line marketing. In summary, personalisation strategies are not suitable for all Web companies. However when customers’ lifetime values and/or needs vary significantly and information, products or services can be tailored to individual needs and wants, personalisation strategies can motivate on-line consumers repeatedly to visit the company’s Web site. In this regard, such strategies can help to establish customer loyalty and make an important contribution to customer retention.

Notes 1. A detailed description of the various preprocessing steps can be found in Mobasher et al. (2000a), p. 146. 2. Many users are extremely averse to the idea of someone monitoring the Web sites they visit and the time they spend on those sites. Some of the privacy issues that arise with Web personalisation are discussed in Srivastava et al. (2000).

References Bagozzi, R. P. (1995) ‘Reflections on Relationship Marketing in Consumer Markets’, Journal of the Academy of Marketing Science, vol. 23, no. 4, pp. 272–7. Chadsey, M. (2000) ‘The Positive Aspects of Personalization’, http://www.digitrends.net/marketing/13637_10900.html. Cooley, R., Mobasher, B. and Srivastava, J. (1997) ‘Web Mining: Information and Pattern Discovery on the World Wide Web’, http://citeseer.nj.nec.com/ cache/papers/cs/3934/http:zSzzSzmaya.cs.depaul.eduzSz~mobasherzSzpapersz Szwebminer-tai97.pdf/cooley97web.pdf. Eggert, A. (2001) ‘Konzeptionelle Grundlagen des elektronischen Kundenbeziehungsmanagements’, in A. Eggert and G. Fassott (eds), eCRM – Electronic Customer Relationship Management (Stuttgart: Schäffer-Poeschel), pp. 87–106. Eggert, A. and Fassott, G. (eds) (2001) eCRM – Electronic Customer Relationship Management (Stuttgart: Schäffer-Poeschel). Grabner-Kräuter, S. (2002) ‘The Role of Consumers’ Trust in Online Shopping’, Journal of Business Ethics, vol. 39, nos 1–2, pp. 43–50. Grabner-Kräuter, S. and Lessiak, C. (2001) ‘Web-Mining als Ansatzpunkt für personalisiertes Internet-Marketing’, der markt-Zeitschrift für Absatzwirtschaft und Marketing, vol. 40, no. 4, pp. 123–41.


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Grönroos, C. (1994) ‘From Marketing Mix to Relationship Marketing’, Management Decision, vol. 32 (January), pp. 4–20. Hanson, W. (2000) Principles of Internet Marketing (Cincinnati, Ohio: South Western College Publications). Herlocker, J. L., Konstan, J. A. and Riedl, J. (2000) ‘Explaining Collaborative Filter Recommendations’, CSCW, 2–6 December, pp. 241–50. Kosala, R. and Blockeel, H. (2000) ‘Web Mining Research: A Survey’, SIGKDD Explorations, ACM SIGKDD, July, pp. 1–15. Mobasher, B., Cooley, R. and Srivastava, J. (1999) ‘Automatic Personalization Based on Web Usage Mining’, http://citeseer.nj.nec.com/cache/papers/ cs/8691/http:zSzzSzmaya.cs.depaul.eduzSz~mobasherzSzpersonalizationzSzper sonalization.pdf/mobasher99automatic.pdf. Mobasher, B., Cooley, R. and Srivastava, J. (2000a) ‘Automatic Personalization Based on Web Usage Mining’, Communications of the ACM, vol. 43, no. 8, pp. 142–51. Mobasher, B., Dai, H., Luo, T., Sun, Y. and Zhu, J. (2000b) ‘Integrating Web Usage Mining and Content Mining for More Effective Personalization’, http://maya.cs.depaul.edu/~mobasher/papers/ecweb2000.pdf. Morgan, R. M. and Hunt, S. D. (1994) ‘The Commitment–Trust Theory of Relationship Marketing’, Journal of Marketing, vol. 58 (July), pp. 20–38. Palmer, A. (2002) ‘The Evolution of an Idea: An Environmental Explanation of Relationship Marketing’, Journal of Relationship Marketing, vol. 1 (January), pp. 79–94. Parvatiyar, A. and Sheth, J. (2000) Handbook of Relationship Marketing (Thousand Oaks, CA: Sage). Payne, A. (2001) ‘Customer Relationship Management’, http://www.ittoolbox. com/peer/AP_website.htm. Peppers, D. and Rogers, M. (1997) Enterprise One to One. Tools for Competing in the Interactive Age (New York: Double Day). Prabhaker, P. R. (2000) ‘Who Owns the Online Consumer?’, Journal of Consumer Marketing, vol. 17, no. 2, pp. 158–71. Rangaswamy, A. and Balakrishnan, A. (2002) ‘From Many to One: Personalized Product Fulfillment Systems’, eBusiness Research Center Working Paper, Penn State University. Srivastava, J., Cooley, R., Deshpande, M. and Tan, P.N. (2000) ‘Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data’, SIGKDD Explorations, ACM SIGKDD, vol. 1 (January), pp. 12–23. Vassiliou, C., Stamoulis, D. and Martakos, D. (2002) ‘The Process of Personalizing Web Content: Techniques, Workflow and Evaluation’, http://www.ssgrr.it/en/ ssgrr2002w/papers/18.pdf. Zaïane, O. R. (1999) ‘Principles of Knowledge Discovery in Databases. Chapter 9. Web-Mining’, http://www.cs.ualberta.ca/~zaiane/courses/cmput690/slides/ ch9s.pdf.

9 Evaluating a Computer-Based Marketing Course by Means of Focus Groups and Interviews Regina Höld

Introduction Being independent of place and time is becoming increasingly important in education and learning (Lang and Pätzold, 2002, p. 112). Therefore learning concepts using new media as a way of acquiring knowledge are increasingly being introduced at education institutions. E-learning, defined as a process of learning in environments with electronic media (Dichanz and Ernst, 2001, p. 7), is the new buzz word in discussions of computer-based learning. In autumn 2002 the Vienna University of Economics and Business Administration (VUEBA) introduced a new marketing course in which students had the option of attending class and/or studying on-line. Course material was provided on a learning platform via the Internet. It consisted of a textbook, slides, text items and links to a variety of topic-related articles on the Web. The new course was evaluated during a pilot study that ran from April to June 2002. The research questions for the study are shown in Figure 9.1. In general, the aim of an evaluation process is to gather and structure participants’ recommendations for improvements and changes to a learning programme (Clarke, 1999, p. vi). Thus it focuses on making explicit practical knowledge for a decision-making process (ibid., p. 2). The evaluation process consists of activities and/or results that reveal the meaning, usefulness and importance of entities (Baumgartner, 1999, p. 71). There is no single method that can be characterised as the evaluation method. However evaluation calls for the application of scientific research methods (Bortz and Döring, 2002, p. 101). In the VUEBA 138

Regina Höld 139

How do students assess the marketing course?

How do students assess the course material? What are its strengths and weaknesses? What suggestions do they have for improving the course and the course material?

How do students use the course material?

How do students profit from attending the course? (Learning transfer)

Figure 9.1 Research questions

evaluation study, the department of marketing’s intention was to obtain students’ feedback on the concept of the new course, their experiences with the course material and information on individual learning habits. The aims of the research and the fact that no secondary data on similar computer-aided courses were available called for an exploratory research design that focused on the opinions, motivations and attitudes of the students participating in the course. Usually the researcher selects methods of data collection that will ensure sufficiently rich and complex data (Morse and Richards, 2002, p. 70). As the VUEBA study would primarily consider students’ verbally expressed recommendations for improvements to the course material a qualitative approach was adopted. The process started with the first focus group session on the students’ expectations, learning habits and motivation, continued with several semistructured interviews on the usability of the material and students’ learning strategies, and ended with the second focus group session, in which the students discussed their experiences of the examination and the course structure. This chapter describes the process of collecting data from students who had just begun their business administration studies and provides a rough description and interpretation of the main results of the study. Its intention is to provide useful hints for the conducting of focus group sessions.

140 Evaluation by Means of Focus Groups and Interviews

Methodology In this study, data collection was done by means of semistructured interviews and focus groups.

Semistructured interviews As pointed out above, data needs to be rich and complex. These requirements are met by choosing the right interview approach. Qualitative interviews give interviewees the freedom to respond in their own words and there is no need for them to try to fit their opinions to a predetermined scale (Patton, 1990, p. 287). Questions are formulated in an open-ended way that encourages the interviewees to talk extensively about the research topic. When the sample size is small, gathering data on experiences, expectations and opinions can be done easily. Depending on the degree to which the questions are formulated in advance, two types of interview can be distinguished: the ‘semistructured interview’ and the ‘interview guide approach’. With the interview guide approach the themes are determined in advance, but during the interviews the sequencing of the questions and their wording are up to the interviewer. A semistructured questionnaire consists of open-ended questions with probes (Morse and Richards, 2002, p. 94) that help the researcher to collect the same information from each interviewee in a systematic way. One advantage of this type of interview is that the conversation between the interviewee and the researcher tends to flow naturally. The following list shows the prerequisites for conducting semistructured interviews: ●

●

● ●

Dictaphone in full working order, spare cassettes, sufficient quantity of batteries. Pretested interview guide with open-ended questions and possible probes. Quiet environment. About an hour of time per interview.

Focus groups Data does not necessarily have to be collected by means of one-to-one interviews. A more efficient way of generating rich data is to organise focus groups. A focus group consists of a number of individuals brought together by the researcher to focus specifically on one topic (Morse and Richards, 2002, p. 95). The idea is to gain insights into peoples’ attitudes

Regina Höld 141 Table 9.1 Categories of focus group questions Question type

Purpose

Opening

Participants become acquainted and start to feel connected; the responses to this question are usually not analysed Begins discussion of topic Moves smoothly and seamlessly into key questions Facilitates insights into the study’s central areas of concern Helps the researchers to determine the relevance of topics and brings the discussion to a close

Introductory Transitional Key Ending

Source: Based on Krueger (1998b), p. 22.

towards and (experience-based) knowledge of an issue via group interaction. A group should consist of six to eight people and the discussion should last about two hours (Patton, 1987, p. 135). A moderator (group facilitator) leads the group through prepared, open-ended questions without stating his or her own opinion (Krueger, 1998a, p. 5). The moderator is responsible for the direction of the conversation and ensuring that it is not dominated by single participants (Morse and Richards, 2002, p. 95). Usually there are 10 to 12 open-ended questions with different levels of importance: ‘The level of importance influences the amount of time spent on the question as well as the intensity of analysis’ (Krueger, 1998b, p. 21). When preparing the ‘questioning route’ for the discussion, five categories of question should be taken into consideration (Table 9.1). Focus group sessions are aimed at collecting ideas and opinions in the early stage of a research project. The results are not claimed to be statistically representative as the participants may not reflect the same structure as the target population. Therefore focus group research should concentrate on analytic generalisability rather than statistical generalisability (Carson et al., 2001, p. 129). The following should be organised in advance: ● ● ●

● ●

Six to eight participants who have about 2.5 hours of time. Dictaphone and cassettes. A list of open-ended questions that follow the ‘questioning route’ (consider the sequencing and timing). Visual demonstrations of questions (slides or flipchart). Refreshments for the participants.

142 Evaluation by Means of Focus Groups and Interviews ●

● ● ●

A suitable conversational environment (consider whether it would be best to have a round table or square one and so on). One moderator, one comoderator (assistant). Name tags for the participants. Note paper for the moderator and comoderator.

Research design Having discussed the methodology, this section considers the application and combination of scientific methods in an evaluation process. The research design defines the frame for the project and the data analysis. In this case, an exploratory approach was chosen to gain insights into the ideas of students attending the new marketing course. The course started on 26 April 2002 and ended with the final exam on 28 June 2002. Usually a research design structures the research process. In this study the research also supported the course’s pilot run, for example it was intended to test the user-friendliness of the material and acceptance of the course concept. Two focus group sessions and interviews with 20 participants were conducted (sixty-five students attended the first class). In subsequent classes, the number of students decreased constantly and only 15 attended the last session; 34 sat the final exam. Figure 9.2 depicts the research process.

Applied methods and their goals This section discusses the role of applied methods and their goals in the project in question. Table 9.2 outlines the research design. Focus group 1 met before the course started as general opinions on e-learning had to be collected. Nine of the students who had registered for the marketing course discussed their expectations, experiences of e-learning and learning strategies. In groups of two or three the students identified the strengths and weaknesses of using computers when

Phase 1 Research problem

Research questions

March to April 2002

Figure 9.2 The research process

Phase 2 – 3 Focus group 1

Interviewguide

Phase 4 Qualitative interviews, feedback

May to June 2002

Focus group 2

Table 9.2 The research design Method of data collection

Phase

Topic

Time

1

Motivation and expectations of students

Before the beginning of the course on 18 April 2002

Focus group 1: 19 participants, recruited via e-mail

2

Acceptance and adoption of the course material Occurrences during the course

26 April 2002 to 14 May 2002

On-line-feedback

24 May 2002 to 27 June 2002

20 interviews based on interview guide

After the exam (28 June 2002)

Focus group 2: the same participants as in phase 1

3

4

Experiences

Aims Evaluation of: ● motivation and expectations ● learning strategies ● opinion on self-study ● advantages of the new course ● experience with e-learning Feedback on: ● usage of course material ● strengths and weaknesses of the course Feedback on: ● acceptance ● anticipated learning benefit ● preparation for the exam ● suggestions for improvement Evaluation of: ● experiences ● anticipated learning benefit ● hints for colleagues ● suggestions for improvement

143


learning marketing and wrote them down on flipcharts. Next, the students were asked to think of a course they had attended in the past that they considered to be optimal. This first focus group provided valuable insights into students’ learning habits and their opinions on individual learning. During the course the students were able to provide feedback at any time (via e-mail). They were handed an information folder inviting them to describe their use of the course material and any problems they had with using it. Two of the students provided positive feedback but also offered suggestions for improvement. Shortly before the exam, some of the students asked for information on when and where the exam would be held and what types of question would be asked. This indicates that the scarce feedback could have been due to the students being very late in getting down to working through the material. The next phase of the project was designed to reveal the students’ perceptions of the course and their strategies for working on-line. In order to obtain deep insights and create rich data, 20 semistructured interviews were conducted. Unlike the session with focus group 1, the questions only concerned the new marketing course. The aims were to discover the strengths and weaknesses of the concept and to obtain the students’ assessments of the course. The anticipated learning transfer was another topic of the interviews. Finally, the students were asked which parts of the course they considered would be helpful in the future. Focus group 2 met immediately after the exam to complete the process of data collection. The session focused on the experiences of students who had used the ‘learning platform’. Eight of the participants told of their experiences with the multiple choice test, talked about the structure of the learning platform and their learning benefits, and offered hints for other students in respect of learning with the material. The students were also asked to demonstrate their navigation habits on the computer and describe problems they had encountered. This encouraged them to talk in detail about their difficulties and offer suggestions for improvements.

Experiences and reflection This subsection summarises my experiences with the methods described above and offers practical suggestions for the preparation of focus group research. For detailed information on focus groups see Morgan (1998).

Recruiting the participants Before conducting interviews or focus group sessions the researcher has to recruit the participants and make sure that they will appear on the

Regina Höld 145

agreed date. For focus groups, six to eight people have to be brought together (Patton, 1987, p. 135). In the present case, motivating students to participate in the project was not very successful when done via e-mail; contacting them by phone was time-consuming but more effective, so recruiting by telephone can be recommended when setting up focus groups with unknown participants. Offering incentives such as credit points or small gifts can encourage students to participate.

Defining the goals Before starting interviews or focus group discussions the researcher has to define the goals and know what he or she wants to find out by applying a specific method. This enables him or her to stay on topic while interviewing or moderating and to probe if more information is required. Moreover it helps the researcher to structure the data analysis.

Preparation of the questions The criteria for productive interviews are non-manipulation, specificity, capturing a wide spectrum, and profundity (Merton and Kendall, 1993, p. 178). Because the wording of the questions can influence the interviewees’ answers, questions should be open-ended, neutral, sensitive and clear (Patton, 1987, p. 122). Pretesting the questions with friends and discussing them with other researchers can reveal where improvements are needed in the wording and sequencing.

Suggestions for conducting focus groups ●

●

●

●

●

Having a comoderator to write down observations and important statements enables the moderator to listen without interruption. Focus group 2 consisted of the same participants as focus group 1 and in the second session the group interaction was noticeably more active. This shows that the flow of group discussions is less inhibited when the participants know each other. In order to enliven the discussion, tools such as group work are useful. They prevent less communicative participants from becoming bored and enhance group cohesion as the participants have to work creatively together. Being a moderator of a discussion requires an ability to let the participants talk and to refrain from pressing them to talk if they are not ready. This means that short periods of silence immediately after asking a question should not trouble the moderator. A focus group session should not exceed two hours. Participants tend to become tired, inattentive and less communicative if the session takes too long.


Data analysis The interviews and the focus group sessions were tape-recorded and transcribed in preparation for the analysis. The verbal data was content analysed. The aim of the content analyses was to reduce complexity and to structure the discussed themes in preparation for interpretation (Lamnek, 1989, p. 171). By creating a system of categories from the data (Titscher et al., 1998, p. 78) the participants’ opinions and perspectives on the addressed topics were revealed (Bortz and Döring, 2002, p. 329). The software QSR N6 supported the building of categories (nodes) from the transcripts and allowed the coding of text parts in the defined categories (Richards, 2002a, p. 2).

Coding The coding strategy was aimed at structuring the verbal data by marking the relevant text units according to the importance of themes in the interviews and focus group sessions. Each theme represented one category. The coding practice applied may be described as topic coding (Morse and Richards, 2002, p. 117). First the documents were read through for the purpose of dividing the text into main categories representing the addressed topics. By repeatedly working through the documents, more and more categories were identified and the text was increasingly structured. In the second step the text in each category was structured by creating subcategories that represented the participants’ statements (Figure 9.3). Having coded all the documents, a tree structure was constructed for each theme. This type of data analysis provides an overview of what was said without neglecting single statements as it is possible to go back to the original data.

Categories The following main categories emerged from the data analysis: ● ● ● ●

● ●

Reasons for registration Expectations Effectiveness of course Learning strategies, use of computers Self-instruction E-learning assessment

● ● ● ●

● ●

Use of material Exam Wishes Strengths/weaknesses Suggestions for improvement Hints for colleagues

Regina Höld 147

Transcript

Main categories: what topic was addressed?

Subcategories: to put it in a nutshell ...

Interview 1: ‘I think that it is a pity that there are no sample tests available.’ ‘I also think that not every student has the right equipment to study on-line. Downloading these things takes a long time.’ ‘It was good that there were four lecturers.’

No sample tests Weaknesses of the course Equipment

Strengths of the course

Four lecturers

Figure 9.3 Example of a coding strategy

Results Qualitative studies produce enormous amounts of text, making it a challenge to draw conclusions. Sticking to the original words of the interviewees makes both the analysis and the reporting exciting. Nevertheless reports call for structure and getting to the point. The results of the study can be demonstrated by showing the tree structure of each theme and describing and interpreting the various opinions and ideas. Students’ quotations illustrate their thinking and make the results come alive. ‘Condensed examples’ (Carson et al., 2001, p. 196) allow an insight into the language of the participants and are a characteristic of qualitative research. The following subsection shows a way of presenting qualitative research results and describes how they were generated from the data. The discussion concentrates on the students’ assessment of the course material and the learning strategies they used when preparing for the exam (for further results see Höld, 2002). Questions to be researched in the study were: How do students assess the course material? What are its strengths and weaknesses? What suggestions do they have for improving the course and the course material? The answers to these questions were coded into the following categories: ● ● ●

E-learning (assessment, material). Strengths/weaknesses. Suggestions for improvement.


Having coded all the documents, conclusions could be drawn from the resulting tree structure by combining the three categories above to obtain a precise overview of what was said about the on-line material (Figure 9.4).

Assessment of the on-line course material Students addressed technical problems to do with navigation and the structure of the material. The following quotations illustrate the problems. ‘Concerning improvement, it would be good to put a little note next to the heading identifying the chapter. It is very hard to find, as

Material Platform

Assessments Additional programs for free (e.g. Acrobat Reader) ~ Design Confusing navigation Password caused problems

Suggestions

• Provide structured index • Instal ‘back’ buttons • Simplify registration procedure

¥ Forum Slides

+

Clarity Design ~ Quantity Content (superficial) ¥ Script

Test items

+

Check on learning progress Multiple choice questions Wording of questions Difficulty ~ Feedback Structure, navigation

• • • • •

Provide references to different articles Provide references to the textbook Fewer slides, more content Sell slides as a script Provide more printer-friendly version of slides (less colour)

• Structure the questions by chapters

• Simplify the loading of questions • Provide detailed feedback with explanations

• Provide sample tests

¥ Sample tests Downloads and links

Information offered ~ Relevance Usage Structure

• Structure by chapter • Current articles should be included

Key: : Ok ~: Positive and negative assessment ¥ : Missing +: Was called a strength

: Needs to be improved

Figure 9.4 Assessment of on-line course material

Regina Höld 149

chapter 1 is below chapter 3.’ ‘I would find it better if you could click on to, say, chapter 1 and there are all the subchapters.’ ‘I find it annoying that there is no numbering of the questions. I constantly got lost.’ Such statements were coded into the categories ‘Suggestions for improvement’ and ‘E-learning material’ (with its subcategories ‘slides’, ‘test items’ and ‘downloads/links’). According to Richards (2002b, p. 77), ‘in qualitative research, coding is not usually an end in itself.’ Coding gathers material that is relevant to special themes and allows questions to be put to the data. Using QSR N6’s ‘boolean search’, which combines text coded in different categories by creating a new category (ibid., p. 79), the following questions were asked: ● ●

What improvements do students suggest for ‘the platform’? What needs to be improved in respect of the downloads/slides/test items?

Figure 9.4 summarises the students’ assessments and suggestions. The suggestions are taken directly from the category ‘suggestions for improvement’ and its subcategories. The assessments resulted from dividing the category ‘e-learning’ into subcategories for each type of material. With regard to strengths and weaknesses, the students evaluated the test items and the slides as strong points of the new course. The test item ‘battery’ was evaluated as a strength as it allowed the students’ to assess their progress. However the structure of the questions and the navigation procedure needed to be improved. Sample tests containing questions about each chapter were requested. Concerning the on-line feedback provided after the test items were answered some of the students called for hints about where the solutions could be found in the textbook, while others wanted more detailed feedback, as the following quotation illustrates: ‘I think that the answers should be explained in more detail. I mean, not only giving reference to the textbook but also explaining the questions on the internet.’

Learning strategies When working and learning with the material, the students concentrated on downloading the slides and answering the on-line test items. The downloads (articles and examples) and links were hardly used as they were not relevant for the test: ‘I really didn’t even look at them because that would have been even more work and I didn’t want to burden myself. But it would be good, very good actually [to download and read the articles].’ ‘They are interesting, but if you are learning for the


test they are not relevant.’ ‘To get a positive mark it’s enough to go through the test items.’ Analysing the data on the on-line material revealed a lack of educational and content-oriented goals. Learning goals needed to be operationalised in order to become measurable (Lang and Pätzold, 2002, p. 101). The students also called for transparent criteria for their grades. In the new course, multiple choice questions were used for the exams. The strategy was to enable students to prepare themselves for the test by going through approximately 400 questions on the Internet, which would help them to determine whether they had properly studied and understood the relevant parts of the textbook. The study revealed that the students prepared themselves for the test by clicking through the multiple choice questions and abandoning the textbook. They thought that the textbook was too thick and was not needed to succeed in the test. The students’ assessment of the slides was positive, although some thought that their content was superficial and provided insufficient help when preparing for the exam. A script with more detailed information would have been of more help. The conclusion to be drawn from this is that there was a lack of communication with regard to the role of the slides. It was not clear whether they were intended to provide a learning structure and to support presentations in the class, or whether they were to be used instead of the textbook. The results clearly showed that the additional links and downloads were not popular. Even though some of the students saw them as a way of elaborating their knowledge, most of the students skipped over them. Elaboration strategies (Metzig and Schuster, 1998, p. 53) are aimed at encouraging the deeper processing of information and facilitating memorisation. The articles and links in the course provided a wide variety of examples that showed the practical relevance of the theoretical contents. A way of making the course material more attractive and useful to students has to be developed. At least the role of the material within the course has to be communicated clearly.

Subsequent measures It was felt that the problems experienced when navigating the test items could be removed by installing a ‘back button’. The structure and design of the platform were improved and a forum was set up to allow students to share their opinions and set up tutorial groups to learn together. The information provided on the platform in respect of how to study effectively was supplemented by information on ways of navigating through

Regina Höld 151

the material. Various links were installed on the university’s homepage and those of the units and departments that had been involved in the development of the platform, making it easier for students to access the platform. Making slides available in space-saving formats reduced the printing costs, and hard copies of the slides were put on sale at the university’s bookshop. The number of test items was increased to about 900, and difficult questions were accompanied by detailed problem-solving suggestions.

Summary This chapter has briefly described how evaluation in education can be conducted by applying qualitative methods. Focus groups and interviews generate a rich body of data that is then organised, interpreted and analysed in a structured way. Software packages help the researcher to store the data, construct categories and code the text in order to make it accessible for further coding, report writing or showing the structure of the text in a tree. In the VUEBA project, the qualitative approach proved to be a good way of obtaining answers to the research questions. Assessment of the students’ statements and opinions provided information on what was actually happening in the course and which learning strategies were being used. At this stage of the project, with no prior experiences or secondary data to draw on, the use of a standardised questionnaire would have resulted in biased data. Moreover any suggestions for improvements would not have been as detailed as presented in this study because they would have been suggested by the researcher and merely judged by the students, not the other way round.

References Baumgartner, P. (1999) ‘Evaluation mediengestützten Lernens’, in M. Kindt (ed.), Projektevaluation in der Lehre (Münster, New York, Munich and Berlin: Waxmann), pp. 63–99. Bortz, J. and Döring, N. (2002) Forschungsmethoden und Evaluation für Human- und Sozialwissenschaftler (Berlin, Heidelberg and New York: Springer). Carson, D., Gilmore, A., Perry, C. and Gronhaug, K. (2001) Qualitative Marketing Research (London, Thousand Oaks and New Delhi: Sage). Clarke, A. (1999) Evaluation Research: An Introduction to Principles, Methods and Practice (London, Thousand Oaks and New Delhi: Sage). Dichanz, H. and Ernst, A. (2001) ‘E-Learning: Begriffliche, psychologische und didaktische Überlegungen zum “electronic learning” ’, Medienpädagogik,

152 Evaluation by Means of Focus Groups and Interviews www.medienpaed.com/00-2/dichanz_ernst1.pdf, Abfrage 17.03.2003, 10:30 MEZ, pp. 1–30. Höld, R. (2002) ‘Qualitative Evaluation computerunterstützten Lernens im Marketing’, diploma thesis, Vienna University of Economics and Business Administration. Krueger, R. A. (1998a) Moderating Focus Groups (Thousand Oaks, London and New Delhi: Sage). Krueger, R. A. (1998b) Developing Questions for Focus Groups (Thousand Oaks, London and New Delhi: Sage). Lamnek, S. (1989) Qualitative Sozialforschung: Band 2 Methoden und Techniken (Munich: Psychologie Verlags Union). Lang, M. and Pätzold, G. (2002) Multimedia in der Aus- und Weiterbildung: Grundlagen und Fallstudien zum netzbasierten Lernen (Cologne: Dt Wirtschaftsdienst). Merton, R. K. and Kendall, P. L. (1993) ‘Das fokussierte Interview’, in C. Hopf and E. Weingarten (eds), Qualitative Sozialforschung (Stuttgart: Klett-Cotta), pp. 171–204. Metzig, W. and Schuster, M. (1998) Lernen zu lernen: Lernstrategien wirkungsvoll einsetzen (Berlin, Heidelberg and New York: Springer). Morgan, D. L. (1998) Planning Focus Groups (Thousand Oaks, London and New Delhi: Sage). Morse, J. M. and Richards, L. (2002) Readme First for a User’s Guide to Qualitative Methods (Thousand Oaks, London and New Delhi: Sage). Patton, M. Q. (1987) ‘How to use qualitative methods in evaluation’, in M. Q. Patton (ed.), Program Evaluation Kit (Newbury Park: Sage), pp. 1–170. Patton, M. Q. (1990) Qualitative evaluation and research methods (Newbury Park, CA: Sage). Richards, L. (2002a) N6 Reference Guide (Melbourne: QSR International). Richards, L. (2002b) Using N6 in Qualitative Research (Melbourne: QSR International). Titscher, S., Wodak, R., Meyer, M. and Vetter, E. (1998) Methoden der Textanalyse: Leitfaden und Überblick (Opladen: Westdeutscher Verlag, 1998).

10 The Job Image of Young Sales Assistants: Introspection and Young Customers’ Assessment Renate Buber

Introduction Although the retail sector is one of the most important employers in Austria, sales jobs in retailing have many negative connotations. Changes in the retailing landscape in the past few decades – for example the phenomenon of the ‘hybrid consumer’ and the polarisation of specialist shops and cut-price supermarkets – have led to a higher degree of self-service. In addition, general changes in societal values have resulted in perceivable changes in customers’ behaviour. These days customers tend to demand less service, and the younger the customers the more comfortable they are with the principle of self-service. Service has become a kind of added benefit that has to be paid for. Naturally this phenomenon is predominantly identified with low-price retailers, discounters and retailers that focus their marketing activities on younger customers. This chapter reports a study on how young sales assistants see their jobs and how their jobs are evaluated by customers of the same age group. Interviews were conducted with both sales assistants and customers, and the interviewees were asked to characterise a typical sales person. The study was carried out in the discount clothing sector and utilised the GABEK method. The data analysis was conducted by means of WinRelan. In 2000 the Austrian trade sector employed about 460 000 people and was the second largest employer in Austria. In 2001 about 239 500 were employed in retailing (almost half of them in the federal states of Vienna and Lower Austria); clothing retailers employed about 30 731 people; 153

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two thirds of the employees were female; and part-time employment was becoming more important and was still considered to be women’s work (Gavac et al., 2002, pp. 11–14). For young people, training to become a sales assistant in retailing is still a popular career choice (about 17 per cent of Austria’s trainees work in retailing); in 2001 there were about 14 300 trainees, mostly women (ibid., pp. 1, 20). Nevertheless personnel managers find it very difficult to attract qualified people for a career in retailing as the sector has had a major image problem for a very long time (Zimmer, 1995).1 In June–July 1999 the Arbeiterkammer2 in Lower Austria conducted a study on working conditions in Austrian retail companies, in cooperation with members of the union that represents retail, transport and traffic workers (Denk et al., 1999). Of the 5500 questionnaires that were sent to potential participants, 1105 were returned; this high response could be interpreted as a strong need among shop workers to talk about their working conditions. The data analysis produced alarming findings. Many areas were dominated by ruthless competition that negatively affected the working conditions of employees. There appeared to be little sense of social responsibility towards their employees. The group researched in the Arbeitkammer study can be related to the sample investigated later in this chapter in that 11 per cent of the interviewees worked for clothing retailers and 10 per cent were aged between 16 and 24. The following subsections summarise some of the findings of that study (ibid., pp. 13–29).

Full-time and part-time employment Ninety-six per cent of part-time employees in retailing were women. Part-time employment tended to be dominated by females and was strongly connected to certain stages of women’s life, with mothers of school-aged children accounting for the biggest proportion of part-time employees. Most of these women said it was their wish to work parttime, but economic pressure should also be taken into account.

Working on Saturdays Eighty-nine per cent of employees in food retailing worked on Saturdays, closely followed by employees in the clothing and textile sectors (85 per cent). Therefore their weekends were very short, and this was a principal cause of the job being evaluated very negatively by those young employees who lived with their families. Eighty per cent of the interviewees said they would refuse to work on Sundays.

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Social benefits Sixty-five per cent of the interviewees were entitled to discounts on products, plus travel and meal grants and company pensions. The extent of social benefits varied by sector and size of company, and depended on the presence of a works council. In the textile sector 83 per cent of the interviewees were provided with social benefits.

Pay In general the pay received by the interviewees was very poor. This can be explained by the fact that most had left school as soon as they had completed their compulsory schooling and tended to be semiskilled or even unskilled. There was a notable income difference between females and males. Fifty-seven per cent of the interviewees described their pay as ‘just enough to live on’, 8 per cent said they could ‘live well on it’, 23 per cent described it as ‘sufficient’ and 7 per cent as ‘not sufficient’.

Satisfaction The highest level of satisfaction expressed was for relationships among colleagues (90 per cent). In small companies the collegial climate was rated higher than in medium-sized and large companies.

Career options An above average proportion of younger and less-qualified interviewees who theoretically had the chance of being promoted were dissatisfied with their educational and promotional possibilities.3 The answers to the question ‘If you could choose again, would you still work in retailing?’ revealed that the older the interviewees the higher the proportion of those who regretted entering retailing. Job dissatisfaction could also be seen in the interviewees’ answers to the question ‘In which aspects does retailing offer advantages to employees compared with other sectors?’ Only the firms’ atmosphere was fairly positively evaluated, while pay, working hours, job image, social benefits, career possibilities and options for further education were viewed as being better in other sectors by 70 per cent of the interviewees.

Changes in the retail business There is a steady movement towards replacing personnel by sales space and efficient presentation. This can be seen in all areas of the retail business, even in those where personal selling was dominant (Görs et al.,


1994, p. 55). Some customers have already adapted to this and become ‘smart shoppers’ who buy at reasonable prices, while those who tend to buy more (‘hybrid consumers’) are seen as stupid (Schmalen, 1994; Beisheim, 1999, p. 9; Diller, 1999; Eggert, 2001, p. 60). A change in societal values has led to less customer loyalty and price being the most important factor in the buying process. Smart shoppers see it as a challenge to buy high-quality products at the lowest possible price, and the ‘added value of performance’ (a shop’s atmosphere, service and so on) is no longer important (Beisheim, 1999, p. 13). Service is now seen as the preserve of high-income customer groups (Baethge et al., 1992, p. 45). In the past 30 years, retailing has evolved from the typical self-service shop in the neighbourhood, to supermarkets, cut-price supermarkets, discounters and self-service warehouses (Diller, 1999, p. 355; Bauer and Jung, 1999, p. 681; Schnedlitz et al., 2000, p. 206). In the future factory outlets, teleshopping and event shopping will be common place (‘wheel of retailing’) (Gilbert, 1999, pp. 8–13; Kotler and Bliemel, 1999, p. 868; Schmalen and Schachtner, 1999; Becker, 2001, p. 533; Liebmann and Zentes, 2001, p. 354; Peterson and Balasubramanian, 2002). The question is, what part will human beings play in this, as seen from the customers’ and sales personnel’s point of view (Beinstein and Maurer, 2002)? There is no doubt that only motivated personnel who identify with their company will be able to establish a convenient and safe buying atmosphere for customers that will give their company a certain advantage over its competitors. This could commit customers to the company and possibly reduce the destructive practice of always having to be cheaper than competitors. Self-service and the enlargement of sales areas have become the leading sales principles (Wald, 1985), causing a dramatic change in the status of salespersons. Personnel have been replaced by capital and individual service has decreased (Müller-Hagedorn and Preißner, 1999, p. 149). More and more salespersons are carrying out menial tasks. This process of dequalification, is ongoing (Tenbensel, 1987, p. 19; Görs et al., 1994) and sales work is generally seen as something that anyone can do (Görs et al., 1994, p. 18). Jobs in the clothing sector have a particularly bad image and are evaluated as unattractive by more than 50 per cent of customers, although boutiques are considered to be a little more interesting than clothing discount shops (Boehm-Kochanski, 1995, p. 9). All in all there is need to avoid the situation where work in retailing will only be chosen by unqualified young people with an unwillingness to learn (Görs et al., 1994, p. 61). Even now, more than 60 per cent of the young people employed in retailing have received no training for

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the task (Bergmann et al., 2001, p. 21). Selling requires sales competence and should be treated as a vital part of firms’ marketing strategy, especially in the case of companies where most sales assistants complain about their treatment by both management and customers (Schwab, 1982; Howe, 1992; Brater and Landig, 1995; Belz, 1996; Reynolds and Beatty, 1999).

The study Aims The aim of the study reported here was to gather information on (1) sales assistants’ perception of their job, and (2) customers’ attitudes towards the job of sales assistant (Figure 10.1). The following research questions were asked: ●

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What is the image of work in sales, as seen by (1) young fulltime employees with at least one year’s experience in the job, and (2) young customers? How has this image been influenced by the various changes in retailing, and in what direction has the importance of personal service changed? How should the ideal selling process be described? How should a typical sales assistant be described?

Introspection; perception of job

:

Needs, wants, attitude towards jobs in sales and selling processes

Sales assistant

Typical salesperson Ideal selling process Selling in the future Figure 10.1 Representation of the research design

Consumer


Methodology The study followed a qualitative approach (Rubin and Rubin, 1995; Kepper, 1996; Flick, 1998; Carson et al., 2001) in order to gain a deeper insight into young people’s assessment of sales work in retailing. The (convenient) sample consisted of two groups of interviewees: 28 sales assistants in retailing (aged 16–25), and 39 customers (aged 16–19). The sales assistants had been working in clothing discount shops (C&A, H&M, Jeans Factory, Mango, Marc O’Polo, MEXX, New Yorker, Orsay, P&C, Pimkie, Schöps, Springfield, Stiefelkönig, Stögmüller, Street One, Turek and Zero)4 in an urban area for a year or longer. Students working part-time and people working for just a few hours per week were excluded. The customers of the shops were predominantly young people. The customers interviewed lived in urban areas and were experienced in buying clothes in discount shops. They consisted of schoolchildren, university students, community service workers, unemployed persons, one entrepreneur and one handball player. Listed below are the open-ended questions that guided the interviews with the sales assistants and customers respectively. All the interviews were closed by asking the interviewees for their age, profession and whether they would like to make any further comments. Questions put to sales assistants ●

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Please tell me about your job as a sales assistant. Why did you become a sales assistant? What do you like/dislike about your job? What do your friends think about your job? How do customers behave in your shop? What happens between their entering and leaving the shop? What is important for your customers? Why? What is a typical selling process in your shop? How important is service for your customers? What does a clothing sales assistant typically look like? Why? If you look five years into the future, what will the sales assistant’s job be like? Will customers behave differently? How important will individual service be? What does that mean for you? With which changes will you have to deal? What do you expect to be pleased with/worried about?

Questions put to customers ●

What is your image of a sales assistant in a clothing shop, for example at H&M?5 In your opinion, why do young people become sales assistants?

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What do your friends think about such jobs and about the people who decide to do them? How do sales assistants behave in the shop? When you go shopping, what happens between your entering and leaving the shop? What is important for you? Why? What is a typical selling process? How important is personal service for you? In your opinion, how do sales assistants view personal service? What does a typical clothing sales person look like? Why? If you look five years into the future, what will the sales assistant’s job be like? Will customers behave differently? How important will individual service be? What does that mean for you? With which changes will you have to deal as a customer? What do you expect to be pleased with/worried about as a customer?

The GABEK method was used for data gathering and analysis. Texts were analysed separately for the two groups of interviewees with the help of WinRelan, the computer application of GABEK (Buber and Kraler, 2000; Zelger, 2000).

The findings The findings of the study are presented very briefly and on a quite general level of analysis, the gestalten tree (Buber, 2003). The aim is to provide an insight into the similarities and differences between the statements of the sales assistants and the customers in terms of perception of the job, the ideal selling process and the likely importance of the job of sales assistant in the future.

The sales assistants’ views Figure 10.2 summarises how the sales assistants saw their job in general and their selling function in particular. The top statement was constructed by summarising the next four groups of findings (hypergestalten – arrow 1). The hypergestalt ‘many people become sales assistants by chance …’ is a summary of three lower-level findings (gestalten – arrow 2). (For more information on the gestalten tree and the different types of gestalten see Buber, 2000.) The sales assistants articulated many disadvantages of their job, including the opening hours and the increasing number of part-timers. They also talked about customers losing their temper with sales assistants. On the other hand they saw the sales assistant’s job as creative if those concerned were interested in selling.

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People become sales assistants by chance and the job has disadvantages. It will change. Whether customers will want service in the future is uncertain, even at H&M. A typical sales process is as follows: the customer enters the shop, asks something, and then tries on clothes. It is important for one to notice whether he/she wants to be served. H&M offers no Service. The perceived disadvantages of a sales job include the working hours, the shop’s opening hours, moody customers and the fact that more and more people work part-time. But being a sales assistant is a creative job as well, as long as one is a bit interested in selling. Many people become sales assistants by chance, having failed to find an adequate job after finishing their studies. Being a sales assistant means one likes contact with people. But friends often find it hard to understand why people like their job. They believe that it is not challenging. To finance my studies I work as a sales assistant on the side. The Internet will change the sales assistant’s job profile in the future. The Internet’s effect on services is uncertain, even at H&M.

Many people become sales assistants by chance. Many wanted to work in another profession, for example in an office, as a hairdresser, as a nurse or in the electricity business. Others could not find an adequate job after obtaining their high-school diploma or finishing their university studies. I like being a sales assistant as I have to deal with people and there is always something new in the job, and it is sometimes funny. Friends and family often find it hard to understand what one loves about this job. I have become a sales assistant because I like dealing with people. I like the job because I meet people. Something new is always happening. I am working as a sales assistant to finance my studies. Education is important for me; that means studying besides working as a sales assistant because the job is not fulfilling. Especially in Vienna, there are many opportunities to study and this means that many young people only take temporary jobs. Young people who are being trained in retailing should not be disregarded. Selling can only be learned by doing, it cannot be learned at school. This is often not understood by friends because they think that working as a sales assistant is not intellectually demanding.

Figure 10.2 How sales assistants view their job

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When asked about their expectations for the future, typical answers were as follows: In the future, let’s say in five, ten or twenty years, the importance of sales employees will decrease. The pay is bad and the work is too monotonous to stay in the job all one’s life. That is why I want to learn and move forward. Changes come very rapidly. In five, ten or twenty years, work as a sales assistant won’t be great. Unlike in the past, one won’t be able to learn the profession. Probably all sales assistants will hold a high school diploma or a university degree. Of course one is afraid of how things will be in five, ten or twenty years; whether small and medium-sized companies will still exist or whether there will be more unemployed people. That’s why one has to be afraid. I will wait and see. It could become better or worse, for example because of stress or low pay. Younger people in particular will be affected by that. It is no longer what it should be. I worry about the future of the job of sales assistants. More and more people will buy clothes by mail order or on the Internet, so fewer sales assistants will be needed. The money and time people can afford to spend on clothing is getting less. It is more convenient to stay at home in the evening and to flick through a catalogue. More and more will be bought via mail order. Nevertheless some customers will still prefer to go to a shop to try on clothes and receive some service. On the matter of dealing with customers the following responses were typical: One of the disadvantages of being a sales assistant is that all customers are not nice. Older women can be difficult. On the one hand older people often want to tell you their life stories; on the other hand they believe that their experience means they always know better than young sales assistants, especially if they are having a bad day. Then we are often the baddies. Nevertheless we try to be tolerant and to listen to them. I would like customers to treat me with respect. People often see sales assistants as part of the furniture and treat them like idiots. Some customers try on clothes and then leave them in a pile as they expect us to clear up after them. Everybody should be treated like a normal human being irrespective of what they look like, how old they are and what profession they are in.


If I enter a shop as a customer I treat the sales assistant as a human being. In this job one has to deal with annoying customers. People expect more than is possible. Sales assistants are only human, but in sales humanity is lost. One is only a number. If one is expected to be friendly to customers one expects to be treated decenctly in return. The motto ‘the customer is king’ has become a bit obsolete. One has to be able to treat customers well because one gets one’s pay because of them. But some of them take out their bad moods on sales assistants. Being a sales assistant means being treated as part of the furniture. Whether people demand service depends on their age. Older customers ask more questions, such as whether things suit them. If older customers are accompanied by female friends we wait at a distance as they tend to consult each other. This also goes for younger customers who come in with their mothers or girlfriends. Younger people need less service than older people.

Customers’ image of sales assistants and their job Figure 10.3 shows what customers expect from sales assistants. Typical views of the job of sales assistant were as follows: Sales jobs are simple and less accepted in society. Anyone can become a sales assistant without being educated, just to earn some money. The career possibilities are few, but it is a good part-time or temporary job for school children or students and it should not be debased. In expensive boutiques sales assistants are expected to have a better education and customers expect better service. Although the sales assistants at H&M are friendly they very rarely offer help. In the future there will be service in shops but the number of sales assistants is bound to decrease.6 In shops such as H&M one already relies less on service and more on cheap products. The Internet will grow in importance and will probably replace catalogue and mail order shopping, but things will not change that quickly. One will continue to buy in shops because one can examine clothes and try them on. To summarise, the image of the job of sales assistant is very poor. The job is never connected with success, challenge or admiration. The only advantage the young interviewees spoke of was the possibility of earning money. A very common attitude was that being a sales assistant required no education at all. Some of the interviewees did not even know that there was a training scheme for those who wished to become

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In large companies such as H&M, personal service hardly exists. Sales assistants should be young, attractive and friendly. They should wear fashionable outfits that one can buy in the shop. Customers, wish that sales assistants would stay in the background, then the customer can look around calmly and try things on. If customers have questions the sales assistants should help immediately and any advice should be fair and credible. However, sales assistants and customers usually only come into contact at the cash desk. Many customers like to look around calmly and try things on. If they need help they should be able to contact a sales assistant and ask him/her. Sales assistants should greet customers in a friendly manner and decide whether they are likely to want help. At H&M it is rare to be offered individual advice, although sales assistants can be asked for missing sizes. Customers prefer sales assistants to stay unobtrusively in the background, but be ready to help if questions arise. Individual advice should be fair and credible, not just aimed at making a sale. At H&M there is no individual service but you can shop faster. Sales assistants should be young, attractive, friendly and helpful. Often sales assistants are women who are interested in fashion. At H&M the sales assistants are young and fashionably dressed. They should follow the current trends; their outfits should be available for purchase in the shop. Contact between customers and sales assistants often only takes place at the cash desk.

Figure 10.3 How customers perceive sales assistants

a sales assistant in retailing. Implicitly, a job in sales was seen as a stopgap solution. The chance to make a career in retailing was evaluated as low or even not possible.

Conclusion The study clearly confirmed the findings presented in the practitioneroriented and scientific-oriented literature. Therefore it is hard to understand why organisations that are concerned by or even responsible for the poor internal and external image of the job of a sales assistant are not making every possible effort to change this image into a more professional one. To this end, marketing campaigns should also address the internal workings of a retail company. In the past, campaigns were held to convince young people of the advantages of becoming an apprentice


or trainee. But most of these were short term and politically motivated to show that an effort was being made to help the young. At this point it can only be suggested that representatives of the retail clothing business and employees’ interest groups meet with the media, teachers, family members and young people in order to start an image campaign. Austria is very well known for its apprentice training, which is unique in Europe. Perhaps people in business, administration and education have forgotten to base their activities on the philosophy behind this system, and need to be reminded of their responsibility to nurture an image that will motivate young people to become sales assistants and motivate retail companies to take better care of their personnel. Last but not least, in accordance with the internal marketing concept, emphasis should be put on convincing retail managers that sales assistants must be trained and treated as competent and responsible marketing people (Buber, 2000). Managers should be aware of the advantages of strongly integrating sales personnel in to the company’s internal marketing concept.

Notes 1. The very bad image of retailing goes back to Plato, who ranked retailing at the lowest level of his ideal society because it did not lead to something new. His pupil Aristotle described the professional retailer as ‘unnatural, profit-seeking, exploiting and corrupt’ (Zimmer, 1995, p. 51). 2. Statutory employees’ organisation. 3. Most of the training programmes in retail companies focus on providing information on new products and so on. Although there is considerable coverage of the psychological aspects of personal selling in the marketing literature (for example Bänsch, 1996; Albers et al., 1999; Nerdinger, 2001), in practical training programmes less emphasis is put on such issues. The training of sales assistants often concentrates on basic rules such as ‘The customer is king’, ‘Smile’ and ‘Be clean and dress properly’, and therefore sales assistants are not informed about their role as a marketing person in a selling process. 4. www.c-and-a.com, www.hm.com; www.jeans-factory.de, www.mango.es, www.mar-o-polo.com, www.mexx.com, www.newyorker.de, http://shop. orsay.com, www.pimkie.de, www.schoeps.at, www.stiefelkoenig.at, www. streetone.de, www.turek.at, www.zero.de, www.spf.com. 5. H&M (Hennes and Mauritz) is a successful chain of clothing discount shops. The company was founded in Sweden in 1947 and entered the Austrian market in 1998 (Huppert, 2000, p. 16). In 2002 H&M was awarded the title of ‘Best international retailer’ by America’s National Retail Federation (H&M, 2002). 6. However Beinstein and Maurer (2002, p. 76) predict that service will experience a renaissance, for which the right personnel will have to be available.

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References Albers, S., Haßmann, V., Somm, F. and Tomczak, T. (1999) Verkauf. Kundenmanagement, Vertriebssteuerung, E-Commerce (Wiesbaden: Gabler). Baethge, M., Grimm, A. and Oberbeck, H. (1992) ‘Neue arbeits- und personalpolitische Konzepte im Groß- und Einzelhandel’, in M. Baetge and H. Oberbeck (eds), Personalentwicklung im Handel. Zwischen Stagnation und neuen Perspektiven (Frankfurt and New York: Campus), pp. 11–105. Bänsch, A. (1996) Verkaufspsychologie und Verkaufstechnik (Munich and Vienna: Oldenbourg). Bauer, H. H. and Jung, S. (1999) ‘Handelsforschung mittels multivariater Methoden: Das Beispiel einer Conjoint Analyse zur Bestimmung der Anforderungsprofile von Betriebsformen des Textileinzelhandels’, in O. Beisheim (ed.), Distribution im Aufbruch. Bestandsaufnahme und Perspektiven (Munich: Vahlen), pp. 679–91. Becker, J. (2001) Marketing-Konzeption (Munich: Vahlen). Beinstein, E. and Maurer, R. (2002) Die Zukunft des Handels (Kelkheim: Zukunftsinstitut). Beisheim, O. (1999) ‘Der Mensch im modernen Dienstleistungs-System’, in O. Beisheim (ed.), Distribution im Aufbruch. Bestandsaufnahme und Perspektiven (Munich: Vahlen), pp. 3–35. Belz, C. (1996) Verkaufskompetenz (St Gallen: Thexis). Bergmann, N., Putz, I. and Wieser, R. (2001) Jugendliche mit und ohne Berufsausbildung. Eine Studie aus Sicht der Betroffenen (Vienna: Hofstätter). Boehm-Kochanski, C. (1995) Karriere im Handel (Frankfurt: Deutscher Fachverlag). Brater, M. and Landig, K. (1995) Der neue Verkäufer: Qualifikationsanforderungen und Qualifizierungsmethoden am Beispiel eines Fachberaters im Handel (Munich and Mering: Hampp). Buber, R. (2000) ‘Die Einstellung von Führungskräften zum internen Marketing – eine empirische Untersuchung mit GABEK’, in R. Buber and J. Zelger (eds), GABEK II: Zur qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 259–99. Buber, R. (2003) ‘Sales personnel in textile retailing: empirical study on the self-perception and the consumers’ perception of young sales assistants’ (unpublished manuscript). Buber, R. and Kraler, C. (2000) ‘How GABEK and WinRelan Support Qualitative Research’, in R. Buber and J. Zelger (eds), GABEK II: Zur Qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 111–37. Carson, D., Gilmore, A., Perry, C. and Gronhaug, K. (2001) Qualitative Marketing Research (London, Thousand Oaks and New Delhi: Sage). Denk, J., Obermair, A., Grund, P., Enne, H. and Lewisch, K. (1999) Arbeitsbedingungen der Beschäftigten im Handel. Bericht (Vienna: Kammer für Arbeiter und Angestellte). Diller, H. (1999) ‘Discounting: Erfolgsgeschichte oder Irrweg?’, in O. Beisheim (ed.), Distribution im Aufbruch. Bestandsaufnahme und Perspektiven (Munich: Vahlen), pp. 351–72. Eggert, U. (2001) Der Handel im 21. Jahrhundert (Düsseldorf and Berlin: Metropolitan).

166 The Job Image of Young Sales Assistants Flick, U. (1998) An Introduction to Qualitative Research (London, Thousand Oaks and New Delhi: Sage). Gavac, K., Gittenberger, E., Grohall, G., Grozea-Helmenstein, D., Helmenstein, C., Hofmann, A., Jung, J., Schneider, C., Schnedlitz, P., Teller, C. and Voithofer, P. (2002) Perspektiven im Handel (Vienna: OeIfGH/WU/WIKA/IHS). Gilbert, D. (1999) Retail Marketing Management (Harlow: Prentice-Hall). Görs, D., Goltz, M. and Iller, C. (1994) Personalentwicklung und Weiterbildung im Einzelhandel. Das Verkaufspersonal zwischen Qualifizierung und Rationalisierung (Bremen: Universität Bremen). H&M (2002) Press releases, finance, www.hm.com/at_de/hm/press/finance.jsp? prid⫽189. Howe, W. S. (1992) Retailing Management (Basingstoke: Macmillan). Huppert, E. (2000) Lebensphase Jugend, eine Einführung in die sozialwissenschaftliche Jugendforschung (Weinheim and Munich: Juventa). Kepper, G. (1996) Qualitative Marktforschung, Methoden, Einsatzmöglichkeiten und Beurteilungskriterien (Wiesbaden: Deutscher Universitätsverlag). Kotler, P. and Bliemel, F. (1999) Marketing Management (Stuttgart: SchäfferPoeschel). Liebmann, H.-P. and Zentes, J. (2001) Handelsmanagement (Munich: Vahlen). Müller-Hagedorn, L. and Preißner, M. (1999) ‘Die Entwicklung der Verkaufstechniken des Einzelhandels: Siegeszug der Selbstbedienung und Aufkommen neuer Medien’, in E. Dichtl and M. Lingenfelder (eds), Meilensteine des deutschen Handel (Frankfurt: Deutscher Fachverlag), pp. 147–79. Nerdinger, F. W. (2001) Psychologie des persönlichen Verkaufs (Munich and Vienna: Oldenbourg). Peterson, R. A. and Balasubramanian, S. (2002) ‘Retailing in the 21st century: reflections and prologue to research’, Journal of Retailing, vol. 78, no. 1, pp. 9–16. Reynolds, K. and Beatty, S. (1999) ‘Customer Benefits and Company Consequences of Customer–Salesperson Relationship in Retailing’, Journal of Retailing, vol. 75, no. 1, pp. 11–32. Rubin, J. H. and Rubin, I. S. (1995) Qualitative Interviewing: The Art of Hearing Data (Thousand Oaks, London and New Delhi: Sage). Schmalen, H. (1994) ‘Das hybride Kaufverhalten und seine Konsequenzen für den Handel’, Zeitschrift für Betriebswirtschaft, vol. 64, no. 10, pp. 1221–40. Schmalen, H. and Schachtner, D. (1999) ‘Discount- vs. Fachhandel im Zeichen des hybriden Konsumenten’, in E. Dichtl and M. Lingenfelder (eds), Meilensteine des deutschen Handel (Frankfurt: Deutscher Fachverlag), pp. 123–46. Schnedlitz, P., Kotzab, H. and Cerha, C. (2000) ‘Betriebstypenspezifische Einkaufsstättenwahl und Nahversorgungsproblematik – Ergebnisse einer empirischen Studie’, in V. Trommsdorff (ed.), Handelsforschung 1999/ 2000. Verhalten im Handel und gegenüber dem Handel (Wiesbaden: Gabler), pp. 195–215. Schwab, R. (1982) Der persönliche Verkauf als kommunikationspolitisches Instrument des Marketing (Frankfurt and Thun: Harri Deutsch). Tenbensel, B. (1987) Arbeit, Qualifikation und Kontrolle im Einzelhandel (Frankfurt and New York: Campus). Wald, R. (1985) Verkaufen – eine Dienstleistung im Strukturwandel (Frankfurt: Campus).

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Zelger, J. (2000) ‘Twelve Steps of GABEKWinRelan: A Procedure for Qualitative Opinion Research, Knowledge Organization and Systems Development’, in R. Buber and J. Zelger (eds), GABEK II. Zur Qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 205–20. Zimmer, D. (1995) ‘Der Handel braucht eine Imagepolitur’, Harvard Business Manager, vol. 4, pp. 51–8.

11 Venture Brazil: Studying Austro-Brazilian Joint Ventures by Means of GABEK and WinRelan Jürgen Mühlbacher and Jochen Penker

Introduction This chapter is based on a study of joint ventures in Brazil, one of the world’s largest and most attractive emerging markets. International joint ventures are complex partnerships that depend on personal and cultural understanding between the partners, strategic harmony in respect of the goals to be achieved, a good resource fit and trust between the partners in the interest of success. Previous research has focused on various aspects of international joint ventures, including planning, management and control, but little attention has been paid to the Latin American market, with its unique characteristics. The study reported in this chapter aimed to identify the key success factors in Austro-Brazilian joint ventures by uncovering the critical internal and external factors that had influenced this kind of cooperation, plus the managers’ perceptions and evaluations of and attitudes towards their joint ventures. The empirical research included both partners’ perspectives, that is, those of the Austrian and the Brazilian managers. It involved the full range of Austrian equity joint ventures in Brazil, covering 12 companies. GABEK was chosen as an appro-priate method for theory building when the existing theories were deemed to be rather inconsistent. This chapter describes the research objectives, questions and design, outlines previous studies’ findings on the key success factors in international joint ventures, explains the first steps of a GABEK analysis, and presents the first results of our empirical study. 168

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Defining international joint ventures The focus here is on equity joint ventures, as opposed to non-equity, cooperative alliances in which the partners do not share ownership of the capital resources. Equity joint ventures are defined as ‘legally and economically separate organisational entities, created by two or more parent organisations that collectively invest financial as well as other resources to pursue certain objectives’ (Anderson, 1990, pp. 19 f.; Pfeffer and Nowak, 1976, pp. 398 f.). According to Yan and Luo (2001, p. 3), ‘among the different forms of interorganisational relations joint ventures are unique and arguably the most complex type of arrangement. … In comparison to the contractual forms of interfirm cooperation, joint ventures represent a longer-term collaborative strategy.’ International joint ventures are broadly defined as joint ventures that involve firms from different countries cooperating across national and cultural boundaries. On some occasions, joint ventures formed by partners from the same country but located in a different country are also considered to be international joint ventures (Geringer and Herbert, 1989, pp. 235 f.).

Keys to joint venture success ‘In the market, everything is important but some things are more important than others’ (Vasconsellos e Sá, 1988, p. 61). This statement constitutes the basic idea behind success factor research. It is argued that ‘in most industries there are usually three to six factors that determine success. These key jobs must be done exceedingly well for a company to be successful’ (Daniel, 1961, p. 116). Key success factors, also called critical success factors, are ‘those characteristics, conditions, or variables that when properly sustained, maintained, or managed can have a significant impact on the success of a firm competing in a particular industry’ (Leidecker and Bruno, 1984, p. 24). The unique features of international joint ventures have made this organisational form both interesting and challenging. Previous research has identified several key areas. In order to synthesise the vast and growing body of literature, Parkhe (1993) has identified four major areas of research on international joint ventures: (1) the motives for venture formation, (2) partner selection, (3) governance and control, and (4) joint venture performance and stability. While this chapter chiefly focuses on the fourth area, the critical success factors identified can also be attributed to other research areas

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(for example the important factor ‘background check’ corresponds to the research area ‘partner selection’). The final goal of our study is to develop an integrated model of joint venture success in Brazil with respect to the multiple perspectives on performance held by various parties in the venture. Since joint ventures involve multiple participants, each with distinct expectations, one of the key tasks is to decide from whose perspective success should be assessed. A joint venture system incorporates three or more different perspectives (Figure 11.1). First, the joint venture partners pursue partial interests such as the growth of their own company, sales or technology transfer. Second, the joint venture managers focus on the growth of the joint venture itself, economic stability, innovation and the sustainability of the venture. The joint venture managers’ challenge is to balance the interests of the two (or more) partners (headquarters). Having analysed the theoretical literature on joint venture performance and stability from 1971 to 2001 we identified and ranked the success factors in international joint ventures (Zielke, 1992; Eisele, 1995; Helm and Peter, 1999; Probst and Rüling, 1999; Kabst, 2000; Yan and Luo, 2001). The ranking was based on the frequency of variables within a theoretically developed category scheme that was deductively adapted during the research process according to the rules of qualitative research (Bortz and Döring, 2002, pp. 139 f.). Table 11.1 shows the ranking of the frequencies in line with our theoretical findings.

Partial interests of Brazilian joint venture partner

Partial interests of Austrian joint venture partner

Joint venture management: balancing different interests, aiming for sustainability of the joint venture

Figure 11.1 Multiple system of interests


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Table 11.1 Key success factors in international joint ventures – results of the literature review Partners’ view

Joint venture view

1. 2. 3. 4. 5. 6. 7.

1. 2. 3. 4. 5. 6. 7.

Common strategy Cultural fit Trust between partners Market situation Control system Legal framework Commitment

Management skills Operative autonomy Learning capacity Human resources Performance Structural fit Incentive system

‘Common strategy’ and ‘cultural fit’ lie at the top of partners’ ranking. Consensus among the partners on the venture’s mission, strategy and cultural fit seem to contribute significantly to success. ‘Trust between partners’ and ‘commitment’ are key factors in establishing a long-term relationship between the partners. ‘Market situation’ secures the economic outcome. The ‘controlling system’ and ‘legal framework’ ensure influence over the local management and development of the joint venture. From the joint venture’s point of view ‘management skills’, which are also part of ‘human resources’, are the most important success factors in any joint venture. ‘Operative autonomy’ and ‘performance’ lead to an economically independent organisational entity (Anderson, 1990). ‘Learning capability’ and ‘structural fit’ aim at sustainable development of the joint venture. Last but not least, ‘human resources’ and an appropriate ‘incentive system’ refer to the qualification and motivation of managers and employees.

The empirical study: Austro-Brazilian joint ventures Problem identification, research objectives and questions The past twenty years have witnessed a rapid increase in the number of joint ventures between companies from all over the world. These joint ventures offer an important alternative to international mergers and acquisitions by allowing for cooperation between firms without loss of corporate identity. International joint ventures, however, are particularly problematic to manage because, by preserving the separate identity of the parent companies, they incorporate several sources of conflict. When joint ventures cross borders and differences in culture and management philosophy come into play, the risk of failure strongly

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increases. Empirical studies prove that only about 50 per cent of joint ventures succeed (Harrigan, 1988; Kogut, 1988; Heck, 2000). Previous research has focused on numerous aspects of joint ventures, including the planning, management and control of international joint ventures. A large number of studies have been conducted in the United States and Western Europe on ventures in the Asian, Central European and Eastern European emerging markets. From an economic point of view, the majority of multinational investments are also concentrated in these areas. Very little attention, however, has been paid to the Latin American market and its unique characteristics. Based on an extensive database and a review of the literature we discovered that Brazil, one of the largest emerging markets in the world and the most important economy in the southern hemisphere, had not been mentioned in any research paper on joint venture success. For this reason alone it was worth taking a closer look at this emerging market. Despite Brazil’s enormous growth potential, only a few Austrian investors had dared to establish a joint venture with a Brazilian partner. Intending to find out more about the opportunities and risks of establishing and managing a joint venture in Brazil, we identified the following research questions: ●

●

●

●

Which internal and external factors do you perceive as crucial to success or failure? How can these factors be assessed and evaluated? Which factors are of greatest importance? Which causal connections are there between the various acts of managing and controlling the joint venture? What are the inherent strengths and weaknesses of specific joint ventures?

Field of research Following a rough interview guide containing nine open-ended questions (Table 11.2), 23 in-depth interviews were conducted with executives at both the Austrian parent firms and the joint ventures. Most interviewees were top-level executives who had personally participated in the venture’s founding negotiations. Each face-to-face interview lasted from 60 to 90 minutes and was tape-recorded. The Austrian executives were first contacted by telephone. After this the potential interviewees received an e-mail in which our research project was briefly described. They were also invited to suggest a time and place for a personal meeting. The CEOs were cooperative and


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agreed to meet an interviewer either at their headquarters or in a nearby hotel or restaurant. One CEO of a large Austrian multinational even picked up the interviewer from the railway station. After a short introduction and project description the interviewees were informed that all information would be kept confidential. They were then asked whether the ‘personal conversation’ (not interview) could be tape-recorded. Only one CEO refused. The interview started with the first question in (Table 11.2). The interviewees’ answering times varied from one to 20 minutes. Questions 1 and 2 turned out to be good ‘appetisers’ because the interviewees felt able to talk about the current situation of their joint venture in a very unconstrained, relaxed and open way. After some time, when the conversation had become more relaxed, the interviewees became more talkative. At the close of the meeting they were asked to arrange an appointment with a representative of the Brazilian partner. Since it appeared to be extremely difficult to make appointments with Brazilian managers, the help of the Austrian managers in making contact with their Brazilian colleagues was very welcome.

Methodology and research design The study adopted the GABEK method (Zelger, 1999) and its computer application WinRelan, which had not yet been applied in the field of success factor research. GABEK is a method of analysing qualitative data such as unstructured text material. It is based on Stumpf’s (1939) theory of ‘Wahrnehmungsgestalten’ (perceptual gestalten), which was Table 11.2 Interview guide 1. 2. 3. 4. 5.

6. 7. 8. 9.

What comes to mind when you think about your joint venture with your Brazilian/Austrian partner? Could you please describe the origins of this venture? In your opinion, what should be considered before forming a joint venture (in Brazil)? In your opinion, which factors are most important during the formation phase? What are the difficulties? In your opinion, what are the success factors in managing a joint venture (in cooperation with a Brazilian/Austrian partner)? What are the challenges and difficulties? If a joint venture is approaching termination, what has to be considered during the termination phase? What are the potential problems? Can you remember an event that substantially influenced the success (or failure) of this joint venture? What would you do differently next time? Would you like to add anything else?

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transformed into a theory of linguistic gestalten by Zelger (1996). When analysing data using WinRelan, the researcher’s aim is to obtain a holistic, integrated view of individual aspects of the situation being investigated (for example opinions and attitudes). This is achieved by developing a rule-based network of data that takes both syntax and semantics into account (Buber and Kraler, 2000, p. 112; for an overview of the characteristics of the GABEK method see Buber and Kraler, 2000, pp. 111–38; and Zelger, 1994, 1996).

Defining text units The first step in GABEK WinRelan analysis is to divide the transcribed text material into text units. Each text unit should be a ‘closed statement’ and the units are called ‘sentences’. When transferred to WinRelan these sentences are automatically written onto index cards. An index card may contain more than one grammatical sentence and should contain a statement from a respondent. This will usually be a meaningful statement when the text from interviews is analysed. In general the length of the text on an index card is determined by the number of keywords the respondent has used in his or her statement and by the researcher’s definition of the text unit (Buber and Kraler, 2000, p. 115).

Coding Generally speaking, coding is hard work and can even be extremely exhausting. However it is an essential part of qualitative analysis and also of GABEK WinRelan analysis. In comparison with other computersupported methods, coding is an especially important process and has a decisive effect on the quality of the overall results (ibid., p. 119). Before starting the coding process an appropriate coding strategy has to be defined. This must be done without any interpretation on the part of the researcher. Moreover it must be done on the respondents’ language level.

The list of keywords Having coded all the text units a list of keywords is generated automatically by WinRelan. The list also shows keyword frequencies within the text. In most cases the number of times a keyword is mentioned gives a first hint of the importance of the factors coded through this word. In the case of our research objective and questions we had to identify keywords that contained relevant information and were positioned at the top of the ranking. The list of keywords could be seen as a brief first impression of important themes.


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Table 11.3 Key success factors in the Austro-Brazilian joint ventures – results of the empirical study Partners’ view

Joint venture view

1. 2. 3. 4. 5. 6. 7.

1. 2. 3. 4. 5. 6. 7.

Human resources Legal framework Cultural fit Market situation Trust between partners Control system Common strategy

Cultural fit Human resources Market situation Structural fit Legal framework Trust Management skills

The definition of criteria For the content of each index card we defined criteria such as the position of the interviewee or his or her identification number. Success factors drawn from the literature (see Table 11.1) were also defined as criteria. When keywords matched one of these criteria (such as trust between partners, human resources and so on) we marked them in the criteria window.

First results The interviews with Austrian executives and joint venture managers generated about 1500 index cards or ‘sentences’ that had to be coded. When looking through our list of keywords and analysing the keyword frequencies we came across several keywords – including ‘joint venture’, ‘partner’ and ‘Brazil’ – that did not provide any information. However five themes seemed to be of particular importance to the interviewees. First, the success or failure of a venture was seen as being influenced by the ‘market situation’, an important external factor that cannot be controlled by decision makers. Second, the area ‘human resources’, including the keywords ‘managing director’, ‘employees’ and ‘experience’, were mentioned frequently by the interviewees. Third, the factor ‘legal framework’ could be of utmost importance to executives and joint venture managers. Finally, the areas ‘trust’ and ‘cultural setting’, namely understanding of the foreign (Brazilian) mentality, was deemed to be relevant. All the success factors identified in previous research (Table 11.1) were also mentioned by the Austrian executives and joint venture managers. However the Austrian partner firms saw ‘human resources’ as the most important factor and this factor does not appear in the ‘partners’ view’ column in Table 11.1. As shown in Table 11.3, ‘human resources’ ranked

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highest, followed by ‘legal framework’, ‘cultural fit’, ‘market situation’, ‘trust’, ‘controlling system’ and ‘common strategy’. From the joint venture managers’ point of view, ‘cultural fit’ was perceived as most significant, followed by ‘human resources’ and ‘market situation’. The partners’ ranking has strong similarities with the variables found in the literature, but the empirical data show no difference between the partners’ view and the joint venture ranking. This could mean that the perspectives of the joint venture partners and the joint venture managers do not differ, as stated before. These results were only valid within the tight framework of our criteria set and merely represented a preliminary framework based on previous research. In order to gain more reliable results and more detailed information on the Austro-Brazilian joint ventures, in the next step of the WinRelan analysis we had to code all the evaluations made by the Austrian executives and joint venture managers.

The evaluation list The evaluation list, which was generated automatically and included words with positive, negative and neutral evaluations, reflected the respondents’ evaluation of given themes. We marked all factors that were perceived by the managers as crucial to success or failure as ‘neutral’ in the evaluation window (Table 11.4). These evaluations may be regarded as preliminary key success factors and will make a precious contribution to the next WinRelan step, gestalten building, which is not presented here. As can be seen in the evaluation list, the neutrally evaluated keywords ‘trust’, ‘legal framework’, ‘lawyer’, ‘human resources’, ‘mentality’ and ‘market situation’ were ranked very high (as they had in the list of keywords) and were seen as especially relevant by the executives and joint venture managers. Legal issues such as finding the right lawyer and working out a mutually acceptable contract were seen as an important first step towards establishing a partnership. The factor ‘personal relationship’ might be specifically related to the Brazilian way of doing business. The negatively evaluated keyword ‘speaking English’ could indicate that the Austrian executives and joint venture managers faced communication problems when speaking or negotiating in English. Among the other negatively evaluated keywords, ‘corruption’ might be another important topic. On the other hand managers positively evaluated the keyword ‘flexibility’. This could mean that the Brazilians were able to react very quickly to changing requirements. However the managers


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Table 11.4 Evaluation list (extract of frequency of keywords) Keyword Trust between partners Mentality Speaking English Personal relationship Lawyer Communication Human resources Legal framework Time Market knowledge Chemistry Understanding Cooperation Control Market situation Support Goal definition clear Cultural fit Sensitivity Flexibility Managing director Scrutinise Background Corruption

01

⫹2

⫺3

24 12 1 7 9 6 7 8 6 7 5 4 4 6 6 6 6 5 5 1 2 5 5 0

2 1 0 3 0 0 2 0 0 1 1 0 3 0 0 0 0 1 0 4 1 0 0 0

1 7 9 0 1 3 0 1 3 0 1 3 0 0 0 0 0 0 0 0 2 0 0 5

Notes: 1. Factors crucial to success or failure (neutral evaluation). 2. Positive evaluation. 3. Negative evaluation.

perceived grave differences between the Austrian and Brazilian mentalities, and coping with this culturally specific factor seemed to be an important issue for them. The keywords ‘understanding’ and ‘chemistry’ point to the importance of building an amicable relationship, not only on a business basis but also on a personal level. ‘Communication’ could be perceived as crucial because joint ventures are based on continuous exchange between partners. The factor ‘time’ is relevant in two specific cases, as described by the interviewees. First, during the planning process it had taken time to analyse the risks and opportunities in the market situation in question. Second, the Austrian managers had found it difficult to understand the culturally specific Brazilian time-frame.

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Hence the Western European managers had been required to exercise a fair degree of patience when establishing joint ventures in Brazil.

Future research steps The results of the first steps of our empirical study (generating the list of keywords and the evaluation list) have been presented above. The next steps will include ‘causal coding’ to identify influences – usually causal relations – between the variables coded through keywords. The causal list will show the number of influences or causal effects that each keyword has on other keywords and the number of keywords that influence that keyword, thus providing more detailed insights into the causal relationships between key success factors (such as whether trust, personal relationships and communication influence performance). Another crucial step of the analysis – the building of gestalten – will be aimed at developing a structural model that includes all the crucial factors in the Austro-Brazilian joint ventures, the ‘gestalten tree’. The ‘relevancy analysis’ will integrate all the results gained so far. It will support the identification and selection of relevant (core) variables in the analysed context, such as basic values, aims, measures and constraints. Next, the ‘analysis of causal nets’ will generate causal network graphics, a powerful interactive instrument to identify the appropriate measures for specific aims. Finally, the ‘gestalt graph’ will integrate the results of the evaluation (the list of evaluations), the gestalten tree and the causal lists in a graphical model.

References Anderson, E. (1990) ‘Two firms, one frontier: on assessing joint venture performance’, Sloan Management Review, vol. 31, no. 2, pp. 19–30. Bortz, J. and Döring, N. (2002) Forschungsmethoden und Evaluation (Berlin, Heidelberg and New York: Springer). Buber, R. and Kraler, C. (2000) ‘How GABEK and WinRelan support qualitative research’, in J. Zelger and R. Buber (eds), GABEK II: Zur qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 111–38. Daniel, D. (1961) ‘Management information crisis’, Harvard Business Review, vol. 5, no. 39, pp. 111–21. Eisele, J. (1995) Erfolgsfaktoren des Joint Venture-Management (Wiesbaden: Gabler). Geringer, J. and Herbert, L. (1989) ‘Control and performance of international joint ventures’, Journal of International Business Studies, vol. 2, no. 20, pp. 235–54. Harrigan, K. (1998) ‘Joint ventures and competitive strategy’, Strategic Management Journal, vol. 9, no. 4, pp. 141–58.


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Heck, A. (2000) ‘Strategische Partnerschaften zum operativen Erfolg führen’, IO-Management, vol. 69, no. 4, pp. 24–31. Helm, R. and Peter, A. (1999) ‘Erfolgsfaktoren von Joint Ventures’, Zeitschrift für Planung, vol. 10, no. 1, pp. 27–46. Kabst, R. (2000) Steuerung und Kontrolle internationaler Joint Venture: eine transaktionskostentheoretisch fundierte empirische Analyse (Munich and Mering: Rainer Hampp). Kogut, B. (1988) ‘Joint ventures: theoretical and empirical perspectives’, Strategic Management Journal, vol. 9, no. 4, pp. 319–32. Leidecker, J. and Bruno, A. (1984) ‘Identifying and using critical success factors’, Long Range Planning, vol. 17, no. 1, pp. 23–32. Parkhe, A. (1993) ‘Messy research, methodological predispositions, and theory development in international joint ventures’, Academy of Management Review, vol. 18, no. 2, pp. 227–68. Pfeffer, J. and Nowak P. (1976) ‘Joint Ventures and Interorganizational Interdependence’, Administrative Science Quarterly, vol. 21, no. 3, pp. 398–418. Probst, G. and Rüling, C. (1999) ‘Joint Ventures und Joint Venture-Management’, in H. Schaumburg (ed.), Internationale Joint Ventures – Management, Besteuerung, Vertragsgestaltung (Stuttgart: Schäffer-Poeschel), pp. 1–34. Stumpf, C. (1939) Erkenntnislehre, Band 1 (Leipzig: Johann Ambrosius Barth). Vasconcellos e Sá, J. (1988) ‘The impact of key success factors on company performance’, Long Range Planning, vol. 21, no. 6, pp. 56–64. Yan, A. and Luo, Y. (2001) International Joint Ventures: Theory and Practice (New York: M. E. Sharpe). Zelger, J. (1994) ‘Qualitative Auswertung sprachlicher Äußerungen. Wissensvernetzung, Wissensverarbeitung und Wissensumsetzung’, in R. Wille and M. Zickwolff (eds), Begriffliche Wissensverarbeitung: Grundfragen und Aufgaben (Mannheim: B.I.–Wissenschaftsverlag), pp. 239–66. Zelger, J. (1996) ‘Linguistic Knowledge Processing by GABEK. The Selection of Relevant Information from Unordered Verbal Data’, Preprint der Universität Innsbruck, Institut für Philosophie, vol. 42, pp. 1–25. Zelger, J. (1999) ‘Wissensorganisation durch sprachliche Gestaltbildung im qualitativen Verfahren GABEK’, in J. Zelger and M. Maier (eds), GABEK. Verarbeitung und Darstellung von Wissen (Innsbruck, Vienna and Munich: StudienVerlag), pp. 41–87. Zielke, A. (1992) Erfolgsfaktoren internationaler Joint Ventures: eine empirische Untersuchung der Erfahrungen deutscher und amerikanischer Industrieunternehmungen in den USA (Frankfurt: Peter Lang).

12 Austrian Furniture Retailers’ Perspective on the Austrian Design Sector Renate Buber and Marion Friedmann

Introduction Good design is becoming increasingly important. In a globally competitive environment where products are becoming more and more similar and interchangeable, design is an important means of differentiating between products. Good design not only has to make quality visible but also has to take aesthetic, functional, technical, ecological, cultural, sociopsychological and cost-optimising factors into account. Until the Second World War Austria had a strong tradition in design. However, after the war, the country failed to continue in that vein. This chapter looks at how experts in furniture design evaluate the industry’s image, identify the various problems involved and make suggestions for solving these problems. The aim of the empirical study reported in this chapter was to gain a detailed insight into the design industry’s image by means of the GABEK method and its computer application WinRelan. One of the main reasons for the negative image of the Austrian furniture industry is the deficit of design consciousness among the Austrian population, which is probably due to poor attention to the subject of design in the education system. Furthermore Austria faces difficulties with the production of furniture in general, the furniture retailing industry and consumers’ buying behaviour. Last but not least, Austria lacks an official institute to promote design in general. Recently, numerous initiatives have been launched to address these problems and promote design. In Austria, furniture retailing is dominated by two large companies and this could have a strong bearing on the inadequacy of furniture design. However demographic and sociodemographic factors suggest that the demand for designer furniture in general and quality products in particular is likely to rise. This means that highly specialised designer 180

Renate Buber and Marion Friedmann 181

furniture shops will stand a good chance of success. Nonetheless further measures will be necessary to convey to people the importance of design. The problem of Austrian designers is not the lack of creativity, rather it seems that their self-confidence is low. The communication and cooperation between economy, education and young designers has to be supported and promoted, otherwise creative designers will migrate abroad to find an outlet for their talent.

The history of Austrian furniture design During the time of the ‘Wiener Werkstätte’ (Vienna Workshops) and the ‘Secession’ group in the Jugenstil era, Austrian design and architecture became very famous and the names of the furniture maker Michael Thonet and architects Josef Hoffmann, Otto Wagner and Adolf Loos spread throughout the world. Until the first decades of the twentieth century furniture production mainly took place in artisans’ workshops, with a single person being in charge of both design and production. This changed with the arrival of mass production, although Thonet had been mass producing furniture since the late 1840s after pioneering a method of steaming and bending solid beech component into a wide range of shapes (Buerdek, 1994, pp. 23, 74). Figure 12.1 shows Thonet’s famous ‘chair number 14’, which was the first real ‘consumer’ chair. By 1930, 50 million had been produced and the chair is still being manufactured today (ibid.) The two world wars had a major impact on furniture production. Due to the massive destruction of buildings there was an acute housing shortage and the average living space shrank. It therefore became more important to rebuild houses than to produce furniture. Furniture production had to be suited to the minimum space available. Countries

Figure 12.1 Thonet’s chair number 14 Source: Gebrüder Thonet, Vienna.

182 Retailer’s Perspective on the Design Sector

such as Italy, Germany and those in Scandinavia led the way in this and Austria faded into the background. This chapter aims to identify the reasons for this development.

Design policy and institutionalisation in Austria Over the years a number of Austrian initiatives have been launched in an attempt to promote better design in product innovation and to encourage design consciousness among the population. Many of these initiatives have proved inefficient or have not produced the expected results, for example: ●

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●

In 1996 the Austrian Design Centre was closed and so far no real alternative has been planned. In 2000 the National Design Award was reintroduced after a four year break. In 1994 the Austrian Chamber of Commerce (WIFI Österreich, 1994) published a booklet on the design landscape in Austria but this has not been updated since.

There is no designated establishment where design and related materials can be exhibited, although the Museum of Applied Arts (MAK) in Vienna has a permanent exhibition of the history of Austrian design. While there are some internationally successful Austrian designers, they tend to be little known in their home country. In Austria the term design is mainly associated with fashion and famous fashion designers, such as Helmut Lang (Bundesministerium für Wirtschaft und Arbeit, 2000). There is still only a very small design component in product innovation – just 7 per cent of Austrian industrial enterprises develop their products in cooperation with a designer (ibid.). Furniture production is dominated by small to medium-sized companies with little risk capital, a high degree of risk-aversion and a rather conservative outlook. There are many places to study design in Austria and the professional training in this respect is considered to be as good as in other countries. However as there are few design-related jobs in the country, many design graduates move abroad or find work in other professions.

Furniture trade in Austria The Austrian furniture retail trade is dominated by two large companies that hold over 55 per cent of the market share (Wohnkultur, 2000, p. 11).


This share is expected to grow in the next few years. Their low-price furniture is mainly imported from Eastern Europe, which has had a negative impact on Austrian manufacturers and many small to mediumsized companies have experienced financial difficulties (HoferWittmann, 1999; IfGH, 2001). The only exceptions are the kitchen and office furniture sectors, where Austrian manufacturers account for 69 per cent and 73 per cent of domestic sales respectively. All in all, market forces are shifting more towards trade (Die österreichische Holzindustrie, 2000, p. 19).

The study: the designer furniture market from the retailer’s point of view Methodology In the scientific marketing literature, image research mostly follows the positivist paradigm. Therefore questionnaires are the method of choice in most empirical studies. There is sufficient prior knowledge to allow the framing of specific hypotheses for investigation, which then guide the research (Churchill and Iacobucci, 2002, p. 316). However, because of the lack of research knowledge in the field addressed by this study a qualitative approach was considered to be more appropriate (Gummesson, 2000; Carson et al., 2001). The aim of the study was to investigate experts’ opinions on and attitudes towards the situation in furniture design and the furniture retail trade. In addition, their suggestions to improve Austria’s furniture design image especially in the European Market, were identified. The study followed the GABEK method (Zelger 1999a, 1999b), supported by WinRelan software. The GABEK method is designed to reveal the often implicit knowledge of those affected by the situation being researched. A convenience sample (Henry, 1990, p. 18) of ten designer furniture outlet owners (one woman and nine men) in Vienna was selected. Interviews were conducted following Meuser and Nagel’s (1991) approach to expert interviews. The interviews were guided by eight questions (Witzel, 1982, p. 90; Meuser and Nagel, 1991, pp. 448 f.) that focused on (1) the position of design and furniture design in Austria in general, (2) Austrians’ attitudes towards the design sector, (3) the demand for designer furniture in Austria, (4) factors relating to customers, (5) the organisation of the industry, (6) the perceived competitors, (7) design education in Austria and (8) the work situation for


young designers in Austria. In most cases only the first question – ‘How would you describe the design and furniture design situation in Austria?’ – was asked as planned. All the other topics were brought up by the interviewees themselves. The interviews were held in the interviewees’ offices, lasted 40–100 minutes, were tape-recorded and then transcribed (Meuser and Nagel, 1991, pp. 455 f.). The text was analysed by means of WinRelan1 (for an overview of the types of result generated by this software see Buber and Kraler, 2000; and Zelger, 2000). The following discussion will pay more attention to the findings than to the methodology.

General findings Figure 12.2 offers an insight into the themes that emerged from the text analysis and provides an overview of the interviewees’ opinions.2 (It should be pointed out that the statements have been translated from German into English, but the interviewees’ sentiments are represented as faithfully as possible.) The summary (at the top of Figure 12.2) is constructed from the four subsummaries and highlights the fundamental problems. It is clear that Austria has a lot of catching up to do in the area of design, especially in respect of the institutionalisation of design and design consciousness. Nevertheless the increasing demand for designer goods and better quality is encouraging. Moreover the teaching of design is considered to be good, although young designers have difficulty finding manufacturers to produce their designs. It is obvious that the design sector in Austria is not well organised and there is insufficient communication between manufacturers and designers. The study revealed many initiatives that a design institution could promote or organise. As there are only a few manufacturers in the designer sector, young designers often have to move abroad to find work. According to the interviewees, not only is the Austrian market small, but also manufacturers are rather risk-averse and the furniture industry as a whole has stagnated. Design is seen as an important means of companies setting themselves apart from their competitors. Moreover well-designed products are likely to sell better. While the teaching of design in Austria is evaluated quite positively, more could be done to boost design consciousness, and at an earlier age. The primary school curriculum should follow the Scandinavian example and devote attention to lifestyle and design matters.


There is no other country in Europe where the share of the furniture retail trade is so concentrated. The demand for design will grow and there is expected to be a demand for better quality. In respect of design, Austria is five years behind the international standard. Design consciousness is low and the design sector is badly organised. Cooperation between designers and manufacturers should be supported. Design education is good but only a few manufacturing companies exist and the main problem for Austria’s young designers is to find someone to produce their work. Therefore many designers move abroad. In respect of design, Austria is five years behind the international standard. Moreover we export too little. In Austria there are only a few manufacturing companies and few well-known young designers. Many of our designers move abroad or work in different professions. There are good educational alternatives and Austria should try to keep people in the country. In Italy and Denmark design is taught at a much younger age and in the Scandinavian countries such matters are taught at primary school. The demand for and interest in good design will grow steadily and it will become more and more difficult to sell badly designed furniture. The market will also grow because young people want better quality. But at the moment it is hard to raise the demand as the furniture market has stagnated. The main problem for Austrian designers is to find a manufacturer. Because of the stupid industrial policies in the 1960s and 1970s, small manufacturing companies collapsed. Today we call the necessary companies to hold production alive. The market for design in Austria is very limited. The companies must find mixed forms and manufacturers must specialise. One has to build up a market. In other countries the profits in relation to the size of the population are much higher. In Austria mostly cheap stuff is sold. For young designers in Austria the situation is a catastrophe; abroad it is better. Often designers do not try to find a manufacturer in Austria. The field of design is also badly organised. One should support cooperation between manufactures and designers. There is good design education in Austria. But design consciousness is low and this should be sharpened. Figure 12.2 The furniture design market: the retailers’ point of view

Some detailed findings The relevancy analysis conducted by means of GABEK and WinRelan identifies ‘basic values’ having a broad consensus among the interviewees (Buber, 2000, pp. 282 ff.). The analysis of the retailers’ perception of


the designer furniture sector revealed that ‘demand’, ‘consumers’, ‘selling’, ‘survival’, and ‘design consciousness’ were basic values (Table 12.1). Some of the findings are strongly connected to marketing issues and may be seen as a suitable basis for furniture retailers’ marketing planning. The basic value ‘survive’ has a more global focus as it deals with operating in a highly competitive and concentrated market. ‘Design consciousness’ can be categorized as person-oriented as it involves psychological aspects of consumer behaviour.

Table 12.1 Detailed results of the study Basic value

Description

Marketing-related findings Demand What influences the demand for designer furniture? Increasing urbanisation and globalisation are having a positive effect on the demand for designer goods. While the prices of designer furniture are high, they are not as high as people expect them to be. Well-designed products can be sold much more easily than badly designed ones. Consumers

What factors influence consumers’ buying behaviour? ‘Feeling comfortable in the shop’, ‘service’, ‘delivery conditions’, ‘price’, ‘visual presentation in the form of a display room’, and ‘expert assistance’ are influencing factors in customer satisfaction. Consumers do not like too broad a range of goods. It confuses them and they do not exactly know what to expect. Today’s IKEA customers are tomorrow’s customers of designer furniture shops.

Selling

Which factors are seen as influencing sales? Working in a niche area and having trained employees has a positive effect on sales. If an object is well designed it attracts more people and therefore has a positive effect on sales. Exhibiting at the Vienna Exhibition is seen as important to a company’s success. The time needed to sell a product grows ever longer.

Company-oriented findings Survival What can small and medium-sized companies do to survive in a very competitive and highly concentrated market? The range of products influences the survival of companies. The products have to be design-oriented and of a high standard. Companies should follow a niche strategy or engage in forms of cooperation that allow for the synergetic use of abilities. Cooperation is seen as a way of increasing the chance of survival. The permanent price war initiated by

Renate Buber and Marion Friedmann 187 Table 12.1 (cont.) Basic value

Description large companies is jeopardising the existence of small, specialist companies.

Person-oriented findings DesignWhat influences design consciousness? consciousness The more self-confident people are, the greater their design consciousness. Exhibitions and articles in newspapers and periodicals can increase design consciousness. Conversely shops that offer junk serve to reduce people’s consciousness of design.

Stakeholders There is a strong need for improvements in the marketing of design. In recent years the importance of marketing has become more obvious to the retailers of designer furniture as well as politicians and various experts in the designer furniture sector. Various initiatives have been launched, but they have often been unsystematic and lacked coordination. Figure 12.3 provides an overview of potential stakeholders from the retailer’s point of view. Each of these groups has a strong influence on the design industry. They are mutually related in terms of the need to improve the image of the furniture industry, but in terms of vertical planning they have to be handled in different ways.

The Austrian population The Austrian people constitute one of the main stakeholder groups. According to the interviewees, the ‘typical’ Austrian lacks selfconfidence and looks abroad for lifestyle ideas, thereby overlooking the potential for creativity and innovation in furniture design and production that exists in Austria. This is why many young designers leave the country and start their careers abroad. Austrians’ design and quality consciousness is thought to be lower than in other countries and their priorities differ. In Switzerland, for example, people place much more importance on their homes than on their cars. In Austria the reverse is true and the car is the main status symbol. But why? The answer to this question has to be sought from historical, sociological and psychological sources, but part of the responsibility lies with another stakeholder group, the educational institutions.


Mass media

Producers

Consumers

Retailer

Design centre

Co

Education

Fa

Population

irs

m

pe

tit

Mu se um

s

Designers

or

s

Representatives of the economy

Figure 12.3 Stakeholders in Austria’s designer furniture sector

Educational institutions This stakeholder group includes primary schools, secondary schools, universities and other institutions of further learning. According to the interviewees, school curricula should be adapted in such a way that lifestyle and design matters flow into different subject areas. For example questions such as ‘How do colours and shapes influence our mind?’ or ‘What makes us feel comfortable?’ could be answered from the viewpoint of a biologist. There is also plenty of scope for design matters to be incorporated into mathematics, physics, psychology and art. In this respect Scandinavia can be held up as a model. While formal design education is considered to be very good in Austria, some of the interviewees thought that university courses pay too little attention to the more practical, economic and marketing aspects of design, so young designers do not know how to market themselves properly.

Designers All the interviewees stated that Austria has very good designers and a very high creative potential, but that there are too many designers for such a small country, especially considering the limited scope of


Austrian production. Consequently many are forced to move abroad or work in other professions.

Producers This group includes furniture manufacturers and producers of handicrafts. The latter are important to a lively design scene. For many special designs, complicated or traditional crafts are needed, along with special production methods and material treatments. Small handicraft specialists are also needed to occupy the niche between mass produced and designer goods. Mass production cannot cope with the unique requirements that go along with special individual designs. The Italian ‘design city’ Milan is the home of many handicraft specialists who offer a wide range of services. Therefore Milan has a lively and substantial design scene and is host to all the major Italian producers of designer furniture. In Austria the poor industrial policies that prevailed in the 1960s and 1970s caused small handicraft production to diminish at an alarming rate. Austria still faces problems at the industrial level. Production is quite limited and it focuses almost exclusively on the Austrian market, to the detriment of Austria’s export performance. Attention needs to be paid to promoting exports to the rest of Europe.

Consumers Potential consumers can be divided into the state institutions, large companies and private consumers. These are the main target groups for marketing strategies or marketing concepts. According to the interviewees the demand for designer furniture is growing in line with the demand for quality, but proper measures to promote demand are still needed. State institutions are a very important target group because if they could be persuaded to purchase well-designed, high-quality products the volume of production would rise enormously. Scandinavia again provides a glowing example in that state purchases of domestically designed and produced furniture support the industry and the people working in it. The state also cooperates with design schools and universities, giving young designers the opportunity to develop products and find employment. So in Scandinavia design policy is highly integrated into economic policy. Banks and insurance companies could also help to boost demand. For example, the style and arrangement of their office furnishings which are seen daily by numerous customers, could influence customers’ taste, design consciousness and demand for designer furniture.


Competitors From the interviewees’ point of view, competition comes mainly from other industries and not from the furniture industry itself. This is a rather interesting view because competition in the Austrian furniture industry is actually very strong. There are two large retail companies that hold over 50 per cent of the market share, but the owners of small and medium-sized designer furniture shops do not see them as real competitors. As one interviewee noted, not all people care to buy ‘that cheap rubbish’. However the large retailers put huge pressure on costs and prices, and as a consequence many smaller furniture companies go bankrupt. Industries such as the leisure industry, the car industry and consumer electronics are seen as the real competitors in that people tend to prefer to spend their money on travel or the latest technology. The design sector and furniture industry should take note of the large variety of competitor groups and set up a marketing mix that focuses on positioning. To position the furniture industry within the sector of the leisure industry, for example, could be one way of making people think more about the importance of living in a well-furnished home: a comfortable, high-quality sofa could last a lifetime.

Design centre In Austria the design sector is not at all organised and there is a pressing need for a central institution such as a design centre to manage design matters by establishing a strong communication network among the various stakeholder groups and functioning as a platform where stakeholders could contribute to the development of a new image. To this end it would promote and actively control exchanges between industry, educational institutions, designers and consumers.

The mass media As supplying information is a key factor in achieving the marketing goals of retail companies, the mass media has to be seen as one of the stakeholder groups in designer furniture. Media such as television, radio, newspapers, design-oriented magazines, periodicals and journals supply information to the general public or to specific target groups, and there is no question that they play an important part in establishing images in the public mind. Accordingly retailers should pursue a systematic information policy to form opinions and attitudes, and of course to create demand.


Museums, exhibitions and fairs Museums and similar institutions could be used more extensively to improve the public’s knowledge and consciousness of design. Fairs provide good platforms for people involved in design in general and for representatives of the furniture industry in particular. For example manufacturers are able to show their most recent products, meet others from the industry, and catch up with the latest news and product developments.

Summary The problems faced by the Austrian design sector are well known to all the experts in the field, as well as to politicians. As this chapter has shown, there are numerous ways of addressing these problems and promoting design consciousness among the Austrian population. While a number of private and public initiatives have been launched and are developing quite well, they should be subject to continuing evaluation to guarantee their future efficiency.

Notes 1. From 446 sentences (text units), 34 gestalten, seven hypergestalten and one hyperhypergestalt were identified. 2. The results are presented on the ‘summarising level’ of a WinRelan-based data analysis. The full results are presented in Friedmann (2001).

References Buber, R. (2000) ‘Die Einstellung von Führungskräften zum internen Marketing – eine empirische Untersuchung mit GABEK’, in R. Buber and J. Zelger (eds), GABEK II. Zur Qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 259–99. Buber, R. and Kraler, C. (2000) ‘How GABEK and WinRelan supports qualitative research’, in R. Buber and J. Zelger (eds), GABEK II. Zur Qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 111–37. Bundesministerium für Wirtschaft und Arbeit (2000) Design Letter (Vienna: BfWA, January). Buerdek, B. E. (1994) Design: Geschichte, Theorie und Praxis der Produktgestaltung (Cologne: DuMont). Carson, D., Gilmore, A., Perry, C. and Gronhaug, K. (2001) Qualitative Marketing Research (London, Thousand Oaks and New Delhi: Sage).

192 Retailer’s Perspective on the Design Sector Churchill, G. A. and Iacobucci, D. (2002) Marketing Research. Methodological Foundations (Australia, Canada, Mexico, Singapore, Spain, UK and US: South Western Thomson Learning). Die österreichische Holzindustrie (2000) Branchenbericht 2000/2001 (Vienna: Die österreichischa Holeindustrie). Friedmann, M. (2001) Zur Struktur der österreichischen Designbranche – eine GABEKAnalyse im Möbeldesignhandel (Vienna: Diplomarbeit, Wirtschaftsuniversität Wien). Gummesson, E. (2000) Qualitative Methods in Management Research (Thousand Oaks, London and New Delhi: Sage). Henry, G. T. (1990) Practical Sampling (Newbury Park, London and New Delhi: Sage). Hofer-Wittmann, H. F. (1999) ‘ohne Titel’, Die Möbelindustrie im Fachverband der Holzindustrie Österreichs (Vienna: Möbelbericht). IfGH (2001) Branchenmonitor (Vienna: Institut für Gewerbe und Handelsforschung). Meuser, M. and Nagel, U. (1991) ‘Experteninterviews – vielfach erprobt, wenig bedacht: Ein Beitrag zur qualitativen Methodendiskussion’, in D. Garz and K. Kraimer (eds), Qualitativ-empirische Sozialforschung: Konzepte, Methoden, Analysen (Opladen: Westdeutscher Verlag), pp. 441–71. WIFI Österreich (1994) ‘Wegweiser durch die Designlandschaft’, Schriftenreihe des Wirtschaftsförderungsinstitutes, no. 255. Witzel, A. (1982) Verfahren der qualitativen Sozialforschung: Überblick und Alternativen (Frankfurt am Main and New York: Campus). Wohnkultur (2000) ‘Verfrühter Jubel?’, Wohnkultur – österreichische Fachzeitschrift für die gesamte Raumausstattung, vol. 35, nos 7/8, p. 11. Zelger, J. (1999a) ‘GABEK. A Method for the Integration of Expert Knowledge and Everyday Knowledge’, in D. J. De Tombe and E. A. Stuhler (eds), Complex Problem Solving: Methodological Support for Societal Policy Making, vol. I (Munich and Mering: Hampp), pp. 20–45. Zelger, J. (1999b) ‘Wissensorganisation durch sprachliche Gestaltbildung im qualitativen Verfahren GABEK’, in J. Zelger and M. Maier (eds), GABEK. Verarbeitung und Darstellung von Wissen (Innsbruck, Vienna and Munich: StudienVerlag), pp. 41–87. Zelger, J. (2000) ‘Twelve Steps of GABEKWinRelan. A Procedure for Qualitative Opinion Research, Knowledge Organization and Systems Development’, in R. Buber and J. Zelger (eds), GABEK II. Zur Qualitativen Forschung – On Qualitative Research (Innsbruck, Vienna and Munich: StudienVerlag), pp. 205–20.

13 Influences of Organisational Learning on Organisational Performance and Success Roland Yeo

Introduction This chapter explores empirical evidence on bridging the gap between organisational learning and performance. The chapter reports on four case studies in which in-depth interviews were conducted to explore a number of issues pertaining to the developmental process of learning, the stages of learning, the factors that influence learning and the relationship between learning and performance. In the past decade, the concept of organisational learning has begun to make an important contribution to organisational studies and management practice. In the 1980s a few companies became aware that corporate learning had the potential to improve organisational performance, competitiveness and success. It has since been asserted that there is a need for organisations to learn faster and respond to rapid changes in the environment; otherwise they simply will not survive (Heaton and Harung, 1999; Harung et al., 1999; Garratt, 1999). Owen (1991) goes as far as to call learning ‘the business of business’. Against this backdrop, this chapter addresses the following questions:

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How and why does organisational learning influence organisational performance? How is organisational learning developed? How appropriate are the three stages of learning (single-loop, double-loop and triple-loop learning)? 193

194 Organisational Learning, Performance and Success ● ●

What factors influence learning during the three stages? How does organisational learning influence organisational performance?

The above questions are based on Yeo’s (2002) theoretical framework for organisational learning and development. This framework is founded on two distinct but related sets of principles for learning: those of cognitive and behavioural learning (Cherrington, 1991). This is motivated by the belief that organisational learning results from attempts to understand changes that occur in the external environment and the consequent adaptation of beliefs. Inherent in the process are new ways of thinking, new attitudes and consequently a new pattern of behaviour (Schein, 1999). There is also constant interaction between the adaptive and generative trends that are characteristic of the behavioural and cognitive learning approaches respectively. At the core of these approaches is the promotion of creative individualism within organisations to make people feel empowered and ready for the challenge of finding better ways of meeting organisational goals and values (Robbins, 1994; Schein, 1999). The lack of empirical information on the relationship between learning and organisational success cannot be overemphasised. Although there have been attempts to explain the performance outcomes of organisational learning, few have adopted a more qualitative approach to account for the behavioural aspect of learning (Levinthal and March, 1993; Swanson and Holton, 1999; Chaston et al., 1999; Dixon, 1999). Hence this chapter aims to provide insights into the latter in order to help fill the gap between the two. The findings can then be used to develop more in-depth case studies or analyses of potential or successful learning organisations.

The research design The research was not totally inductive as deduction based on prior theory formed part of this study. Therefore the approach lay somewhere between purely exploratory or inductive (Eisenhardt, 1989) and explanatory or deductive (Yin, 1993). Data triangulation was achieved through the use of in-depth interviews and documents, plus discussions of the data with colleagues and academics in order to identify unclear or ambiguous descriptions. This helped to achieve construct validity (when the results obtained from the use of measures fit the theories around which the test is designed – Yin, 1994). Moreover, two academics

Roland Yeo 195

reviewed the draft analysis, thus satisfying investigator triangulation. Internal validity was achieved through within-case analysis, cross-case analysis and pattern matching. Through the use of multiple case studies, the research design satisfies external validity. The qualitative orientation of the study was an attempt to balance the quantitative orientation towards organisational learning in the existing literature (see for example Goh and Richards, 1997; Zairi, 1999; Van Deusen and Mueller, 1999). That said, some form of quantitative analysis was utilised in order to provide a more objective evaluation of the thematic patterns in the qualitative data. This was largely done at the data codification stage, when simple numeric figures were assigned to denote the number of times topics were mentioned in the interviewees’ responses. More importantly, the relevance of an intangible notion such as organisational learning could only be realised through insights gained from the cognitive and behavioural patterns of individuals. These two aspects of learning were the underlying theories behind the study.

Data collection Twelve interviews were conducted in four Singaporean organisations: two private organisations (one large and one medium sized) and two public ones (again, one large and one medium). In this case ‘large’ refers to a staff of 1000 or more and ‘medium’ to a staff of 500–1000. The reason why small organisations were not considered was that their internal organisational structure is often less apparent, the roles are less defined and the level of group interaction is less dynamic. With too narrow a structure the individual, team and organisational goals are likely to be unclear, weakening the validity of the theoretical framework and consequently the results. In each case three interviews were conducted with male and female interviewees at the supervisory or management level. One of the criteria used to select the interviewees was that they had to be involved in reviewing, deciding and endorsing organisational policies. The evidence was mainly drawn from in-depth interviews and documents. For the interviews, both open-ended and closed questions were used to elicit responses from the interviewees. The open-ended questions allowed them to tell stories, provide examples and discuss broader issues, while the closed questions gave them the opportunity to evaluate certain issues by rating their level of importance on a given scale (see Tables 13.3, 13.6, 13.7 and 13.9). For the documentary

196 Organisational Learning, Performance and Success

evidence, company newsletters and website information were of particular importance as they served to confirm/refute the statements made by interviewees. They also enabled the researcher to make inferences and provided avenues for further investigation. These dual sources of information helped to ensure that the research reflected significant issues and questions that were relevant to contemporary cases (Yin, 1994). All the field notes, interview protocols, interview transcripts, collected documents and materials were coded. The coding technique was applied to positive and negative incidents relating to each category of topic. Two pilot studies were conducted to test the data collection instruments and provide conceptual clarification of the research design.

Background information The organisations studied will be referred to as organisations A to D in order to facilitate the discussion and satisfy ethical considerations. The interviewees will be identified as A1 (organisation A, first interviewee), D4 (organisation D, fourth interviewee) and so on (Table 13.1). All four organisations aspired to be learning organisations and were making preliminary efforts in that direction.

Display and analysis of the data Two techniques were used in the process of cross-case analysis: (1) putting information in different arrays to make a matrix of categories, and Table 13.1 Background information on and coding of the organisations and interviewees Type

Private

Size Large

Medium

A (Media) A1: Human resources A2: Editorial

B (Consulting) B1: Human resources B2: Customer relationship marketing B3: Management consulting

A3: Training Public

C (Quality and productivity) C1: Planning and corporate development C2: Human resources C3: Corporate planning

D (Information) D1: Public communications D2: Human resources D3: Research and planning

Roland Yeo 197 Table 13.2 Degree of importance given to each factor Number of mentions

Degree of importance

1–4 5–6 7–9 10–11 12

Of minor importance Somewhat important Very important Highly important Crucial

Note: Twelve was the maximum number of interviewees.

(2) tabulating the frequency of different events and putting the information in chronological order (see Yin, 1994). The numbers in each column in Tables 13.3 to 13.10 indicate the number of times a factor was mentioned by the interviewees and its degree of importance (Table 13.2).

Research issues How is organisational learning developed? The opening question, which was broad, general and designed to establish a rapport, asked the interviewees to share their views on or experiences of organisational learning. Several common themes emerged in the responses. First, there was a general feeling that a learning organisation promotes the sharing of information and experience between one another in an open and flexible manner. Second, to a large extent the direction and content of learning are influenced by the organisation’s mission and vision. This perspective is further reinforced by the belief that systemic thinking is key to the development of organisational learning. Third, the extent and scope of learning should be driven by top managers with a strong commitment to the development of learning initiatives. Fourth, learning prepares the organisation for outside competition and helps it to gain competitive advantage over time. Fifth, there is a time factor associated with organisational learning in that the learning organisation is an ongoing process. Next the interviewees were asked to rate a range of activities involved in organisational learning – the findings are presented in Table 13.3. As can be seen, a learning climate was rated as the most important consideration in the development of organisational learning, followed by a flexible reward system, and inquiry and dialogue. To substantiate

Table 13.3 Degree of importance of organisational learning factors (number of mentions per ratings)* Cases

Learning climate Selfdevelopment Inquiry and dialogue Flexible organisational structure Team building Flexible reward system Participatory policy making Systemic thinking Learning strategies Total

A (private)

B (private)

C (public)

D (public)

Total

5

4

3

2

1

5

4

3

2

1

5

4

3

2

1

5

4

3

2

1

5

4

3

2

1

3

–

–

–

–

2

1

–

–

–

3

–

–

–

–

1

2

–

–

–

9

3

–

–

–

2

–

1

–

–

2

–

1

–

–

1

–

2

–

–

1

1

1

–

–

6

1

5

–

–

1

1

–

–

1

1

2

–

–

–

1

1

–

–

1

1

–

2

–

–

4

4

2

–

2

1

–

1

1

–

2

–

1

–

–

–

–

1

2

–

1

–

2

–

–

4

–

5

3

–

1

–

2

–

–

1

1

1

–

–

–

1

1

1

–

–

3

–

–

–

2

5

4

1

–

2

1

–

–

–

3

–

–

–

–

–

2

1

–

–

1

2

–

–

–

6

5

1

–

–

1

2

–

–

–

1

2

–

–

–

–

–

1

1

1

1

–

–

2

–

3

4

1

3

1

1

–

2

–

–

1

1

1

–

–

2

1

–

–

–

1

1

1

–

–

5

3

4

–

–

1

1

1

–

–

–

2

1

–

–

–

1

1

1

–

–

–

1

2

–

1

4

4

3

–

13

5

7

1

1

13

9

5

–

–

7

6

7

5

2

7

9

7

4

– 40 29 26

10

3

* 5 ⫽ most important; 4 ⫽ very important; 3 ⫽ important; 2 ⫽ fairly important; 1 ⫽ least important.

Roland Yeo 199

the view on the need for a learning climate, interviewee A1 commented: ‘People need to learn in a safe environment where it is acceptable to fail and learn from one’s failures.’ With regard to a flexible reward system, interviewee D1 felt that rewards satisfy the needs of individuals. Interviewee C3 emphasised that inquiry and dialogue are an integral part of the learning process and that ‘Questions and feedback help to clarify issues and offer solutions.’ Participatory policy making was viewed as one of the least important considerations as ‘It is the job of the top management, who should be able to communicate clearly the organisation’s vision to all employees’ (interviewee B2). Table 13.4 shows a number of factors that strengthen, weaken, revive or terminate learning. The responses listed are based on common mentions by all twelve interviewees. With reference to the degree of importance scale in Table 13.2, two highly important factors that strengthen learning are a strong management commitment to learning (11 mentions) and clear communication of vision, mission and outcomes (10 mentions). These are consistent with the earlier view that strategic direction by top management is of utmost importance to organisational learning. On the other hand, two very important factors that weaken learning are the lack of concerted effort (nine mentions) and the lack of reward and recognition (nine mentions). These factors are in line with the common views discussed earlier that organisational learning operates by systemic thinking and collective effort, and that rewards are a key consideration in organisational learning. As for factors that revive learning, a supportive environment was rated as somewhat important (six mentions). This is reinforced by the observation that a lack of commitment and support from top management (six mentions) is a somewhat important factor in terminating learning. These two perspectives are complementary in that they reinforce the earlier point that strong management commitment to learning (11 mentions) is essential to the development of a learning organisation. As can be seen in Table 13.5, the key personnel in directing organisational learning are the chief executive officer, senior management and managers (11 mentions, rated highly important). The human resource department came next with eight mentions and a rating of very important. As interviewee C2 commented: ‘Learning programmes are part of human resource functions. More importantly, the human resource department designs reward and recognition systems, is responsible for communicating the corporate values and mission, and helps to promote an open culture.’ This comment clearly summarises the key

Table 13.4 Factors that strengthen, weaken, revive or terminate learning (number of mentions) Organisation

Factors that weaken learning: Lack of concerted effort Lack of reward and recognition Weak leadership Information overload Narrow/myopic human resource policies Factors that revive learning: Frank assessment and willingness to change for the better Supportive environment Strong leadership and clear directions Recognition that learning provides a competitive advantage Assistance from external consultants Factors that terminate learning: No commitment and support from the top management Lack of need Unnecessary punishment or penalisation Entrenched habits Poor incentives Total

B

C

D

Total

Mean

2 3 1 2

2 3 – 1

1 3 1 –

– 2 – 1

5 11 2 4

1.25 2.75 0.5 1.0

1

1

1

3

6

1.5

2

3

2

3

10

2.5

2 1 3 – 2

3 3 1 3 1

2 2 1 1 –

2 3 1 – 2

9 9 6 4 5

2.25 2.25 1.5 1.0 1.25

1 2 1

– – 2

– 2 –

1 2 1

2 6 4

0.5 1.5 1.0

2 1

1 1

1 –

– –

4 2

1.0 0.5

2 – 2 1 1

1 1 – 2 1

2 1 1 – –

1 – 2 1 –

6 2 5 4 2

1.5 0.5 1.25 1.0 0.5

32

30

21

25

108

27.0

200

Factors that strengthen learning: Strong interface between training programmes and work Strong management commitment to learning Staff empowerment Strong team spirit Open culture promoting trust and respect for each other Clear communication of vision, mission and outcomes

A

Roland Yeo 201 Table 13.5 Key personnel that drive organisational learning, and the benefits of organisational learning (number of mentions) Organisation

Key personnel: Human resource department CEO, senior management and managers Employees Benefits of learning: Enhances personal development Allows organisation to stay ahead of changes Allows organisation to be proactive by introducing products and services Promotes new knowledge creation and innovation Builds more open organisations that attract talent Improves quality of employee Improves financial performance of organisation Increases confidence in job functions and work improvement Total

A

B

C

D

Total

Mean

3

2

1

2

8

2.0

3 1

3 1

3 –

2 1

11 3

2.75 0.75

2

1

1

3

7

1.75

1

1

2

–

4

1.0 1.75

1

3

2

1

7

2

3

2

1

8

2.0

1 –

– 1

– 1

1 1

2 3

0.5 0.75

2 1

2 1

2 –

2 2

8 4

2.0 1.0

17

18

14

16

65

16.25

characteristics of organisational learning discussed in this section. Two very important benefits of learning are improved financial performance and increased knowledge creation and innovation (eight mentions each), the latter being associated with the cognitive development of individuals. The next two very important benefits are the introduction of new products and services and enhanced personal development (seven mentions each), the latter being associated with cognitive and behavioural aspects of individuals. In summary, organisational learning develops when every individual adopts a systemic perspective and a shared vision is communicated and practised throughout the organisation. Three factors are important to the developmental process: a conducive learning climate, a flexible reward system, and constant inquiry and dialogue among employees.


The three stages of learning One of the questions asked was ‘What are the characteristics of a learning organisation?’ Prompts were used to elicit responses on the likely existence of three stages of learning: individual, team and organisational learning. All 12 interviewees felt that the three stages of learning do exist in organisations, although it was generally agreed that the third stage – organisational learning – is a collective effort. This implies that ‘Both individual learning and team learning are subsets of organisational learning’ (interviewee B3). It was also pointed by 10 out of the 12 interviewees that learning at the organisational level cannot occur without the proper institutionalisation of individual and team learning. It was generally acknowledged that organisational learning imbues a systemic perspective where a shared understanding of the corporate vision is essential to the success of learning initiatives. As noted by interviewee A2: ‘Without clear direction from senior management, learning at the individual or team level may be truncated as learning goals become confused and diverse.’ Hence individual and team learning goals should be in line with those set out by the organisation, so that ‘Organisational learning will be focused on the overall vision and mission of the organisation without being distracted by external circumstances’ (interviewee B3). There was a general consensus that learning goals are not static as they are constantly challenged by a variety of factors. According to 10 of the interviewees, this is largely due to the dynamic interaction of the three stages of learning. It was generally agreed that the relationship between the various stages is not necessarily straightforward. As expressed by interviewee D2: ‘The relationship is also not clearly demarcated to determine when learning becomes individual, team or organisational.’ This is especially so when learning is associated with single-loop, double-loop or triple-loop learning, where learning is taken to be a problem-solving process. The ways in which the problem is approached, analysed and solved all impact on the extent of learning. There is a perceivable link between the relevance of different goals and the capacity to learn. As shown in Table 13.6, all the interviewees agreed that organisational goals are very important to the development of organisational learning. Individual and team goals were also viewed as important in influencing the capability to learn during the relevant stages.

What factors influence learning during the three stages? According to the data collected, the five factors associated with Senge’s (1990) ‘fifth discipline’ are crucial to organisational learning.

Table 13.6 Importance of goals in influencing learning capabilities during the three stages of learning (number of mentions per rating)* Organisation A

B

C

D

Total

VI

I

MI

VI

I

MI

VI

I

MI

VI

I

MI

VI

I

MI

Goals Individual Team Organisational

2 1 3

– 2 –

1 – –

1 3 3

2 – –

– – –

2 2 3

– 1 –

1 – –

1 2 3

1 1 –

1 – –

6 8 12

3 4 –

3 – –

Total

6

2

1

7

2

–

7

1

1

6

2

1

26

7

3

* VI ⫽ very important; I ⫽ important; MI ⫽ moderately important.

203


These factors are personal skills, mindset, working environment, job scope and vision. In the study an additional factor, teamwork, was identified. These six factors were presented in closed questions for the interviewees to rank – not one was ranked as unimportant. As can be seen in Table 13.7, teamwork (12 mentions) was regarded as the most important factor of the five. As interviewee B2 put it, ‘Teamwork leads to the crossfertilisation of ideas and a sense of belonging which influences learning.’ Personal skills and vision (10 mentions each) were rated as the next very important factors. According to interviewee C2: ‘Personal skills increase a person’s competence in handling the job well. Learning will largely be driven by personal motivation derived from the ability to achieve more at work.’ For interviewee A3, the development of personal skills should ultimately be in line with the direction of the organisation’s vision: ‘I learn what appeals to me and I try to make it work for me first and see how it can contribute to the company’s overall performance.’ The next important factor is mindset (nine mentions) which ‘all boils down to habits, attitude, beliefs and expectations’ (interviewee D2). It is the cognitive state of every individual and governs a specific set of behavioural patterns. Mindset is central to the development of organisational learning theory as it deals with both the social psychology (cognitive) and social science (behavioural) aspects of it (Yeo, 2002). The working environment was also regarded as essential to organisational learning as ‘An employee may be motivated to learn if his or her peers are all doing well and learning at a fast pace due to some social factor’ (interviewee B1). A healthy environment promotes team learning, where members learn together and influence each other to bring about positive outcomes. According to interviewee A1, ‘There are two elements associated with the working environment – psychological and physical. It also depends on whether they are supportive or detrimental. It can either foster or make learning more difficult. For example a supportive team environment can help ameliorate a difficult job while a destructive team environment can make it more difficult.’ Job scope was seen as the least important of the six factors, with five respondents rating it as only moderately important. According to interviewee D1, ‘What is more important is the clarity of job scope, which in turn affects how the department learns in relation to the rest of the organisation.’ In the view of several respondents, job scope does not necessarily determine an individual’s capacity to learn. For instance ‘job scope is important for the higher-level employees rather than lower ones as their jobs may be routine and repetitive’ (interviewee B2). Hence it seems that job scope does not play a significant part in organisational learning.

Table 13.7 Rating of factors that influence organisational learning (number of mentions per rating)* Organisation A

Personal skills Mindset Working environment Teamwork Job scope Vision Total

B

VI

I

MI

VI

3 2

– –

– 1

2 3

1 3 2 2

2 – – –

– – 1 1

13

2

3

I

C

D

MI

VI

I

MI

VI

1 –

– –

3 2

– –

– 1

2 3 1 3

1 – 1 –

– – 1 –

2 3 – 2

1 – 1 1

14

3

1

12

3

Total

I

MI

2 2

1 1

– –

– – 2 –

1 3 – 3

2 – 2 –

3

10

6

VI

I

MI

10 9

2 1

– 2

– – 1 –

6 12 3 10

6 – 4 1

– – 5 1

1

50

14

8

* VI ⫽ very important; I ⫽ important; MI ⫽ moderately important.

205


The above factors relate to Argyris and Schön’s (1978, 1996) learningloop theory in the following ways. First, organisational learning occurs when the individuals in question reflect upon and develop their own thinking processes, as characterised by ‘mindset’ (rated as very important by nine respondents, the third highest of the six factors). Second, organisational learning is a process in which members use knowledge to guide their behaviour in such a way as to promote the ongoing adaptation of the organisation (Edmonson and Moingeon, 1998). Using taskrelevant knowledge is particularly important when assessing collective performance and progress towards goals. In this regard two factors – teamwork (rated as very important by 12 interviewees) and corporate vision (rated as very important by 10 interviewees) – relate to collective performance and goals respectively. Guiding behaviour involves choosing actions based on experience as well as actions designed to test inferences. As can be seen in Table 13.7, ‘personal skills’, which guide such actions, were rated as very important by 10 respondents. Adaptation refers to an organisation’s response to external changes, including both problems and opportunities. ‘Working environment’, which was rated as very important by six respondents, is concerned with such adaptations. In essence these factors relate to an iterative cycle of action and reflection, which is described by Argyris and Schön (1978, 1996) as integral to effective organisational learning. In short there was a general perception among the interviewees that all six factors discussed here have a significant influence on learning at all three levels. None felt that there are factors that apply only to one particular stage of learning. A quick cross-case analysis also reveals a high level of consistency in the responses across the four organisations. For instance the total number of ‘very important’ ratings for all six factors was 13 for organisation A, 14 for B, 12 for C and 10 for D. Most importantly, the analysis here contributes to the body of knowledge on the subject of organisational learning as it shows that prioritisation of these factors is essential to the implementation of learning strategies in any organisation.

How does organisational learning influence organisational performance? As is clear from Table 13.8, organisational performance involves both financial and non-financial factors. At the bottom line, targets and turnover are the main concerns of all organisations. The interviewees rated this financial category as crucial (12 mentions; based on the degree of importance scale in Table 13.2). Other very important factors

Roland Yeo 207 Table 13.8 Constituents of organisational performance (number of mentions) Organisation

Bottom-line targets and turnover Productivity and efficiency, turnaround time, defect rate Public image and perception of organisation Employee attrition Employee satisfaction Skills level of employees Management of resources Customer satisfaction Creative and innovative products and services Internal capabilities, e.g. agile teams Total

A

B

C

D

Total

Mean

3

3

3

3

12

3.0

2

3

2

2

9

2.25

1 – 2 2 1 1

– – 2 2 2 3

1 1 2 1 2 1

– 1 1 2 – 3

2 2 7 7 5 8

0.5 0.5 1.75 1.75 1.25 2.0

1

3

1

1

6

1.5

1

–

–

1

2

0.5

14

18

14

14

60

15.0

are productivity and efficiency (nine mentions) and customer satisfaction (eight mentions), which are linked to the quantity, quality and sale of products, which in turn affect market share. Two very important non-financial factors are employee satisfaction and skills level (seven mentions each). Thus organisational performance can be measured at several levels. The highest level relates to how well the organisation has performed with respect to the goals or targets set in line with its vision and mission, such as productivity growth in the manufacturing sector. At the lower levels, organisational performance can be measured against operational targets such as the defect rate, turnaround time, skills level and employee satisfaction. Table 13.9 shows the interviewees’ ratings of a variety of performance measures. As can be seen in the 5 and 4 columns in the total section, financial measures were rated as most important or very important by all 12 interviewees, while products and services, and human resources both attracted 10 such ratings, followed by markets with nine. Human resources can be measured by the attrition rate, although employee satisfaction is harder to quantify. Nonetheless it is a very important non-financial performance indicator as ‘Happy staff are motivated to perform even better’ (interviewee B1).

208

Table 13.9 Degree of importance of organisational performance measures (number of mentions per rating)* Organisation A

B

C

D

Total

5

4

3

2

1

5

4

3

2

1

5

4

3

2

1

5

4

3

2

1

5

4

3

2

1

Financial Budgetary Use of assets Operational Markets Products and services Human resources

1 –

2 –

– 3

– –

– –

3 2

– 1

– –

– –

– –

3 1

– –

– –

– 1

– 1

1 1

2 –

– 1

– 1

– –

8 4

4 1

– 4

– 2

– 1

2 – 2

– 3 –

– – 1

– – –

1 – –

1 3

2 1 –

1 1 –

– – –

– – –

– – 2

2 1 1

1 2 –

– – –

– – –

2 3 –

1 – 1

– – 1

– – 1

– – –

4 4 7

5 4 2

2 4 2

– – 1

1 –

2

–

1

–

–

3

–

–

–

–

–

2

1

–

–

2

1

–

–

–

7

3

2

–

–

2

–

–

1

–

2

1

–

–

–

1

1

1

–

–

1

2

–

–

–

6

4

1

1

–

Total

9

5

5

1

1

14

5

2

–

–

7

7

6

1

1

10

7

2

2

–

40 23

15

4

2

* 5 ⫽ most important; 4⫽ very important; 3⫽ important; 2⫽ fairly important; 1⫽ least important.

Roland Yeo 209

According to 11 of the interviewees, the way in which organisational learning influences performance is not necessarily straightforward. For instance interviewee A3 suggested that ‘learning leads to excitement’ and a willingness ‘to do the job well above expectations’, indicating that non-financial aspects of learning can ultimately bring financial benefits. According to interviewee D3, ‘The ability for an organisation to deliver and delight its customers, both external and internal, is crucial to performance success. Aspects associated with performance include leadership, culture and values, for example whether there is a customer focus, as well as creativity and imagination in product design and the management of resources in such areas as human, financial and information.’ In interviewee C2’s view, ‘Organisational learning should not be for learning’s sake. It is for the purpose of organisational performance; it facilitates the pursuit of excellence’. In essence, organisational learning improves organisational performance by upgrading employees’ skills and knowledge to match the job requirements set out in the corporate vision and mission. Through a reward and recognition system, employees can be encouraged to handle greater responsibilities and challenges. This will ensure better management of the resources needed to produce innovative products and services in the fight against external challenges. With strong internal capabilities such as good teamwork and motivated employees, productivity, efficiency and customer service will be greatly enhanced, leading to increased sales and better financial performance.

Conclusions First, the findings on the process of organisational learning reveal that a time factor is associated with learning, and that learning is to do with preparing the organisation for the future rather than meeting immediate needs. Second, the findings on the three learning stages – the individual, team and organisational stages – suggest that their relationship is complex and dynamic. Hence these stages should be coordinated within work processes. Third, two factors are crucial to motivating learning: the strategic stance of the organisation based on its vision and mission, and effective leaders who provide clear directions to achieve organisational goals. Finally, the findings on the relationship between learning and performance suggest that improvements in employees’ attitudes, motivation and commitment constitute cognitive and behavioural evidence of organisational learning.


Organisational learning affects performance in different ways. As perceived by all 12 interviewees, the achievement of bottom-line targets is crucial to organisational success and shows that the organisation has a sound and viable business plan. According to interviewee A2, ‘Financial performance also depends on whether the organisation can get every individual employee to think and function as if he or she is in the business himself or herself. For instance the Ritz Carlton hotel was reputed to have just such a corporate culture of empowerment, which accounts for its consistent financial success.’ Organisations with a strong financial performance and cultural diversity intend to attract talented employees. Through constant learning and innovation, organisations such as Microsoft have been able to produce a series of competitive products that have ensured their financial advancement. Organisational learning also leads to competitive advantage in that organisations become motivated to ‘do the right thing at the right time. Organisations must be constantly on their toes and ready to strike when the iron is hot’ (interviewee C2). This requires learning from competitors and other entities in the external environment in order to develop innovative products and services to meet market demands. In this sense performance determines the perceived value of the organisation’s products (measured in terms of creativity, quality and reliability) in comparison with the products of competitors. As can be seen in Table 13.10, there are proportionate returns between organisational learning and performance. The learning of valuable lessons should be translated into results, whether tangible or intangible. For instance a highly important outcome of organisational learning is a change in mindset (10 mentions; based on the degree of importance scale in Table 13.2). This refers to individuals undergoing a positive change in attitude, enabling knowledge and experience to be used to gain a competitive edge (rated as very important by nine interviewees) by finding better ways of doing things (seven mentions), thereby producing the right products and performing better than competitors (six mentions). Moreover individuals will react more quickly to business changes (seven mentions) and think of strategies to address the changes. With a collective effort and a shared vision of the corporate goals, employees’ commitment will be enhanced (six mentions). These cognitive and behavioural changes among employees will help the organisation to achieve better financial results (eight mentions), which will in turn benefit the employees financially in the form of bonuses and so on. The data presented in Table 13.10 lend support to the definition that organisational learning is a process that requires individual cognition

Roland Yeo 211 Table 13.10 Relationship between organisational learning and performance (number of mentions) Learning outcomes

Able to transform knowledge and experience into a competitive edge Better ways of doing things Able to produce the right products and do better than the competitors Better financial results Faster learning curve and fewer mistakes Change in mindset Learned responses and quicker response to business changes Greater commitment from employees Greater commitment from management Organisation becomes more agile and responsive to external challenges Total

Organisation A

B

C

D

Total

Mean

2 2

1 2

3 2

3 1

9 7

2.25 1.75

1 3

3 2

1 2

1 1

6 8

1.5 2.0

2 2

1 3

1 2

2 3

6 10

1.5 2.5

2 1

1 2

2 1

2 2

7 6

1.75 1.5

–

2

2

–

4

1

2

2

1

–

5

1.25

17

19

17

15

68

17.00

and supports organisational adaptiveness (Edmonson and Moingeon, 1998). The five learning outcomes that attracted the highest number of mentions relate to cognitive and behavioural aspects of human development. The two highest rated outcomes – ‘change in mindset’ (10 mentions) and ‘the ability to transform knowledge and experience into a competitive edge’ (nine mentions) – deal primarily with the cognitive development of individuals through organisational learning. The next two highest rated outcomes – ‘better ways of doing things’ and ‘learned responses and quicker response to business changes’ (seven mentions each) – are concerned with behavioural changes among individuals as a result of cognitive input, while ‘faster learning curve and fewer mistakes’ (six mentions) are cognitive and behavioural consequences of organisational learning. In essence, organisational learning is a process of acting, assessing and acting again in an ongoing cycle of reflection and action, a theme proposed by Senge (1990) and Argyris and Schön (1978, 1996).


In summary, organisational learning and organisational performance are intertwined in that individuals learn to enhance their jobs, which in turn has an impact on team goals and organisational performance. As pointed out by interviewee B3, ‘Assuming the job requires new technology, naturally those who are quick to pick up new skills related to the technology will be at an advantage, improving work efficiency and producing a greater output.’ Through a collective effort, employees apply the knowledge and experience they have gained to enable the organisation to be more agile and responsive to external changes. This study has successfully demonstrated the link between organisational learning and performance through the use of a qualitative case study methodology and quantitative data in a simple numeric form.

References Argyris, C. and Schön, D. A. (1978) Organisational Learning: A theory of action perspective (Reading, Mass.: Addison-Wesley). Argyris, C. and Schön, D. A. (1996) Organisational Learning II: Theory, method, and practice (Reading: Addison-Wesley). Chaston, I., Badger, B. and Sadler-Smith, E. (1999) ‘Small Firm Organisational Learning: Comparing the perceptions of need and style among UK support service advisors and small firm managers’, Journal of European Industrial Training, vol. 23, no. 1 , pp. 36–43. Cherrington, D. J. (1991) The Management of Human Resources (Boston, Mass.: Allyn & Bacon). Dixon, N. M. (1999) The Organisational Learning Cycle: How we can learn collectively (Alderson: Gower). Edmonson, A. and Moingeon, B. (1998) ‘From Organisational Learning to the Learning Organisation’, Management Learning, vol. 29, no. 1, pp. 5–20. Eisenhardt, K. (1989) ‘Building Theory from Case Study Research’, Academy of Management Review, vol. 14, no. 4, pp. 532–50. Garratt, B. (1999) ‘The Learning Organisation 15 years on: Some personal reflections’, The Learning Organisation, vol. 6, no. 5, pp. 202–6. Goh, S. and Richards, G. (1997) ‘Benchmarking the Learning Capability of Organisations’, European Management Journal, vol. 15, no. 5, pp. 575–83. Harung, H. S., Heaton, D. P. and Alexander, C. N. (1999) ‘Evolution of Organisations in the New Millennium’. Leadership and Organisation Development Journal, vol. 20, no. 4, pp. 198–207. Heaton, D. P. and Harung, H. S. (1999) ‘The Conscious Organisation’, The Learning Organisation, vol. 6, no. 4, pp. 157–62. Hedges, P. (1998) Increasing Profitability by the Effective Use of Learning (London: Kogan Page). Levinthal, D. A. and March, J. G. (1993) ‘The Myopia of Learning’, Strategic Management Journal, vol. 14, pp. 95–112. Owen, H. (1991) Riding the Tiger: Doing business in a transforming world (Maryland: Abott).

Roland Yeo 213 Robbins, S. P. (1994) Management (Englewood Cliffs, NJ: Prentice-Hall). Schein, E. H. (1999) ‘Empowerment, Coercive Persuasion and Organisational Learning: Do they connect?’, The Learning Organisation, vol. 6, no. 4, pp. 163–72. Senge, P. M. (1990) The Fifth Discipline: The art and practice of the learning organisation (London: Century). Swanson, R. A. and Holton III, E. F. (1999) Results: How to Assess Performance, Learning, and Perceptions in Organisations (San Francisco, CA: Berrett, Koehler). Van Deusen, C. A. and Mueller, C. B. (1999) ‘Learning in Acquisitions: Understanding the relationship between exploration, exploitation and performance’, The Learning Organisation, vol. 6, no. 4, pp. 186–93. Yeo, R. (2002) ‘From Individual to Team Learning: Practical perspectives on the learning organisation’, Team Performance Management, vol. 8, nos 7–8, pp. 157–70. Yin, R. K. (1993) Applications of Case Study Research (Thousand Oaks, CA: Sage). Yin, R. K. (1994) Case Study Research: Design and Methods (Thousand Oaks, CA: Sage). Zairi, M. (1999) ‘The Learning Organisation: Results of a benchmarking study’, The Learning Organisation, vol. 6, no. 2, pp. 76–81.


Index Key: f = figure; n = note; t = table, bold = extended discussion or heading emphasised in main text. management 63–4, 64f population 187 retail sector 153–4 Austria: Federal Ministry for Economic Affairs and Labour xiv Austria: National Design Award 182 Austrian Chamber of Commerce 182 Austrian Design Centre 182 Austrian National Bank xii awareness (noetic/autonoetic) 14

aboutness 18 abstraction 30, 47 action 10, 37 activation 17, 36, 46, 80 actor networks 40 adaptation 18, 206 advertising 119t, 122 Albers, S., et al. (1999) 164, 165 Amazon.com 133 Andelfinger, U. 56 Anforderungsanalyse (Andelfinger) 56 anger 82 Arbeiterkammer (statutory employees’ organisation) 164(n1) architecture 181 conceptual 82 hierarchical cognitive 87 neural 16, 18 synaptic 16 Argyris, C. 206, 211 Aristotle 164(n1) artefacts viii, 3, 5, 7, 8, 17, 28, 36, 37, 39, 40, 104 artificial intelligence 4, 35 artisans’ workshops 181 attitude 210 Austria apprentice training 164 design policy and institutionalisation 182, 184 design sector 180–92 furniture retailers 180–92 furniture trade 182–3, 185f, 190 industrial policies (1960s, 1970s) 185f, 189

Bangkok: Chulalongkorn University xiii Bangkok: Rhamkhamhaeng University xii bankruptcy 190 banks 189 Bänsch, A. 164(n3) Barsalou, L. 90(n1) batteries 114 behaviour vii, viii, 7, 11, 12, 24(n3), 35–6, 206 Beinstein, E. 164(n6) belief granters 85 belief systems 89 beliefs 10, 194 Bereiter, C. 29 Berlin Free University xii, xv Berry, D. C. 32 Billig, M., et al. (1988) 89, 90 Blockeel, H. 129 bonuses 211t Boolean searches 101, 149 Börsch, S. 33 215

216 Index

boutiques 156, 162 brain 4, 35, 36, 38, 39, 82 Brazil 168, 170, 172 see also joint ventures (Austro-Brazilian) Broadbent, D. E. 32 et al. (1986) 32, 41 Broadbent, M. H. P. 41 BroadVision 132 Brooks, R. 36 Buber, R. ix, x, xii, 61, 115, 159, 174, 184, 185 Bukowitz, W. R. 4 CASE (computer-assisted self-explication) 132–5 categories 78, 90(n3), 100 analysis 56 hierarchical trees 99 idiosyncratic 99 sub-categories 99 categorisation 77–9 non-Aristotelian 76 causality 80, 85, 178 Cdnow.com 133 centre-periphery 77–9, 78f chains/chaining 78–9, 83 action chains 80, 81f Chi, M., et al. (1981) 30, 31, 32, 41 chief executive officers (CEOs) 199 children 49, 154 Churchland 4 Clark, A. 38 Cleeremans, A. 32 Club of the Green Party (Austrian Parliament) xii codevelopment 17 coding 61, 83, 90, 96–8, 102–8 Austro-Brazilian joint ventures 174, 178 ‘dramatic advances for researchers’ 97 evaluation of VUEBA computer-based marketing course 146, 147f, 148, 149, 151 iterative searching 102, 108

‘managing knowledge by managing codes’ 98–101 ‘coding on’ 103–4, 107 cognition 3–10, 35, 36, 46, 75, 77 centrality, chaining, overlap 77 ‘essentially interactive’ 45 individual 210 ‘intersubjective conversational process’ 69 on-line 79 situated, embodied 36–9 cognitive abstraction 30 anthropology 89 dissonance 88–90 economy (principle) 38 grammar (space grammar) 79, 80 neurobiology 64–5 neuroscience 6 processes 23, 28, 69 cognitive linguistics ix, 75–92 aspects of gestalt theory 77 zoom, link, scope 80 cognitive systems 5–6, 14, 17, 19 centripetal and centrifugal factors 89 individual 9 internal domain 5–6 relation with reality 6 Cognitive Theory of Cultural Meaning (Strauss and Quinn, 1997) 89 collaboration 106–7 collective effort 199 collective property 50, 51 commitment 10, 101, 171t, 171, 197, 209 commoditisation 125 communication 29, 50–1, 69, 113, 122–3, 150, 177t, 177, 178, 190, 199, 200t Austrian design sector 184, 185f man-machine 35 competitive advantage 197, 200t, 210, 211

Index

competitiveness 193 complexity ix, 69, 70, 71, 71(n1), 77, 87, 90(n2), 93–109 conceptual models 77, 90(n2) cultural ‘thoughtscapes’ 85 on-line manageability 81 see also GABEK compression 56, 80–1, 90(n4) automatic 87–8 computer science 4 computer-assisted self-explication (CASE) 132–5 computers 36 complexity managers 94 epistemological foundation 8 concepts 37, 68, 70, 71, 77, 80, 82, 100 complexity of meaning 70, 71(n1) defined by network graphs 64 hierarchies 65 conceptual clustering 90 integration networks 85 knowledge viii knowledge network 65 network graphs 63–4 networks 61–3, 63f, 65, 68 Condor, S. 90 connectionism 15, 35–6, 37, 38 construction 18 constructivism 9, 11, 18, 19 consumer profiles 127, 131 consumers 186t attitudes towards EEB components 114–15, 123(n1) Austrian designer furniture 189 knowledge and experience 61–2 qualitative approach 114 context 70–1, 87, 96, 104 everyday 89 meaningful wholes 65, 71 controlled ventilation systems (consumers’ knowledge) ix, 62f, 62, 67, 113–24

217

communication and promotion 122–3 cost 119t, 120, 121t, 121–2 data analysis 115–16 distribution and installation issues 122 evaluation profiles 115–16, 117–20, 121t lead users 115, 116, 117t, 117–20, 121t, 123 marketing 120–3 methodology 115–16 needs and wants 123 non-users 115, 117, 123 occupants of apartments 116, 117f, 119–20, 121t, 123 occupants of one-family houses 116–19, 123 results 116–20, 121t conventional knowledge see information convergent evidence 76 conversation 61, 69, 80 conversation theory (Pask) 60, 69 Cook, N. 88 Cooley, R. 137 et al. (1997) 129n, 131, 136 corporate administration 43 community 54 culture 57, 199, 209, 210 structures and activities 55 corruption 176, 177t creative individualism 194 creativity 209 credence 133 culture cross-border 171–2, 175t, 175–6 diversity 210 customer focus 209 groups 127 loyalty 125, 136 relationship marketing 126 retention 136 satisfaction 207t, 207 service 161, 162, 163f, 164(n6), 209

218 Index

customer databases 125, 127, 130–1, 132, 133, 135–6 preprocessing steps 130, 136(n1) privacy issues 131, 136(n2) customers job image of sales assistants x, 153–67 life-time value ix, 127, 134–5, 136 needs ix, 135, 136 cybernetics 17 Dahrendorf, R. 34 Dai, H. 137 Darmstadt University xv data 19–20, 21, 55, 89, 105, 129 coding x, 96–8 conceptual ix demographic 97 ideas and 101 patterns 100 qualitative 95 quantitative 95 retrieval 101, 102, 108 verbal 56 data analysis ix data collection ix data-mining vii data-processing 36 databases vii, 54, 61, 98 ‘DataBite’ hyperlinks 106 Davenport, T. H. 4 decision-making strategies 113 default tools 82 defect rate 207t, 207 demand 210 Denmark 185f dequalification 156 Deshpande, M. 137 design centre 190 design-consciousness x, 184, 185f, 186, 187t design schools 189 designer furniture sector (Austria) 180–92 basic values 185–6 competitors 190

emigration of young talent 184, 185f, 187, 189 export promotion required 189 findings (detailed) 185–7 findings (general) 184–5, 191(n2) history 180, 181–2 image 187, 190 methodology 183–4 qualitative approach 183 retailer’s point of view x, 183–7, 188f stakeholders 187–91 teaching 184, 188 designers 188–9 dilemmas 89 cognitive 75 Kohlberg 56 dimensionalising 104 discourse 38, 54, 77 cultural 88 hierarchical scaffolding of knowledge 76 ‘invisible’ context 81 master maps 76 new conceptual inferences 85 reconstruction of theme structures 75 scientific 51 discourse fields 82, 90(n2) discourse management: dynamic mental space model 75 distributed representation 18 distributedness 15 documentation 194, 195–6 Dörner, D. 29 dynamic systems 17 e-learning 138, 142, 147, 149 education 22, 160f educational institutions 187, 188 Edwards, D. 90 EEB see energy-efficient buildings efficiency 207t, 207, 209, 212 embodiment 35, 36 emotions 81, 82

Index

employees 57, 209 corporate 50 highly-qualified 50 idiosyncratic knowledge 53 image of jobs x performances and cognitive structures 55 satisfaction x, 207t, 207 social activities 46–7 well-being 53 employment 189 image (low-price clothing shops) x part-time employment 96, 97, 101, 154, 159 empowerment 210 energy-efficient buildings (EEB) ix, 61–2, 67, 113 attitudes towards EEB components 114–15, 123(n1) consumers’ attitudes 114–15 knowledge about ix, 67 environment 5–6, 8, 16, 18, 23, 23–4(n2–3), 35, 36, 38, 39, 45, 69, 194, 199, 200t epistemological equilibrium 7 epistemology 4–5, 6, 9, 10, 14, 19, 45, 87 genetic (Piaget) 49 three central questions 23(n1) European Council (Lisbon, March 2000) 24(n2) evaluation profiles 115–16, 117–20, 121t exhibitions 191 experience 33, 36, 38, 49, 70, 71, 101, 114, 117, 123, 133, 141, 197, 210, 211, 212 expert activity viii inter-disciplinary cooperation 34 methodological consequences 39–40 qualitative analysis 39 situatedness and artefacts 28–42 experts 76, 88 explicit strategies 38

219

facts 19–20 fairs 191 families 154 family resemblances 78–9 family values 103 Fauconnier, G. 81, 85 Feltovich, P. 41 field research 106 figure-ground operations 79–80 Fillmore, C. 79 FitzGerald, P. 41 flexibility 30, 38, 176, 177t focal spread (Zelger) 80 focus groups ix, 126, 128 evaluation of VUEBA computer-based marketing course 139, 140–2, 143t, 144–6, 151 questions 141t recruiting participants 144–5 suggestions 145 force chains 85 force schemas 80, 81f formal concept analysis (method) 56 frame semantics 79–80 frames 24(n3) Freud, S. 14 Fribourg University (Switzerland) xv Friedmann, M. x, xii, 191(n2) furniture retailers 180–92 GABEK (Ganzheitliche Bewältigung von Komplexität, Holistic Processing of Complexity) method (Zelger) ix, x, 56, 60–1, 62–3, 68, 69, 71, 115 Austrian design sector 180–92 Austro-Brazilian joint ventures 173–4 features shared with real-life thought 76–7 gestalt-based linguistics 75–92 job image of sales assistants 153, 159 method for theory-building 168 ‘qualitative research approach’ 61, 71

220 Index

Gadamer, H. G. 60, 69 Gadner, J. viii–ix, xii, 65, 66, 68, 101 Gane, M. 90 Geiger, D. vii generalisability 30, 141 generalisation (‘public concepts’) 67 Germany 182 gestalt clusters 75, 90 cognition 76, 85 embedding 82–3, 84f graph 178 psychology 75 theory (Stumpf, 1939) 61, 77, 82 gestalt-building Austro-Brazilian joint ventures 176, 178 automated ix bottom-up process 65, 67f computer-supported process ix gestalten 29, 71, 76, 191(n1) compressed 90(n4) features of thought and discourse 83, 85 GABEK 87 global images 80, 81 high-level 80–2, 88 higher-level 66, 77 linguistic 61 lower-order 68 organisation of thought 82 schematic 89 ‘spans slightly different senses’ 90(n2) spatial 78 gestalten trees 60, 64–8, 71, 90, 146, 148 definition 67 GABEK 87 ‘general knowledge structure’ 67 job image of sales assistants 159 loss of detailed information 66–7

relevance 68 syntactic and semantic rules 68 Glaser, R. 41 GOFAI (good old-fashioned artificial intelligence) 38 Goh, S. 195 Goodwin, C. 39 Grabner-Kräuter, S. ix, xii–xiii, 133, 135 Granada University (Spain) xv Gregory, D. 64, 65, 67, 68 grounded theory method 97, 106 group therapy 4 grouping rules 83 guilt 97, 101 H&M see Hennes and Mauritz handicrafts 189 Hanson, W. 132, 132f, 134f hardware 4 Haßmann, V. 165 Hendriks-Jansen, H. 38, 39 Hennes and Mauritz (H&M) 158, 160f, 162, 164(n5) hermeneutics 60, 69 Herstatt, C. 114, 115 heuristic (procedural) strategies 30 Hoffmann, J. 181 Höld, R. ix, xiii, 147 Holland, D. 33 Holz-Mänttäri, J. 34–5 homeostasis 7 HTML tags 129 Huberman, M. 94, 96 human resources 171t, 171, 175t, 175–6, 199, 200t, 201t, 207, 208t, 209 humanitarian aid 30 Hütter, A. 34 ‘hybrid consumer’ 153, 156 hypergestalten 68f, 76, 88, 90, 191(n1) clusters 76 disconnected 89 GABEK 87, 88 incompatible 89

Index

hypergestalten – continued interpreting 87–8 job image of sales assistants 159 hyperhypergestalten 67, 68f, 76, 85, 87, 191(n1) ‘highest level of generalisation’ 67 hyperlinks 105, 106, 129 hypertext 105 ideas 101, 105 identification 33–5 identity (professional) 34, 40 ideology 85, 89 idiosyncratic knowledge 45–9, 50, 52, 53 emotional quality 49 not observable 48–9 properties and conditions 48–9 two fundamental sources 49 IKEA 186t imagery/images 77, 81, 82, 86 incentive system 171t, 171 index systems 99–100 individual history 35, 39 information (‘conventional knowledge’) 10, 20, 21, 48, 50–2, 54, 95, 105, 106, 197, 209 conditions and properties 51–2 interpretation (open-ended process) 52 ‘objectified knowledge’ 43 storage 98 verbal signs (multiple meanings) 51–2 virtual nature 51 information and communication technology (ICT) 125, 127 information technology advantages 54 limitations and dangers 54–5 Innsbruck: Leopold Franzens University xii, xiii Innsbruck University xiv input–output relationships 40

221

inquiry and dialogue 197, 198t, 199, 201 Institute of Knowledge Organisation viii, xii see also IWO insurance companies 189 ‘integrated oppositions’ 90 intelligence 38 intentionality 10–11 interaction 9, 54 linguistic 69 social process 69 interactivity (recursivity) 29 internalisation 46 international joint ventures 168 definition 169 ‘four areas of research’ (Parkhe) 169 keys to success 169–71 partner selection 169–70 performance and stability 169 see also joint ventures (Austro-Brazilian) internet 3, 12, 20, 95, 127, 128, 138, 160f, 161, 162 characteristics 125 interpretation 55, 56, 69, 82 ontogenetic and phylogenetic history 7 open-ended process 52 verbal information 52 interviews/interviewees ix, 33, 61, 63–4, 68, 75, 76, 85–7, 89, 98, 100, 115, 117, 122, 126, 128 Austrian design sector 183–90 Austro-Brazilian joint ventures 172–3, 176–7 defining goals 145 evaluation of VUEBA computer-based marketing course 139, 140, 142–7, 151 job image of young sales assistants 153–5, 158–63 organisational learning 193–213 preparations of questions 145 recruiting participants 144–5

222 Index

interviews/interviewees – continued selection criteria 195 semi-structured 55, 56–7 types 140 intranet 54 investment 131 irony 85, 89–90 Italy 182, 185f IWO (Institut für Wissensorganisation) viii, xii job image of sales assistants 153–67 aims of study 157 customers’ views 157, 162–3 findings 159–63 methodology of study 158–9 qualitative approach 158 questions 156, 157, 158–9 research design 157f sales assistants’ views 157, 159–62 job scope 204, 205t joint ventures (Austro-Brazilian) x coding 174, 178 defining text units 174 empirical research 168, 171–8 evaluation list 176–8 field of research 172–3 future research 178 GABEK WinRelan study 168–79 ‘integrated model of joint venture success’ 170 joint venture view 171t keywords 174, 175, 178 methodology and research design 173–4 partners’ view 171t perspectives 170, 170f previous research 168, 169, 172, 175, 176 problem identification, research objectives, and questions 171–2 qualitative approach 174 results 175–6

success factors x, 171t, 175t see also international joint ventures Kallikourdis, D. 72 Karnas, G. 32 keywords 30, 31 ‘meeting’ 63–4, 64f Kimmel, M. ix, xiii kinaesthetics 77, 80 King John (Shakespeare) 89 Klagenfurt: University of Klagenfurt xii knowing 14 knowledge 18, 21, 24(n3), 206, 210, 211, 212 general: ‘aboutness’ and intentionality 10–11 acquisition 7 application in reality 5 and behaviour 11 ‘characterised by three factors’ vii–viii concept 4–5, 45–52 currency 15, 16 definitions vii, 45, 60 dimensions 11–19 domains 5–6 epistemological relationships 18–19 evolutionary perspective 11 goal 9 homomorphic/isomorphic mapping 15–16, 18 iceberg analogy 13 mapping, adaptation, construction 18–19 ‘missing link’ between input and output 6 nature viii, 10–11 ontological status 4 proper sense 21–2 properties viii ‘radically tied to cognitive processes’ 9 and reality (circular relationship) 7–8 reality cognition 3–10

Index

knowledge – continued reality- and object-oriented approach 19–23 representation by conceptual networks 61–3 representational strategies versus behavioural strategies 18 ‘spreading activation’ dynamics 16 sub-symbolic models 76 wider view 40 types: actualised 16–17 conceptual viii, 46, 47, 49, 60–74 conventional 50–1, 51–2 cultural 70 declarative 12, 13, 30, 32 distributed 14–16 embodied 76 episodic 13–14, 88 everyday 3–27, 113 expert viii, 13, 113 explicit 12–13, 44, 46, 55, 76 external 7–8 idiosyncratic 45–9, 50, 52, 53 internal 7–8, 9 intuitive 56 local 14–16 new sort 98 objectified 43, 50–1 organisational 12 performed 46–7, 48 personal 43, 48, 49, 55–6, 69–70 potential 16–17 procedural 12, 13, 30 propositional viii qualitative intellectual 22 qualitatively different forms 45 representational versus embedded 17 rule-based 16, 21, 76 scientific 20 semantic 13–14 sensory motor 46 situated 18 social 12, 70

223

socialised 51 tacit/implicit 12–13, 16, 32, 43, 44, 46–7, 55, 56, 70, 115 unconscious versus conscious 14 verbal 50 knowledge effect 7 knowledge management viii, 3–5, 6, 10, 13, 17, 22, 48 corporate self-reflection 44 goal 53 hermeneutic 53, 55 human foundation 43–59 joint activity 54 ‘kind of research activity’ 55–7 methods and tools 55 not formalistic 54–5 ontological domains 8–10 prestige 44 principles 52–7 qualitative 55–7 quantitative 55 knowledge organisation 3–5, 10, 13, 43, 60–74 case studies ix–x, 113–213 conceptual network graphs 63–4 core reason for viii definitions viii, 10 gap (scientific research v economic use) vii gestalten trees 60, 64–8, 71 managing complexity 93–109 ‘new aspect of academic research’ vii outline of book viii–x problem of understanding 68–71 qualitative 71 qualitative questions 101–3 qualitative software 93–109 storage and access methods 93, 94 theory and methodology viii–ix, 3–109 knowledge representation 6, 10, 16, 17

224 Index

knowledge society 22, 24(n2) knowledge systems 68, 88 Quine’s definition 62 knowledge transfer 7, 9, 13, 17, 22 knowledge units 36 Kohlberg, L. 56 Kosala, R. 129 Kövecses, Z. 81, 82, 86 Kraler, C. 61, 115, 159, 174, 184 Krems: Danube University xv Krueger, R. A. 141t Lakoff, G. 76, 78, 82, 151 Lang, H. 182 Langacker, R. 79, 80 language 5, 13, 14, 36, 47, 50, 51, 55, 65, 68, 69, 70, 76 English 176, 177t language theories 36 Latin America 168, 172 leadership 209 learned procedures 38 learning 9, 13, 17, 38, 70 activities 197–9 behavioural 194, 204, 210, 211t, 211 benefits 201t, 201 cognitive 194, 204, 210, 211t, 211 conducive climate 201 developmental process x, 193 extent and scope 197 factors influencing x, 193, 202, 204–6 factors that strengthen, weaken, revive, terminate 199, 200t relationship with performance x, 193 single-loop, double-loop, tripleloop 202 stages x, 193 systemic perspective 202 three stages (individual, team, organisational) 202, 203t, 209 see also organisational learning

learning climate 197, 198t, 1999 learning loop theory (Argyris and Schön) 206 ‘learning platform’ 144, 148f, 150–1 legal framework 175t, 175–6 libraries 12 Liebowitz, J. 4 lifestyle 187, 188 linguistic gestalten (Zelger) 65, 174 ‘seven syntactic and semantic rules’ 65 linguistic hypergestalten 65 linguistics cognitive 75 gestalt-based 75–92 linking 83, 105 live node browser 103–4 Llull, R. 82 Lofland, J. 96 Lofland, L. H. 96 London: Anna Freud Centre (London) xii London: University College London (UCL) xii Loos, A. 181 Lower Austria 153, 154 Luo, T. 137 Luo, Y. 169 Macbeth (Shakespeare) 81 macrostructures 81 mail order 161, 162 management 101, 171t, 171 activities vii cross-border/intercultural 63, 171–2 practice 193 systems 38 top/senior 197, 199–202 managers 57 manufacturers/manufacturing 189, 191, 207 mapping 6, 9, 18 cognitive dissonance, real dilemmas, irony 88–90 environmental structures 18

Index

market communication 113 segmentation 113 situation 171t, 171, 175t, 175 marketing 115, 163 Austrian design sector 186–9 computer-based course ix controlled ventilation systems 120–3 cycle of personalisation 127f dominance of technical information 114 personalised 125–37 target-group strategies 113 marketing course (computerbased): evaluation 138–52 aims of research 139 applied methods and goals 142–4 categories 146 course material 148–9, 148t data analysis 146–7 focus groups and interviews 138–52 methodology 140–2 qualitative approach 139, 140, 149, 151 research design 142–5 research process 142f research questions 138, 139f, 145, 147 results 147–51 subsequent measures 150–1 suggestions for improvement 149 marketing managers 114, 122, 123 marketing strategies ix, 115 markets 125, 135, 207 marriage 86 mass media 122, 164, 190 mass production 181 Maturana, H. R. 5, 7, 9, 18 Maurer, R. 164(n6) meaning 10, 37, 50, 51–2, 54–5, 70, 71(n1) of concepts and actions 70 higher-level definition 64

225

semantic comprehension 50, 52 semiotic attribution 50 shared 64 mega-metaphors 90(n4) Melbourne: La Trobe University 95 memory 32, 36, 50, 54, 79, 82 mental models 4 mental spaces 77, 85 mentality 176, 177t, 177 Merge for NVivo 107 Merkwelt 38 metacognition 32 metadata 129 metaknowledge 12 metaphor 86, 87 ‘method of loci’ 82 methodology 39–40, 96, 105–6, 114 Austrian designer furniture market 183–4 Austro-Brazilian joint ventures 173–4 ‘code and retrieve’ 96, 97 comparative 107 consumers’ knowledge of controlled ventilation systems 115–16 evaluation of VUEBA computer-based marketing course 140–2 job image of sales assistants 159–9 organisational learning 212 practical 33 qualitative 107 scientific 33 ‘system closure’ (L. & T. Richards) 102 Meuser, M. 183 Michigan State University xiii Microsoft 210 Middleton, D. 90 Milan 189 Miles, M. 94, 96 ‘mindmapping’ 82 mindset 204, 205t, 206, 210, 211t, 211

226 Index

mission 207, 209 mnemotechnics 82 Mobasher, B. 131, 136 et al. (2000a) 136(n1), 137 et al. (2000b) 131f, 137 models, layered 105 Morgan, D. L. 144 Morse, J. xiv, 104, 139, 140, 141, 146 motivation 33–5, 53, 101, 139, 143t, 145, 156, 164, 171, 209 motor action 11 output 6, 7 system 6, 8, 17 MovieFinder.com 133 Mueller, C. B. 195 Mühlbacher, J. x, xiii museums 191 music 88 mystical knowledge 22–3 Nagel, U. 183 narrative 88 Nerdinger, F. W. 164 network graphs 63–4, 71, 115, 116–17, 178 lead users’ product knowledge 116–17 neural activation 16 architecture 16, 18 mechanisms 9 networks 35 processes 23 system 6, 14, 17, 24(n3) neuroscience 6, 15, 18 niche markets 186t, 189 nodal/key concepts 62f, 62, 63f, 65, 66f node schemas 83 nodes 97–101, 102 evaluation of VUEBA computer-based marketing course 146 flexible altering 99 optional free (N4 software) 99 ‘parent’ 99

non-equity cooperative alliance 169 Non-numerical Unstructured Data: Indexing Searching and Theorising (NUD*IST) software xiv, 94–9, 101, 103, 106–7 aims 95–6 coding complex data 96–8 N4 software 99, 101, 103, 105, 107 N5 software 95, 107 N6 software ix, 95, 107, 146, 149, 151 third revision 101 see also Qualitative Solutions in Research Nonaka, I. 4, 10, 13 nouns 79 novices 30–3, 38 NUD*IST see Non-numerical Unstructured Data NVivo software 94, 97–107 object-orientedness 19, 39 objectification 53 objectified knowledge has to be conventionalised 51 ‘idiosyncratic knowledge attached to material signs’ 50 nature and function 50 observation 48 Oeser, E. vii, 20, 60 Ohnesorge, D. viii–ix, xiii–xiv, 63 on-line cognition ix conceptual activity 85 discourse shifts (theory) 77 dynamics of discourse 85 retailers 133 ontogenetic development 45 opening hours 159 operative autonomy 171t, 171 organisation adaptiveness 211t, 211 organisational learning: influence on organisational performance x, 193–213

Index

organisational learning – continued background information 196, 196t cross-case analysis 196, 206 data analysis 196–7 data collection 195–6 development 197–201 empirical evidence 193 prioritisation 206 qualitative and quantitative data 195, 212 research design 194–5 research issues 197–209 research questions 193–4 research validity safeguards 194–5 results 206–9 organisations public and private sectors 195 size 195 structure viii ‘organising systems’ (Tesch) 96 organism phylogenetic and ontogenetic history 17, 18 Orissa (India) 88 Ornetzeder, M. 124 ‘over’ (preposition) 82–3, 84f Owen, H. 193 Paivio, A. 82 Paris xv Parkhe, A. 169 partner software 107 Pask, G. viii, 60, 64, 67, 68, 69 et al. (1973) 69, 72 Penker, J. M. x, xiv perception 37, 64–5, 70, 76, 77 perceptual gestalten (Wahrnehmungsgestalten) (Stumpf) 173 perceptual symbol systems (Barsalou) 90(n1) performance 171t, 171, 178 financial 210, 211t financial and non-financial factors 206, 207t, 208t, 209

227

influence of organisational learning x, 193–213 Perger, J. 34 personal judgment 70 selling 155 personalisation systems ix personnel managers 154 Peschl, M. F. viii, xiv, 9, 18, 19, 24(n3) philosophy 4, 6, 10 of the mind 15 of science 6 Piaget, J. xv, 45, 46, 49 ‘assimilation’ process 70 ‘plan as resource’ 38 planning 113 Plato 164(n1) plus/minus grids 85 Polanyi, M. 12–13, 46, 69–70, 115 policy-making, participatory 198t, 199 Pöchhacker, F. 34 prepositions 79 prestige 34, 44 prices 186t, 190 privacy 131, 136(n2) problem-solving 31–2, 33, 203 procedurality 31 processes cognitive 9, 23, 28, 69 corporate 53 hidden 5 internal 40 knowledge organisation 60 neural 23 off-line 130–1, 131f on-line processes 87, 130–1, 131f processuality bias 88 productivity 207t, 207, 209 products 4, 207–10 professionalism 38, 40 profiling 77, 79–80, 81 propositional discourse comprehension models 81 protolanguage (Pask) viii, 61 prototypes 78–9, 83, 88, 90(n3)

228 Index

Prusak, L. 4 psycholinguistics 87 psychology 4, 10 cognitive 82 developmental 35, 49 public concepts 67–8 Putz-Osterloh, W. 30 qualitative analysis x, 31 qualitative research creativity and organisation 108 editing, linking, modelling 104–6 exploration 108 methods viii–ix software ix, 93–109 see also GABEK Qualitative Solutions in Research (Melbourne) xiv, 95 QSR International xiv QSR Merge 107 QSR N6 146, 149 QSR software 103, 106–7 see also Non-numerical Unstructured Data quality 20, 180, 185f, 189, 207 quantitative models 114 quantitative values 20 questionnaires 55, 56, 114, 132, 136, 140, 183 biased data 151 working conditions (Austrian retail companies) 154 Quine, W. V. O. 62 Quinn, N. 33, 65, 85–7, 89 Quintilian 82 racist ideology: ‘othering’ 81 Radley, A. 90 realism, naïve 19 reality 5–6, 11, 17, 18, 19, 23(n2) cognitive 87 knowledge and (circular relationship) 7–8 relation with cognitive system 6 ways of knowing 19–23

reality cognition 3–10 reasoning 88 recipe knowledge 21 ‘recipe-application society’ 22 recommendation 133–4 recontextualising 103–4 reference models: flexible/general viii reflection 33, 49, 104 reflexive consciousness 47 reflexivity 29 reification: epistemological problems of 87 religion 97–8, 103 repraesentandum (entity to be represented) 15 repraesentans (representing entity) 15 representational structures 18 respect 161, 200t results: methods of analysing 56 retail managers 164 retail sector ‘added value of performance’ 156 career options 155, 164(n3) changes 155–7, 161, 162, 164(n6) clothing 153, 154, 156, 158 food 154 full-time and part-time employment 154 image problem 154, 156, 162, 163, 164(n1) internal workings 163–4 pay 155, 161 polarisation 153 satisfaction/dissatisfaction 155, 159, 160f, 161 social benefits 155 textiles 154, 155 ‘vital part of marketing strategy’ 157 working on Saturdays 154 reward system 197, 198t, 199, 201, 209 ‘rich text’ 104, 105 Richards, G. 195

Index

Richards, L. ix, xiv, 95, 97, 98, 102, 104, 105, 139, 140, 141, 146, 149 Richards, T. xiv, 95, 99, 102, 104 risk-aversion 182, 184 Risku, H. viii, xiv–xv, 34 Ritz Carlton hotel 210 robotics 35 Rohracher, H. et al. (2001) 123(n1), 124 Rosch, E. 77–8 Rubin, H. J. 115 Rubin, I. S. 115 Sadoski, M. 82 sales assistants expectations 161 job image x, 153–67 ‘marketing person in a selling process’ 164, 164(n3) sales process 160f ‘scaffolding’ 38 Scandinavia 182, 184, 185f, 188, 189 Scardamalia, M. 29 schema theory 80 schemas 31, 37, 77, 80, 81f, 81 causality 85 cultural 86, 87 form and content 82–3 hierarchy 85 Schön, D. A. 206, 211 Schreyögg, G. vii Schwarz, M. 32 science cognitive 4, 6, 14, 15, 35, 36, 76 natural 6, 21 scientific research 29 scientific theories 20–1, 22 scope 80 Scott, B. C. 72 search tools 102, 107 searching power 103 ‘Secession’ group (Jugenstil era) 181 Seiler, T. B. viii, xv, 47, 50, 55, 56, 70, 77 self-confidence 187, 187t

229

self-consciousness 50 self-definition 33 self-service 153, 156 semantic networks 36 semantics 17, 20 semiotic links 51 Senge, P. M. 202, 211 sensory input 6, 7 sensory system 6, 17 services 207, 208t, 209, 210 Shakespeare, W. 81, 89 Shore, B. vii, 70 Shweder, R. 88 signification 50 Singapore x, 195 Singapore: Temasek Engineering School xv Singapore: Temasek Polytechnic xv situatedness viii, 28, 35, 40 situationism 37 skills 12, 69 cognitive 40 intellectual 13 intuitive knowledge 56–7 management 171t, 171 manual 13 new 212 personal 204, 205t, 206 social 40 skills level (of employees) 207t, 207 Skövde University (Sweden) xv slides 148f, 149, 150, 151 small businesses (SMEs) 30, 132, 182, 183, 186t, 190 ‘smart shoppers’ 156 social position 34 psychology 204 science 76, 102, 204 society 53 sociology 4 software ix, 4, 71 qualitative 93–109 software designers 106 software firms 135 solipsism 18, 20 Somm, F. 165

230 Index

space grammar (cognitive grammar) 79, 80 spatialisation of form hypothesis (Lakoff) 82 spreading activation principle 16 Srivastava, J. 131, 136, 137 et al. (2000) 136(n2), 137 status symbols 187 Strauss, A. L. 96 Strauss, C. 65, 89 structural coupling 76 students 158 assessments and suggestions 148f, 149 expectations 142, 143t experiences 142, 143t, 144–5 learning strategies 142, 143t, 144, 149–50, 151 perceptions of course 144 VUEBA computer-based marketing course 139, 142 Stumpf, C. 61, 65, 173 Sun, Y. 137 supermarkets: cut-price 153, 156 Superzeichen (Dörner) 29 suppliers 133 survival 11, 24(n3), 186, 186t Sweden 164(n5) Sweetser, E. 90(n3) Switzerland 187 symbol manipulation 35–6 symbolism 90(n4) symbols 3, 24(n3) system analysis 30 system knowledge 30 systematic psychological displacement 90 systematicity (second-level) 89 systemic thinking 199 systemisation and linear condensation principle 89 Takeuchi, H. 4, 10, 13 Tampere University (Finland) Tan, P. N. 137 teaching 17, 38

teamwork 100, 106–7, 204, 205t, 209 technology 21, 212 telecommunications 36 telephones 36 Tesch, R. 96 text viii, 55, 60, 61 selective representation 65, 66f text clusters 63f text units 69, 71 Thailand: management 63–4, 64f theory construction 106, 108 thinking-aloud (TA) method 33 thinking/thought 38, 76–7, 82, 83, 85 on-line processes 87 principles 75 qualitatively different forms 45 Thonet, M. 181 chair number fourteen 181, 181f ‘thoughtscapes’, cultural 85–7 THOUGHTSTICKER 61, 64, 67 time 177t, 177, 209 Tomczak, T. 165 topics 68, 69, 98 inter-relationship 95–6 nodes 97 training 28, 155, 156–7, 160f, 162–3, 164, 164(n3), 186t transduction process 6 transformability 30–1 transformation function 6 translating/translation 32, 34–5, 36 use of idiom 31 tropes 85 trust 168, 171t, 171, 175t, 175, 176, 177t, 178, 200t turnaround time 207t, 207 Turner, M. 81, 85, 89

xv understanding 68–71 unemployment 161

Index

United States of America 88, 89, 172 Università degli Studi di Roma/La Sapienza xiv universities 188, 189 University of Northern Iowa xiv Van Deusen, C. A. 195 Varela, F. J. 5, 7, 9, 18 Venkula, J. 34 verbal data viii expression viii reasoning 32 signs 51–2 verbalisability 31–3 verbalisation 32, 33, 45, 47, 56 verbs 79 Vester, F. 31 Vienna 153, 160f, 183 Vienna Business School xiii Vienna Exhibition 186t Vienna Museum of Applied Arts (MAK) 182 Vienna University xii, xiv, xv Vienna University of Economics and Business Administration (VUEBA) xii, xiii, xiv marketing course (evaluation) 138–52 vision 199, 200t, 202, 204, 205t, 206, 207, 209, 210 von Foerster, H. 9, 69, 70 von Glasersfeld, E. 5, 7, 9, 10, 69 von Hippel, E. 114, 115 Wagner, I. 40 Wagner, O. 181 Wahrnehmungsgestalten (Stumpf) 173 web content mining 129, 129f, 130 web mining 126 categories 128–30 prerequisite for personalised on-line marketing 128–30 taxonomy 129f

231

web personalisation adequacy 131–5 appropriate systems 125–37 CASE systems 132–3, 132f, 134f, 135 collaborative filtering systems 132, 132f, 133–4, 134f, 135 content- and usage-based 130–1 definition 128 endorsement systems 132, 132f, 133, 134f, 135 four types (Hanson) 132, 132f off-line and on-line processes 130–1, 131f principles 126–8 quantitative and qualitative research 126, 128 risks 126 rule-based systems 132, 132f, 134f, 135 web structure mining 129, 129f web usage mining ix, 129, 129f, 130–1, 136(n2) definition 130 websites 95, 128, 133, 136, 151, 164(n4), 196 wheel of retailing 156 Wiener Werkstätte (Vienna Workshops) 181 Williams, R. L. 4 Windows Relational Analysis (WinRelan) 60–1, 71, 115 Austrian design sector 180–92 Austro-Brazilian joint ventures 173–4, 176 job image of sales assistants 153, 159 Wittgenstein, L. 70, 71(n1), 78 wisdom 22, 106 women 154 working environment 204, 205t, 206 works councils 155 XML tags

129

232 Index

Yan, A. 169 Yeo, R. x, xv, 194 Yin, R. K. 194, 196 youth 153–67 Zaïane, O. R.

129n, 137

Zairi, M. 195 Zelger, J. 56, 60–1, 65, 75, 76, 80, 87, 88, 173, 174, 183, 184 Zhu, J. 137 zooming 80, 81, 83, 85

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