Effective Knowledge Transfer in Multinational Corporations

Tina C. Chini

Effective KnowledgeTransfer in Multinational

Corporations

Effective Knowledge Transfer in Multinational Corporations

Effective Knowledge Transfer in Multinational Corporations

Tina C. Chini Vienna University of Economics and Business Administration

© Tina C. Chini 2004 Foreword © Bodo B. Schlegelmilch 2004

All rights reserved. No reproduction, copy or transmission of this publication may be made without written permission.

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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 EuropeanUnion and other countries.

ISBN 1–4039–4220–X

This book is printed on paper suitable for recycling and made from fullymanaged and sustained forest sources.

A catalogue record for this book is available from the British Library.

Library of Congress Cataloging-in-Publication Data Chini, Tina C., 1979–

Effective knowledge transfer in multinational corporations / Tina C. Chini. p. cm.

Includes bibliographical references and index. ISBN 1–4039–4220–X 1. Knowledge management. 2. International business enterprises. I. Title. HD30.2.C47445 2004 658.4′038—dc22 2004045424

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

To my mother on her 50th birthday

vii

Contents

List of Exhibits ix

Foreword by Bodo B. Schlegelmilch xii

Preface xiv

Acknowledgements xvi

1 Positioning 1

The knowledge management challenge 1 Aim of the research 2

2 Knowledge and the MNC 5

Introduction to knowledge management: a conceptual background 5

The relevance of knowledge management in the MNC 19

3 Knowledge-Based Determinants of MNC Strategic Configuration 37

Headquarters–subsidiary relationships 37 Strategic mandates, coordination and control 39 The capability perspective 49 Contingency factors 52

4 A Model of Knowledge Transfer in MNCs 58

Strategic mandate 58 Value of knowledge stock 60 Knowledge transfer capabilities 61 Knowledge transfer effectiveness 64 Organizational distance 65 Cultural distance 66

5 Research Design and Methodology 68

Research context 68 Data collection 70 Operationalization and measures 76

viii Contents

6 Analysis and Results 81

Descriptives of the unit of analysis 81 Analysis of the model’s constructs 82 Hierarchical relationships and culturally close subsidiaries 107 Hierarchical relationships and culturally distant subsidiaries 124 Lateral relationships of subsidiaries 129

7 Conclusion, Limitations and Implications 138

Conclusion 138 Limitations 145 Implications for future research 146 Managerial implications 147

Appendix 1 Specification of the Measurement Model 149

Appendix 2 Model Identification and Assessment 153

Notes and References 157

Bibliography 159

Index 170

ix

List of Exhibits

2.1 The continuum of data – information – knowledge 7 2.2 Tacit and explicit knowledge 9 2.3 Definitions of knowledge management 11 2.4 The knowledge management value chain 13 2.5 A simplified communication model 15 2.6 Nine knowledge transfers 16 2.7 The knowledge spiral 18 2.8 Studies on intra-MNC knowledge transfers 30 2.9 Areas of empirical contributions 35 3.1 Subsidiaries’ strategic mandate typologies 41 3.2 Control mechanisms 45 4.1 A model of intra-MNC knowledge transfer 59 4.2 Strategic mandates of subsidiaries 59 4.3 Overview of hypotheses 1a–1d 64 5.1 Industry weights of target and final sample 73 5.2 Industries in the final sample 735.3 Regions in the sample 74 5.4 Location of headquarters 74 5.5 Location of headquarters in Central/Western Europe 75 5.6 Location of subsidiaries 75 5.7 Knowledge flows to and from headquarters 76 5.8 Knowledge flows to and from the focal subsidiary 76 6.1 Employees at units 82 6.2 Positions of respondents 83 6.3 Strategic mandates of subsidiaries 84 6.4 Cross-tabulation: strategic mandate and number

of employees 85 6.5 Strategic positions of headquarters 87 6.6 Strategic positions of global versus regional headquarters 87 6.7 Knowledge stock of subsidiaries compared with peer

subsidiaries 88 6.8 Knowledge stock of subsidiaries compared with their

headquarters 89 6.9 Knowledge stock of headquarters compared with subsidiaries 89 6.10 Hierarchical formal transmission channels 92

x List of Exhibits

6.11 Lateral formal transmission channels 92 6.12 Informal transmission channels 93 6.13 Knowledge management infrastructure 94 6.14 Use of knowledge management tools 96 6.15 Composition of knowledge transfer processes 97 6.16 Knowledge transfer processes 98 6.17 Subsidiaries’ perceptions of similarity vis-à-vis

headquarters and peer subsidiaries 99 6.18 Headquarters’ perceptions of similarity vis-à-vis

culturally close and distant subsidiaries 100 6.19 Impact of culture on knowledge transfers 101 6.20 Subsidiaries’ benefits of knowledge transfers from

headquarters 103 6.21 Subsidiaries’ benefits of knowledge transfers from

culturally close subsidiaries 104 6.22 Subsidiaries’ benefits of knowledge transfers from

culturally distant subsidiaries 104 6.23 Headquarters’ benefits of knowledge transfers from

culturally close subsidiaries 105 6.24 Headquarters’ benefits of knowledge transfers from

culturally distant subsidiaries 105 6.25 Satisfaction with knowledge management 107 6.26 Creation of a structural equation model 109 6.27 Composition of latent variables 110 6.28 Formative and reflective variables 111 6.29 Selection of variables 112 6.30 Model 1 113 6.31 Model 2 113 6.32 Model 3 114 6.33 1st Dataset 114 6.34 Global measures of fit of models 1–3 115 6.35 Hypotheses and direct effects 117 6.36 Differences between groups 120 6.37 2nd Dataset 125 6.38 Hypotheses and direct effects (2nd Dataset) 126 6.39 3rd Dataset 129 6.40 Hypotheses and direct effects (3rd Dataset) 131 6.41 General assessment of hypotheses 134

A1.1 Unidimensionality of constructs 149 A1.2 CFA results: hierarchical relationships 151

List of Exhibits xi

A1.3 Indicator reliability, factor reliability and average varianceexplained 152

A2.1 Model descriptives 154 A2.2 Overview of measures of fit 155 A2.3 Suggested assessment of measures of fit 156

xii

Foreword

Doctoral students are usually an enthusiastic and intelligent lot and, inthe majority of cases, it is a pleasure to work with them. However, veryoccasionally there are students who are truly outstanding. In these rarecases, working together is more than a pleasure, it is a privilege. TinaChini has been such an exceptional student, both in terms of abilityand drive. And the outcome in form of this book showcases the qualityof her work.

Her topic, ‘Effective Knowledge Transfer in Multinational Corporations’is most relevant for multinational firms. Given that knowledge is nowwidely regarded as the key determinant of competitiveness, it is vital forcompanies to understand how knowledge transfer between globallydispersed units is best organized. The problem, however, is that this isnot an easy topic to tackle. First, knowledge is a complex constructthat is difficult to capture. Second, the data collection invariably hasto be conducted in different countries and different cultures. TinaChini manages to handle these and other methodological challengesvery well.

She initially points out that not quantity but quality of knowledgetransfer is driving the success of multinational corporations. While thisappears obvious, it is often ignored in empirical studies that find it easierto assess quantity rather than quality of knowledge flows. In contrast,Tina Chini skilfully integrates both perspectives. Based on a compre-hensive literature review, she subsequently develops pertinent researchhypotheses and a causal model depicting the complexities of know-ledge transfer effectiveness in multinational firms.

Next, considerable room is given to provide details of the empiricalresearch design and the development of appropriate measurement scales,including reliability and validity checks. The merits and limitations ofthe selected analytical technique, namely causal analysis, are alsodiscussed. Together, these insights underline once more the methodo-logical challenges faced by Tina Chini.

Her discussion introduces the results and carefully reflects on them inthe light of the existing literature. Based on this, Tina Chini explainsthe diverse implications of her work for the managerial practice of

Foreword xiii

multinational companies as well as for theory development. The scopefor future research avenues is also outlined.

With this book Tina Chini has managed to produce a highly relevant,methodologically sound and comprehensive analysis of knowledge trans-fer effectiveness in multinational companies. Taken her methodologicalskills and drive, it is likely that we will see more such high-qualitycontributions by this researcher.

BODO B. SCHLEGELMILCH

Chair of International Marketing and ManagementWirtschaftsuniversität Wien (WU Wien)

xiv

Preface

Nine out of ten prefaces start by thanking the authors’ family andfriends for supporting them during the stressful time of working ontheir book. Probably this indicates that a particularly hard time is overnow and ‘things are getting better’. Having reached this stage myself,I would also like to thank a number of people without whose supportthis book and the underlying research would not have been possible.

Those who have really enabled this research – and will, I hope, alsobenefit from its outcome at one point or other – are the managers whoparticipated in the study. I hope my lengthy questionnaire and mynumerous follow-up calls have not discouraged them to support furtherempirical studies. Special thanks go to my advisers, Professors Bodo B.Schlegelmilch and Constantine S. Katsikeas, who always had an openear for my ideas (and complicated models) and shared their experiencewith me. Next in line are my colleagues, who have gone through similartortures and were among the few people able to understand what I wasdoing. Elisabeth deserves to be mentioned separately here as shepatiently endured all my beginner’s questions while she was in the finalstages of her PhD thesis. I must acknowledge the deepest debt to Nilsfrom the University of Hamburg who has dedicated much time teachingme structural equation modelling. After some more time, however,I reached the point when no one was able to understand what I wasreally doing and then writing a PhD became a very lonely activity –especially when the most intense phase of writing happens to be in thehottest summer of the century . . . Spending most of the time in a boil-ing office, I started to divide my friends into two groups: those I wouldnot call any more and those who had to listen to my problems quitefrequently. I would like to thank both groups: the first one for still‘knowing’ me – although some of them might think they were reallylucky to be in the first group; the second one, especially Philippa, Deniseand Ulli, for lending me an ear and for all the intense discussions wehad – obviously not only about my thesis. I must not forget to mentionPhilipp, who, despite not being in a daily-glass-of-wine reach, supportedme a great deal. I will always keep the weekend breakfasts with Theresain good memory as well as all the nice evenings spent with Kathi andNina. And I am especially grateful to Tom, who always advocated a

Preface xv

healthy mind–body balance and made me do all kinds of physicalexercise from climbing to yoga. I thank you all for being my friends andfor supporting me during this stressful time. But, in my case, this doesnot indicate that the hard times are over. Times might even get harderas our endeavours seem to lead many of us to different places.

I could not have published this thesis had it not survived the criticalcomments of my reviewers Björn, Philippa and my father. At this pointI thank Jacky Kippenberger, my editor at Palgrave Macmillan, and copy-editor Keith Povey for helping me to turn my thesis into a book. As faras my own survival is concerned, I owe much to my grandmother, whomade sure that my fridge contained some food from time to time, mygrandparents and my brother. My last years would not have been sowonderful without Björn. I thank him for sticking with me through allmy ups and downs and for constantly challenging me. Most important,not only during the last years but during my entire life, was the supportof my parents who – despite not always sharing my views – alwayshelped me to accomplish my goals. This book is dedicated to mymother on her 50th birthday.

TINA C. CHINI

xvi

Acknowledgements

Exhibit 2.4: KM Value Chain, from From Knowledge Theory to Manage-ment Practice: Towards an Integrated Approach by Minsoo Shin, TonyHolden and Ruth A. Schmidt, copyright 2001 Elsevier Ltd. Used by per-mission of Elsevier Ltd.

Exhibit 2.6: The Nine Knowledge Transfers, from A Knowledge-BasedTheory of the Firm to Guide in Strategy Formulation by Karl-Eric Sveiby,copyright 2001 Emerald Group Publishing Ltd. Used by permission ofEmerald Group Publishing Ltd.

Exhibit 2.7: Four Modes of Knowledge Conversion, from The Knowledge-Creating Company: How Japanese Companies Create the Dynamics ofInnovation by Ikujiro Nonaka and Hirotaka Takeuchi, copyright 1995 byOxford University Press, Inc. Used by permission of Oxford UniversityPress, Inc.

Exhibit 3.1: Subsidiary Strategy Typologies, from Configurations of Strat-egy and Structure in Subsidiaries of Multinational Corporations byJulian Birkinshaw and Allen J. Morrison, copyright 1995 by PalgraveMacmillan Ltd. Used by permission of Palgrave Macmillan Ltd.

Exhibit 3.2: Classification of Control Mechanisms on Two Dimensions,from Managing the Multinationals by Anne-Wil Harzing, copyright 1999Edward Elgar Publishing Ltd. Used by permission of Edward ElgarPublishing Ltd.

Exhibit 6.26: Structural Model Conceptualization and Exhibit 6.29:Measurement Model Conceptualization, from Introducing LISREL byAdamantios Diamantopoulos and Judy A. Siguaw, copyright 2000 SagePublications Ltd. Used by permission of Sage Publications Ltd.

Every effort has been made to trace all copyright-holders, but if anyhave been inadvertently overlooked the publishers will be pleased tomake the necessary arrangement at the first opportunity.

1

1Positioning

The knowledge management challenge

The emerging discussion on knowledge management during the lastdecade has led to a plethora of theories and models in the business litera-ture. Despite these efforts, most companies do not make use of morethan the half of the knowledge available to them (Bullinger, Wörnerand Prieto 1998). The major reason given to support that argument isthe lack of methods to identify and edit expert knowledge. The fewmethods used to utilize knowledge that do exist also tend to lack empiricalvalidation (Easterby-Smith and Araujo 1999; Huysman 1999; Lähteenmäki,Toivonen and Mattila 2001).

Reviewing the wide body of literature on knowledge managementleads to the three main conclusions:

• Literature consists either of studies of organizations’ efforts to implementknowledge management or of detailed descriptions of specific know-ledge tools.

• Studies tend to be descriptive, largely of a non-empirical nature andtend to address fragmented topic areas rather than the entire processof knowledge transfer.

• As the complex process of knowledge transfer is difficult to capture,empirical studies take a very narrow focus. The literature reviewbelow will show that the few studies explicitly investigating theintra-MNC knowledge transfer process do not provide a comprehensivemodel that is based on empirical findings. The notion of knowledgetransfer effectiveness, in particular, is hardly ever addressed.

2 Effective Knowledge Transfer in MNCs

Many authors have recently outlined the singular importance ofknowledge transfer as a challenge in managing knowledge, and theissue becomes especially critical in multinational corporations (MNCs).A major competitive advantage of MNCs is their ability to exploitlocally created knowledge worldwide (Kogut and Zander 1995; Nohriaand Ghoshal 1997; Gupta and Govindarajan 2000). As MNCs aim toreplicate their success across borders, they ‘will need to focus not juston “what” they know, but “how” they gain that knowledge and diffuseit throughout the enterprise’ (Riesenberger 1998, p. 97). Despite the factthat knowledge dissemination across locally dispersed units stronglyimpacts MNCs’ practices and has thus to be aligned with the companies’overall strategy, most studies ignore the international dimension andlargely fail to research the ultimate effectiveness of intra-MNC know-ledge transfers.

Aim of the research

The aim of this study is to investigate knowledge transfers taking placebetween locally dispersed MNC units. In order to exploit the organiza-tion’s knowledge stock and support knowledge creation, functionalunits of MNCs have to share knowledge across organizational entities.This implies that such companies have to be able to transfer know-ledge within organizational networks characterized by separation throughtime, space, culture and language. Viewing knowledge as a corporateresource illustrates the relevance of the international strategy litera-ture: Perlmutter and Heenan (1979), Porter (1980), Bartlett and Ghoshal(1987), Prahalad and Doz (1987), Asakawa (1995) and many othershave focused on intra-company transfers and how MNCs attempt tooptimize sourcing strategies in terms of location advantages and eco-nomies of scale. All of these researchers address the central problem ofan organization in a setting of physical separation through time andspace, and separation of key members by culture and language. Thus,as Grant (1996, p. 118) puts it: ‘Many current trends in organizationaldesign can be interpreted as attempts to access and integrate the tacitknowledge of organizational members while recognizing the barriersto the transfer of such knowledge.’ It has always to be recognized thatthe human capability to capture and understand complex facts isrooted in a cultural setting and thus tends to differ across cultural areas.Organizational functions which are dependent on the cultural contextand have to cooperate across locally dispersed units lend themselvesespecially well to an investigation of knowledge transfers in MNCs.

Positioning 3

Beyond the general recognition that cultural differences are likely toimpinge on the success of international knowledge transfer, concreteproblems emerging in cross-cultural knowledge transfer are rarelyaddressed in the literature.

Current research – discussed in detail in the literature review below –indicates the absence of an unifying framework that could serve asa basis for a research agenda on intra-MNC knowledge transfers. Respond-ing to the need for such a framework, this study aims to develop a con-ceptual model of knowledge transfer. Based on this model, researchhypotheses are advanced and subsequently tested through conductingand analysing the results of a large-scale empirical survey.

The study is structured in six main areas.

Knowledge and the MNC

First, the general issue of knowledge and knowledge management ispresented and theoretical concepts which are relevant for the studyhighlighted (Chapter 2). Special focus is placed on the conceptualiza-tion of the knowledge transfer process and the relevance of know-ledge management in MNCs is discussed. The in-depth literaturereview reflects selected strategic and organizational topics discussedin the research, and a categorization of relevant knowledge transferstudies is given.

Knowledge-based determinants of MNC strategic configuration

Then the theoretical building blocks which have a bearing on intra-MNCknowledge transfers are presented and discussed (Chapter 3). Four sectionsfocus on headquarters–subsidiary relationships; strategic mandates,coordination and control; organizational capabilities; and contingencyfactors.

A model of knowledge transfer in MNCs

The research fields discussed in the preceding literature review aredrawn together to propose a conceptual model of intra-MNC knowledgetransfer (Chapter 4). Based on this model, research hypotheses aredeveloped.

Research design and methodology

The methodology section then sheds light on how the survey is con-ducted (Chapter 5). It explains the general research challenges, thesampling and data collection process and the operationalization ofconstructs.


Analysis and results

The analysis (Chapter 6) is split into the investigation of the particularconstructs and the structural equation model where the hypotheses aretested.

Conclusion, limitations and implications

The conclusion (Chapter 7) summarizes the main findings and tries tointegrate them into current research by demonstrating managerialimplications and avenues for future research.

5

2Knowledge and the MNC

Introduction to knowledge management: a conceptual background

The question of how knowledge should best be defined has been thesubject of a lively epistemological debate (Shin, Holden and Schmidt2001). An examination of the various perspectives on the definition ofknowledge and their implications for knowledge management formsa useful starting point, enabling researchers and practitioners alike tounderstand the directions of knowledge management research and theapproach taken in this study.

First, some concepts of knowledge and how this term is distin-guished from data and information are presented. At this stage, it isuseful to introduce the dichotomies of tacit and explicit and individualand organizational knowledge. The theoretical approaches to know-ledge management are then discussed, starting with an overview ofseveral key definitions. To illustrate that knowledge can be managedsuccessfully only when several processes are leveraged, the concept ofthe ‘knowledge management value chain’ is introduced. The particularlink of the value chain which is the subject of this study is analysed inmore detail, and some concepts about how to depict knowledge transferare presented.

Conceptualizing knowledge

Data – information – knowledge

Although numerous definitions can be found in the literature, researchersseem to agree on the fact that ‘data, information and knowledge are notinterchangeable concepts’ (Davenport and Prusak 1998, p. 1). Since the


intention is not to give an exhaustive list, only those positions arepresented which are regarded as important in understanding the approachto knowledge management in this study.

Data. Data can be defined as a set of objective facts. They are structuredrecords without information of how to use them in a given context.Data consist of signs and are the raw material to be processed, but theygive no hint on how to do so and are thus of limited use (Willke 1998).From the perspective of systems theory, there are no data per se but onlydata which are constructed by perception. Besides, to be existent datahave to be codified – e.g. in numbers, language, or pictures (Willke1998). In modern organizations, data are usually stored in some sort ofinformation technology (IT) system. As data are only raw material forthe creation of information, large quantities of data without any inform-ation about their importance or irrelevance can create problems: ‘moredata is not always better than less’ (Davenport and Prusak 1998, p. 2f ).

Information. Information can be defined as data with significance (Kriwet1997, p. 81; Davenport and Prusak 1998, p. 4). Hence, data consideredvaluable by a user constitute information. Data in one context may berelevant information in another (Kriwet 1997). Thus, in order to beinformation, data has to be provided with a meaning which is specificfor and dependent on the respective system (Willke 1998).

Knowledge. Knowledge combines various pieces of information withan interpretation and meaning (Nevis, DiBella and Gould 1995; Kriwet1997). It is created by the target-oriented combination of information,includes a component of subjectivity, insecurities and paradoxes andis subject to ambiguity (Wagner 2000, p. 37). While information derivesfrom data, knowledge derives from information (Davenport andPrusak 1998). While information is a static concept, knowledge is con-stantly changing. And while information is descriptive and explicit,knowledge includes a normative component and can be explicit ortacit. In line with Plato, Nonaka and Takeuchi (1995) define know-ledge as ‘justified true belief’. It is created if somebody makes sense outof a new situation by holding justified beliefs. Sveiby (2001, p. 2),however, defines knowledge as ‘the capacity to act (which may or maynot be conscious)’.

What all these different approaches seem to have in common is thatknowledge is located at the top of the hierarchical structure (Shin,

Knowledge and the MNC 7

Holden and Schmidt 2001). Probst, Raub and Romhardt (1999, p. 38)describe the transition from data via information to knowledge asa continuum, an approach especially applicable to an investigation ofknowledge transfers in MNCs (Exhibit 2.1). It can be seen as the lowestcommon denominator, which has to be agreed upon as managersoperating in different contexts are likely to hold divergent perceptionsof these terms.

If information is to be transformed into knowledge, people have tomake this transformation happen by comparing the information indifferent situations, through the combination of various bits ofknowledge, conversation with others about the information, or assess-ing the consequences of the information for decision-taking (Davenport1998). In organizations, data can be found in records, and inform-ation in messages, whereas knowledge is embedded in documents ordatabases, in organizational processes, routines and norms and isobtained from individuals, groups, or organizational routines eitherthrough structured media or through person-to-person contact(Davenport 1998).

Classifications of knowledge

On the basis of the distinction between the different cognitive levelsof data, information and knowledge, a useful dichotomy seems to be

Data Information Knowledge

UnstructuredIsolatedContext-independentLow behavioural controlSignsDistinction

StructuredEmbedded

Context-dependentHigh behavioural control

Cognitive behavioural patternsMastery/Capability

Exhibit 2.1 The continuum of data – information – knowledge

Source: Based on Probst, Raub and Romhardt (1999, p. 38).


‘knowing what’ (Wissen, savoir-connaître) and ‘knowing how’ (Können,savoir-faire). While the former refers to procedural types of know-ledge – i.e. know-how, the latter can be called ‘declarative know-ledge’ – i.e. know-what (see also Kogut and Zander 1993; Simonin1999b; Gupta and Govindarajan 2000; Becerra-Fernandez and Sabherwal2001). This distinction is vital to the knowledge transfer process asit is decisive for the choice of media and storage devices. It is alsoimportant to distinguish between the necessity of ‘higher-order’knowledge in contrast to mere data for managing certain organ-izational tasks.

Other classifications distinguish between specific and general know-ledge or divide knowledge into the three aspects (Hedlund 1994, p. 75):

• Cognitive knowledge • Skills • Knowledge embodied in artifacts, e.g. products.

Cognitive knowledge comes in the form of mental constructs. It residesin the minds of people and is also denominated ‘brainware’ (Bennettand Gabriel 1999, p. 216). Skills are competences and capabilities. Shin,Holden and Schmidt (2001), however, suggests that we should distin-guish between a school of thought that regards knowledge as an object(cf. Zack 1999; Tenkasi 2000) and another that defines knowledge asa application-related process (cf. Kogut and Zander 1993; McDermottand O’Dell 2001).

Explicit and tacit knowledge

Knowledge is generally distinguished along two dimensions which goback to the philosopher Michael Polanyi (1966), who wished to criticizepositivist science. The distinction refers to the kind of knowledge, anddifferentiates between explicit or articulated and tacit or implicit know-ledge. Polanyi observed that skills could be exercised without theperformer being able to fully account for their cognitive basis. He elabo-rated a theory according to which all actions included tacit and explicitelements of knowledge and that it was especially hard to articulate thetacit elements, and consequently to pass them on to others. Today, mostresearchers base their theories on this distinction of tacit and explicitknowledge.


Explicit knowledge. This consists of some systematic language and iscodified through words, numbers and codes (Hedlund 1994). This codifi-cation makes it amenable to transfer (Riesenberger 1998).

Tacit knowledge. This is non-verbalized, intuitive and unarticulated(Hedlund 1994), depends on the experience of the individual, includesbeliefs and emotions (Nonaka and Takeuchi 1995; Riesenberger 1998),personal skills and acquired knowledge (Bennett and Gabriel 1999). Onthe organizational level, tacit knowledge is embedded in organizationalroutines. Because of its implicit nature it is difficult to formalize and totransfer, but it is precisely this experience-based tacit knowledge which –because of the difficulty in imitating it – creates the basis for a sus-tainable advantage (Zack 1999).

The most prominent model building on the dichotomy of tacit andexplicit knowledge is that of Nonaka and Takeuchi (1995). In theirwork, the authors draw the distinctions shown in Exhibit 2.2.

The concept can be criticized by referring back to Polanyi’s (1966)original ideas. In fact, Polanyi states that every piece of knowledgecontains both tacit and explicit elements. Researchers have recentlyrecognized this fact and have concluded that the importance for firmslies in their ability to articulate the tacit elements. Hedlund (1994) hasemphasized the significance of firms as articulation machines. Basedon this insight, Hakanson (2003, p. 9) elaborates on the power andlogic of articulation, stating that ‘it seems that most tacit skills of eco-nomic interest are at least potentially articulable’. Critics of Nonakaand Takeuchi’s clear distinction also state that the boundary betweentacit and explicit knowledge is rather blurred and flexible and thattacit knowledge may be overemphasized (Shin, Holden and Schmidt

Exhibit 2.2 Tacit and explicit knowledge

Source: Based on Nonaka and Takeuchi (1995, p. 36).

Tacit knowledge (Subjective) Explicit knowledge (Objective)

Knowledge of experience (body) Knowledge of rationality (mind)

Simultaneous knowledge (here and now)

Sequential knowledge (there and then)

Analogue knowledge (practice) Digital knowledge (theory)


2001). It is important to note that the value of tacit knowledge doesnot come from the fact that it cannot be articulated but that it has notbeen articulated yet.

Organizational and individual knowledge

According to the ontological level of the knowledge bearer, Hedlund(1994) distinguishes among the levels of individual, group, organizationand inter-organizational domains. Individual knowledge reflects individualexperience and constitutes the basis for the development of organizationalknowledge. Organizational knowledge is embedded knowledge andcomprises belief systems, collective memories, references and values.It ‘resides in the relations between individuals, and is therefore morethan the sum of individual knowledge bases’ (Kriwet 1997, p. 83). Theinter-organizational domain comprises suppliers and customers (Hedlund1994). Seen from an even broader perspective, the term ‘social knowledge’addresses knowledge residing within groups of people.

The tension between individual and organizational knowledge isespecially critical to the firm as a knowledge integrating institution. Assuch, knowledge has to be managed as resource.

Theoretical concepts of knowledge management

Definitions of knowledge management

Apart from any interest in knowledge for economic theory building,there is the question of how to manage knowledge in organizations. Amultitude of definitions for knowledge management can be found, andExhibit 2.3 presents a small choice of how prominent researchers in thefield have conceptualized knowledge management.

Some authors differentiate between a human resources-oriented approachto knowledge management and a technology-oriented approach ( Jacoband Ebrahimpur 2001). However, recent research has predominantlyapplied the so-called ‘integrative’ approach, based on a fundamentalbelief that human and technological components have to be combinedin order to reach an optimal result. As the potential of knowledge man-agement to create and maintain a competitive advantage can be realizedonly by an interplay between technological systems and aligned struc-tures of human communication, the integrative approach is also usedin this study.

Although the research agenda in knowledge management seems to berather fragmented and, as Gallupe states (2001, p. 62), there appears tobe no systematic framework to provide guidance, some studies have put


an effort into the formulation and categorization of research. Somestudies, on the other hand, attempt to explain and theorize the entireknowledge management system and aim to provide an insight into thewhole knowledge management process. Such management processes,each including various forms of knowledge, are very complex, and

Exhibit 2.3 Definitions of knowledge management

Source Definition

Birkinshaw (2001, p. 12)

Knowledge management can be seen as a set of techniques and practices that facilitates the flow of knowledge into and within the firm.

Buckley and Carter (1999,p. 82)

Knowledge management contains ‘the internal mechanisms for coordination, that is, for pooling the key information garnered by managers whose task it is to monitor external volatility and discover new opportunities’.

Davenport etal.(2001, p. 117)

Knowledge management is ‘the capability to aggregate, analyze, and use data to make informed decisions that lead to action and generate real business value’.

Demarest (1997, p. 379)

‘knowledge management is the systematic underpinning, observation, instrumentalization, and optimization of the firm’s knowledge economies’.

Leonard-Barton (1995, p. xiii)

‘The primary engine for the creation and growth and of technological capabilities is the development of new productsand processes, and it is within this development context that we shall explore knowledge management . . . The management of knowledge, therefore, is a skill, like financial acumen, and managers who understand and develop it will dominate competitively.’

Malhotra (1998, p. 59)

‘Essentially, it embodies organizational processes that seek synergistic combination of data and information processing capacity of information technologies, and the creative and innovative capacity of human beings.’

Stewart etal.(2000, p. 42)

‘The premise is that knowledge assets, like other corporate assets, have to be managed in order to ensure that enterprises derive value from their investment in knowledge assets.’

Tsoukas and Vladimirou (1996, p. 973)

Knowledge management ‘is the dynamic process of turning an unreflective practice into a reflective one by elucidating the rules guiding the activity of the practice, by helping give a particular shape to collective understandings, and by facilitating the emergence of heuristic knowledge’.


capturing an organization’s entire knowledge management system canbe very difficult. Some authors, however, have made an attempt tocategorize knowledge management systems. Typical of this genre is thework of Birkinshaw (1999), Hansen, Nohria and Tierney (1999), Hong(1999), McAdam and McCreedy (1999), Zack (1999), Bloodgood andSalisbury (2001), Earl (2001), Gallupe (2001), and Holsapple and Joshi(2001). The way authors attempt to derive a categorization differs con-siderably. Romhardt (2000), for example, approaches the problem froma systems theory perspective and Tsoukas and Vladimirou (1996) applya constructionist view.

Von Krogh and Venzin (1995) suggest a useful systematization ofresearch streams in knowledge management:

• Knowledge management models • Knowledge, conversation, cooperation • Measurement and assessment of knowledge • Knowledge transfer • Knowledge structures • Epistemology • Knowledge and information technology • Knowledge and power • Knowledge, networks, and innovation.

Generally, different assumptions are made about the boundaries ofknowledge management. They range from comprising only one individualto the integration of business partners and external institutions into theknowledge management system. In this study, knowledge managementis understood as an organization-wide activity, and the leveraging of know-ledge between different organizational units (i.e. headquarters andsubsidiaries) in different countries forms the centre of analysis.

The knowledge management value chain

While there is a general debate on whether to conceptualize knowledgeas an object or as a process, most researchers seem to agree on thedescription of knowledge management as a process (Grant 1996). Evenso, researchers have presented different links to build the value chain.Hong (1999), for example, divides it into four stages:

• Knowledge acquisition • Information distribution


• Information interpretation • Organizational memory.

A slightly different approach is taken by Hedlund (1994), who distin-guishes between the storage, transfer and transformation of knowledge.Shin, Holden and Schmidt (2001) aim to consolidate different contributions(Exhibit 2.4).

The first link of the knowledge management value chain in thesedepictions is the creation of knowledge. Obviously, the prerequisite forknowledge management is the identification of the source, in other wordsrecognizing that unique knowledge exists in an internal source (e.g. anorganizational unit) or an external source (e.g. a research institution,a customer, etc.). In the latter case, this link could also be named ‘know-ledge acquisition’. It also has to be determined whether the knowledgecreated and embedded in the context of one country or one subsidiaryis of value for the rest of the organization (Kriwet 1997; Gupta andGovindarajan 2000). While deeply ingrained knowledge is primarilyrelevant to the stakeholders in a specific project and of minor value forother subsidiaries, generalizeable knowledge provides insights withrelevance to various subsidiaries.

Storage means that knowledge has to be maintained in some kind ofindividual or organizational memory. This link has to be customized tothe type of knowledge, the potential recipient and, thus, the search process.In order to permit storage, not only the existence of such a memory

Consciousness

Construction

Construction

Creation

Creation

Creation

Access

Organization

Extension

Creation Storage

Storage

Transformation

Distribution

Dissemination

Share

Application

Implementation

Use

Evaluate

Sell

KMValue Chain

Holzner and Marx(1979)Pentland (1995)

Nonaka andTakeuchi (1995)Demarest (1997)

Daal, Hass andWeggeman (1998)Davenport, De Longand Beers (1998)

Liebowitz (1999) Store

Draw-up

Embodiment

Distribution

Dissemination

Dissemination

Transference Asset Management

Apply

Apply

Application

Application

Identify Capture

Vision and Strategy

Exhibit 2.4 The knowledge management value chain

Source: Shin, Holden and Schmidt (2001, p. 341).


device but also the willingness of employees to detach the knowledgegained from the individual, is vital. This release of knowledge is likelyto have effects on power relations.

Power relations may also be a concern where knowledge distribu-tion is concerned. Who should be given access to knowledge, andwhich knowledge is relevant to whom? Such questions are likely toemerge in the distribution stage; this link of the knowledge managementvalue chain is referred to as distribution, transformation, dissemination,transference, or sharing. But problems often emerge, since differenttypes of knowledge are not easy to distribute – a characteristic VonHippel (1994) calls ‘stickiness’ (see also Szulanski 1995) – and one hasto be aware that transfer always includes some form of transformationas well. It then depends on the type of knowledge to decide whichstrategy should be applied to manage the transfer process (Davenport1998). Due to codification, knowledge arriving at the receiver will notbe exactly identical with the knowledge of the sender. Another criticalpoint is the choice of transmission media to channel knowledge. Thedetails of knowledge transfer will be discussed in depth in the follow-ing chapters.

The access and transfer of knowledge does not yet ensure that knowledgewill be used. Therefore, an application phase is included in the knowledgemanagement value chain. As the goal of transferring knowledge is toincrease value, knowledge transfer is successful only if the newlyabsorbed knowledge leads to changes in behaviour or the creation ofnew ideas (Davenport 1998). Knowledge application ultimately seeks tolocate the source of competitive advantage (Shin, Holden and Schmidt2001).

Last but not least, it is essential that the knowledge managementvalue chain is strategically driven in order to realize the objectives of anorganization. The central assumption is that each knowledge managementsystem has to be intricately interwoven with corporate strategy, structureand processes. Vision and strategy form a ‘control circuit’ for the knowledgemanagement value chain.

The process of knowledge transfer

As mentioned above, the process of managing the different types ofknowledge is split up into several parts in the knowledge managementvalue chain, and knowledge transfer forms just one stage. The emphasishere is on the ‘distribution’ stage of the knowledge management valuechain. It has already been pointed out that the knowledge transfer processis quite complex in itself, and on a broad level, two general approaches


can be distinguished: the communication model and the knowledge spiralmodel (Inkpen and Dinur 1998).

Knowledge transfer as a communication model

The classical communication model (Shannon and Weaver 1957) depictsmessages’ flow from a sender to a recipient. While parts of the messageare likely to be transformed or even destroyed by ‘noise’, it is importantthat the core of the message arrives at the sender. Two critical stages arethe encoding phase, when the sender packages the message to fit the media,and the decoding phase, when the receiver has to decipher it again. Asimplified model depicting only the building blocks of the Shannonand Weaver (1957) theory is shown in Exhibit 2.5.

Szulanski (1996) was among the first to introduce this conceptinto the knowledge management literature, conceptualizing knowledgetransfer as a message transmission from a source to a recipient in a givencontext. Knowledge transfer is thus seen as a dyadic exchange of know-ledge between source and recipient. Inkpen and Dinur (1998) extendedthis model, and identified four groups of related factors:

• Source-related factors • Recipient-related factors • Factors relating to the relationship and distance between the two units • Factors related to the nature of the knowledge transferred.

Four stages are then necessary for the transfer process:

• Initiation: transferred knowledge is recognized. • Adaptation: knowledge is changed at the source location to the

perceived needs of the recipient.

‘NOISE’

SENDER

Context

RECIPIENT

Context

Source: Based on Shannon and Weaver (1957).

Exhibit 2.5 A simplified communication model


• Translation: alterations occur at the recipient unit as a part of thegeneral problem solving process of adaptation to the new context.

• Implementation: knowledge is institutionalized to become an integralpart of the recipient unit.

Although this process is quite easy to understand at first glance, itbecomes quite complex at the organizational level when eligible sendersand recipients have to be defined. To shed some light on this issue,Sveiby (2001, p. 349) identified nine different knowledge transfers,which are depicted in Exhibit 2.6.

For this study, four of these knowledge transfer processes are relevant.It is important to note that a critical feature of modern knowledgemanagement is the time-lag between sender and recipient. WhileShannon and Weaver’s (1957) communication model builds ona nearly simultaneous transfer, the knowledge transfer process canbe interrupted and knowledge can be stored in the meantime. Froma conceptual point of view, however, it is clear that every medium

1. Knowledgetransfersbetweenindividuals

4. Knowledgetransfers fromindividualcompetenceto internalstructure

5. Knowledgetransfers frominternal structureto individualcompetence

9. Knowledgetransfers withininternal structure

8. Knowledgetransfers from internalto external structure

7. Knowledgetransfers fromexternal to internalstructure

6. Knowledgetransfers withinexternal structure

Externalstructure

Internalstructure

Individualcompetence

$

2. Knowledgetransfers fromindividuals toexternal structure

3. Knowledgetransfers fromexternal structureto individuals

Source: Sveiby (2001, p. 349).

Exhibit 2.6 Nine knowledge transfers


represents an intermediate storage device, so the approach is notthat revolutionary:

• Transfers between individuals: How can we improve the transfer ofknowledge1 between people in our organization? This kind of transfertakes place in communication between employees, and here, thesimplified model of communication can be applied. Issues such astrust and exposure to different kinds of expertise in the company areimportant.

• Transfers from individual competence to internal structure: How can weimprove the conversion of individually held competence to systems,tools and templates? When knowledge of individual competences isstored in repositories, it becomes accessible in the organization’sstructure and can be shared by everyone. This definition of ‘transfer’comprises only the first half of the communication model, namelyfrom the sender to the media.

• Transfers from internal structure to individual competence: How can weimprove individuals’ competence by using systems, tools andtemplates? Employees’ capacity to act should be improved by know-ledge accessible in the internal system. An important issue at thisstage of transfer is the interface between humans and knowledgestorage systems. Here, the second half of the communication processis depicted, the transition from the media to the recipient.

• Transfers within the internal structure: The strategic question is howthe organizations’ systems, tools and processes can be integratedeffectively.

All transfer processes involving external senders or recipients areexcluded from this study. The transfer processes ‘between individuals’and ‘with the internal structure’ is a necessary phase of the transferprocess, but is not highlighted. The first becomes almost irrelevant inglobal organizations where sender and recipients are characterized bydispersed locations and time-zones and, as stated above, at least fora short time knowledge is stored in a medium. The latter explains onlythe management of knowledge within the storage system, and thusexceeds the focus of this investigation. Nevertheless, it is important toexplain Sveiby’s intentions in order to visualize the complexity of organi-zational knowledge transfer.

The idea of adapting the communication model to explain know-ledge transfers not only between individuals but also between entities


in global organizations is extremely useful for the development of theconceptual model in this study, and will be taken up later.

The spiral of knowledge

Nonaka and Takeuchi (1995) attribute the success of Japanese companiesto their effectiveness in creating knowledge. In their pioneering work theauthors propose a model of knowledge creation – the spiral of know-ledge (Exhibits 2.7). They build on the epistemological dimension ofexplicit and tacit knowledge and on the ontological dimension whichsymbolizes the number of people involved in the process.

The core assumption of this model is that tacit knowledge has to bemobilized and converted. This means that the model does not onlyexplain knowledge creation but describes processes of transferringknowledge, specifically the so-called ‘conversion’ processes. A spiral iscreated when the conversion of tacit and explicit knowledge results inhigher epistemological and ontological levels. Nonaka and Takeuchi(1995) identify four specific conversion processes:

• Socialization (tacit to tacit): Individuals exchange tacit knowledgewithout codifying it during the transfer phase, e.g. shared mentalmodels, technical skills.

• Externalization (tacit to explicit): In this process, tacit knowledge ismade explicit by codifying it in the form of metaphors, analogies,

Internalization(Operational knowledge)

Combination(Systemic knowledge)

Tacit knowledge

Tacitknowledge

Explicit knowledge

Explicitknowledge

To

From

Socialization(Sympathized knowledge)

Externalization(Conceptual knowledge)

Source: Nonaka and Takeuchi (1995, p. 62).

Exhibit 2.7 The knowledge spiral


hypotheses, models, etc. Through such a transformation personal know-ledge can be made available on a corporate-wide basis. Externalization isthus the most important process for knowledge creation.

• Combination (explicit to explicit): Through combination, concepts aresystematized within a knowledge system. Existing elements of know-ledge are combined in order to create new explicit knowledge.Several media support combination, e.g. documents, meetings,phone calls.

• Internalization (explicit to tacit): The conversion of explicit into tacitknowledge is called ‘internalization’, meaning that incoming knowledgeis integrated into an individual’s knowledge base.

By combining these two models – the communication model and theknowledge spiral – the four knowledge conversion processes can also beseen as singular transfers between a sender and a recipient. This leads tothe conclusion that every sending and every receiving unit has to engagein some of these processes in order to process the inflowing or outflowingknowledge. As described in the communication model, it has to be remem-bered that no knowledge transfer is context-free. At a later stage, thisargument will be recaptured and used to explain knowledge transfers inglobal organizations.

The relevance of knowledge management in the MNC

The outstanding relevance of knowledge management for MNCs is nowconsidered. Theoretical perspectives, such as the conceptualization ofthe MNC, the growing role of knowledge management in organ-izational design and the transition from a resource-based view toa knowledge-based view of the firm, form the base of this study. Morespecifically, knowledge transfers in the MNC are discussed. Havingoutlined the various levels on which knowledge can be transferred inthe MNC – and the focus of this research – the general transferability ofknowledge as a resource is briefly addressed. This is followed by an over-view and a categorization of the major studies which are relevant forthe current research.

The conceptualization of the MNC

The overall aim of the theory of the MNC is to explain the level andpattern of the foreign value-added activities of firms (Dunning 1993). Inthis context, a multitude of definitions of the MNC are given in theinternational business literature and the question of the decisive criteria


of the MNC is still unresolved (see also Rugman and Brain 2003).Almost all authors agree that, in comparison to a domestic company, anMNC has fundamental distinguishing characteristics – with regard tosocial and cultural norms, government regulations, customer tastes andpreferences as well as social and economic structures, the MNC facesvarious conflicting demands and pressures in its multiple host countries.While domestic companies are confronted with competition withina single market, the MNC is challenged by complex competitive strategiestaking place on an international level and requiring extensive coordin-ation efforts. The MNC also manages a complex organizational structureand management system that requires control over its product and itsfunctional and geographic diversity, including linguistic and culturalaspects (Bartlett and Ghoshal 1989).

This study follows Bartlett and Ghoshal’s (1989) approach; they classifyfirms as MNCs if they fulfil two requirements:

• They need to have substantial direct investments in foreign countries, notjust an export business. (Vernon 1966 set the – more or less arbitrary –requirement to be engaged in six overseas production operations.)

• They need to be engaged in the active management of these offshoreassets rather than simply holding them in a passive financial portfolio.

Bartlett and Ghoshal regard the second issue as the key differentiatingcharacteristic of an MNC, as it highlights the importance of strategicand organizational integration: ‘What really differentiates the MNC isthat it creates an internal organization to carry out key cross-border tasksand transactions internally rather than depending on trade through theopen markets’ (Bartlett and Ghoshal 2000, p. 3). This extends to thenotion that, in general, knowledge can be transferred more effectivelyand efficiently through internal than through external market mech-anisms (Kogut and Zander 1992). The aim to internalize knowledge flowsis thus seen as a primary motivation for foreign direct investment (FDI),which results in a view of the MNC as a repository of valuable knowledgethat can be exploited either through the development of new productsor through the dissemination of existing products to new locations.

The question of how to structure and to manage the relationshipsbetween the headquarters and its foreign subsidiaries is one of the coreissues when dealing with the study of MNCs (Bartlett and Ghoshal 1989;Birkinshaw etal. 2000; Paterson and Brock 2002). Traditionally, organiza-tional forms of MNCs have been conceptualized in terms of regionalscope, product, functional divisions, or matrix structures. By focusing


on the control of world-wide operations, factors such as the extent andthe nature of these operations, the national origin, information-processingcapabilities and the mind-set of senior managers can be identified asdeterminants of the MNC’s choice of a structure (Malnight 2001). Buckleyand Casson (1998, p. 22) argue that: ‘Efficient information processing iscrucial to cope with the resultant increase in the complexity of decision-making. This has important implications for the organizational structureof the MNE.’

Although empirical studies have not yet found clear support for a well-developed typology of MNCs, some concurrence can be found in the recentliterature about the strategy and structure of MNCs. With regard to thedescription of strategy, all authors implicitly or explicitly refer to a con-tinuum of integration, coordination, or globalization advantages versusdifferentiation, responsiveness, or localization advantages (Harzing 2000b).

In recent international business research, the conceptualization ofthe MNC as a learning organization which has continuously to upgradeits competitive advantage has come to the fore. In this context, twoschools of thought have developed (Sölvell and Zander 1995):

• First, the Home-Based Model, which builds mainly on the ideas of Porter(1990) and Sölvell, Zander, and Porter (1991), proposes that, in spiteof globalization, the MNC makes a clear distinction between core andperipheral activities and remains dependent on local environments.

• Second, extending the frameworks of Perlmutter and Heenan (1979)and Bartlett (1984), the Heterarchical Model stresses that ‘traditionalheadquarter functions are geographically dispersed and none of thedimensions – country, product, or function – is uniformly super-ordinate in the process of generating new firm-specific knowledge orin strategy formulation and implementation’ (Sölvell and Zander1995, p. 25).

An in-depth discussion of the different models would go beyond thescope of this research (for more insight see Sölvell and Zander 1995),but it has to be emphasized that this study builds on the latter school ofthought. Its implications for structuring the MNC will how be discussed.

In this study, the firm is conceptualized as a network of units. In thisnetwork, units have strategic mandates and thus access and transferknowledge from different positions (cf. Gupta and Govindarajan 1991;Asakawa 1995; Tsai 2001). Although their network positions differ, thecorporate embeddedness (Granovetter 1985) of organizational units inthis network provides a basic social context which is common for all units.


The Integrated Network Model developed by Bartlett and Ghoshal (1988)‘models the MNC as a geographically-dispersed set of value-addingactivities, each activity of which can be viewed as a semi-autonomousentity, with ownership ties, normative links and certain obligation tohead office’ (Paterson and Brock 2002, p. 323). The network approachcombines the characteristics of integration, responsiveness and world-wideinnovation and learning simultaneously, with some functions coordinatedat the global level while others remain local (Harzing 2000a). Due to itsability to deal with complex, large MNCs, the network approach hasbecome one of the preferred designs when conceptualizing MNCs.

Bartlett and Ghoshal (1988) propose their theory of the IntegratedNetwork Model and the Transnational Organization as a response tomanagerial and environmental challenges. Many of these network charac-teristics have also been emphasized by Hedlund (1994), conceptualizingthe MNC as a heterarchy, also called the N-Form, in direct antithesis tothe traditional Hierarchy Model of the organization. Other similar modelsinclude the Multifocal Organization developed by Doz (1986), the Hori-zontal Organization developed by White and Poynter (1990) and theMetanational Organization by Doz, Santos and Williamson (2001).

Within the Transnational Organization, all subsidiaries and the head-quarters are part of an interdependent network. Organizational units areseen as a unique source of skills, capabilities and knowledge. Their con-tributions to the interdependent network of worldwide operationstherefore differ considerably and learning has to take place on a globalbasis. This implies that headquarters do not necessarily play a dominantrole in the organization, and the firm’s ability to react flexibly to fastchanging market conditions is increased.

The Heterarchy Model of the organization challenges the assumptionsof the traditional Hierarchy Model that are unable to reflect the fullcomplexity of the modern MNC and its peripheral operations (Hedlund1994). In this sense, ‘hierarchy’ can be characterized by prespecified andstable relationships, instrumentality and additive influence of parts,unidirectionality and universality and knowledge and people hierarchies.In contrast, the Heterarchy Model implies many different kinds of sub-sidiary centres with loose coupling between the units and normativecontrol systems and brings in the necessary multidimensionality of theorganizational structure. Analogies to the Transnational Organizationinclude the dispersion of resources, skills and decision making through-out the organization rather than concentration at the top. Activities aresimultaneously coordinated along the three primary dimensions –knowledge, action and position of authority – rather than product,


geography and function. Hedlund (1994) argues that cohesion andprotection from anarchy is achieved through normative goal-directedintegration comprising shared objectives, knowledge and a commonorganizational culture as important mechanisms.

The increasing complexity of differentiated subunits within con-ceptualizations of the MNC can be viewed as a response to the multiple,fast-changing environments the MNC faces. Structural variationsacross geographic units reflect different cultural and economic envir-onments and result in differences in strategic roles and organizationalstructures. Functional variations reflect differences in strategic objectives.Common structural patterns are thus found among centralized upstreamoperations like R&D – to take advantage of economies of scale andscope – and decentralized downstream operations like marketing andsales – to respond to differences in national market requirements(Malnight 2001).

Knowledge management and the structure of the MNC

It thus becomes evident that knowledge management – specificallyknowledge requirements and distribution – influence the structure of theorganization (see also Hedlund 1994). According to Galbraith’s (1972)information-processing view of organizational design, and in responseto intensifying global competition, increasing the firm’s information-processing capacity becomes a necessity if the MNC is to cope with thechallenge of growing environmental and organizational complexity(see also Ghoshal, Korine and Szulanski 1994).

As already indicated, a critical proposition behind the networkapproach is that the interdependence of the world-wide units createsadditional value through extensive cross-border exchanges. The demand foracquiring, collocating and interpreting environmental and competitiveinformation on a global basis is an important characteristic of the networkmodel. Kogut and Zander (1992) argue that the firm’s combinativecapacity reflected in the Integrated Network Model is the primary deter-minant of the MNC’s superiority vis-à-vis open market transactions. TheMNC has the ability to generate additional strategic advantages by com-bining distributed knowledge, resources and capabilities. Expandingefficiency and scale, accessing specialized and locally embedded resources,enhancing innovation through operations across markets and creatingoperational flexibility are all aimed to respond to factors outside a firm’sdirect control (Kogut and Zander 1992). All these factors refer to theknowledge-based view of the firm (see p. 25) which proposes that thebuilding of world-wide learning capabilities to develop and rapidly


diffuse innovations is a vital source of competitive advantage for the MNC(Bartlett and Ghoshal 2000).

In this context, Hedlund (1994) presents a coherent argument toexplain why, in terms of knowledge management, the IntegratedNetwork Model is superior to the traditional hierarchical and divisionalview of the firm:

The challenge is not to divide a given task in a way ensuring maximallyefficient performance. Rather, it is to position the company so thatnew tasks can be initiated, often on the basis of separate knowledgepieces from different organizational units. Instead of bringing theinformation to the given decision point, it becomes a matter ofbringing the decision to the knowledge bases. (Hedlund 1994, p. 87)

Hedlund emphasizes the importance of integrating mechanisms in orderto foster the combination of pieces of knowledge through dialogue andknowledge assimilation. Dialogue and assimilation, as well as the interplaybetween tacit and explicit knowledge, require shifting groupings ofindividuals while pooling from a permanent personnel pool – one ofthe key features of the Transnational Organization. To achieve a sufficientlevel of dialogue and knowledge assimilation, lateral communication isan important mechanism. In addition, redundancy and repetition ofknowledge – formerly regarded as inefficient – are seen as necessary todistribute it throughout the organization.

In his research about knowledge management within MNCs, Hedlund(1999, p. 6) refers to two dimensions:

• The knowledge intensity of a firm or activity, which describes thedegree to which a firm is dependent on an internal supply of advanced,complex, and recent knowledge.

• The knowledge extensity of a firm or activity, meaning the increaseddispersion of knowledge in terms of geography, organization (betweenindividual firms and between their departments, subsidiaries, etc.)and substantion (dispersion of knowledge over technical fields andtypes of knowledge) relevant to the competitive distinctiveness ofthe firm.

The firm, conceptualized as a Heterarchical Transnational Organization,has the ambition to both intensify and extend its knowledge. The keycharacteristic of the MNC is its ability continuously to combine andrecombine knowledge.


From resource-based view to knowledge-based view

‘Contemporary strategy analysis has seen a shift in emphasis from thestructure–conduct–performance [SCP] paradigm which emerged fromindustrial economics and towards theories which focus on the internalresources of individual firms as a key determinant of competitiveadvantage’ (Galunic and Rodan 1998, p. 1193). The resource-based viewof the firm, first articulated by Wernerfelt (1984), looks at firms in termsof their resources rather than their products and aims to identify stra-tegic options through the exploitation and the development of theseresources. Among the most notable works which contributed to theestablishment of this theory are Barney (1986), Prahalad and Hamel(1990) and Conner (1991). The term ‘resource’ refers to the firm’s stock oftangible and intangible assets. In the centre of the analysis are resourceswhich are difficult to imitate and constitute the basis for the firm’s com-petitive advantage. In this context, every firm is regarded as a uniquebundle of idiosyncratic resources and capabilities. ‘Capabilities’ or ‘compe-tencies’ represent the organization’s collective capacity for undertakinga specific type of activity’ (Lieberman and Montgomery 1998, p. 1112).

Even before the formation of the resource-based view, the internationalstrategy literature had tended to view knowledge as a corporateresource. Perlmutter and Heenan (1979), Porter (1980), Bartlett andGhoshal (1987), Prahalad and Doz (1987), Asakawa (1995) and manyothers all focused on intra-company transfers and how MNCs canattempt to optimize sourcing strategies in terms of location advantagesand economies of scale. All these researchers address the central problemof organization in a setting of physical separation through time andspace and separation of key members by culture and language, callingfor a management of dispersed knowledge assets. Thus, as Grant (1996, p. 118)puts it: ‘Many current trends in organizational design can be interpretedas attempts to access and integrate the tacit knowledge of organizationalmembers while recognizing the barriers to the transfer of such know-ledge.’ Conner and Prahalad (1996) suggest that performance differ-ences between firms can be traced back primarily to asymmetries inknowledge. Consequently, within the resource-based literature, the viewof knowledge as a primary resource has become increasingly common(cf. Grant 1996).

The emerging ‘knowledge-based view of the firm’ can be seen as anoutgrowth of several streams of research: epistemology, organizationallearning, resource-based view of the firm, organizational capabilitiesand competences, and innovation and new product development (Grant


and Baden-Fuller 1995). This view suggests that knowledge is by far themost important resource and that social networks facilitate knowledgesharing within an organization. Knowledge as a resource, however,requires organizational capabilities in order to be productive – an argu-ment that is fully in line with the resource-based view.

The knowledge-based theory of the firm is also used to identify circum-stances in which collaboration between and within firms is superior toeither market or hierarchical governance mechanisms which inefficientlyintegrate specialized knowledge. This perspective is further developedby Kogut and Zander (1992) and highlighted in the Evolutionary Theoryof the firm. Kogut and Zander postulate the inefficiency of markets asmeans of knowledge transfer and integration, as knowledge – tacit aswell as explicit – is highly specific and efficient integration must preservethe efficiencies of specialization in the acquisition and storage of knowledgewhich is better achieved in a firm. The firm is seen as reservoir of socialknowledge that structures cooperative action. There are epistemic communi-ties, in which discourse and coordination play complementary roles inreplicating behaviours and exploring new options (Kogut and Zander 1995).

Although researchers commonly agree on these characteristics of theknowledge-based view, there is insufficient consensus for it to be recognizedas a theory. Grant (1996, p. 110) states that the knowledge-based viewconcerns issues that go far beyond the traditional concerns of strategicmanagement and extends the knowledge-based view of the firm to:

• Explain the existence of the firm as an institution for the organizationof production

• Explore the nature of coordination within the firm • Analyse organizational structure • Determine the boundaries of the firm.

Grant (1996) also stresses the discrepancy of organizational andindividual knowledge and insists that the roots of the knowledge-based approach have to lie in individual knowledge creation. Subsequently,organizational knowledge has to be created through the firm’s combinativecapability.

Knowledge transfers in MNCs

Varieties of knowledge transfer in MNCs

In the MNC context, various directions and levels of knowledge transfersare possible. Although researchers assign different labels to these transfer


activities – such as ‘knowledge diffusion’, ‘knowledge sharing’, ‘knowledgedissemination’, ‘transfer of capabilities’, ‘transfer of best practice’ – alltransfers of procedural and declarative knowledge will be referred tohere as ‘knowledge transfer’. The epistemological concept underlyingthe specific understanding of knowledge transfer in this research hasbeen presented above (p. 14).

The most obvious classification of knowledge transfers in MNCs isthe one between external and internal transfers. ‘External transfer’ refersto all sources and recipients of knowledge outside the firm – i.e. otherfirms, institutions, customers, etc. There is a large body of literaturefocusing on transfers, strategic alliances between different firms, and(international) joint venture ( JVs). ‘Internal transfers’, however, con-cern all activities inside the global organization of the MNC. In thisrespect, transfers between organizational units and within organizationalunits can be distinguished. As mentioned earlier, this research centreson intra-organizational knowledge transfers between organizational unitslocated in different countries. Following Gupta and Govindarajan(2000), a dyadic and nodal perspective is taken. In the centre of the analysisare hierarchical and lateral flows – i.e. between headquarters and sub-sidiaries and between subsidiaries and subsidiaries.

In the following section, the transferability of knowledge within theMNC will be discussed in relation to the characteristics of knowledge as aresource. A literature review categorizing some relevant studies in thefield is then provided.

Transferability of knowledge within the MNC

As discussed previously, knowledge transfer cannot mean the ‘transfer’of one piece of knowledge in terms of delivery from A to B. Knowledgeis always context-bound and related to the overall history of the know-ledge-holder. It therefore encounters the problem of the ‘hermeneuticcircle’: to store knowledge in the organization it has to be abstractedfrom its context to a certain extent, but as soon as it is taken up bya human being it is linked to a specific context again. Therefore, thecommon idea of transfer is – if at all – possible only for parts of theoriginal piece of knowledge. (The term ‘piece of knowledge’ is alreadyproblematic in itself, as it already demands uncoupling something ofa person’s knowledge stock.) What is, of course, critical for the effec-tiveness of such transfers is that the core message is still conveyed,despite being found in a totally different mantle. The term ‘knowledgetransfer’ is thus used throughout this study bearing these significantlimitations in mind.


These considerations have an important effect on the view of know-ledge as a corporate resource. Unlike other resources, it does not diminishin value when shared, but is actually doubled (Sveiby 2001). Althoughthis is true on the organizational level, restrictions may occur at thepersonal level when power relations are concerned. The notion that‘knowledge is power’ can have negative effects on the firm if people tryto protect their knowledge and are not willing to share it. Foss andPedersen (2002) conclude that this issue is less important in dynamiccontexts, as the likeliness of gaining power through the transfer ofknowledge is higher. Although the constraints of employee motivationand their willingness to share knowledge are supposed to play a rolein the knowledge transfer process, earlier studies could not provideconvincing confirmation (see also Foss and Pedersen 2002; Szulanski1995).

Many researchers also argue that the transfer of knowledge willdepend on the type of knowledge transferred. They note that knowledgecharacteristics differ:

• According to industries (cf. Jacob and Ebrahimpur 2001) and subjectareas (cf. Gupta and Govindarajan 1991) – i.e. technological knowledge,marketing knowledge, etc.

• According to their level of creation (Foss and Pedersen 2002) – i.e.internal, network, cluster and

• According to their degree of tacitness (Kogut and Zander 1993;Simonin 1999b).

While the differentiation among industries and subject areas is wellreflected in this research, the degree of tacitness is not found as anappropriate criterion for distinction. As argued earlier, the distinction oftacit and explicit knowledge prevalent in the majority of publicationsseems to be obsolete (see also Hakanson 2003), though it has to beadmitted that knowledge differs in composition, which impacts on theease of transfer. A simple differentiation criterion which seems suitableis knowledge ‘complexity’. This term can be defined as the number ofindependent routines and resources linked to a particular knowledgeasset (see also Zander and Kogut 1995; Simonin 1999b). Grant (1996)assumes that the efficiency of knowledge transfer depends partly uponthe aggregation potential of the specific knowledge – aggregation isenhanced when knowledge can be expressed in terms of commonlanguage, for example. Idiosyncratic knowledge, which is difficult to


aggregate at a single place, is therefore hard to transfer. Doz and Santos(1997) address a similar issue when they discuss the packaging of know-ledge. They argue that for every piece of knowledge to be transferredthere is an appropriate form.

A characteristic of knowledge opposed to the view that knowledge isan easily transferable resource is ‘stickiness’. Von Hippel (1994) andSzulanski (1995) researched this phenomenon and state that stickinessis either intrinsic to the knowledge itself or pertinent in the situation.Their argument is specifically aimed at refuting the neoclassical viewthat knowledge is a public good and transferable without incurringcosts. They also emphasize the importance of established linkagesbetween units to enhance knowledge transfer.

State of the art: studies on intra-MNC knowledge transfer

Although there is a large number of studies discussing knowledge trans-fer in general, relatively little attention has been paid to the full scopeof knowledge flows that are to be found within a single organizationalsetting (Teigland, Fey and Birkinshaw 2000). The studies in this field arequite diverse, approaching the issue from various perspectives. Someregard knowledge flows between headquarters and subsidiaries of MNCsas control or administrative mechanisms (cf. Gupta and Govindarajan1991), others see the optimization of such flows as means for creatingcompetitive advantage (cf. Zander and Kogut 1995; Subramaniam andVenkatraman 2001). In terms of chronological development, the inves-tigation of technology transfers opened the avenue for a more generalextension of the topic after the mid-1980s. Generally speaking, whileearlier studies concentrated exclusively on technical knowledge, laterstudies incorporate a more comprehensive field of knowledge – namelymanagerial and administrative knowledge and marketing knowledge(see also Simonin 1999b).

In Exhibit 2.8, the most relevant contributions addressing intra-organizational knowledge transfer are categorized as conceptual orempirical. Only studies either providing a model of intra-MNC know-ledge transfer or addressing the transfer process as such are analysed indepth. Requirements or contingencies of knowledge transfer are addressedlater (Chapter 3). Works which focus specifically on inter-companytransfers and knowledge transfer in JVs, strategic alliances or mergersand acquisitions (M & As) are excluded from this analysis. Exhibit 2.8shows the most important contributions, which have all been pub-lished in major academic journals, in chronological order. The type of

30

Exhi

bit

2.8

Stu

die

s o

f in

tra-

MN

C k

now

led

ge t

ran

sfer

s

Stu

dy

Typ

e o

f K

no

wle

dg

e A

im

Mai

n F

ind

ing

s M

eth

od

Gup

ta a

nd

Gov

inda

raja

n (1

991)

Pro

cedu

ral a

ndde

clar

ativ

e kn

owle

dge

Influ

ence

of k

now

ledg

eflo

ws

on c

ontro

l mec

hani

sms

For

mal

and

info

rmal

adm

inis

trat

ive

mec

hani

sms

refle

ct k

now

ledg

e flo

ws

Qua

ntita

tive

Gup

ta a

nd

Gov

inda

raja

n (1

944)

P

roce

dura

l and

de

clar

ativ

e kn

owle

dge

Rol

es o

f sub

sidi

arie

s in

the

MN

C a

ccor

ding

to k

now

ledg

eflo

ws

Diff

eren

tiate

d kn

owle

dge

flow

rol

es

are

linke

d to

diff

eren

tiate

d pr

oces

ses

and

syst

ems

with

in th

e M

NC

Qua

ntita

tive

Dar

r, A

rgot

e an

d E

pple

(1

995)

N

ot s

peci

fied

Lear

ning

in s

ervi

ce

orga

niza

tions

Kno

wle

dge

is fo

und

to tr

ansf

er

with

in th

e sa

me

fran

chis

e bu

t not

ac

ross

oth

ers

Qua

ntita

tive

Zan

der

and

Kog

ut

(199

5)

Pro

cedu

ral

know

ledg

e D

eter

min

ants

of t

he s

peed

of

kno

wle

dge

tran

sfer

and

im

itatio

n

Cod

ifiab

ility

, tea

chab

ility

and

the

thre

at o

f mar

ket p

reem

ptio

n ar

e cr

itica

l

Qua

ntita

tive

Gilb

ert a

nd

Cor

dey-

Hay

es (

1996

) In

stru

men

tal a

nd

deve

lopm

enta

l kn

owle

dge

Con

cept

ual m

odel

of

know

ledg

e tr

ansf

er

Acc

epta

nce

is c

ritic

al fo

r tec

hnol

ogic

al

chan

geQ

uant

itativ

e

Gro

sse

(199

6)

Tec

hnol

ogy

know

ledg

e,

proc

edur

al

Tec

hnol

ogy

tran

sfer

in s

ervi

ce

indu

strie

sT

rain

ing

and

tran

sfer

of e

xper

ts is

vi

tal

Qua

ntita

tive

Szu

lans

ki (

1996

) B

est p

ract

ice

Com

preh

ensi

ve ta

xono

my

to b

arrie

rs to

intr

a-fir

m

know

ledg

e tr

ansf

er

Maj

or b

arrie

rs a

re la

ck o

f abs

orpt

ive

capa

city

, cau

sal a

mbi

guity

and

ar

duou

s so

urce

–rec

ipie

nt re

latio

nshi

p

Qua

ntita

tive

31

Doz

and

San

tos

(199

7)

Not

spe

cifie

dK

now

ledg

e tr

ansf

er in

ge

ogra

phic

al d

ispe

rsio

n an

d co

ntex

t diff

eren

tiatio

n

Kno

wle

dge

man

agem

ent g

ets

even

tful i

n di

ffere

nt c

ultu

res

and

cont

exts

and

has

to b

e pa

ckag

ed

diffe

rent

ly

Con

cept

ual

Inkp

en a

nd D

inur

(1

998)

O

rgan

izat

iona

l kn

owle

dge

The

impa

ct o

f con

text

on

know

ledg

e tr

ansf

ers

Con

text

sim

ilarit

y an

d th

e na

ture

an

d ex

tent

of k

now

ledg

e tr

ansf

er

mec

hani

sm e

mpl

oyed

are

the

key

succ

ess

fact

ors

Qua

litat

ive

Nah

apie

t and

Gho

shal

(1

998)

In

telle

ctua

l cap

ital

The

rol

e of

soc

ial c

apita

l in

rel

atio

n to

inte

llect

ual

capi

tal w

ithin

the

orga

niza

tion

Soc

ial c

apita

l acc

ount

s fo

r th

e su

perio

rity

of fi

rms

over

mar

kets

to

crea

te a

nd tr

ansf

er k

now

ledg

e

Con

cept

ual

O’D

ell a

nd G

rays

on

(199

8)

Not

spe

cifie

dId

entif

icat

ion

and

tran

sfer

of

bes

t pra

ctic

es

Bes

t pra

ctic

e tr

ansf

er a

s a

benc

hmar

king

pro

cess

Q

ualit

ativ

e

Von

Kro

gh a

nd K

öhne

(1

998)

In

divi

dual

and

or

gani

zatio

nal

know

ledg

e

Cat

egor

izat

ion

of

know

ledg

e tr

ansf

ers

and

the

influ

enci

ng fa

ctor

s

Pha

ses

of k

now

ledg

e flo

ws

and

taxo

nom

y of

influ

enci

ng fa

ctor

s C

once

ptua

l

Han

sen

(199

9)

Pro

cedu

ral

know

ledg

e E

ffect

s of

the

stre

ngth

of

inte

r-un

it tie

sS

tron

g tie

s fa

cilit

ate

the

tran

sfer

of

com

plex

kno

wle

dge

Qua

ntita

tive

Hoo

pes

and

Pos

trel

(1

999)

N

ot s

peci

fied

Kno

wle

dge

tran

sfer

and

pr

oduc

t dev

elop

men

t pe

rfor

man

ce

‘Glit

ches

’ as

a m

easu

re o

f kn

owle

dge

tran

sfer

per

form

ance

Q

ualit

ativ

e

Dye

r an

d N

obeo

ka

(200

0)

Not

spe

cifie

d M

NC

–int

erna

l kno

wle

dge

shar

ing

Net

wor

k-le

vel k

now

ledg

e sh

arin

g pr

oces

ses

are

vita

l Q

uant

itativ

e

32

Exhi

bit

2.8

(Co

nti

nu

ed)

Stu

dy

Typ

e o

f K

no

wle

dg

e A

im

Mai

n F

ind

ing

s M

eth

od

Gup

ta a

nd

Gov

inda

raja

n (2

000)

P

roce

dura

l and

de

clar

ativ

e kn

owle

dge

Det

erm

ina

nts

of s

ubsi

diar

y kn

owle

dge

flow

s D

iffer

ent d

eter

min

ants

of

know

ledg

e in

flow

s an

d ou

tflow

s Q

uant

itativ

e

Hak

anso

n an

d N

obel

(2

001)

T

echn

ical

kno

wle

dge

Rev

erse

kno

wle

dge

tran

sfer

sw

ith r

espe

ct to

uni

t’sem

bedd

edne

ss a

nd

inte

grat

ion

Uni

ts w

hich

are

hig

hly

embe

dded

an

d hi

ghly

inte

grat

ed tr

ansf

er m

ost

Qua

ntita

tive

Tei

glan

d, F

ey a

nd

Birk

insh

aw (

2000

) R

&D

kno

w-h

ow

Kno

wle

dge

tran

sfer

bet

wee

n R

&D

uni

ts

Tra

nsfe

rs a

re fa

cilit

ated

thro

ugh

one-

com

pany

cul

ture

Q

ualit

ativ

e

Bec

erra

-Fer

nand

ez

and

Sab

herw

al (

2001

) P

roce

dura

l and

de

clar

ativ

e kn

owle

dge

Con

tinge

ncy

view

of

know

ledg

e tr

ansf

er

effe

ctiv

enes

s

Tas

k ch

arac

teris

tics

influ

ence

the

effe

ctiv

enes

s of

kno

wle

dge

tran

sfer

Q

uant

itativ

e

Gol

d, M

alho

tra

and

Seg

ars

(200

1)

Not

spe

cifie

dO

rgan

izat

iona

l cap

abili

ties

as

prec

ondi

tions

for

effe

ctiv

e kn

owle

dge

trans

fer

Soc

ial c

apita

l and

kno

wle

dge

inte

grat

ion

are

key

to e

ffect

ive

know

ledg

e tr

ansf

er

Qua

ntita

tive

Sub

ram

ania

m a

nd

Ven

katr

aman

(20

01)

Tac

it kn

owle

dge

Kno

wle

dge

tran

sfer

and

tr

ansn

atio

nal p

rodu

ct

deve

lopm

ent

Diff

eren

ces

amon

g co

untr

ies

and

rich

info

rmat

ion

proc

essi

ng

mec

hani

sms

impr

ove

prod

uct

deve

lopm

ent c

apab

ilitie

s

Qua

ntita

tive

Sve

iby

(200

1)

Indi

vidu

al a

nd

orga

niza

tiona

l kn

owle

dge

Epi

stem

olog

ical

app

roac

h to

st

rate

gy fo

rmul

atio

n Le

vels

of k

now

ledg

e tr

ansf

er a

nd

thei

r ch

arac

teris

tics

Con

cept

ual

33

Tsa

i (20

01)

Not

spe

cifie

dIm

pact

of a

uni

t’s a

bsor

ptiv

e ca

paci

ty a

nd n

etw

ork

posi

tion

on k

now

ledg

e tr

ansf

er

Uni

ts in

cen

tral

net

wor

k po

sitio

ns

crea

te m

ore

inno

vatio

n Q

uant

itativ

e

Bha

gat,

etal

. (20

02)

Org

aniz

atio

nal

know

ledg

e T

rans

fer

of k

now

ledg

e be

twee

n di

ffere

nt c

ultu

res

Diff

eren

ces

in c

ultu

ral p

atte

rn a

nd

cogn

itive

sty

le m

oder

ate

effe

ctiv

enes

s of

kno

wle

dge

tran

sfer

s

Con

cept

ual

Fos

s an

d P

eder

sen

(200

2)

Not

spe

cifie

dS

ourc

es o

f tra

nsfe

rabl

e su

bsid

iary

kno

wle

dge

Kno

wle

dge

from

diff

eren

t sou

rces

w

ithin

the

unit

has

to b

e tr

ansf

erre

d di

ffere

ntly

Qua

ntita

tive

Mud

ambi

(20

02)

Not

spe

cifie

d N

etw

ork

pers

pect

ive

of

know

ledg

e tr

ansf

ers

with

in

MN

Cs

Mic

ro a

nd m

acro

per

spec

tive.

D

iffer

entia

tion

of in

tra-

MN

C fl

ows

Con

cept

ual

Chi

ni, A

mbo

s an

d S

chle

gelm

ilch

(200

3)

Pro

cedu

ral a

nd

decl

arat

ive

know

ledg

e H

eadq

uart

ers’

ben

efits

of

know

ledg

e tr

ansf

ers

from

su

bsid

iarie

s

Sub

sidi

arie

s’ s

trat

egic

man

date

and

th

e he

adqu

arte

rs a

bsor

ptiv

e ca

paci

ty a

re c

ritic

al

Qua

ntita

tive


knowledge referred to in the research, the overall aim, the main findingsand the method used are presented in order to provide an overview.

The conceptual studies presented above strengthen the theoreticalbackground of the knowledge-based view of the firm, provide frameworksof how to conceptualize knowledge transfers in MNCs, or focus on theproblems and influencing factors in those transfers.

Nahapiet and Ghoshal (1998) concentrate on the role of social capitaland aim to explain the antecedents of the knowledge-based view fromthat perspective. Social capital is said to be the reason why firms outper-form markets in knowledge transfer. Mudambi (2002) provides a conciseliterature review and argues in favour of the network perspective. Hesuggests viewing intra-MNC knowledge transfers from both a macroand a micro perspective. Another strong theoretical contribution is offeredby Von Krogh and Köhne (1998), who identify three phases of knowledgetransfer: initiation, actual transfer and integration. The authors alsodescribe several factors – such as the nature of knowledge, the interactionof sender and recipient, motivation and corporate and local culture –that have a bearing on the knowledge transfer process. The frameworkof knowledge transfer outlined by Sveiby (2001) has already been dis-cussed above (p. 16). The issue of knowledge transfer between differentcontexts in different geographies central to the study in hand has beenaddressed conceptually by Doz and Santos (1997). They argue thatknowledge management becomes ‘eventful’ in the case of geographicaldispersion and context differentiation and discuss strategies of packagingknowledge. A cross-cultural perspective is given by Bhagat et al. (2002),who theorize that differences in cultural patterns and cognitive stylesmoderate the effectiveness of knowledge transfers.

Turning to the empirical contributions, researchers concur withGupta and Govindarajan’s (2000, p. 474) observation that ‘Very littlesystematic empirical investigation into the determinants of intra-MNCknowledge transfer has so far been attempted.’ Among the notable excep-tions are those who have contributed comprehensive empirical studieson intra-organizational knowledge transfer. Despite the overall dearthof empirical contributions, there are some studies that are of particularrelevance for the present work. They can be categorized according to thefive areas shown in Exhibit 2.9.

Hansen (1999) finds that strong ties are especially useful when highlycomplex knowledge is concerned. In contrast, weak ties prove as efficientmeans of transfer when knowledge is less complex. Tsai’s (2001) studydiscusses intra-organizational knowledge transfer from the viewpoint of


the unit’s centrality in the network and emphasizes its absorptive capacity.As our research includes knowledge transfers from headquarters as wellas subsidiaries, it is important to acknowledge the characteristics ofreverse knowledge transfers – i.e. from subsidiaries to headquarters – asoutlined by Hakanson (2001) and Chini, Ambos and Schlegelmilch(2003) and to differentiate between diverse sources of subsidiary know-ledge, as shown in Foss and Pedersen (2002). As mentioned above, thestudies of Gupta and Govindarajan (1991, 1994, 2000) are among thosewhich see knowledge flows as control or administrative mechanisms.Some ideas articulated in their (2000) study are applicable in ourresearch. The studies listed as integrating capabilities and context andtransferability in Exhibit 2.9 will not be further explained at this point.The discussion on (p. 27) as well as that in Chapter 3 focuses specificallyon these issues.

It has to be noted that only a few studies explicitly investigate theintra-MNC knowledge transfer process. Although these studies addgreatly to our knowledge in this field, most of them take a very narrowperspective and fail to provide an integrative model. Moreover, hardlyany study integrates the concept of knowledge transfer effectiveness/success into their empirical investigation: notable exceptions are Becerra-Fernandez and Sabherwal (2001) and Gold, Malhotra and Segars (2001).Another attempt to account for this issue empirically was made byChini, Ambos and Schlegelmilch (2003). As the organizational advantagegained from knowledge transfer is hard to isolate, and therefore hard to

Exhibit 2.9 Areas of empirical contributions

Topic Study

Ties and the network perspective Hansen (1999); Tsai (2001)

Subsidiary perspective Chini, Ambos and Schlegelmilch (2003); Foss and Pedersen (2002); Hakanson and Nobel (2001)

Coordination and control issues Gupta and Govindarajan (1991, 1994, 2000)

Capabilities and context Becerra-Fernandez and Sabherwal (2001); Bhagat, et al. (2002); Gold, Malhotra and Segars (2001); Inkpen and Dinur (1998); Subramaniam and Venkatraman (2001)

Transferability of knowledge Zander and Kogut (1995); Szulanski (1996)


measure, most authors concentrate on the incidence of knowledgetransfers without distinguishing between effective/successful/beneficialtransfers and others. In contrast, our research focuses on the effective-ness of knowledge transfers, assuming that not every transfer of knowledgeis effective.

37

3Knowledge-Based Determinants of MNC Strategic Configuration

Chapter 3 focuses on the knowledge-based determinants of the MNC’sstrategic configuration. As shown in the literature review in Chapter 2,research on knowledge transfers within MNCs is marked by severalstreams of theory, such as ties and the network perspective, the subsidiaryperspective, coordination and control issues, capabilities and contextand the transferability of knowledge. Having identified these, the follow-ing sections will shed some light on the origins and the importance ofthese topics and show why they need to be integrated into a compre-hensive model of intra-MNC knowledge transfer.

Headquarters–subsidiary relationships

Traditionally, the research stream on headquarters–subsidiary relationshipsfocuses on two aspects: the centralization and formalization of decisionmaking and the integration of the portfolio of subsidiaries to maximizetheir usefulness within the MNC (Paterson and Brock 2002). It has recentlybeen recognized that viewing the global network from the peripheryinstead of from the centre could add strategic value to managerial deci-sions. The management of the MNC as a network implies the need tobalance local responsiveness and central coordination. However, perceptionsare likely to differ as they take their perspective from different parts of theorganization. Headquarters–subsidiary relationships can generally becompared to a principal–agent relationship. Nohria and Ghoshal (1994)emphasize that the structure of headquarters–subsidiary relationshipshas to fit its context. Authors such as White and Poynter (1990) also notethat subsidiaries are confronted with different challenges and requiredifferent administrative practices. MNCs can no longer rely exclusivelyon their home base (Doz et al. 1997) – neither in terms of tangible


resources nor in terms of knowledge. Consequently, the power ofsubsidiaries vis-à-vis the headquarters increases and an important streamof scholarly research now focuses on the role of subsidiaries in knowledgecreation (Holm and Pedersen 2000; Foss and Pedersen 2002).

The shift from a hierarchical to a heterarchical conceptualization of thefirm has led to a change in perspective from the MNC level to the subsidiarylevel (Birkinshaw et al. 2000). Moreover, the role of network actors hasto be seen from a dynamic viewpoint. First, the configuration of thenetwork is constantly changing: network positions are not stable butreactive to changes in the environmental as well in the firm context.Second, not only headquarters ‘introduce’ strategy, subsidiaries alsotake strategic decisions (Paterson and Brock 2002).

In terms of power relations, a subsidiary is likely to push for more andpossibly inappropriate levels of autonomy. Sensitive management stylesare required so that the subsidiary can easily see and evaluate its con-tribution to the overall organizational success (Taggart and Hood 1999).In this context, power can be based on authority as well as on the controlof critical resources. Resource dependence between actors is an importantbase of subsidiary power and autonomy. Subsidiaries’ power is alsoincreasingly based on their role as a source of ideas, skills, knowledge andcapabilities. In recent years, a separate research stream has thus focusedon the development of subsidiaries (see also Birkinshaw and Hood 1998).

Complexity is augmented through the twofold embeddedness ofsubsidiaries (see also Andersson and Forsgren 2002). A subsidiary’snetwork consists of relationships with different degrees of embeddedness.On the one hand, units are integrated into their own local context,forming relationships with external partners. On the other hand, theyare integrated in the MNC network. Therefore, subsidiaries are facingtwo separate and sometimes contradictory forces. External embeddednesshas to be seen as a source of local knowledge and innovation and can becritical to the MNC’s corporate advantage. Admitting high degrees ofexternal network relationships means that headquarters’ control of thesubsidiary, and thus over corporate strategy, decreases (Andersson andForsgren 1996; Ambos and Reitsperger 2004). Another problem is thatheadquarters’ knowledge about subsidiaries’ networks is limited and thuscontrolling the development of subsidiaries becomes a decision made underuncertainty. For this reason, understanding the network can be a sourceof power independent of the unit’s formal position (Holm, Johansonand Thilenius 1995).

It thus becomes evident that the problematic of headquarters–subsidiaryrelationships has an important bearing on the theory of intra-MNC

Knowledge-Based Determinants of MNC Strategic Configuration 39

knowledge transfers. Not only is the question where knowledge is createdimportant, but also which parts of the organization it can be used inbecomes critical. The design of coordination and transmission channels isalso likely to be inefficient unless at least some actors possess sufficientknowledge about the network configuration.

Strategic mandates, coordination and control

While headquarters–subsidiary relationships have been discussed in thepreceding section, the concrete differences between organizational unitsare now the centre of analysis. As argued above, the MNC can no longerbe seen as a centrally managed entity. This implies that the headquarters’position is likely to differ in the same way as subsidiaries’. While thereis a broad stream of literature about the varying strategic mandates ofsubsidiaries, headquarters’ strategic positions have not been discussed indepth. Despite a lack of classifying frameworks, it is important to note thatheadquarters also occupy a dynamic network position in the MNC.

To ensure that knowledge is sent to the locus in the organizationwhere it can ultimately add to value creation, coordination and controlmechanisms constitute important transmission channels for knowledgeflows. Having discussed different strategic mandates and the relevanceof coordination and control in the MNC for this study, an attempt toalign strategic mandates and coordination and control mechanisms ismade. In final pages the issue is recaptured, focusing explicitly on theoptimization of knowledge flows.

Strategic mandates of subsidiaries

Structural variations across geographic units reflect different cultural andeconomic environments, resulting not only in differences in organ-izational structures, but also in various strategic roles for subsidiaries(Malnight 2001, p. 1189). Substantial differences across subsidiaries withinMNCs exist because of the different strategic contexts in terms of themagnitude and directionality of capital, product and knowledge flowssubsidiaries face in the MNC network. While certain subsidiaries engagein intra-corporate transactions, others do not. If they do, then thevolume and criticality of these transactions is decisive. For each type oftransaction, a subsidiary may engage in high or low levels of transactioninflow as well as high or low levels of transaction outflow. Subsidiariescan also be distinguished as receivers or providers of what is beingtransferred. As a consequence, the degree of lateral interdependence amongsubsidiaries also varies across roles (Gupta and Govindarajan 1991).


In the literature, the terms subsidiary ‘strategy’, ‘role’, ‘mandate’ or‘charter’ are often used interchangeably. The utilization of differentterminologies is mostly due to the contributor’s research background –organizational behaviour, strategic management, etc. However, oneimportant difference in the conceptualization is whether such mandatesare assigned by headquarters or achieved by the subsidiary autonomously.Birkinshaw and Morrison (1995) define ‘role’ as the outcome of a deter-ministic process whereby the subsidiary fulfils an imposed function.In contrast, ‘strategy’ suggests a higher degree of freedom on the part ofsubsidiary management to define its own destiny. Although this dif-ferentiation is especially critical in subsidiary development, it is not thatrelevant for the study at hand, because the aim of this study is a surveyof the status quo rather than a dynamic view. Throughout this study,the term ‘strategic mandate’ will be used.

Several authors (Bartlett and Ghoshal 1986; Gupta and Govindarajan1991; Martinez and Jarillo 1991) have recognized the different strategicmandates taken by subsidiaries within the MNC, and classified themalong certain dimensions. The theoretical foundations and implicationspresented in literature vary significantly since different criteria were usedto categorize the different subsidiaries (Ambos and Reitsperger 2004).Bartlett and Ghoshal (1986) model subsidiary mandates as a function ofthe significance of the unit’s local environment to the company’s globalstrategy and its unique resources and capabilities. Gupta and Govindarajan(1991) array subsidiaries according to the extent to which a subsidiaryreceives knowledge inflows from the rest of the corporation and the extentto which it engages in knowledge outflows to the rest of the corporation.Exhibit 3.1 gives an overview of some subsidiary mandate typologiesthat have been developed in the literature. Although the underlyingrationales vary considerably, Birkinshaw and Morrison (1995) suggestthe categorization shown in Exhibit 3.1.

Most frameworks take into account the importance of subsidiaryresponsiveness/autonomy versus integration/interdependence/coordi-nation to characterize subsidiary mandates (Paterson and Brock 2002).However, not all elements of the typologies in Exhibit 3.1. are completelycomparable. Ambos (2002) notes that many studies assign the samelabels while building on different theoretical bases. To outline the mostimportant characteristics of the typologies which are especially helpfulfor this research, the approaches of Bartlett and Ghoshal (1986), Jarilloand Martinez (1990), Gupta and Govindarajan (1991), and Birkinshawand Morrison (1995) were chosen. Although these mandates build ondifferent dimensions, they approximate each other to a certain extent.It has also to be noted that a single subsidiary is likely to have multiple


roles at the same time (Birkinshaw and Morrison 1995). Birkinshaw andMorrison’s (1995) research adds that the structural context varies accordingto the subsidiary’s mandate. A subsidiary’s structural context can bedefined as ‘the set of formal and informal management systems thatdetermine the relationship of the subsidiary to its parent and affiliates’(Birkinshaw and Morrison 1995, p. 730). The structural context is thusconsistent with the subsidiary’s strategic objectives and shapes thesubsidiary’s mandate. In turn, the subsid-iary’s autonomous actions alsoshape its structural context. In other words, there are three perspectivesfor the determination of subsidiary mandates:

• First, headquarters assign a mandate to the subsidiary which is con-trolled through a variety of formal and informal mechanisms.

• Second, having sufficient freedom, the subsidiary defines its ownmandate.

• Third, the local environment determines the subsidiary’s mandatethrough the influences of the host country characteristics.

Some subsidiary mandates which are comparable to a certain extent arenow described:

Exhibit 3.1 Subsidiaries’ strategic mandate typologies

Source: Birkinshaw and Morrison (1995, p. 733).

LOCALIMPLEMENTER

SPECIALIZED CONTRIBUTOR

WORLD MANDATE

White and Poynter (1984) Minature Replica

Rationalized Manufacturer

Global Mandate

D’Cruz (1986) Branch Plant Globally Rationalized Product Specialist

World Product Mandate

Bartlett and Ghoshal (1986)

Implementer Contributor Strategic Leader

Jarillo and Martinez (1990) Autonomous Receptive Active

Gupta and Govindarajan(1991)

Local Innovator,Implementer

Global Innovator

Integrated Player

Roth and Morrison (1992) Integrated Global SubsidiaryMandate


The Local Implementer (or Autonomous Subsidiary)

This is characterized by limited geographic scope, typically in a singlecountry, and by severely constrained product or value-added scope(Birkinshaw and Morrison 1995). This subsidiary is relatively independ-ent from the rest of the organization since it carries out most of theactivities of the value chain (Jarillo and Martinez 1990). Gupta andGovindarajan (1991) use the term ‘Local Innovator’ for this type of sub-sidiary, and define it through low knowledge outflows and inflows. Thesubsidiary has high local responsibility for the creation of relevantknow-how in all key functional areas. This knowledge, however, is likelyto be idiosyncratic and is of only limited competitive use outside thesubsidiary’s host country.

In the case of global integration, Local Implementers could also berestricted to fewer value chain activities. Bartlett and Ghoshal’s (1986)as well as Gupta and Govindarajan’s (1991) Implementers correspondto this type. In this context, Local Implementers are highly dependenton the parent and engage heavily in inter-affiliate purchases as theytypically specialize in downstream activities such as sales and marketing(Birkinshaw and Morrison 1995). These subsidiaries engage in littleknowledge creation on their own and therefore rely on knowledge inflowsfrom headquarters and peer subsidiaries (Gupta and Govindarajan 1991).In this situation, units in less strategically important markets do not needaccess to critical information; they are aimed at operational executionand at capturing the economies of scale and scope which are crucial tomost companies’ global strategies (Bartlett and Ghoshal 1986).

The (Specialized) Contributor (or Receptive Subsidiary)

This performs only few activities, typically marketing and sales, and ishighly integrated in the rest of the firm (Jarillo and Martinez 1990). Highinternal product flows and internationally configured value chains arecharacteristic for this role (Birkinshaw and Morrison 1995). Contributorsare mandated to execute strategies and decisions developed elsewhere.However, despite operating in a small or strategically unimportant market,these subsidiaries, which approximate Gupta and Govindarajan’s (1991)Global Innovator, have distinct internal capabilities. They serve as thefountainheads of knowledge for other units and can also be characterizedby low knowledge inflows and high knowledge outflows. Their activitiesare thus tightly coordinated with the activities of other subsidiariesresulting in high levels of interdependence with other affiliates andthe parent.


The Black Hole

A Black Hole is assumed to offer a potential country-specific advan-tage to the MNC, but has low firm-specific advantages. FollowingBirkinshaw and Morrison’s (1995) classification it can be either considereda low-performing World Mandate Subsidiary (see below) or a high-potentialSpecialized Contributor. As Black Holes are located in important markets,a strong local presence is essential for maintaining the company’s globalposition. However, a Black Hole is not seen as an acceptable strategicposition but a state of transition that has to be overcome (Bartlett andGhoshal 1986).

Strategic Leader (World Mandate, Active Subsidiary or Integrated Player)

This has world-wide or regional responsibility for a product line orentire business or function. Its activities are integrated world-wide, butmanaged from the subsidiary and not from the headquarters (Birkinshawand Morrison 1995). An Active Subsidiary performs many activities inclose interdependence with the rest of the firm. It constitutes an activenode in a tightly knit network (Martinez and Jarillo 1991). Exhibitinghigh knowledge inflows and outflows, this subsidiary type also has theresponsibility for creating knowledge that can be utilized by othersubsidiaries. However, it is not self-sufficient in the fulfilment of itsown knowledge needs (Gupta and Govindarajan 1991). FollowingBartlett and Ghoshal (1986), a Strategic Leader operates in a strategicallyimportant market and has high levels of resources and expertise. It thushas no reliance on lateral product flows, external sourcing of raw materialsand external selling of products (Birkinshaw and Morrison 1995). Thishighly competent national subsidiary serves as a partner of headquarters indeveloping and implementing strategy. It not only has to detect signalsof change but also help to analyse the threats and opportunities anddevelop appropriate responses (Bartlett and Ghoshal 1986).

Coordination and control within the MNC

The increased share of sales and profits from overseas and the increasedself-sufficiency of subsidiaries is a trigger for engagement in the study ofcoordination and control mechanisms. In view of the dispersedresources and complex internal flow patterns, the expanding array ofcoordination and control mechanisms has been an important topic inthe MNC literature (Malnight 1996).

MNC management faces the question where decisions are made, howoperations can be optimized globally and how the country units should


report to the headquarters. Even more than the subsidiary’s resourcedependence, the design of coordination and control mechanisms canbe viewed as a source of headquarters’ control over the subsidiary(Andersson and Forsgren 1996). Control as an integrating mechanism isalso aimed at reducing uncertainty and at ensuring that behavioursoriginating in separate parts of the organization are compatible andsupport common organizational goals. The fit between the organizationalmodel and the coordination and control mechanisms applied is found tobe one of the most influential factors explaining performance differenceswithin MNCs (Harzing 1999).

Harzing (1999) provides an in-depth literature review on the classi-fication of coordination and control mechanisms. Definitions of controlinclude two different aspects:

• First, control can be seen as a means to direct behaviour in an organ-ization towards the goals of this organization.

• Second, there is an element of power in this relationship.

Although many authors use the terms ‘control’ and ‘coordination’synonymously (cf. Martinez and Jarillo 1989) coordination can be viewedas the process of ‘integration, harmonization or linking different parts ofan organization towards a common goal’ (Harzing 1999, p. 9). In contrastto control, the power element is more implicit. With regard to networkrelationships in the MNC, coordination and control can be viewed asthe way headquarters make sure that subsidiaries behave in concord-ance with headquarters’ policy. In this study, control is seen as a meansto achieve an end called ‘coordination’. To optimize knowledge flowswithin the MNC, efficient coordination has to ensure that units link upand exchange knowledge through appropriate transmission channels.

Most researchers distinguish between formal and informal controlmechanisms (Martinez and Jarillo 1989; Gupta and Govindarajan 1991).Gupta and Govindarajan (2000) specifically distinguish between formalintegrative mechanisms and corporate socialization mechanisms astransmission channels for knowledge flows. Harzing (1999) classifiesdiverse control mechanisms prominent in the literature into the fourcategories shown in Exhibit 3.2.

Personalized centralized control describes the decisions taken at the topmanagement level and the personal surveillance of their execution. Incontrast, bureaucratic formalized control is an impersonal, indirect mech-anism, usually in the form of written rules or manuals. By definition,output control does not focus on behaviour but on outputs. Monitoring


or reporting systems are the means through which financial data, salesfigures, etc. are transmitted. The final category, control by socializationand networks, is the most complex. Harzing (1999, p. 22) further distin-guishes between:

• Socialization: a control by common organizational culture and sharedvalues

• Informal, lateral and horizontal exchange of information: non-hierarchicalcontrol

• Formalized lateral and cross-departmental relations: non-hierarchicalbut temporarily formalized control.

However, these mechanisms cannot be regarded as mutually exclusive.Different control mechanisms are instead implemented simultaneouslyfor different types of subsidiaries, employees and parts of the organization,or for different situations and environments the MNC faces (Ambos2002; Martinez and Jarillo 1989). Organizational characteristics (sizeand interdependence) and environmental characteristics (uncertainty andheterogeneity/complexity) are supposed to have the largest influenceon the choice of control mechanisms in MNCs (Harzing 1999). Generally,any combinations of these categories are possible, but the literature stressesthat some combinations are more probable than others (Harzing 1999).Output control and control by socialization and networks for example,are likely to appear together in situations with high environmentaluncertainty, complex technology and limited knowledge of the trans-formation process. A more recent stream of research finds that strategicmandates and cultural distance have an impact on the choice of coord-ination and control instruments (Nobel and Birkinshaw 1998; Ambosand Reitsperger 2002). The coordination and control mechanisms usedfor the different strategic mandates are now discussed.

Exhibit 3.2 Control mechanisms

Source: Harzing (1999, p. 21).

PERSONAL/CULTURAL (FOUNDED ON SOCIAL INTERACTION)

UNPERSONAL/ BUREAUCRATIC/ (FOUNDED ON INSTRUMENTAL ARTEFACTS)

Direct/Explicit Personal centralized control Bureaucratic formalized control

Indirect/Implicit

Control by socialization and networks

Output control


Coordination and control of different strategic mandates

The fit between the role of the subsidiary and the type of coordination andcontrol exercised over the subsidiaries proves to be one of the influentialfactors on the performance of the MNC (Harzing 1999). Research hasfound evidence for a strong relationship between the role an MNC assigns toa subsidiary and the level of coordination and control that is required(Nobel and Birkinshaw 1998; Ambos and Reitsperger 2002).

Specialized Contributors, for example, prove to be more involved in allkinds of control mechanisms than Local Implementers. Local Implementersrequire less coordination, since they are relatively independent. However,these subsidiaries represent the MNC’s opportunity to capture learningwhich is the source of its competitive advantage. Bartlett and Ghoshal(1987) propose that, generally, units with implementation responsibilityshould be managed through tight operating controls with standardizedsystems used to handle much of the coordination. This corresponds toBirkinshaw and Morrison’s (1995) findings which suggest high levels ofbureaucratic formalized control and low strategic autonomy for LocalImplementers, respectively. However, in Martinez and Jarillo’s (1989)study, Local Implementers showed the lowest levels of total controlcompared to the other mandates. In subsidiaries exhibiting high degreesof integration with the rest of the MNC (such as Specialized Contributorsand World Mandates) all control mechanisms, especially the subtle ones(control by socialization and networks), are used more extensively(Martinez and Jarillo 1991).

The Specialized Contributors seem to be the tightest controlled man-dates. They are characterized by high levels of interdependence with peersubsidiaries. Normally, they specialize in a narrow set of value chainactivities (Birkinshaw and Morrison 1995), so headquarters have to putlocal interests above global ones when managing these units.

Strategic Leaders must be given freedom to develop responsibility andan entrepreneurial approach although headquarters’ support must still beintensive. For this unit, operating controls may be light and quite routine,but coordination of information and resource flows to and from theunit will probably require intensive involvement of senior management(Bartlett and Ghoshal 1987). The complex local environment accountsfor the value of knowledge stock gathered in these subsidiaries; to beable to source and integrate this knowledge, Strategic Leaders demandgreater autonomy and less centralization and formalization. Comparedto the other mandates, these subsidiaries have the highest levels ofstrategic autonomy and, thus, the lowest levels of bureaucratic formalizedcontrol (Birkinshaw and Morrison 1995). In order to establish control


among Strategic Leaders, the literature suggests the imposition of tightsocial control (Bartlett and Ghoshal 1986).

However, negative arguments against social control also exist.When referring to Strategic Leaders as Centres of Excellence (CoEs), theimposition of a company-centric culture may inhibit the CoE from inte-grating into the local research community. In this case, the acquisitionof new knowledge is inhibited (Egelhoff 1999). Moreover, Hansen (1999)concludes that strong inter-unit ties through tight social control impedethe acquisition of new knowledge and increase the risk of redundantknowledge generation in the CoE. Strong social control of off-shore CoEscan even have a negative impact on CoEs’ performance (Ambos andReitsperger 2004).

This shows that the strategic mandate is an important determinant ofintra-MNC knowledge transfer. Units have to be managed differently tomaximize overall performance and to build the basis for a global networkthat provides appropriate transmission channels for knowledge flows.

Managing strategic mandates in order to optimize knowledge flows

As mentioned earlier, according to Gupta and Govindarajan (1991, 1994,2000) knowledge flow patterns between organizational units represent acore dimension along which subsidiaries’ strategic contexts can differ.As this is among the main concerns of this research, it is seen as importantto address the question of how to manage and control these patternsseparately.

This study centres on declarative and procedural types of knowledgeflows. As far as procedural knowledge is concerned, expertise, skills andcapabilities are involved. This type of knowledge can take the form ofinput processes (e.g. purchasing skills), throughput processes (e.g. productdesigns, process designs and packaging designs) or output processes(e.g. marketing know-how). Declarative knowledge, such as externalmarket data, is mirrored in the transfer of globally relevant data about keycustomers, competitors, or suppliers (Gupta and Govindarajan 1991).

Three specific major determinants of control mechanisms can beidentified that reflect the variations across the subsidiary mandates arisingfrom its position in the MNC’s knowledge flow network (Gupta andGovindarajan 1991):

• Subsidiary differences in lateral interdependence, • Subsidiary differences in global responsibility and authority, • Subsidiary differences in the need for the exercise of autonomous

initiative.


Lateral interdependencies between subsidiaries can be expected to bea positive function of the extent of both knowledge inflow andknowledge outflow (Gupta and Govindarajan 1994). Administrativemechanisms must be implemented that determine the appropriate degreeof information-processing capacity for the subsidiary (Galbraith 1972;Egelhoff 1991). Complex formal integrative mechanisms such as liaisonpositions enhance the information-processing capacity and are thusexpected to be high for subsidiaries playing the role of an IntegratedPlayer (Gupta and Govindarajan 1991). Liaison personnel also serve as animportant transmission channel for knowledge flows between organiza-tional units. Another function affecting the information-processingcapacity is the intensity of communication linkages in terms of fre-quency, informality, openness and density, that is generally higherfor lateral interdependent subsidiaries such as Integrated Playersthan for Global Innovators, Implementers and Local Innovators (Guptaand Govindarajan 1991).

The perspective and attitude of subsidiary managers also has to beaddressed. The greater the degree of lateral interdependence betweensubsidiaries the greater is the need for global as opposed to local subsidiarymanagement orientation. The ratio of expatriate managers – who aremore likely to have a comprehensive understanding of the MNC’s overallglobal strategy – in contrast to local managers is therefore expected to behigh for Integrated Players, medium for Global Innovators and Imple-menters and low for Local Innovators (Gupta and Govindarajan 1991).Effective management of high lateral interdependence also requiresclose identification with and commitment to the entire MNC. Subsidiarymanagers of an Integrated Player have thus to be more corporatelysocialized than those of subsidiaries mandated as Global Innovators andImplementers – and, of course, much more socialized than those ofLocal Innovators (Gupta and Govindarajan 1991).

With regard to subsidiary differences in global responsibility andauthority, mechanisms such as special bonus systems, budget evaluationstyles, etc. aim to motivate managers ‘to think of their responsibilities ineither global or local terms, as appropriate, and mitigate the emergence offrustration in those contexts where the manager’s responsibility exceedshis or her authority’ (Gupta and Govindarajan 1991, p. 781). Finally,with respect to the variations in the degree of autonomous initiative ofsubsidiary managers, mechanisms such as decentralization of decisionmaking authority or the size of the subsidiary managers’ bonus relative totheir salary have to be tailored to the magnitude and scope of knowledgecreation expected from a subsidiary (Gupta and Govindarajan 1991).


From the preceding section, it can be concluded that units are assigneddifferent strategic mandates, and that these mandates require appro-priate coordination and control mechanisms in order to reach overalleffectiveness in the MNC. In the context of knowledge flows, it isespecially important that formal and informal coordination and controlmechanisms are in place because they provide the pattern for globalexchange. It was also shown that strategic mandates can be based on thedistinction of their knowledge flows. Knowledge flows are thus primarilyseen as control and administrative mechanisms.

The capability perspective

The last two theoretical building blocks have both focused on theorganizational unit’s strategic position in the MNC. Even though werecognize the importance of these issues, they cannot account for theentire explanation of knowledge transfer in the MNC. What seems to beequally important is the capability perspective presented here. Thisperspective stresses the relevance of organizational skills and routines. Verydifferent schools of thought have all come to the conclusion that certaincapabilities have an effect on the way knowledge transfer is designedand also on the effectiveness of knowledge transfer. First, some differenttheoretical approaches to the capability perspective are presented andsome distinct knowledge management capabilities are then highlighted.

Approaches to organizational capabilities

Drawing on the resource-based and knowledge-based view, a firm isregarded as a unique bundle of idiosyncratic resources and capabilities.Knowledge as a resource needs organizational capabilities to be productive(Grant 1996). Capabilities represent the organization’s collective capacityfor undertaking a specific type of activity (Lieberman and Montgomery1998). The MNC’s capabilities differ in terms of their intrinsic tech-nological opportunities and their correspondence with market opportunities(Zander and Kogut 1995). In this context, the skills and routinesdeveloped by the MNC can be viewed as organizational underpinnings.In turn, the creation and maintenance of superior organizational routinesis vital for the development and renovation of the firm’s competitiveadvantage (Kogut and Zander 1995).

The most prominent and most widely discussed capability in thisrespect is the ‘combinative capability’ outlined by Kogut and Zander(1992). It is noticeable that, despite having a different explanation for theexistence of the MNC, several research streams stress the importance of


this combinative capability. The internalization theorists Buckley andCasson (1976) argue that the very existence of a MNC lies in its abilityto internalize externalities by putting together attributes, resources andactivities within an internal market at a more efficient rate thanmarkets do. The argument that firms create value through combiningdispersed knowledge fits this perspective well, especially if one accepts thatmarkets often fail to transfer this knowledge at a price – a perspectivethat is proposed by transaction cost economists (Hymer 1976; Teece1981). Comparing different economic theories of the firm, Chandler(1992) notes the importance of organizational capabilities and findsthat learning such capabilities requires trial and error as well as a guidedprocess of learning and experimentation. Following a very different logic,Kogut and Zander (1993) come to a similar, if not even stronger, conclu-sion regarding the MNC as a knowledge integrating institution. Buildingan evolutionary theory of the firm, they state that knowledge exists insocial relations among cooperating members of a community withoutfixed boundaries and ground their explanation of the MNC in the firm’sskills and capabilities and their nature. In this school of thought, whichis also adopted in this study, the MNC is seen as a social community,whose productive knowledge defines a competitive advantage.

Knowledge management capabilities

Although combinative capability has been the centre of discussion – lastbut not least because of its degree of theoretical abstraction and itsdeficiency in providing practical guidance – it is not the only capabilitycritical to knowledge transfers in MNCs. In order to capitalize on theirasset knowledge, firms have to develop learning capabilities: they need tofocus on how to create and diffuse knowledge (Riesenberger 1998).Particularly, if one looks at intra-MNC flows, knowledge transfer has tohappen before combinative capability can occur. In order to initiallyengage in the knowledge transfer process, additional capabilities arenecessary. These can also partly be viewed as components of combinativecapability. Gold, Malhotra and Segars (2001) find empirical evidence thatfirms may possess a predisposition for successful knowledge managementthrough the development of key capabilities. Many terms have been usedto describe specific knowledge management capabilities (Gold, Malhotraand Segars 2001):

• Capture, transfer, use (De Long 1997) • Acquire, collaborate, integrate, experiment (Leonard-Barton 1995) • Create, transfer, assemble, integrate, exploit (Teece 1998) • Create, transfer, use (Skryme and Amidon 1997), etc.


At the beginning, units have to seek for new knowledge. Whether thesources are inside or outside the firm is not relevant for this particularresearch. With regard to the capabilities required, organizational unitsmust be able to ‘sense’ new knowledge in their environment (Ghoshaland Bartlett 1988; Brockhoff 1998). To integrate incoming knowledge,however, another capability is necessary. Cohen and Levinthal (1990)first introduced the construct of absorptive capacity which has recentlybeen applied very frequently in theoretical and empirical studies (for amore detailed discussion see Zahra and George 2002). This constructhas also been used to mirror combinative capability, as it is defined asthe ability to use prior knowledge to recognize the value of newinformation, assimilate it and apply it to create new knowledge andcapabilities.

Assuming that organizational units already possess knowledge, they haveto be capable of aggregating it within the unit at an organizational – incontrast to an individual – level. Aggregation at the organizational levelimplies a certain awareness, at least by the top management team or atfunctional specialists, that valuable knowledge exists inside the unit.The maintenance and the continuous update of this knowledge stock isimportant. All these factors can be encapsulated as ‘maintaining valuableknowledge stock’. Of course, this construct also comes close to combin-ative capability if it is assumed that various pieces of knowledge have tobe organized efficiently in order to result in a useful ‘stock’, but aims attotally different issues than absorptive capacity.

When it comes to the dissemination of knowledge within the firm,it is argued that certain capabilities are constitutive for the transferprocess. Combining the different perspectives highlighted in the litera-ture, the interplay of three mutually reinforcing capabilities seemsnecessary:

• First, the ability to coordinate with other units at formal and informallevels is critical. This issue has been addressed in the section aboutcoordination and control (p. 43), but it is important to show thatthese processes can also be seen from a capability perspective.

• Second, infrastructure facilitates knowledge transfers and determineshow knowledge travels throughout the organization. The importance oftechnical support has long been stressed in the debate about knowledgemanagement (cf. Hansen, Nohria and Tierney 1999). Infrastructureincludes business intelligence, collaboration, distributed learning, know-ledge discovery, knowledge mapping, opportunity generation as well assecurity. But infrastructure alone is not enough. Profound knowledgeabout how to use different tools has also to exist (Leonard-Barton 1995).


Infrastructure also has to be continuously developed – ultimately byits use; long-distance learning tools cannot function without anycontent, for example. Infrastructure is thus useful only if employeesare capable of using it.

• Third, knowledge transfer capability is probably most widely recognizedin the knowledge management literature, as it concerns the distinctknowledge transfer processes. Depending on the type of knowledgeand how it is packaged, different processes have to be applied inorder to transfer knowledge either between people or between peopleand technology (see also Sveiby 2001). As emphasized in the discussionabout the transferability of knowledge (p. 27), not every piece ofknowledge is available in a form that is amenable to transfer. Forexample, in some situations brainstorming might be appropriate,while in others entries into repositories of lesson-learnt or databasesare possible. To send and to receive knowledge, certain processeshave to be applied to transform the knowledge into the appropriateform. The choice of processes depends on the characteristics of theknowledge as well as on the sender, the receiver and their context.Such combination and exchange of knowledge is supported by thepresence of social capital (Nahapiet and Ghoshal 1998), which isdefined as the sum of actual and potential resources embedded within,available through, and derived from, the network of relationships.

This brief compilation of knowledge management capabilities showsthat research in this field is rather fragmented. Although authors seemto agree that the development of capabilities is important for effectiveknowledge transfer, they use different terminologies and hardly everprovide clear definitions of the respective capabilities. The threeknowledge process capabilities described above were combined because –according to the literature reviewed – they reflect the most importantfacets of intra-MNC knowledge transfer.

Contingency factors

The conceptualization of knowledge transfers (Chapter 2) builds onShannon and Weaver’s (1957) communication model. Despite being avery simplified approach, this model lends itself well to facilitate theknowledge transfer process as a dyadic relationship. In this section,the ‘noise’ of the communication model is in the centre of analysis.As transfers between MNC units located in different countries are inves-tigated, some contingency factors are supposed to play an important role


in the transfer process. First, some contingencies which make knowledgemanagement ‘eventful’ are outlined. Then, the two factors – organizationalisomorphism and national culture – which are supposed to have the highestimpact on the effectiveness of intra-MNC knowledge transfers are high-lighted and discussed.

‘Eventful’ knowledge management

Doz and Santos (1997) argue that in MNCs, knowledge managementbecomes ‘eventful’ because of the dispersion in space and time anddifferentiation of context. Dispersed organizational units – and theirknowledge – are embedded in a twofold manner (Granovetter 1985;Andersson and Forsgren 1996). First, local culture highly impacts theway knowledge management is organized. At the same time, MNCsface a pressure to conform to conditions in the local environment andan imperative for consistency within the MNC (Rosenzweig and Singh1991). Interactions across distance are especially critical because theyhave to ensure the MNC’s integration and existence as a single entityacross cultures (Manev and Stevenson 2001). ‘Thus, organizational andcultural barriers internal to the firm become a prime concern when thefirm’s management is seeking the most effective use of its intangibleknowledge assets’ (Buckley and Carter 1999, p. 80). The investigationof knowledge transfers between dispersed settings has also led to therecognition that the transfer of knowledge does not imply a ‘full’ repli-cation of knowledge in a new location (cf. Doz and Santos 1997).Indeed, ‘transfer of knowledge is often associated with modification ofthe existing knowledge to the specific context’ (Foss and Pedersen2002, p. 54).

As far as the advantages and disadvantages of context similarities inknowledge transfers are concerned, two schools of thought havedeveloped (Hansen 1999; Asakawa 2002). Capability-based theories andthe product innovation literature hypothesize that a higher degree ofinteraction between units leads to more familiarity and subsequently toa better understanding of the knowledge transferred (Subramaniam andVenkatraman 2001). In contrast, the network school (cf. Granovetter1973) argues that units with weak network ties, which are not in regularcontact with the rest of the organization, operate in a different contextand, thus, are able to introduce new knowledge, and they are viewed asan important source of innovation. But when it comes to transfers ofcomplex knowledge that tend to be characterized by a high degree oftacitness, the instrumental benefits of weak ties are called into question(Hansen 1999). On the one hand, chances to receive more innovative, and


potentially more beneficial knowledge, might increase when the sourcehas weak network ties. On the other hand, the likelihood of correctlyunderstanding and subsequently benefiting from such knowledgesources might be lower because of a lack in context similarities.

Institutional isomorphism

Functional entities in MNCs are organized differently in terms of organiza-tional structure, corporate culture and core processes. All these componentslargely determine the effectiveness of knowledge management (Daft andLengel 1986; Hansen, Nohria and Tierney 1999; Becerra-Fernandez andSabherwal 2001). Units pursuing different strategic mandates are likelyto develop characteristic nodes and processes, and consequently accountfor boundaries in information processing (Martinez and Jarillo 1989;Gupta and Govindarajan 2000) (see also p. 46). These mandates tendto be manifested in the development of specific characteristics, such astransmission channels, infrastructure and capabilities. Becerra-Fernandezand Sahberwal (2001) found that contingencies related to the taskcharacteristics have an impact on the choice of knowledge transferprocesses, and ultimately on the effectiveness of knowledge management.

These facets of institutional isomorphism are supposed to have a bearingon the unit’s approach to structure and process knowledge (Asakawa 1995).Institutional isomorphism thus leads to organizational distance betweenindividual organizational units of the MNC. Although we can recognizethe innovation potential of loosely coupled networks (Hansen 1999),organizational distance between two organizational units of a MNC islikely to be detrimental to smooth knowledge transfers. Organizationaldistance is specifically viewed as differences between organizational unitsin terms of structures, processes and values.

National culture

National culture is embedded deeply in everyday life and is relativelyimpervious to change. When management practices are inconsistent withthese deeply held values, employees are likely to feel dissatisfied, distracted,uncomfortable and uncommitted. Congruence between managementpractices and the characteristics of the national culture therefore producebetter performance outcomes (Newman and Nollen 1996). Correctlyadapted strategies, such as communication across cultures, are potentialsources of competitive advantage in the global business environment.The main strategic challenge is the alignment between key characteristicsof the national culture and a firm’s strategy, structure, systems andpractices (Griffith and Harvey 2001).


It has to be recognized that knowledge is not culture-free. People indifferent contexts will attempt to interpret each other’s ideas based ontheir unique thought worlds. The human capacity to capture andunderstand complex facts is rooted in cultural settings and, thus, tendsto differ across cultural areas. In order to adopt knowledge effectively ina new cultural context, a creative synthesis of different cultural andmeaning systems has to be achieved (Tenkasi 2000). The reception ofknowledge from another cultural context is likely to be easier when thesystem of underlying conventions fits the system of meanings of thoseexpected to implement these procedures (Macharzina, Oesterle andBrodel 2001). However, cultural diversity in MNCs has proved to fostercreativity and innovation (Gomez-Mejia and Palich 1997). Culturaldiversity is thus especially relevant to knowledge creation and long-termperspectives on performance. A lack of context similarities requiresknowledge to be transformed so that it conforms to existing culturalexpectations (Tenkasi 2000).

Beyond the general recognition that cultural differences are likely toimpinge on the success of international knowledge transfer, concreteproblems emerging in cross-cultural knowledge transfer are hardly everaddressed in the literature, with the notable exception of Bhagat et al.(2002), Subramaniam and Venkatraman (2001) or Doz and Santos (1997).Morosini (1998), for example, has conducted a study on strategy andexecution across cultures in global corporate alliances. He concludes that,in order to be transferred successfully, knowledge has to be adaptedto the recipient culture’s specifications, and not the other way round.‘The knowledge thus disseminated can in itself be modified, enriched,and fed back from the local context to the rest of the organization andvice versa’ (Morosini 1998, p. 288).

If a dyadic relationship is in the centre of analysis, the concept ofcultural distance between two organizational units comes into play. Cul-tural distance can be defined as the difference between the national cul-tural characteristics of the home and the host countries (Hennart andLarimo 1998), or as the degree to which the cultural norms in onecountry are different from those in another country (Kogut and Singh1988). Originally, this concept was suggested by Johanson and Vahlne(1977), who observed that Swedish firms progressively expand fromtheir home base into countries characterized by less ‘psychic’ distance.Subsequently, this approach became known as the ‘Uppsala Model’.Although cultural distance was first primarily researched in combin-ation with entry strategies – i.e. modes of entry and country risk (Kogutand Singh 1988; Brouthers and Brouthers 2001; Shenkar 2001) – it is


now used as a key variable in strategy, management, organizationbehaviour and human resource management (Shenkar 2001) and isoften mentioned in relation to intra-organizational knowledge transfer.Cultural distance has also become an important variable in investigat-ing organizational control systems, particularly when headquarters–subsidiary relationships are the centre of research (Johanson andVahlne 1977; Kogut and Singh 1988; Nohria and Ghoshal 1994; Rothand O’Donnell 1996). In this context, cultural distance is used from twodifferent angles:

• It can be approached from a transaction cost perspective, arguing thatcultural distance increases transaction/coordination costs, as well asfrom

• A resource-based view, regarding the very ability to bridge cultural dis-tance as a unique advantage (Shenkar 2001).

Cultural distance also has implications for the micro and the macrolevel (Manev and Stevenson 2001). At the macro level, higher culturaldistance affects the integration of a subsidiary into the network (see alsoJemison and Sitkins 1986), whereas at the micro level it may lead tomisunderstandings, conflicts, or friction between managers. Smallercultural distance means a higher degree of similarity in personal back-ground, which facilitates social relationships and the interaction in adyad, while larger cultural distance mostly hinders. Due to its wide use,implications of this concept can be found in various areas. For this study,the impact of cultural distance on the management of dispersed unitsand on the transferability of knowledge between cultures, is especiallyrelevant.

‘Cultural distance has largely been taken to represent a hindrance tothe performance of the MNE and its affiliates’ (Shenkar 2001, p. 522).Shared attributes often lead to homophily because managers are morelikely to establish strong ties with colleagues who have similarattributes, values and perceptions (Manev and Stevenson 2001). Manevand Stevenson (2001) find that cultural distance has a negative impacton the strength of MNC network ties. Similarly, Rosenzweig and Singh(1991) conclude that choice of control mechanisms is dependant on theparent country culture and on the cultural distance between the parentand the subsidiary. If the parent and the subsidiary are from similarcultures, there may be less of a need to impose formal control mechanisms.Cultural distance may also have positive effects, as the firm’s creativityand ability to change is enhanced by more diversity. Especially in


cross-border acquisitions, the target culture may provide a specific setof characteristics which can hardly be replicated in the bidding country(Morosini 1998). Augmentation of cultural distance in MNCs may alsothus have positive performance effects. As far as the exchange ofknowledge is concerned, Griffith, Zeybek and O’Brien (2001, p. 95)states that:

It is important to recognize that the greater the cultural distance(inclusive of national and organizational) between people who areattempting to communicate effectively, and thus the less consistentthe communication environment, the less likely there will be sufficientsocial bonding among individuals to facilitate effective communication.

It can be concluded that knowledge management across dispersed unitsis likely to be influenced by several contingency factors. The phases ofdecontextualization in the transmitting culture and recontextualizationin the recipient culture are very critical for the effectiveness of the transferprocess. In particular, organizational and cultural distance is supposedto have a strong impact. However, it is difficult to account for directeffects as, in both cases, greater distance leads to more innovationpotential whereas more obstacles are inherent in the transfer process.

58

4A Model of Knowledge Transfer in MNCs

According to the literature review, a comprehensive model of cross-cultural knowledge transfer within MNCs that can serve as a basis formore systematic empirical work seems to be missing. To respond tothis need, a conceptual model of the knowledge transfer processacross geographically dispersed units of MNCs is developed. As strat-egy, organizational structure and functions differ across MNCs, it issuggested that no single best way of transferring knowledge exists.On the contrary, the model proposes that the transfer of knowledgehas to correspond to the strategic network position of the organiza-tional unit as well as to the unit’s internal capabilities to manageknowledge.

Integrating the literature, Exhibit 4.1 depicts the constructs formingthe proposed conceptual model. Each of these constructs is nowdescribed in detail and research hypotheses are presented.

Strategic mandate

Subsidiaries often vary in the nature of their operations. While somesubsidiaries are mandated to contribute to the MNC by generatingand disseminating new knowledge, the primary aim of others is toimplement or exploit headquarters’ knowledge in the local context(Gupta and Govindarajan 1991; Kuemmerle 1997; Asakawa 2001a;Birkinshaw 2002; Ambos and Schlegelmilch 2004) (Exhibit 4.2). Thestrategic mandate based on patterns of knowledge inflow and outflow isthus also likely to be a key construct within flows between headquartersand subsidiaries. Based on Gupta and Govindarajan (1991, 1994), fourgeneric subsidiary mandates need to be distinguished:

59

Strategic Mandate

Value ofKnowledge Stock

Cultural Distance

Organizational Distance

Knowledge TransferCapabilities

KnowledgeTransferEffectiveness

ImplementersCulturalDistance

OrganizationalDistance

Satisfaction

PerceivedBenefit

TransmissionChannels

Infrastructure

ProcessCapabilities

LocalInnovators

GlobalInnovators

IntegratedPlayers

Value ofKnowledgeStock

Exhibit 4.1 A model of intra-MNC knowledge transfer

KnowledgeInflows

KnowledgeOutflows

LocalInnovators

GlobalInnovators

Implementers IntegratedPlayers

Hig

Low

Low High

h

Exhibit 4.2 Strategic mandates of subsidiaries


• Global Innovator (high outflow of knowledge from the subsidiary tothe corporation and low inflow from the corporation to the subsidiary)

• Integrated Player (high outflow, high inflow) • Implementer (low outflow, high inflow) and • Local Innovator (low outflow, low inflow).

In line with the network approach described in the literature review,headquarters are also likely to differ according to their integration in theglobal organizational network. Although no labels have so far been assignedfor headquarters fulfilling different strategic positions, they should beincluded in this construct.

Thus, we can state a first general hypothesis:

Value of knowledge stock

Organizational units require access to other units’ knowledge and haveto possess certain internal capabilities in order to engage in knowledgetransfer (Tsai 2001). Thus, having reviewed mandates as the units’strategic positions in the organization, the focus needs to move to theunits’ potentiality to transfer knowledge. The knowledge actually transferredis likely to be influenced by the attractiveness of a unit’s knowledgestock in relation to other units.1

Normally, each organizational unit pursues a dual task: it sends know-ledge to others (source unit) and it receives knowledge from others (targetunit). The value of a source unit’s knowledge stock strongly determines theunit’s propensity to engage in knowledge transfer: if a unit’s knowledgeis not attractive, it will not be asked to share its knowledge. The avail-able knowledge also has to be non-duplicative and useful for other units’purposes (Gupta and Govindarajan 2000). Thus, a model depictingknowledge transfer within MNCs needs to take account of a unit’s value ofknowledge stock relative to headquarters’ and to peer subsidiaries’ stock.

Looking at an organizational unit as a recipient of knowledge, itsabsorptive capacity (Gupta and Govindarajan 2000) is likely to affect

Hypothesis 1

The development of knowledge process capabilities depends on the strategicmandate of the organizational unit.

A Model of Knowledge Transfer in MNCs 61

a unit’s ability to handle the incoming knowledge. Cohen and Levinthal(1990) define absorptive capacity as the ability to use prior knowledgeto recognize the value of new information, assimilate it and apply it tocreate new knowledge and capabilities. A valuable knowledge stock istherefore likely to enhance a unit’s absorptive capacity. Prior knowledge(Cohen and Levinthal 1990) and homogeneity of the receiving andsending unit is expected to facilitate the assimilation and exploitationof new knowledge (Gupta and Govindarajan 2000). Foss and Pedersen(2002) also found strong empirical support for the view that the moreknowledge the unit creates and absorbs, the more knowledge will betransferred. Thus, we have a second general hypothesis:

Knowledge transfer capabilities

In addition to acquisition, creation, utilization and storage, transfer isviewed as a key process in managing corporate knowledge (Marquardt1996). Free knowledge flow has also been identified as one of the keyelements of successful knowledge management (Riesenberger 1998). Inorder to support the free flow of knowledge, the company has todevelop a certain organizational architecture – i.e. cross-functional,flexible structures (Nevis, DiBella and Gould 1995), free flow ofcommunication (Argyris 1994) and a learning culture (Slater and Narver1995). The actual knowledge transfer process is extremely complexand difficult to capture, since it has both, inter-personal and inter-organizational dimensions. Moreover, as pointed out in the literaturereview (p. 35), transmission channels, infrastructure and processes haveto be distinguished.

Transmission channels have been identified as key to intra-MNCknowledge transfer (Bartlett and Ghoshal 1989; Gupta and Govindarajan2000). Gupta and Govindarajan (2000) further differentiate into formal(formal integrative mechanisms) and informal channels (socializationmechanisms). In their empirical study, formal channels show a positivesignificant influence on the knowledge flow between the subsidiary and

Hypothesis 2

A high value of knowledge stock positively affects the development ofknowledge transfer capabilities.


the parent corporation, on the one hand, and peer subsidiaries, on theother. When it comes to the influence of informal channels, the resultsare significant only in the knowledge flow between peer subsidiaries. Toanalyse knowledge transmission channels, lateral and vertical channelshave to be distinguished, on the one hand, and formal and informalchannels, on the other.

The knowledge management infrastructure of MNCs has to be highlydeveloped in order to maximize the exploitation of resources that areembedded within, available through and derived from a network ofunits. An important aspect is the technological dimension, whichaddresses the technology-enabled ties that exist in a firm. Technicalinfrastructure within the field of a company’s knowledge managementincludes business intelligence (knowledge regarding competition andthe broader economic environment), collaboration of individuals withinthe company and distributed learning, knowledge discovery (discoverinternal and/or external knowledge), knowledge mapping (track sourcesof knowledge), opportunity generation (track knowledge about customers)and security (prevent inappropriate use) (Gold, Malhotra and Segars2001).

Knowledge process capabilities refer to the practices knowledge-sendingand knowledge-receiving organizations perform. Following Nonaka andTakeuchi’s (1995) knowledge spiral, four modes of knowledge conversioncan be identified:

• Socialization (from individual tacit knowledge to group tacitknowledge)

• Externalization (from tacit knowledge to explicit knowledge) • Internalization (from explicit knowledge to tacit knowledge) • Combination (from separate explicit to systemic explicit knowledge).

As these modes cover the possibilities of knowledge conversion betweenindividual and organizational knowledge, they can equally be appliedto knowledge transfer between organizational units.2 This view has pre-viously been taken by Doz and Santos (1997), Inkpen and Dinur (1998)and Sveiby (2001), who argue that from an organizational viewpoint,knowledge shared is actually knowledge doubled – i.e. new knowledgeis created with every transfer within the organization.

If the modes of knowledge conversion are applied to intra-organizationalknowledge transfer, externalization occurs before a unit sends know-ledge to another. Socialization generally occurs in both organizationalunits – sender and recipient – as they have to interact mutually. To start


socialization, however, a field of interaction has to be established (Nonaka,Umemoto and Senoo 1996), mostly initiated by the source unit. Froma recipient point of view, internalization is used to integrate knowledgeinto the target unit. Finally, by combination, explicit knowledge isconverted into even more complex sets of explicit knowledge (Nonakaand Takeuchi 1995). This mode is more often used by a target unit thathas recently received new knowledge and is starting to ‘break it down’(Nonaka and Takeuchi 1995) into systemic explicit knowledge.

In view of the composition of knowledge transfer capabilities, we canrefine our general hypothesis as follows.

Hypothesis 1a: Organizational units characterized by high know-ledge outflow and high knowledge inflow (for subsidiaries: IntegratedPlayers) are expected to develop the highest3 formal and informal trans-mission channels with peer subsidiaries/headquarters and the highestlevel of knowledge management infrastructure. Integrated players areexpected to develop all knowledge processes at an equal and high level.

Hypothesis 1b: Organizational units characterized by high know-ledge outflow and low knowledge inflow (for subsidiaries: GlobalInnovators) are expected to develop intermediate4 formal and informaltransmission channels with peer subsidiaries/headquarters and anintermediate level of knowledge management infrastructure. GlobalInnovators are expected to emphasize externalization and socialization.

Hypothesis 1c: Organizational units characterized by low knowledgeoutflow and high knowledge inflow (for subsidiaries: Implementers) areexpected to develop intermediate5 formal and informal transmissionchannels with peer subsidiaries/headquarters and an intermediate levelof knowledge management infrastructure. Implementers are expected toemphasize internalization and coordination.

Hypothesis 1d: Organizational units characterized by low knowledgeoutflow and low knowledge inflow (for subsidiaries: Local Innovators)are expected to develop the lowest formal and informal transmissionchannels with peer subsidiaries/headquarters and a low6 level ofknowledge management infrastructure. Local Innovators are expectedto develop all knowledge processes at an equal and low level.


Exhibit 4.3 gives an overview of hypotheses 1a–1d. From a systemic point of view, the development of these knowledge

transfer capabilities – channels, infrastructure and processes – followsa unit’s specific strategic mandate and its ability to transfer knowledge.Knowledge transfer capabilities are mutually reinforcing and have to becoordinated in order to be employed efficiently.

Based on these insights we can introduce general hypothesis 3:

Knowledge transfer effectiveness

The aim of knowledge transfer at the recipient unit is to integrate thenew knowledge in the unit’s context and to make use of it. Effectiveutilization refers to the potential to turn knowledge into a competitiveadvantage-yielding capability (Grant 1996). Effectiveness is generally,

Hypothesis 3

Appropriately developed knowledge transfer capabilities (in terms ofchannels, infrastructure and processes) have a positive impact on theeffectiveness of knowledge transfer.

Exhibit 4.3 Overview of hypotheses 1a–1d

STRATEGIC MANDATE DEVELOPMENT OF TRANSFER CAPABILITIES

Transmission Channels Infrastructure Processes

Integrated Players Highest Highest All processes at a high level

Global Innovators Intermediate Intermediate Externalization, Socialization

Implementers Intermediate Intermediate Internalization, Combination

Local Innovators Lowest Lowest All process at a low level


seen as one dimension of performance, besides efficiency and adaptiveness(see also Katsikeas, Leonidou and Morgan 2000).

Buckley and Carter (1999) note that an important requirement foreffective knowledge transfer is for the source unit to recognize theknowledge requirements of the recipient unit in order to provide what isappropriate, in a form that is appropriate. As human behaviour, knowledgeand cognition is guided by the contextual rules and resources residentin social structures and conventions, knowledge has to fit these contextualrequirements of the recipient unit. The transfer of knowledge – especiallyof organizational procedures and management practices – from onecultural context to another is likely to fail

unless the governing philosophy – the system of underlying structu-rational conventions – fits the system of meaning of those expectedto implement these procedures from day to day, or unless the organ-izational routines are transformed so that they conform to existingcultural expectations. (Macharzina, Oesterle and Brodel 2001)

The effectiveness of knowledge transfer is thus likely to be influencedby two moderators identified in the literature (Asakawa 1995; Inkpenand Dinur 1998; Buckley and Carter 1999; Simonin 1999b) – organiza-tional distance and cultural distance.

Organizational distance

‘Organizational distance’ refers to differences between organizationalunits (headquarters–subsidiary, subsidiary–subsidiary) in terms ofstructures, processes and values. It attempts to capture issues such asdifferences in approaches towards decision making, for example (Simonin1999a, p. 473) defines organizational distance as follows: ‘[It] capturesthe degree of dissimilarity between the partners’ business practices,institutional heritage, and organizational culture.’ Asakawa (1995)suggests that institutional isomorphism has a strong impact on the waylocal units approach and structure knowledge. With regard to knowledgetransfer, it is assumed that organizational distance amplifies ambiguity;a large organizational distance may thus lead to a ‘lack of understandingof the logical linkages between marketing actions and outcomes, inputsand outputs, and causes and effects that characterize a broadly definedmarketing-based competency and its transferability’ (Simonin 1999a,p. 467). The following general hypothesis can thus be advanced.


Cultural distance

‘Cultural distance’ is the second moderator the literature takes intoaccount. Knowledge and cognition, as human behaviour, is guided bythe contextual rules and resources residing in social structures andconventions; transferred knowledge has thus to fit these contextualrequirements of the recipient unit. The transfer of knowledge from onecultural context to another is likely to fail if the underlying assumptionsare divergent from the system of meaning of those expected to imple-ment the knowledge received. Alternatively, organizational routineshave to be transformed so that they can conform to existing culturalexpectations (Macharzina, Oesterle and Brodel 2001). Or, as Doz andSantos (1997, p. 23) put it: ‘effective transfer of knowledge is a dialoguebetween the sender and the receiver about their own contexts andabout the object of knowledge.’ Cultural distance may also hamper theidentification of market opportunities and understanding of marketmechanisms (Simonin 1999b).

Apart from recognizing the role of these moderators, there is littleresearch that addresses the effectiveness of knowledge transfer. Amongthe noteworthy exceptions are empirical contributions by Becerra-Fernandez and Sabherwal (2001) and Gold, Malhotra and Segars (2001).Some researchers have already integrated the condition of effectivenessor success into their definition of knowledge transfer (Doz and Santos1997) – i.e. knowledge transfer as such is always effective transfer(Jensen and Meckling 1995). It is argued, on the other hand, that theeffectiveness of knowledge transfer processes depends on the perceived

Hypothesis 4

The lower the organizational distance between units the higher theeffectiveness of knowledge transfer.

Hypothesis 5

The lower the cultural distance between units the higher the effectivenessof knowledge transfer.


benefit (Foss and Pedersen 2002, p. 60) and the overall satisfaction withknowledge management (Becerra-Fernandez and Sabherwal 2001).

To sum up, a model of knowledge transfer in MNCs which aims toexplain the effectiveness of knowledge transfer between organizationalunits in different countries has been introduced. Five general hypothe-ses have been developed to describe the impact and the relations of theproposed constructs. As already mentioned, it is important to note thatthe effectiveness of knowledge transfer between MNC units is contingenton the appropriate development of specific organizational capabilities,developed in response to the unit’s strategic position in the networkand the value of its knowledge stock. The effectiveness of knowledgetransfer is also likely to be affected by organizational and culturaldistance.

Chapter 5 will focus on the design and the methodological aspects ofthe empirical study. Concrete operationalizations of constructs to testthe hypotheses developed will then be presented.

68

5Research Design and Methodology

Chapter 5 presents the method applied in the empirical study. First,some general issues about the research context and possible challengesof knowledge management research in MNCs are discussed. Based onthis discussion, it is demonstrated that some of these issues are addressedin the choice of the unit of analysis and the questionnaire design. Twostandardized questionnaires are generated, one for headquarters andone for subsidiaries, and pre-test interviews were helpful in refining theinstrument. The second section describes the steps in data collection,starting with the choice of the target sample and the sampling process.Informant selection is then described and the final sample presented.The operationalization of constructs is finally outlined and measuresused in the study discussed.

Research context

Challenges in knowledge management research in MNCs

Knowledge transfers between organizational units of MNCs provide theempirical setting for this research. To survey knowledge flows betweenMNC units, it is necessary to investigate multiple network nodes, andglobally dispersed units lend themselves especially well to the investigationof locally adapted as well as globally standardized knowledge.

The complex requirements of empirical multi-unit MNC research,however, also account for the scarcity of empirical work in the field ofknowledge transfer. When multiple nodes are included, power relationsbetween headquarters and subsidiaries, cultural distance and organizationalstructure play a critical role, which can hardly be analysed in isolation.Objective measures are also hard to find on the nature, quantity, or quality

Research Design and Methodology 69

of knowledge. Researchers largely have thus to rely on subjective measures.Scholars have long since pointed out that headquarters’ and subsidiaryperceptions might differ. Reviewing the literature on embeddedness ofunits (Granovetter 1985; Andersson and Forsgren 1996, 2002), there isreason to believe that this is also the case for knowledge transfer.Researchers address the problem of capturing perception gaps betweenheadquarters and subsidiaries by investigating corresponding relations ina multi-unit perspective. The lack of objective measures and the need tocontrol for perception gaps are the main reasons which suggest the valueof a perceptional approach. The literature (cf. Birkinshaw et al. 2000;Asakawa 2001a) suggests that we should distinguish dyad headquarters–subsidiary relationships from subsidiary–subsidiary relationships. Thisstudy focuses on dyadic perceptions and distinguishes between knowledgeflows that occur laterally among subsidiaries and hierarchical flowsbetween headquarters and subsidiaries.

Unit of analysis

The unit of analysis in this study is the organizational unit of a MNC – i.e.headquarters or a subsidiary – and its knowledge transfers. Relation-ships and knowledge transfers between several units will be analysed.The aim was that one sampled MNC should provide five respondents –one in headquarters and four subsidiary managers in different countries.

In limiting the study to one functional area in the MNC – i.e. marketing –it was sought to reduce complexity to a manageable level. Earlier researchconvincingly noted that different functions will differ in terms of knowledgeprocessed and management approach (Reger 1997; Harzing 1999; Ambos2002). The presence of a primary up-stream activity like research anddevelopment (R&D) and manufacturing or of a primary down-streamactivity like marketing and sales is expected to moderate effects in theanalysis of knowledge flows (Gupta and Govindarajan 2000). Focusingon the marketing function in headquarters and subsidiaries seems appro-priate in terms of the primary research aim – the investigation of knowledgetransfers – because coordination and integration of world-wide marketingknowledge is generally perceived as particularly important (Buckley andCarter 1999; Simonin 1999b). Marketing is also assigned a high level ofcompetence in subsidiaries (Foss and Pedersen 2002), and thus marketingknowledge is also likely to be created in a decentralized fashion.

Despite the problems associated with the key informant approach,such as measurement error and common method variance (see also Harzing1999, p. 183), this approach was chosen for the survey. Owing to thecomplexity of the research design and the problems of response in this


research field, it was unlikely that enough multiple responses would bereceived; however, as a several units are involved in the survey, theconsistency of answers throughout a company can be analysed.1

Questionnaire design and pre-test

Measures of all constructs of the model were developed, based on anin-depth review of the literature, and some validated scales were adoptedfrom major earlier studies. Subsequently, a preliminary questionnaire wascomposed: as headquarters’ and subsidiaries’ managers were addressed,two slightly different versions of the questionnaire were created. Thequestionnaire was carefully designed to be simple to complete. It wasstructured in ten subsections, each highlighting the question of therespective section. To enable thematic orientation, the research topicswere used as headlines, e.g. Management Systems. The questionnairepredominantly consists of 7-point Likert scales; respondents had to answertwo questions by filling in numbers from 1 to 7 and one open questionwhere they had to indicate the locations of subsidiaries they were incontact with. The last page included demographics questions about theunit’s address, its set up, size and position in the market.

To evaluate content validity of the measures selected, the questionnaireswere pre-tested by four potential respondents in headquarters andsubsidiaries. These interviews gave further insights into knowledgemanagement practices and helped to refine the research instrument. Inthe pre-test interviews, some questions arose about the meaning of differentknowledge management tools. Some managers had problems in answeringquestions, as they did not know some of the ‘highly sophisticated’ know-ledge management tools, such as ‘groupware’. In these cases, exampleswere added in order to foster a better understanding. Another problem wasthe instruction to refer to other subsidiaries. Managers found it difficult torefer to culturally close and distant subsidiaries, indicated as subsidiary Aand B, and to come back to these categories several times. An additionalissue was the length of the questionnaire. Pre-tests took about one hour,including additional questions, clarifications and discussions. It wasassumed that managers answering the questionnaire on their own wouldtake about 15–20 minutes. In response to the feedback received from theseinterviews, the questionnaires were modified to create a final version.

Data collection

Target sample

The European Top 5002 served as a sample frame for the empiricalinvestigation. Companies were selected on the basis of their turnover,


their degree of internationalization and their industrial affiliation. Theresearch plan involved data collection at two levels – headquarters andsubsidiaries. Earlier research indicated that dyadic relationships arenotoriously difficult to sample because of the need to include pairs inthe survey (Asakawa 2001a; Ambos 2002). Furthermore, to address networkeffects adequately among headquarters and subsidiaries as well as sub-sidiaries and subsidiaries, only MNCs with more than six overseas units(Vernon 1966) were targeted. Because of resource limitations, an initialtarget sample of sixty MNCs was set.

Given the European Top 500 as a sample frame, the sample size ofeach industry was calculated on the basis of its contribution to grossdomestic product (GDP) and the industries were weighted according totheir economic input to the European Union’s GDP in 2000. As industrystructures highly influence the organization of MNCs, and subsequentlytheir knowledge transfer practices, it was found important to control forindustry-specific results. According to Jacob and Ebrahimpur (2001, p. 15)‘organizational culture and the nature of the industry in question areimportant determinants of the value of tacit knowledge’. Industrieswere classified based on NAICS (North American Industry ClassificationSystem) codes, and industry strata were set. A more detailed differentiationof industries was not conducted, as the sample size was not sufficient toallow a meaningful comparison.

Given these methodological considerations, the initial sample framewas screened and firms qualifying – i.e. operating at least six overseassubsidiaries (Vernon 1966) – were contacted in descending orderaccording to their revenues. Whenever a company declined to cooperatein the survey, the next largest company in terms of turnover wasapproached. After this sampling procedure, sixty MNCs agreed to cooperate,resulting in a sample of 240 subsidiaries and sixty headquarters, i.e. 300units.

Informant selection

Informants were selected based on the following procedure and question-naires were subsequently mailed to them:

1 Telephone contacts with headquarters or subsidiaries located in German-speaking countries to secure support of research.

2 Headquarters providing contact persons in four subsidiaries in differentcountries or agreeing to distribute the questionnaire themselves tofour subsidiaries.

3 Mailing of questionnaire.


Informant contacts were first established by telephone. Starting withthe largest corporations, senior managers (usually in charge of marketing)were contacted and asked to cooperate. Research shows that pre-contactsusually increase response rates, response speed, and data quality (Harzing2000a). Headquarters were asked to coordinate the participation of theirunits because they are supposed to have more influence on the inter-national coordination necessary. They were asked to give contact personsin marketing positions in four subsidiaries abroad or distribute thequestionnaire directly to their colleagues.

After carefully weighing the advantages of other methods (cf. Harzing1997; Salzberger, Sinkovics and Schlegelmilch 1999), it was decided toconduct the survey by mail. To enhance response rates, the possibilityof sending back-up questionnaires by e-mail was considered as an optionbecause of the speed of transfer to remote subsidiaries. Several follow-upcalls and e-mails were needed in order to collect the questionnaires. Assome managers asked to receive the questionnaire by e-mail, someelectronic versions were mailed and returned by fax. By the end ofOctober 2002, data collection was finished.

Final sample

The final sample consisted of 162 MNCs units belonging to forty-fivecompanies. Thirty-eight headquarters and 124 subsidiaries participated.Despite their initial agreement, fifteen MNCs did not provide any dataat all; eighteen companies returned the required five questionnaires,providing data on the headquarters and on four subsidiaries. TwoMNCs even involved six units. In six cases, four questionnaires werecompleted, seven MNCs returned three, six gave information about twounits and in three MNCs it was possible to survey only one unit.

The sample represents leading MNCs of diverse industries, such asmanufacturing (56 per cent), finance and insurance (21 per cent) andother services (11 per cent), including consulting companies, for example.Exhibits 5.1 and 5.2 depict the industry partitions of the target sampleand the final sample. 70 per cent (114 units) of all surveyed companieswere among the industry’s top five performers, and 102 of those marketleaders had held their position for more than five years.

Surveyed companies operated in twenty-nine countries in differentgeographic regions (Exhibit 5.3). Austria, Belgium, France, Germany,Ireland, the Netherlands, Switzerland and the United Kingdom are cat-egorized as Central/Western Europe; Italy and Spain as Southern Europe;Norway and Sweden as Northern Europe; Bulgaria, Croatia, the CzechRepublic, Hungary, Poland, Romania, Russia, Slovakia and Slovenia as


Central and Eastern Europe (CEE); China, Japan and Singapore as SouthEast Asia. The majority of headquarters were located in Central/WesternEurope (Exhibit 5.4), followed by CEE countries and Northern Europe.

Out of the thirty-three headquarters located in Central/WesternEurope, 26 were situated in Austria (Exhibit 5.5), and the response rateof Austrian companies was significantly higher. As noted by Harzing

Exhibit 5.1 Industry weights of target and final sample

Industry weighted as contribution to EU GDP (2000)

Target sample (%)

Final sample (%)

Agriculture, Forestry, Fishing 2.52 5.6Utilities 3.21 0

Construction 8.03 0

Manufacturing 22.78 56.2

Trade (wholesale + retail) 14.06 1.9

Transportation and Warehousing + Information

8.15 4.3

Finance, Insurance 7.80 21.0

Real Estate, Rental and Leasing 14.92 0

Accommodation and Food Services 3.96 0

Other Services 14.57 11

SUM 100 100

Other Services11.1%

Finance/Insurance21.0%

Transportation4.3%

Trade1.9%

Manufacturing56.2%

Agriculture/Forestry5.6%

Exhibit 5.2 Industries in the final sample


(1999), Austrian managers felt a stronger affiliation to a study con-ducted by a local research institution, and contacts made in previousprojects could be used. Although the Austrian bias of the sample has tobe borne in mind, there are no indicators that country of origin effectsplayed an important role in the study. Out of the twenty-six Austrian

Asia3.1%

North America 3.1%

Australia0.6%

CEE30.2%

Northern Europe2.5%

Southern Europe4.9%

Central/Western Europe55.6%

Exhibit 5.3 Regions in the sample

CEE5.3%

Northern Europe2.6%

Southern Europe5.3%

Central / Western Europe86.8%

Exhibit 5.4 Location of headquarters


headquarters, ten were regional headquarters, mostly responsible forthe CEE region. In total, twenty-four global and fourteen regional head-quarters were represented in the sample.

Most subsidiaries are also found in Central/Western Europe andCEE countries (Exhibit 5.6). As headquarters were free to choose the

Belgium3.0%

United Kingdom3.0%

Switzerland6.1%

Germany9.1%

Austria78.8%

Exhibit 5.5 Location of headquarters in Central/Western Europe

Asia4.0%

North America 4.0%

Australia0.8%

CEE37.9%

Northern Europe2.4%

Southern Europe4.8%

Centr. / West. Europe 46.0%

Exhibit 5.6 Location of subsidiaries


subsidiaries participating in the study, they were influenced by geo-graphic proximity. Moreover, many Central/Western European head-quarters directly controlled CEE subsidiaries; fifty-seven subsidiariesresponded that they had been created as acquisition or merger, forty-five subsidiaries were set up as a greenfield operation. The remainingseventeen did not provide information about their set-up mode.

Operationalization and measures

Strategic mandate

In order to measure the strategic mandate of the unit, Gupta andGovindarajan (1994) have developed a useful instrument. The mandate,they argue, is actually defined by the intensity and the direction of knowledgeflows. In this measure, different areas of data (i.e. market data on customers,market data on competitors) and procedural knowledge (i.e. marketingknow-how, distribution know-how, technology know-how and pur-chasing know-how) essential to marketing departments are addressed.Exhibits 5.7 and 5.8 depict the knowledge flows which this study surveyed.

INFLOW

OUTFLOW

Subsidiary A Subsidiary BHeadquarters

Exhibit 5.7 Knowledge flows to and from headquarters

INFLOW

OUTFLOW

Focal Subsidiary Peer subsidiaryHeadquarters

Exhibit 5.8 Knowledge flows to and from the focal subsidiary


Following Gupta and Govindarajan (1991, 1994), the subsidiary mandateis operationalized as a product of knowledge outflows and knowledgeinflows. As shown in Exhibits 5.7 and 5.8, respondents were asked toindicate the extent of knowledge flow from/to their subsidiary/head-quarters on a Likert-type scale from 1 (not at all) to 7 (a very great deal).3

Knowledge flows were assessed on a six-item instrument. Consistentwith Gupta and Govindarajan (1991, 1994), the resulting four types aretermed ‘Global Innovators’, ‘Integrated Players’, ‘Implementers’ and‘Local Innovators’.


As mentioned above, a unit’s potentiality to transfer knowledge isdetermined by the value of its knowledge stock. This construct accountsfor the unit’s ability to send knowledge, as well as its ability to processincoming knowledge, also called absorptive capacity.

The theoretical assumptions of Gupta and Govindarajan (2000) provideguidance in the identification of a source unit’s value of knowledge stock.The authors suggest that a knowledge stock which is non-duplicativeand relevant for the rest of the global network is an indication of aunit’s high value of knowledge stock. Gupta and Govindarajan (2000)operationalize this construct in terms of mode of entry, subsidiary size andthe relative economic level of the host country. Despite their theoreticalguidance, it was decided to develop a new scale which explicitly aimsat surveying the unit’s perception of its own knowledge stock in relationto other units deemed to be useful. Again, a Likert-type scale was used(1 = much lower; 7 = much higher) and managers were asked:

Generally, compared to others (all your subsidiaries), your subsidiary’s(headquarters’) knowledge stock in the following area is . . .

For practical reasons, respondents should refer to the same types ofknowledge (know-how) as explored by the ‘strategic mandate measure’.


Three different scales, which have been used in major empirical studies,are proposed in order to capture the three dimensions of knowledgetransfer capabilities referred to above.

The existence and richness of transmission channels scale was adoptedfrom Gupta and Govindarajan (2000). Here, the use of liaison personnel,temporary task forces and permanent teams as lateral and hierarchicalcoordination instruments was explored on a scale from 1 (very infre-quently) to 7 (very frequently). For the use of informal coordination


channels, working experience in other units, the use of mentors and theparticipation in executive development programmes involving employeesfrom different subsidiaries were taken as proxies. For these questions,binominal answers (‘yes’ or ‘no’) were provided.

The scale for knowledge management infrastructure stems from Gold,Malhotra and Segars (2001). Respondents were asked to assess howdeveloped several aspects of their unit’s infrastructure are. While thisscale primarily captured infrastructure-related practices, the next scaleincluded different knowledge management tools in order to identifythe four knowledge conversion processes suggested by Nonaka andTakeuchi (1995). Becerra-Fernandez and Sabherwal (2001) developedempirical measures for evaluating the extent to which each of the fourknowledge process capabilities – externalization, internalization, com-bination and socialization – are used. As the scale includes differentknowledge management tools, the question is formulated as follows:

Please indicate how frequently each of the following knowledgemanagement processes and tools is used in your company: If one ofthese does not exist in your company, please choose ‘Not applicable.’

Possible answers range from 1 (very infrequently) to 7 (very frequently),‘not applicable’ is an additional option if the tools do not exist in thecompany.


Organizational distance has been measured in two ways. As a firstapproach, a means of self-perception of the differences between the ownunit and the headquarters/other subsidiaries were chosen. For the former,it was decided to adapt the scale used by Simonin (1999a) and to choosethe following questions:

Generally, business practices and operational mechanisms are verysimilar.

Generally, corporate culture and management style are very similar.

Again, respondents were asked to provide information about agreement(1 = strongly disagree; 7 = strongly agree) with this statement, relating tohierarchical and lateral relationships versus culturally close and distantunits. However, the measure proposed by Simonin (1999b) focuses onlyon selected aspects of organizational practices and the resulting distance


between units. It was thus decided to implement another, more sophis-ticated, organizational distance measure.

Several strategic tools were integrated into the questionnaire in orderto measure the company’s ‘strategic value disciplines’ based on Treacyand Wiersema (1995). For this purpose, a scale was developed based onthe detailed specifications in the literature. Variables explored the orientationof a unit’s organization. Answers from headquarters and subsidiaries werecompared and the distance calculated according to their divergent ratings.As different units of one company were surveyed, a comparison of eachunit’s perceptions, and thus a relative measure of organizational distance,became possible.

Cultural distance

To include cultural distance as a construct, several questions exploringwhether cultural distance was explicitly perceived to be a barrier toknowledge transfer were formulated, based on the discussion in theliterature:

It seems to me that national culture influences the way of doingbusiness heavily.

Many misunderstandings and cultural conflicts emerge from know-ledge transfer between units in different countries.

Generally, language differences are major obstacles in communicatingwith and understanding subsidiaries.

Questions integrated into measures of the strategic mandate, transfercapabilities and perceived benefit were aimed at exploring whetherthere was less transfer to culturally distant units within the company.Shenkar (2001) published an interesting review about measuring culturaldistance which is essential to this approach. Kogut and Singh’s (1988,p. 422) formulas were also used in order to measure country culturedifferences ‘objectively’. Based on Hofstede’s (1980) indices, a compositeindex was formed which captured the deviation along each of the fourcultural dimensions.


In line with the other parts of the model, a self-perception measure wasproposed, which aimed to capture the perceived benefits originating fromknowledge inflows. This approach can be seen as an ex post analysis ofknowledge transfer, assuming that successful transfer of knowledge will


lead to perceived benefits. A scale was developed in order to measurethe perceived benefits (1 = not at all; 7 = a very great deal) stemmingfrom the different areas of data/know-how mentioned above. At thesame time, respondents were asked where this knowledge inflow camefrom, in order to survey the quality of knowledge transfer from thedifferent organizational units.

On a broader scale, the individual satisfaction with processes relatedto knowledge transfer on different organizational levels was captured(Becerra-Fernandez and Sabherwal 2001). Three organizational levels ofknowledge management were addressed: the employee’s own context,the unit in question and the global organization. Possible answersranged from 1 (strongly disagree) to 7 (strongly agree).

81

6Analysis and Results

The analysis follows through the calculation of empirical results anddiscusses the implications of the findings. First, some descriptives aboutthe unit of analysis are presented. Second, each of the model’s theoreticalconstructs are discussed and descriptive statistics and bivariate testsgenerated in order to provide an overview about the units’ characteristicsand their interdependencies. Third, the constructs are integrated intothe model and the hypotheses are tested using structural equation mod-elling. As data can be combined to three different sets, the three modelsare analysed and differences outlined.

Descriptives of the unit of analysis

As outlined above (Chapter 5), the 162 surveyed units belong to forty-fiveMNCs that are mostly market leaders. They operate in twenty-ninecountries and in ten geographic regions. In the sample there is a pre-ponderance of firms located in Central and Western Europe – on theheadquarters as well as on the subsidiary level. MNCs operate in diverseindustries. However, the leading industry groups, manufacturing andfinance and insurance constitute 77 per cent of the sample. To learnmore about the characteristics of the headquarters and subsidiaries sur-veyed, the units’ size and their set-up are analysed, and an overview ofthe respondents’ position is given.

The unit’s size is measured by their number of employees. On average, theheadquarters and the subsidiaries in the sample employ 654 employees(Exhibit 6.1).

Separating headquarters and subsidiaries, it becomes evident thatheadquarters tend to be more personnel-intensive. The average number of


employees in headquarters is 1,019, whereas the average subsidiaryemploys 638 people.

As mentioned earlier, fourteen headquarters fulfil the task of a regionalheadquarters, and twenty-four are the global centre of operations. In thecase of subsidiaries, it is important to know how they have been foundedas the mode of set-up often accounts for the extent of integration in theglobal network and the importance of the unit as a source or a recipientof knowledge. Among the subsidiaries surveyed, 44 per cent have beenset up as a greenfield, 56 per cent as a merger or acquisition.

The identification of respondents is vital for the validity of the data.As mentioned above, vice-presidents of marketing and top marketingmanagers were targeted in this study. At the end of the questionnaire,respondents were asked to fill in their position. Grouping together similartitles, Exhibit 6.2 lists the positions participants indicated in the survey.The key informant approach was successfully accomplished and dataare assumed to represent the view of the companies’ top management.

Analysis of the model’s constructs

In this section, the different constructs just discussed are analysed inturn. First, descriptives are generated to get an overview. Second, if possible,

> 2,5008.5%

2,000–2,4990.7%

1,500–1,9991.4%

1,000–1,4994.2%

500–99912.7%

250–49921.8%

< 24950.7%

Exhibit 6.1 Employees at units

Analysis and Results 83

comparisons between different groups (i.e. headquarters and subsidiaries,culturally close and culturally distant units) are calculated. As some con-structs are measured in different ways, the various results are outlined. Insome cases, different operationalizations are used to perform robustnesschecks. The results and their impact on current research are then discussed.

Antecedents to knowledge transfer

The subsidiary’s strategic mandate and the unit’s value of knowledgestock have been theoretically identified as antecedents in the model ofintra-MNC knowledge transfer. The analysis of the strategic mandatewill be conducted here at the subsidiary level and the characteristics thatsupport knowledge transfer investigated separately on the headquartersand the subsidiary level.

Strategic mandate

To identify strategic mandates, the analysis was conducted at the subsidiarylevel; 124 subsidiary cases were included.

As a first step, the means of the different knowledge flows were calculated.On a scale from 1 (not at all) to 7 (a very great deal), the means of hierarch-ical flows (inflows from headquarters and outflows to headquarters)were 3.27 and 2.25, and for lateral flows (inflows from peer subsidiaries

Exhibit 6.2 Positions of respondents

POSITION NO. OF RESPONDENTS

CEO/General Manager 53

Board Member 8

Vice-President/Marketing 18

Country Manager 8

Head of Marketing Group/Department 13

Advertising & PR Management 5

Business Development Manager 2

Marketing Manager 35

Communication/Information Service/Knowledge Manager 10

Controller 2

Executive Assistant 1


and outflows to peer subsidiaries) means of 2.20 and 3.10 were generated.This result showed that inflows from headquarters had the highest value,slightly higher than inflows from subsidiaries and outflows to headquarters.Interestingly, outflows to subsidiaries ranked second.

Next, subsidiary mandates were calculated, following Gupta andGovindarajan (2000). Taking the overall knowledge inflow and outflowover all knowledge types, the four subsidiary mandates discussed previouslywere derived by using median splits along these two composite measures.Looking at the sum of inflows and outflows, means and medians layvery close (inflow mean=3.64; outflow mean=3.58; inflow median=3.58;outflow median = 3.54). Calculation results indicated that thirty-eight ofthe subsidiaries surveyed could be called ‘Global Innovators’, twenty-nine‘Local Innovators’, nine ‘Implementers’ and seven ‘Integrated Players’(Exhibit 6.3). Cases containing one or more missing values in one of theoriginal scales were excluded from the analysis. This resulted in forty-onemissing values after the sequence of calculations.

The predominance of ‘Integrated Players’ and ‘Local Innovators’ in thesample suggested that sixty-seven of the subsidiaries surveyed (81 per cent)were characterized by equal knowledge inflows and outflows. Onlya minority (19 per cent) were characterized by a divergent level of know-ledge flows. Integrated Players by definition contribute extensively tothe knowledge base of the firm. However, other than global innovatorsthey are not self-sufficient, but are dependent on knowledge residingelsewhere in the firm. Gupta and Govindarajan (1991) define LocalInnovators as units dealing with idiosyncratic knowledge resources.

KnowledgeInflows

KnowledgeOutflows

LocalInnovators29 (35%)

IntegratedPlayers38 (46%)

Implementers9 (11%)

GlobalInnovators

7 (8%)

Low

Low High

H

h

gi

Exhibit 6.3 Strategic mandates of subsidiaries


In order to check for the robustness of the construct, K-means clusteranalysis was used. A two-cluster solution was generated which con-firmed the median-split calculation. The question then arose whetherthe typology of strategic mandates represented groups of subsidiarieswhich differed considerably from each other. To investigate differencesbetween the four mandates, cross-tabulations were generated for the sizeof the company, referring to the number of employees and the subsidiaries’mode of set-up (Exhibit 6.4). No significant differences between theunits’ sizes were found. Most subsidiaries lay in the smallest segments.While 52 per cent of Integrated Players had fewer than 249 employees,18 per cent had 500–999 and 18 per cent 1,000–1,499. Local Innovatorswere similarly distributed, with 50 per cent below 249 employees,26 per cent 500–999 and 12 per cent 1,000–1,499.

As far as the mode of set-up is concerned, 39 per cent of IntegratedPlayers were established as acquisition or merger, 61 per cent as a green-field. Local Innovators, however, showed an inverse pattern – 67 per centhad been set up as an acquisition or merger, 33 per cent as a greenfield.However, there was evidence that the influence of the mode of set-upwas not persistent, but the integration of units originally set up as a mergeror acquisition changed over time (Hakanson and Nobel 2000).

At this stage, one might wonder if the prevalence of only two strategicmandates – one highly embedded and one rather independent – wasa unique characteristic of the sample. From a theoretical point of viewthere was no such evidence. Possibly, the fact that headquarters had to

Exhibit 6.4 Cross-tabulation: strategic mandate and number of employees

MANDATE * No. of employees Crosstabulation

Count

No. of employees

Total< 249 250 –499 500–999 1000–1499 > 2500

MANDATE Implementer 3 4 7

Global Innovator

7 7

Local Innovator

13 6 3 1 3 26

IntegratedPlayer

17 6 6 3 1 33

Total 40 16 9 4 4 73


nominate the units to participate in this survey influenced this result.Headquarters were asked to involve one subsidiary perceived as culturallyclose and one perceived as culturally distant in the study. A reason couldbe that managers perceived subsidiaries as culturally distant that werethe least integrated into the global communication and control processesand largely relied on their own knowledge (Local Innovators). However,a correlation analysis of cultural distance according to Kogut and Singh’s(1988) distance measure and organizational distance1 with the two man-dates reveals that the correlations were not significant. Another possibleexplanation could be that headquarters tended to involve subsidiaries thatthey were regularly in contact with, on the one hand (Integrated Players),and rather autonomous subsidiaries (Local Innovators), on the other.

To sum up the analysis of subsidiaries’ strategic mandates, the distinctionof four strategic mandates does not make much sense in this case. Onlytwo types, Integrated Players and Local Innovators, prevail. One reasoncould be that the typology suggested by Gupta and Govindarajan (2000),based on knowledge flows, is too simplistic, another that the randomchoice of subsidiaries participating in the survey led to biased results:

• A better classification approach might be the differentiation betweenglobal and local mandates. Birkinshaw and Morrison (1995), forexample, adopt the term ‘world mandate’ for subsidiaries playinga role as a strategic leader. As opposed to locally centred subsidiaries,a subsidiary assigned a world product mandate engages in special-ization and has decision making authority over a broad array ofinterconnected functions. Contrasting highly integrated to ratherautonomous subsidiaries might lead to more convincing results.

• To account for possible problems in the measure, the strategicmandates will not be included in the structural equation model.Instead, the original scales measuring lateral and hierarchical know-ledge flows will be used.

As mentioned earlier, headquarters are also likely to differ in their strategicposition in the global organizational network. Applying the same analysisas in the subsidiary cases but, of course, including only hierarchical flows,the following characteristics were found: the means ranked 3.7 for inflowsand 4.6 for outflows. Medians lay at 4.0 for inflows and 4.5 for outflows.

For headquarters, a more balanced distribution can be seen (Exhibit 6.5).Among headquarters that provided data, most were characterized byhigh knowledge outflows and high knowledge inflows, fulfilling a trad-itional central position in the organization. It is important to notice that


the smallest group engaged in high knowledge outflows and low inflows.This position can be explained by a type of headquarters that sends manydirections and advice to subsidiaries but does not get much knowledgeback. It is possible that headquarters implemented mere output controlmechanisms and were not engaged into more qualitative control.

When this compilation was split between global and regional head-quarters, it became evident that a larger percentage of global headquartersreceived high inflows whereas regional headquarters showed a similarpattern as subsidiaries’ knowledge flows. Exhibit 6.6 refers to the twenty-eight headquarters that provided sufficient information to calculate theirstrategic mandate.

KnowledgeInflows

KnowledgeOutflows

7(25%)

11(40%)

7(25%)

3(10%)

Low

Low High

H

h

gi

Exhibit 6.5 Strategic positions of headquarters

Exhibit 6.6 Strategic positions of global versus regional headquarters

TYPE

Global HQ Regional HQ Total

MANDATE IN high/OUT low 6 1 7

IN low/OUT high 2 1 3

IN low/OUT low 4 3 7

IN high/OUT high 7 4 11

Total 19 9 28



While the strategic mandate aims to map the unit strategically, thevalue of knowledge stock aims to explain the unit’s potentiality to engagein knowledge transfer. The unit’s value of knowledge stock was analysed,first on the subsidiary level and subsequently the headquarters level.The results were also calculated using the operationalization of Guptaand Govindarajan (2000).

As far as the subsidiaries’ knowledge stock compared to their peersubsidiaries was concerned, the majority of units perceived themselvesto have a higher value of knowledge stock than their peers. The value ofknowledge stock was measured on a scale from 1 (much lower) to 7(much higher). The midpoint is thus located at 3.5. All means higherthan 3.5 pointed towards a higher value of knowledge stock comparedto others. As shown in Exhibit 6.7, the most valuable area seems tobe ‘market data on customers’, followed by ‘marketing know-how’.‘Purchasing know-how’ has the lowest mean.

The knowledge stock of subsidiaries vis-à-vis their headquarters inthe different areas is displayed in Exhibit 6.8. As Exhibit 6.8 shows, sub-sidiaries generally perceived their own knowledge stock as more valuablethan headquarters’. All means were higher than 3.5 – i.e. the majorityof subsidiaries perceived their knowledge stock in the respective areasto be higher than the knowledge stock of headquarters. Most valuableappear to be ‘market data on customers’, followed by ‘market data oncompetitors’. ‘Technology know-how’ ranked last. But even here, themean indicated that most subsidiaries’ perceived their knowledge stockas higher than that of their headquarters.

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7Much lower

No

. of

com

pan

ies

Much higher

market data oncustomers

market data oncompetitors

marketing know-how

distribution know-how

technology know-how

purchasing know-how

Exhibit 6.7 Knowledge stock of subsidiaries compared with peer subsidiaries


When it came to headquarters, ‘technology know-how’, ‘purchasingknow-how’ and ‘marketing know-how’ were the areas where headquartersperceived their knowledge stock to be far more valuable than the know-ledge stock of subsidiaries (Exhibit 6.9). But, in parallel to subsidiaries,no area was found where headquarters perceived their knowledge stocklower compared to that of subsidiaries.

What is evident from these comparisons is that all units somehowoverestimate their own knowledge stock. It is remarkable that in thecomparison of headquarters with subsidiaries, the highest means werereached, followed by subsidiaries’ estimations vis-à-vis headquarters.The lowest means were reached in the comparison of peer subsidiaries.

05

101520253035404550

1 2 3 4 5 6 7

Much lower

No

. of

com

pan

ies



marketing know-how


technology know-how

purchasing know-how

Much higher

Exhibit 6.8 Knowledge stock of subsidiaries compared with their headquarters

0

2

4

6

8

10

12

14

16

1 2 3 4 5 6 7

Much higher

No

. of

com

pan

ies



marketing know-how


technology know-how

purchasing know-how

Much lower

Exhibit 6.9 Knowledge stock of headquarters compared with subsidiaries


In sum, centralized functions such as technology and purchasingseem to lie with headquarters. This confirms the presumption madeearlier that sampled headquarters mostly fulfil a traditional centralfunction. Local market analysis is mostly done by the subsidiaries. Thisresults in high values for ‘market data on customers’ and ‘market dataon competitors’. The results for marketing know-how are ambiguous.This can be explained by a division of strategic tasks in the headquartersand local adaptation in subsidiaries.

To compare the results of these newly developed measures with theoriginal operationalization of Gupta and Govindarajan (2000), the modeof entry, subsidiary size and the relative economic level of the hostcountry were measured. The underlying rationale is that units possessa valuable knowledge stock if their knowledge is non-duplicative andrelevant for other units. Thus, the authors hypothesized that the highestvalue of knowledge stock will reside in units which are product of anacquisition/merger, which are relatively large and which are situated ina host country that is economically more advanced than the homecountry. To calculate the value of a unit’s knowledge stock, a formulaassigning equal weight to the three variables is suggested:2

Relative size + Mode of set-up + Relative level of development =Value of knowledge stock

To leverage differences between MNCs of different sizes, the unit’ssize is taken relative to all surveyed units. A value of 1 is assigned tothe units’ set-up as acquisitions or mergers and 0 to greenfield operations.If the relative level of economic development of the host country ishigher than the home country’s, a value of 1 is entered. An equal levelof development equals 0.5 and a lower level 0. Thus, the maximum tobe reached for the value of knowledge stock is 3, the minimum 0. Forthe analysis, ninety-four subsidiary cases are valid because of missingvalues. The mean ranks at 0.85 and 75 per cent of subsidiaries havea level of knowledge stock lower then 1.50 according to this calculation.

Gupta and Govindarajan (2000) only tested knowledge flows onthe hierarchical level and did not take inter-subsidiary relationships intoaccount. Lateral relationships will thus also be excluded from this analysis.Nevertheless, it was found important to assess headquarters’ value ofknowledge stock, too. Obviously, the mode of set-up becomes irrelevantin the investigation of headquarters and the economic level of develop-ment cannot be assessed vis-à-vis all subsidiaries. For this purpose thesame formula was applied without the variable ‘Mode of set-up’. Instead


of the relative level of economic development the absolute developmentwas taken, assigning 1 to highly developed and 0 to emerging economies.To achieve comparable values, headquarters’ results were standardizedon the 0–3 level. The results indicated that headquarters’ value ofknowledge stock reached higher overall values with a mean of 1.68.25 per cent of headquarters ranked lower than 1.52 and 50 per centwere distributed between 1.52 and 1.83.

To compare the results, the two measures of the units’ value ofknowledge stock were standardized using z-scores. A correlation analysisrevealed a Pearson correlation coefficient of 0.067 that is not significant.It can thus be concluded that the two measures do not generate the sameoutput. Overall, the perceptional measure reached a higher mean (1.83)compared to the (1.05) of Gupta and Govindarajan’s (2000) measure.

The divergence of the two measures is most likely due to the differentmeasurement approaches. First, variables are not comparable and aim atdifferent aspects – i.e. the perceived knowledge stock versus a derivedmeasure to investigate non-duplicative characteristics and relevance ofknowledge. Second, the new scale builds on respondents’ perceptions,whereas the Gupta and Govindarajan’s (2000) measure builds on fac-tual data. The obvious disadvantage of the perceptional measure is thetendency to overestimate the unit’s own knowledge stock and the exist-ence of perception gaps. As mentioned above, all units assess their ownvalue of knowledge stock higher than others. Nevertheless, Gupta andGovindarajan’s (2000) measure seems very problematic; although, theirexplanations about the influence of relative size, mode of set-up andrelative level of economic development appear admissible, the conceptsseems to be too general.

Given that other measures in the survey relating to the extent ofknowledge transfers are also perceptional, the perceptional measureof knowledge stock is favoured for this study.


Three different scales, which have been used to capture the threedimensions of knowledge transfer capabilities – transmission channels,knowledge transfer infrastructure and knowledge transfer processes –are now the subject of analysis.

Transmission channels

The formal knowledge transmission channels, operationalized as coord-ination instruments between units – liaision personnel, temporary taskforces and permanent teams – are examined for all surveyed units,


headquarters and subsidiaries. Judging from the means, liaision personnelwere used most frequently for coordination between headquarters andsubsidiaries. Independent sample t-tests revealed that there were nosignificant differences between headquarters and subsidiaries as far ashierarchical formal transmission channels were concerned (Exhibit 6.10).3

Between peer subsidiaries, the same mean ranking for formal coord-ination instruments was observed: liaision personnel, followed by tempor-ary task forces and permanent teams. The means for lateral transmissionchannels reached slightly lower values (Exhibit 6.11). When it came toinformal channels, 29 per cent of subsidiary managers had workingexperience in headquarters and 22 per cent in another subsidiary

05

10

15202530

354045

1 2 3 4 5 6 7Frequency

No

. of

com

pan

ies liaision

personnel – hierarchical

permanentteams – hierarchical

temporary taskforces – hierarchical

Exhibit 6.10 Hierarchical formal transmission channels

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7

Frequency

No

. of

com

pan

ies liaision

personnel – lateral

permanentteams – lateral

temporary taskforces – lateral

Exhibit 6.11 Lateral formal transmission channels

93

Exhi

bit

6.12

Info

rmal

tra

nsm

issi

on c

han

nel

s

Wor

king

Exp

erie

nce

at H

eadq

uart

ers

Wor

king

Exp

erie

nce

at A

noth

er S

ubsi

diar

yM

ento

r in

H

eadq

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ers

Men

tor

in a

S

ubsi

diar

yP

artic

ipat

ed in

E

xecu

tive

Dev

elop

men

t P

rogr

amm

es w

ith

Sev

eral

Sub

sidi

arie

s

Per

cent

age

of

Sub

sidi

ary

Man

ager

s

29

22

45

18

57

Wor

king

Exp

erie

nce

at C

ultu

rally

Clo

se

Sub

sidi

arie

s

Wor

king

Exp

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at C

ultu

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Dis

tant

S

ubsi

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ies

Men

tor

in C

ultu

rally

C

lose

Sub

sidi

ary

Men

tor i

n C

ultu

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D

ista

nt S

ubsi

diar

yP

artic

ipat

ed in

E

xecu

tive

Dev

elop

men

t P

rogr

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es w

ith

Sev

eral

Sub

sidi

arie

s

Per

cent

age

ofH

eadq

uart

ers

Man

ager

s

30

11

41

24

74


(Exhibit 6.12). More mentors were situated in headquarters. 30 per centof headquarters managers were sent to culturally close subsidiaries to getwork experience and only 11 per cent to culturally distant subsidiaries.Their mentors were rather found in culturally close subsidiaries too. Insum, slightly more headquarters (65 per cent) than subsidiary managers(63 per cent) had a mentor. The participation in executive developmentprogrammes where participants from several subsidiaries are involvedwas higher on the side of headquarters managers (74 per cent) than onthe side of the subsidiary managers (57 per cent).

Knowledge management infrastructure

When all surveyed units were analysed, the results in Exhibit 6.13 weregenerated for knowledge management infrastructure. To give more detailson their means and medians, descriptives are added.

Knowledge management technology that allowed people to collabo-rate inside the unit ranked among the most frequently used infrastruc-ture in companies, followed by technology that allowed collaborationwith people outside the unit. Another important component of knowledgetransfer infrastructure was instruments to retrieve and use knowledge

Categorize product knowledge

Categorize process knowledge

Monitor practices

Unit-internal collaboration

Collaboration across units

Single learning

Multiple learning

Search for new knowledge

Map location of knowledge

Product /process knowledge

Market/compet. knowledge

Generate new ideas

Frequency

7.06.05.04.03.02.01.0

Exhibit 6.13 Knowledge management infrastructure


about products and processes, and about markets and competition.This point is closely linked to technology to search for new knowledge,which constitutes another important facet of the company’s infra-structure. It can be concluded that collaboration tools (inside the unitand outside) are most important. Communication in the company seemsto be the first priority. Closely behind communication tools rankedvarious technologies to retrieve, use and search for knowledge. Techno-logies to monitor competition and business partners fell in the samecategory.

The lowest values were assigned to infrastructure that allowed learningas a group from multiple sources or at multiple points in time, andtechnology to map the location of specific types of knowledge. Thesefindings pointed towards a low development of sophisticated learningand training infrastructure and the lack of directories or navigationinstruments within the knowledge management systems.

When the responses of headquarters and subsidiary managers wereanalysed in isolation, no major differences between the groups could befound in the results of independent sample t-tests.4

Knowledge transfer processes

The measure of knowledge transfer capabilities included the usagefrequency of different tools (Exhibit 6.14). 162 managers were asked toindicate on a scale from 1 (very infrequently) to 7 (very frequently),how often they used the different instruments.

The result of this analysis is shown below. First, the use of the differenttools is described and discussed, and the four knowledge conversionprocesses suggested by Nonaka and Takeuchi (1995) then are explored.

The most frequently utilized knowledge management tools wereface-to-face meetings, learning-by-doing and on-the-job training – allknowledge management tools closely tied to personal contacts. Thesewere followed by case-based problem solving technology, databases,and intra-/internet. A separate analysis of headquarters and subsidiarycases using independent sample t-tests does reveal some differences.5

For the tools capture and transfer of experts’ knowledge, decision supportsystems, employee rotation and projects across subsidiaries significant dif-ferences were found. In these cases headquarters’ means ranked higherthan subsidiaries’. As the mean differences were very small (max. 0.92 foremployee rotation), no important implications are expected.

To give an example, Exhibit 6.15 presents the use of face-to-facemeetings, which was the most prominent tool used by managers; forty-seven managers indicated that they used this tool very frequently.


Nobody indicated that face-to-face meetings were used very infrequentlyin his/her company.

Another pattern is shown by chat groups/web-based discussiongroups. Thirty managers indicated they use this tool very infrequently;only two managers used this tool very frequently. This phenomenoncould be due to the fact that this tool requires a substantial amount ofinfrastructure investment and employee’s IT skills.

Although these results seem to be unspectacular, it is important torecognize that a rather balanced use of knowledge management toolscan be observed. Despite the prevalence of ‘conservative’ tools, such asdatabases or face-to-face meetings, which existed long before the trendtowards a knowledge economy developed, nearly every company hasestablished sophisticated tools such as web-based groupware or decisionsupport systems. But the infrequent use of these tools demonstrates thatmost technology-intensive instruments are not yet accepted by employees.

The four knowledge conversion processes proposed by Nonaka andTakeuchi (1995) are the next subject of analysis. Based on the categor-izations developed by Becerra-Fernandez and Sabherwal (2001), the

Capture and transferDesision support sys.

Learning by observ.Chat groups

Employee rotationSubsidiaries project

Analogies/metaphorsTeam collaboration

DatabasesWeb-based data

‘Yellow pages’Apprentices/mentorsBrainstorming camps

Best practicesIntra- and internetLearning-by-doingOn-the-job training

Problem solving tech.Face-to-face meeting

Frequency

765432

Exhibit 6.14 Use of knowledge management tools


different items were aggregated to result in ‘externalization’, ‘combin-ation’, ‘socialization’, and ‘internalization’, as in Exhibit 6.15.

As seen in Exhibit 6.16, internalization was the knowledge transfer pro-cess mostly used by the 162 units surveyed. Second ranked combination,followed by socialization and externalization.

Again, independent sample t-tests6 were used to calculate differencesbetween headquarters and subsidiaries. Results showed, that only social-ization differed significantly between the groups, where the headquarters’mean ranked slightly higher.

Exhibit 6.15 Composition of knowledge transfer processes

Capture and transfer of experts’ knowledge

Decision support systems

Problem solving technology EXTERNALIZATION

Analogies and metaphors

Pointers to expertise

Chat groups/web-based discussion groups

Team collaboration tools

Best practices and lessons learned

Databases COMBINATION

Web-based access to data

Intra- and internet pages

Apprentices and mentors

Brainstorming camps SOCIALIZATION

Employee rotation

Subsidiaries projects

Learning-by-doing

On-the-job training INTERNALIZATION

Learning by observation

Face-to-face meetings


Earlier, it was hypothesized that units engaging in high outflow ofknowledge would emphasize externalization and socialization processes,and units generating high inflows would make more use of internal-ization and combination. From a descriptive point of view, it can beconcluded that units engage more in knowledge absorbing activities, wherethey have to internalize knowledge by transforming it from explicit intotacit knowledge and to combine separate explicit to become systemicexplicit knowledge. This finding does not seem to be in line with thestrategic mandate of the units surveyed discussed earlier. There, themajority of units were found to exhibit rather low knowledge inflows.What can be concluded at this stage is that units seem to use the processesinternalization and combination more often. An explanation could bethat the processes of a recipient unit are more demanding, or more time-intensive, so that units have to apply them more to integrate incomingknowledge. Alternatively, units might find it easier to cope with thoseprocesses. To shed some light on these issues, the concrete hypotheseswill be tested later in the structural equation model.

What has to be noted at this stage, however, is the problematicnature of the construct ‘strategic mandate’ in relation to knowledgetransfer capabilities. One-way analysis of variance (ANOVA)7 was used

Externalization

Mea

n6.0

5.5

5.0

4.5

4.0

3.5

3.0InternalizationSocializationCombination

Exhibit 6.16 Knowledge transfer processes


to test for differences between the four subsidiary groups in the develop-ment of knowledge transfer capabilities, and no significant results werefound in any of the categories. Thus, as already stated, only knowledgeinflows and outflows of units’ representing the units integration in theglobal network will be subject of the analysis in the structural equationmodel. The specific mandates of the units analysed are apparently notvaluable for this particular sample.

Context factors


Organizational distance was measured in two ways. As a first approach,the perceived similarity of business practices and operational mechanismsand of corporate culture and management style were explored. Again,this was done on the headquarters’ as well as on the subsidiary’s level.Subsidiary managers had to rate the two types of similarity vis-à-visheadquarters and vis-à-vis peer subsidiaries, resulting in the resultsshown in Exhibit 6.17.

Business practices and operational mechanisms between the focalsubsidiary and peer subsidiaries reached the highest value, meaning thatthey are perceived to be very similar on the subsidiary level. However,

Style subsStyle HQPractices subsPractices HQ

Mea

n

4.9

4.8

4.7

4.6

4.5

4.4

4.3

Exhibit 6.17 Subsidiaries’ perceptions of similarity vis-à-vis headquarters andpeer subsidiaries


corporate culture and management style seemed to be more similarbetween headquarters and the focal subsidiary. Generally, it should benoted that these mean ratings were all relatively close to each other,within a range of 4.3 to 4.9 on a scale from 1 to 7.

Headquarters’ managers had to assess the similarity of business practicesand operational and corporate culture and management style comparedto culturally close and culturally distant subsidiaries (Exhibit 6.18). Thepattern in Exhibit 6.18 clearly shows a much broader range of results(from 4.0 to 5.5) and a higher similarity between headquarters and cultur-ally close subsidiaries with regard to both categories – business practicesand operational mechanisms and corporate culture and management style.

Summing up, subsidiaries indicated that they perceived higher simi-larity to their peers with regard to business practices and operationalmechanisms, and higher similarity to headquarters with regard to corpor-ate culture and management style. Headquarters found culturally closesubsidiaries more similar (in terms of organizational distance) thanculturally distant subsidiaries, in respect of both business practices andoperational mechanisms and corporate culture and management style.

Organizational distance was also calculated as a relative measurebetween headquarters and subsidiaries. For this purpose the strategic

Style distant subsStyle close subs

Practices distant subsPractices close subs

Mea

n

6.0

5.5

5.0

4.5

4.0

3.5

Exhibit 6.18 Headquarters’ perceptions of similarity vis-à-vis culturally closeand distant subsidiaries


orientation scales of headquarters and every subsidiary were comparedand a distance measure, following the guidelines of Kogut and Singh’s(1988) cultural distance index, was generated. As a result, a mean of1.9 was found among 118 cases. The highest value reached was 9.78,but 75 per cent of all cases lay below 2.22.

To compare the measures, a Pearson correlation analysis was performed.To standardize data, the perceptional measure of organizational distancewas generated as mean of both variables. The perceptional measureand the relative organizational distance index were entered as z-scores.However, the relative organizational distance measure did not correlatesignificantly with the aggregated perceptional measure. A reason forthis divergence could be that the relative measure is, of course, based ona multitude of function-centred variables and does not build on generalimpression.

Cultural distance

The question of cultural distance has been addressed in various ways(Exhibit 6.19). First, explicitly aiming at cultural problems occurringin knowledge management, three issues were surveyed on a scale from1 to 7:

Language differencesMisunderstandingsNational culture

Mea

n

5.5

5.0

4.5

4.0

3.5

3.0

Exhibit 6.19 Impact of culture on knowledge transfers


• The influence of national culture on the way of doing business • Misunderstandings and cultural conflicts emerging from knowledge

transfers between different countries • Language differences as obstacles in communication.

The results were quite straightforward. According to the managerssurveyed, national culture heavily influenced the way of doing business,but they did not believe that misunderstandings and cultural conflictsimpeded the knowledge transfer between units in different countries.Language differences also did not seem to be major obstacles.

These results might be due to respondents’ backgrounds. They all heldtop management positions and were, at least to some extent, involvedin international operations. As the analysis of informal communicationchannels has shown, many respondents had gained working experienceabroad and were sensitized to cultural differences.

Second, Kogut and Singh (1988) indices were used to calculate thecultural distance. Only hierarchical relationships between headquartersand subsidiaries were analysed. The highest distance reached was 4.96,the mean scores 1.68. Kogut and Singh’s (1988) cultural distance indexcorrelates significantly with the aggregated measure of the impact ofculture on knowledge transfers. Astonishingly, the correlation was nega-tive. Units which were more culturally distant, according to the Kogutand Singh’s definition, perceived less impact of cultural differences ontheir knowledge transfers.

Although the operationalization of cultural distance adopted fromKogut and Singh’s (1988) Hofstede-based formula is commonly used inthe business literature, this approach has received much criticism duringrecent years. Shenkar (2001) identifies deficient hidden conceptual andmethodological assumptions in the cultural distance construct. Some referdirectly to Hofstede’s concept of culture (cf. Au 2000), such as spatialand corporate homogeneity, or consider that the concept of long-termorientation (Hofstede and Bond 1988), which is especially relevant forresearch in Asian countries, has never been incorporated into theformula. It is has also been questioned, whether the different indicesshould be equally weighted or that the use of the concept of distance isinaccurate, as the definition implies symmetry and linearity (cf. Blackand Mendenhall 1991). Alternatives to the widely used formula mightbe a more comprehensive view of national culture, including cognitivemeasures. To avoid such problems, suggested measures aimed explicitlyat respondents’ subjective perceptions of cultural distance are used inthe following analysis.


Third, the differences in knowledge management between culturallyclose and culturally distant subsidiaries are outlined in the discussionof the constructs ‘transmission channels’, ‘organizational distance’ and‘benefit of knowledge transfers’. As can be seen from the detailed analyses,the well-known phenomenon that interaction with culturally close unitsseems to be easier generally holds true.


Knowledge transfer effectiveness was measured by two scales – thebenefit from knowledge transfers and the satisfaction with knowledgemanagement.

Benefit from knowledge transfers

On the subsidiary level, benefits from knowledge transfers from threesources were assessed (Exhibit 6.20):

• Headquarters • Culturally close subsidiaries • Culturally distant subsidiaries.

As in other constructs, six different areas of data and know-how weresurveyed.

Technology and marketing know-how represented the most beneficialknowledge flows from headquarters (Exhibit 6.21). Distribution, purchas-ing and marketing data on competitors were found at a quite low level,the mean ranking being around 3.5–4 (on a scale from 1 to 7). Marketdata on customers was regarded as the least valuable knowledge inflows.

05

10

15202530

354045

1 2 3 4 5 6 7

Benefit

No

. of

com

pan

ies



marketing know-how


technology know-how

purchasing know-how

Exhibit 6.20 Subsidiaries’ benefits of knowledge transfers from headquarters


Market data on customers was the only area of knowledge transfersfrom culturally close subsidiaries whose mean ranked higher than thosefrom headquarters (Exhibit 6.22). Marketing and technology know-hownotably ranked significantly lower from this perspective.

When knowledge transfers from culturally distant subsidiaries werecompared to the previous exhibits, the pattern looked quite differentand means did not reach the same level. The most beneficial transferwas perceived to be marketing know-how but the mean scored only at2.5. The ranking was similar to the benefits from headquarters, but ona lower level.

0

5

10

15

20

25

1 2 3 4 5 6 7

Benefit

No

. of

com

pan

ies



marketing know-how


technology know-how

purchasing know-how

Exhibit 6.21 Subsidiaries’ benefits of knowledge transfers from culturally closesubsidiaries

05

101520253035404550

1 2 3 4 5 6 7

Benefit

No

. of

com

pan

ies



marketing know-how


technology know-how

purchasing know-how

Exhibit 6.22 Subsidiaries’ benefits of knowledge transfers from culturallydistant subsidiaries


At the headquarters level, information about the benefits from know-ledge transfers from culturally close subsidiaries and culturally distantsubsidiaries were provided. The results shown in Exhibit 6.23 and 6.24were found.

Interestingly, market data on customers and competitors were amongthe most beneficial categories. This pattern was very clear when inflowsfrom culturally distant subsidiaries were considered. From culturallyclose subsidiaries, marketing know-how ranks second.

To sum up, these findings point towards the fact that transfers fromdifferent units were perceived differently in terms of their benefits tolocal operations. On a general level, most beneficial to subsidiaries seemed

02468

101214161820

1 2 3 4 5 6 7Benefit

No

. of

com

pan

ies



marketing know-how


technology know-how

purchasing know-how

Exhibit 6.23 Headquarters’ benefits of knowledge transfers from culturallyclose subsidiaries

0

2

4

6

8

10

12

1 2 3 4 5 6 7

Benefit

No

. of c

om

pan

ies



marketing know-how


technology know-how

purchasing know-how

Exhibit 6.24 Headquarters’ benefits of knowledge transfers from culturally distantsubsidiaries


to be transfers from headquarters. Only transfers of market data oncustomers from culturally close subsidiaries outpaced benefits fromheadquarters. Transfers from culturally distant subsidiaries were leastbeneficial, but showed the same pattern as benefits from headquarters.

Astonishingly headquarters did not benefit most from higher-orderknowledge such as know-how, but market data about competitors.One reason might be that the cost-benefit relation was perceived tobe higher when no complicated processes of decoding and adaptationwere involved. Another reason could be the aggressive competitivebehaviour of the companies in the sample, which consisted largely ofindustry leaders. It is also feasible that subsidiaries were not givenenough freedom to develop valuable knowledge – or, at least, that per-ceived competence varied across subsidiaries. This could explain whymarketing know-how from culturally close subsidiaries was quite import-ant to headquarters. What does not seem to add much value, neitherto headquarters nor to subsidiaries, was purchasing know-how.

Satisfaction with knowledge management

The other scale sought to measure knowledge transfer effectivenesswas the satisfaction with knowledge management (Exhibit 6.25). Theoverall satisfaction with knowledge management was indicated bythe availability of knowledge, the knowledge sharing and the improve-ment of effectiveness through available knowledge on a personallevel, a unit level and a company-wide level. Two additional variablesexplored the clarity of the knowledge transferred and the speed ofknowledge transfer. Exhibit 6.25 shows the means found for theseitems, by including all respondents, headquarters and subsidiarymanagers.

Results showed that the improvement of effectiveness achieved thehighest values in all categories – the personal, the unit and the wholeorganization. The general pattern showed that satisfaction with knowledgemanagement was higher in the personal areas and declined when theunit level and the organizational level was considered. Overall, all meansranked above 3.5, which indicated a positive rating in all categories. Itcan thus be concluded that managers tended to be satisfied with know-ledge management in their companies. Most satisfaction was found inthe personal area. This could be explained by the higher autonomy inthis field and the possibility to adjust knowledge management topersonal needs. Least satisfaction was perceived in organization-wideknowledge sharing and in overall satisfaction with knowledge manage-ment in the global organization.


A separate analysis for headquarters and subsidiaries lead to verysimilar results. Using t-tests, no major differences could be detected.8

Hierarchical relationships and culturally close subsidiaries

The following section, which constitutes the major part of the analysis,is dedicated to structural equation modelling.

A short introduction to structural equation modelling is given inorder to explain the aims of this method, and why it was chosen. Thissection elaborates on the theoretical composition of such models ingeneral and on the construction of the ‘Model of Intra-MNC KnowledgeTransfer’ in particular. Having chosen the optimal measurement model,the structural equation analysis is performed. Hypotheses tests are pre-sented and the results discussed.

Different sets of data which either refer to hierarchical, (betweenheadquarters and subsidiaries), or lateral (between peer subsidiaries)relationships will be included. To contrast them with each other, furtheranalyses are run and the models’ results are compared.

Available (my tasks)

Effective (my tasks)

Satisfied (person)

Available (unit)

Sharing in unit

Effective (unit)

Satisfied (unit)

Available (org)

Sharing (org)

Effective (org)

Satisfied (org)

Clarity

Speed

Agreement

7.06.05.04.03.02.01.00.0

Exhibit 6.25 Satisfaction with knowledge management

Source: Diamantopoulos and Siguaw (2000, p. 15).


Structural equation modelling

‘Structural equation modelling (SEM) is a statistical methodology thattakes a confirmatory (i.e. hypothesis-testing) approach to the analysis ofa structural theory bearing on some phenomenon’ (Byrne 2001, p. 3).SEM thus falls into the category of multivariate methods, and typicallyconveys causal processes. The processes under study are represented bya series of structural (i.e. regression) equations. Covariance matrices depictthe associations of observed variables, which lead to the explanation ofrelations of a smaller number of underlying constructs. Causal analysisenables the modelling and the estimation of complex structures of depend-ence simultaneously, which is especially useful in the behaviouralsciences where researchers are often interested in studying theoreticalconstructs that cannot be observed directly (Byrne 2001, p. 4). Thisrequirement is often recognized in the knowledge management literature,where constructs tend to be complex and, by definition, not directlyobservable. Many authors in the field have used SEM (cf. Simonin 1999b;Becerra-Fernandez and Sabherwal 2001; Gold, Malhotra and Segars 2001).

A major characteristic of causal analysis is the differentiation ofobservable (manifest) and latent variables. The latter are more complexconstructs which cannot be observed directly. Latent variables arecommonly called factors and observed (or manifest) variables are namedindicators. Moreover, it is helpful to distinguish between exogenouslatent variables and endogenous latent variables. ‘Exogenous latentvariables are synonymous with independent variables; they “cause”fluctuations in the values of other latent variables in the model . . .Endogenous latent variables are synonymous with dependent variablesand, as such, are influenced by the exogenous variables in the model,either directly or indirectly’ (Byrne 2001, p. 5). Exhibit 6.26 providesguidance on how to construct a structural model.

The structure of hypotheses includes endogenous and exogenousvariables. The unit’s strategic mandate, its ability to transfer knowledge,and cultural and organizational distance are exogenous variables, whereasknowledge transfer capabilities and the effectiveness of knowledge transferare endogenous. The development of the structural model follows thediscussion of hypotheses and the underlying arguments (see Chapter 4).

A model that specifies direction of cause from one direction only, asin this case, is termed recursive. One that allows for reciprocal feedbackeffects is called nonrecursive (Diamantopoulos and Siguaw 2000, p. 18).Exhibit 6.27 indicates the endogenous and exogenous factors in therecursive structural model. Exogenous factors are represented by shadedellipses, endogenous factors by white ellipses.

109

START

Past theories,evidence, experience,exploratory research

Identification ofconstructs (latent

variables for inclusionin the model)

Designation of latentvariables as exogenous

or endogenous

Can eachvariable be clearly

designated asexogenous orendogenous?

Ordering of endogenousvariables

Isorderclear?

Specification of expectedrelationships for eachendogenous variable

(including zerorelationships)

Final model for testing

Considerdevelopment of

alternative models

Irrelevantconstructsincluded?

Irrelevantrelationships

included?

Relevantrelationships

omitted?

Relevantconstructsomitted?

Yes Yes

Yes

Yes Yes

Yes

No

No

No

NoNo

No

No

No

Exhibit 6.26 Creation of a structural equation model



Owing to the high theoretical requirements of the conceptualizationof knowledge flows the model can become quite complex. A second levelof latent variables is included, as some comstructs consist of multiplefacets. This is shown in Exhibit 6.28. In contrast to the analysis of themodel’s constructs only metric scales can be used for SEM. To allow fora more differentiated measurement, the original scales of knowledge inflowsand outflows are used instead of the calculated strategic mandate. Moreover,the perceptional variables are used for organizational and cultural distance.As a result, all scales entered in the model are 7-point Likert scales.

When it comes to construct-to-indicator relationships, recent researchdistinguishes between formative and reflective indicators (Diamantopoulosand Siguaw 2000, p. 14). A construct with reflective indicators meansthat the indicators are expressed as a function of the construct: in thiscase, the latent variable is thought to ‘cause’ observed indicators. A forma-tive construct, however, is expressed as a function of those indicators:indicators are thought to ‘cause’ the latent variable. A characteristic ofconstructs with formative indicators is that the indicators of the sameconstruct ‘can have positive, negative, or no correlation’ with one another(Bollen and Lennox 1991, p. 307). Validation of constructs with formativeindicators rests mainly on the thoroughness with which the constructdomain is tapped (i.e. content validity) (Johnson et al. 1996).

Strategic Mandateexogenous

Value ofKnowledge Stockexogenous

Cultural Distanceexogenous

Organizational Distanceexogenous

Knowledge TransferCapabilitiesendogenous

Knowledge TransferEffectivenessendogenous

Exhibit 6.27 Composition of latent variables


In Exhibit 6.28, reflective variables are labelled ‘R’. To describe the procedure of structural equation modelling, studies

generally distinguish between the structural model and the measurementmodel (cf. Homburg and Pflesser 1999b; Byrne 2001). The structuralmodel depicts the hypothesized relations between the latent variables –i.e. the constructs. The measurement model, however, relates the indi-cator variables to the factor variables.

Exhibit 6.29 shows a decision tree for the construction of the meas-urement model. More detail on the adoption of existent scales and thedevelopment of new scales for the hypothesized model is provided onp. 76.

It is assumed that each indicator represents an erroneous meas-urement of one, or several, latent variables (Homburg and Pflesser1999b, p. 641). As it is highly unlikely that a perfect fit will be achieved,the model-fitting process can be summarized as follows (Byrne2001, p. 7):

Data = Model + Residual





KnowledgeTransferEffectivenessendogenous

KnowledgeOutflow

KnowledgeInflow


KnowledgeStock

CulturalDistance


PerceivedBenefit

T1: FormalChannels

T2:Infrastructure R

T3: ProcessCapabilities R

Satisfaction R

Exhibit 6.28 Formative and reflective variables


The residual represents the discrepancy between the observed data inthe sample and the hypothesized model.

In order to be of scientific use, a model has to be overidentified – i.e.the number of data points has to exceed the number of parameters tobe estimated (Byrne 2001, p. 35). Of course, due the large number ofindicators and the complex structure of relationships, an estimation of theoriginal model including all indicators would lead to an underidentified

Past methodologies,scales, exploratory

research

Identify potentiallyrelevant manifest variables

to act as indicators forlatent variables

Are measuresavailable for the

latent variables inthe model?

Consider development ofnew measure(s)

Selection ofmanifest variables

for inclusion inmeasurement

model

No

No

No

Yes

Yes Yes

START

Aremultiple measures

available for allconstructs?

Is use of asingle

measurerealistic?


Exhibit 6.29 Selection of variables


model. It was therefore decided to split the model into three partialmodels and reduce the number of included indicators. Exhibits 6.30 – 6.32depict the specifications of these structural models.9

As an extensive amount of variables were gathered, including inter-dependencies of various subsidiaries and headquarters at hierarchicaland lateral levels, choices on which variables are most suitable for SEMhad to be made. It was thus decided to include either hierarchical or lateralrelationships and to limit the analysis to the relations to culturally closeor culturally distant subsidiaries. As shown in Exhibit 6.33, the first set ofdata to be analysed was composed of headquarters relating to culturallyclose subsidiaries, and subsidiaries relating to their headquarters.



Outflow

Inflow

KnowledgeStock

T1: FormalChannels

T2:Infrastructure R



Exhibit 6.30 Model 1

Knowledge TransferCapabilitiesexogenous

Knowledge TransferEffectivenessendogenousPerceived

Benefit

T1: FormalChannels

T2:Infrastructure R


Satisfaction R



Assessment of the model

After the specification of the model (see also Appendix 1, p. 149) and theparameter estimation, the model has to be assessed (see Appendix 2,p. 153). If this step leads to negative assessment, a modification of themodel is necessary. In case of positive assessment, a detailed interpretationof results must follow.

Exhibit 6.34 shows that models 1–3 generally reached good measuresof fit and could be assessed as reliable and valid at this stage.

All NFI and CFI values between 0.918 and 0.961 were excellent asthey were close to 1.00. Chi-square/df scores were above 2.5 in all cases



Knowledge TransferEffectivenessendogenous

CulturalDistance


Satisfaction R

PerceivedBenefit


38Headquarters

124 CulturallyDistantSubsidiaries

124Subsidiaries

1st DATASET 2nd DATASET 3rd DATASET

124 CulturallyCloseSubsidiaries

38Headquarters

Exhibit 6.33 1st Dataset


and reached even 3.749 in model 2, which could be seen as a very goodreproduction of reality. RMSEA, however, was slightly too high in allcases, between 0.11 and 0.13. Ideal values would be smaller than 0.05 orlie between 0.08 and 0.10. The explanation could be that aggregated andreduced factor variables did not allow a close approximation of reality.

To test the significance of hypotheses, one-tailed t-tests were used.The AMOS software module provides these results as critical ratios (c.r.).For models with more than 200 degrees of freedom, a minimum valueof 2.345 is requested to meet α= 0.01 significance, 1.658 for α= 0.05 and1,286 for α= 0.1. For more than 70 degrees of freedom, cut-off values of2.381 (α= 0.01) 1.667 (α= 0.05) and 1.294 (α= 0.1) are recommended(Backhaus et al. 1996, p. 573). Based on these recommendations, models1–3 were tested. The results showed that most hypotheses were significantat the 0.05 level.

An overview of all hypotheses presented in the three models is nowprovided and their theoretical direction – i.e. positive or negative – indi-cated. The direct effects drawn from the structural equation models’output symbolize the causal relationships. Unstandardized results aredependent on the unit of measurement of the observed variable and arethus difficult to interpret. Therefore, standardized values are typicallyexamined (Byrne 2001, p. 89). As outlined above, some hypotheses didnot reach significant levels, and some effects shown by the models werecontradictory to the hypothese (Exhibit 6.35).

The results are then discussed in detail and possible implicationsoutlined.

Discussion of Results

Hypothesis 1

The development of knowledge process capabilities depends on the strategicmandate of the organizational unit.

Exhibit 6.34 Global measures of fit of models 1–3

Model Chi2 df p-value RMSEA Chi2/df NFI CFI

Model 1 805.04 269 0.000 0.111 2.993 0.918 0.943

Model 2 768.66 205 0.000 0.131 3.749 0.919 0.939

Model 3 220.76 75 0.000 0.110 2.944 0.961 0.974


It was hypothesized that the strategic position in the organizationalnetwork would act as an antecedent to the development of knowledgetransfer capabilities. As already discussed (p. 85), two strategic subsidiarymandates prevailed in the sample, Integrated Players and Local Innovators.At this stage, the impact of the units’ strategic mandate, as determinedby existent knowledge flows, was tested in the structural equation model.This was also done for headquarters. To achieve comparable results forheadquarters and subsidiaries, only hierarchical knowledge flows wereincluded.

Hypothesis 1a: Organizational units characterized by high know-ledge outflow and high knowledge inflow (for subsidiaries: IntegratedPlayers) are expected to develop the highest10 formal and informaltransmission channels with peer subsidiaries/headquarters and thehighest level of knowledge management infrastructure. Integrated playersare expected to develop all knowledge processes at an equal and highlevel.

Hypothesis 1b: Organizational units characterized by highknowledge outflow and low knowledge inflow (for subsidiaries:Global Innovators) are expected to develop intermediate11 formaland informal transmission channels with peer subsidiaries/headquartersand an intermediate level of knowledge management infrastructure.Global Innovators are expected to emphasize externalization andsocialization.

Hypothesis 1c: Organizational units characterized by low know-ledge outflow and high knowledge inflow (for subsidiaries: Imple-menters) are expected to develop intermediate12 formal and informaltransmission channels with peer subsidiaries/headquarters and anintermediate level of knowledge management infrastructure. Imple-menters are expected to emphasize internalization and coordination.

Hypothesis 1d: Organizational units characterized by lowknowledge outflow and low knowledge inflow (for subsidiaries:Local Innovators) are expected to develop the lowest formal andinformal transmission channels with peer subsidiaries/headquartersand a low13 level of knowledge management infrastructure. LocalInnovators are expected to develop all knowledge processes at anequal and low level.

117

Exhibit 6.35 Hypotheses and direct effects

Hypotheses Direct Effect

Hypothesisaccepted

Model 1OUT---> T1 High levels of knowledge outflow positively affect the development of formal transmission channels

0.976*** √

OUT ---> T2 High levels of knowledge outflow positively affect the development of knowledge management infrastructure

0.922*** √

OUT ---> T3 High levels of knowledge inflow positively affect the development of knowledge transfer processes

0.918*** √

IN ---> T1 High levels of knowledge inflow positively affect the development of formal transmission channels

not sig. X

IN ---> T2 High levels of knowledge inflow positively affect the development of knowledge management infrastructure

–0.176*** X

IN ---> T3 High levels of knowledge inflow positively affect the development of knowledge transfer processes

–0.221*** X

ABIL1---> T1 A unit’s high value of knowledge stock positively affects the development of formal transmission channels

not sig. X

ABIL1 ---> T2 A unit’s high value of knowledge stock positively affects the development of knowledge management infrastructure

–0.345*** X

ABIL1 ---> T3 A unit’s high value of knowledge stock positively affects the development of knowledge transfer processes

–0.330*** X

Model 2T1 ---> EFF1 A high development of formal transmission channelspositively affects the perceived benefit of knowledge transfers

0.511** √

T1 ---> EFF2 A high development of formal transmission channelspositively affects the satisfaction with knowledge management

–0.626** X

118

Notes:*** significant at the 0.01 level (one-tailed). ** significant at the 0.05 level (one-tailed). * significant at the 0.10 level (one-tailed). Hypothesis accepted √.Hypothesis rejected X.

Exhibit 6.35 (continued)

Hypotheses Direct Effect

Hypothesisaccepted

T2 ---> EFF1 A high development of knowledge management infrastructure positively affects the perceived benefit of knowledge transfers

0.147** √

T2 ---> EFF2 A high development of knowledge management infrastructure positively affects the satisfactionwith knowledge management

0.452*** √

T3 ---> EFF1 A high development of knowledge management processes positively affects the perceived benefitof knowledge transfers

0.847*** √

T3 ---> EFF2 A high development of knowledge management processes positively affects the satisfaction with know-ledge management

0.636*** √

Model 3ORG ---> EFF1 High organizational similarity between the units – i.e. low organizational distance – positively affects the perceived benefit of knowledge transfers

0.967*** √

ORG ---> EFF2 High organizational similarity between the units – i.e. low organizational distance – positively affects the satisfaction with knowledge management

0.583*** √

CULT---> EFF1 High cultural similarity between the units – i.e. low cultural distance – positively affects the perceived benefit of knowledge transfers

not sig. X

CULT ---> EFF2 High cultural similarity between the units – i.e. low cultural distance – positively affects the satisfactionwith knowledge management

not sig. X


Looking at results, units characterized by high levels of outflowdeveloped all knowledge transfer capabilities at a high level. The strongestimpact was found on knowledge management infrastructure (0.922), butprocesses were also causally related to knowledge outflows by a regressioncoefficient of 0.918. The lowest, but still significantly positive, impactwas seen on the development of formal transmission channels.

Another picture was shown by units exhibiting high levels of know-ledge inflows. While the impact on the development of formal transmissionchannels was not significant, the other two hypotheses reached signifi-cant levels but showed a negative instead of the hypothesized positiverelationship. However, the negative empirical relationships did not reachhigher regression scores than 0.221 and thus had only a low impact.Consequently, all hypotheses concerning the impact of high knowledgeinflows on the development of knowledge transfer capabilities had tobe rejected.

The conclusion drawn from these findings was that units exhibitinghigh knowledge inflows did not develop their knowledge transfer cap-abilities at a high level. With regard to units that engaged heavily inknowledge outflows, the hypotheses could be confirmed. Thus, units withhigh knowledge outflow and high knowledge inflow did not necessarilydevelop the highest level of knowledge transfer capabilities. It could alsobe that units characterized by high knowledge outflows and low inflowsreached equal levels of knowledge transfer capabilities.

However, the measure of knowledge inflow might be inflated byperception differences and overestimation, resulting in inflow levelsthat were proportionally larger than the degree of transfer capabilitiesdevelopment. In this context, common method variance could playa role, as both categories were surveyed in one questionnaire. Add-itionally, those units characterized by high levels of knowledge inflowmight not be cognizant of the channels, infrastructure and processesthey used.

To test whether differences between four groups (high inflow – highoutflow; high inflow – low outflow; low inflow – high outflow; lowinflow – low outflow), created through median splits in inflows andoutflows, were significant, one-way ANOVA was used. Only variableswhere significant differences on the 0.1 level were found are reported inExhibit 6.36.

As shown above, only six variables exhibited significant differencesbetween two of the four groups, and the mean difference was negligiblylow. It can thus be stated that no important differences between the groupsexisted with regard to the characteristics of knowledge transfer capabilities.


Summing up, there is empirical evidence that units characterized byhigh levels of knowledge outflow tended to highly develop all threeknowledge transfer processes. Units heavily engaging in outflows didput efforts into the development of knowledge transfer capabilities tomake sure that the knowledge they sent reached the receiver. On theother side, units did not seem to mind how they got the knowledge in –i.e. units characterized by high inflows did not develop high levels ofknowledge transfer capabilities. It could be assumed that the development

Exhibit 6.36 Differences between groups

VARIABLE SIGNIFICANT DIFFERENCES BETWEEN GROUPS

Sig.

Infrastructure

Categorize Product Knowledge Low Inflow – Low Outflow AND

Low Inflow – High Outflow

0.098

Categorize Product Knowledge Low Inflow – Low Outflow AND

High Inflow – High Outflow

0.032

Multiple Learning from Single Source and Time

Low Inflow – Low Outflow AND

Low Inflow – High Outflow

0.098

Multiple Learning from Single Source and Time



0.003

Map Location of Specific Type of Knowledge



0.000

Retrieve and Use Knowledge About Products and Processes



0.018

Process

Analogies and Metaphors Low Inflow – Low Outflow AND


0.025

Brainstorming Camps Low Inflow – Low Outflow AND


0.087


of transfer capabilities was a task of those units which were in the sendingposition; this phenomenon could be interpreted as ‘ignorant’ behaviourof the recipient. However, when four groups were created, differenceswere significant for only six variables of knowledge management infra-structure and knowledge processes.

Additional antecedents to the development of knowledge transfer cap-abilities were hypothesized to be units’ characteristics that supportedthe knowledge transfer, namely the value of knowledge.

A high value of knowledge stock was seen as a unit’s ability to sendknowledge, hypothesized to enhance the development of knowledgetransfer capabilities. But results showed that all three hypotheses concern-ing the development of knowledge transfer capabilities had to be rejected.The impact on the development of formal transmission channels wasinsignificant. Although the two other hypotheses reached significance,the empirical direction, which was negative, did not correspond to thepredictions.

The explanation of these facts could lie in motivational aspects. Althoughthe findings of earlier studies (Szulanski 1995; Foss and Pedersen 2002)concluded that motivational issues were not relevant and could beignored, model results could be explained by integrating such points.Possibly, a unit which possessed a high value of knowledge stock thatwas non-duplicative and relevant to others did not want to give thatasset away. Consequently, it did not engage in the development oftransfer capabilities. As it is not possible to include such measures intothe study ex post, a strong recommendation for further research is toaccount for such motivational factors.

Another reason could be the lack of coordination in the MNC. Unitsdid not share knowledge because they simply did not know that othershad valuable knowledge which could be applicable in their own context.As shown in the analysis of the model’s constructs (p. 88) units tendedto evaluate their own knowledge higher than others’. This could lead toa self-reliant attitude. If there seemed to be no valuable knowledge ‘outthere’ it was not worth developing any transmission channels.

Hypothesis 2

A high value of knowledge stock positively affects the development ofknowledge transfer capabilities.


The impact of the three knowledge transfer capabilities on two com-ponents of knowledge transfer effectiveness was tested. As describedearlier, knowledge transfer effectiveness was represented by the perceivedbenefit of knowledge transfers and the satisfaction with knowledgemanagement. In this model, only the effectiveness of hierarchical trans-fers was explored.

All hypotheses concerning the perceived benefit of knowledge transferscould be confirmed. The impact of the development of formal transmis-sion channels, knowledge management infrastructure and knowledgeprocesses was positive and highly, significant reaching values of 0.511,0.147 and 0.847. Thus, the development of knowledge managementinfrastructure had only a small effect, while formal transmission channelsand processes reached impact levels which were among the highestthroughout the partial models.

The development of formal transmission channels had a clearly positiveimpact on the perceived benefit, but satisfaction with these tools wassignificantly negative (−0.626). At first glance, it seemed paradoxicalthat an item had a positive effect on the perceived benefit but anegative impact on satisfaction, but a simple explanation is at hand.Formal transmission channels are often implemented in a centralized andstrict manner and fulfil coordination and control functions. As controlis often seen as coercive, satisfaction is rather low. However, formaltransmission channels seem to be effective as they clearly augment theperceived benefit. Thus, they might be enforced, but they work efficiently.

It has to be speculated why the development of infrastructure has onlylow effects on the perceived benefit of knowledge transfers. One reasoncould be that this category largely consists of technical facilities that linkdirectorates across the firm. Often they are complex to understand andit is difficult for employees to get acquainted with such technologies.Maybe, employees take the cost of learning how to use these tools intoaccount in their assessment. A further explanation could be that thebeneficial effects of knowledge management infrastructure are not yetrecognized. Theoretically, it could also be that the existence of infra-structure in the company is seen as a given prerequisite and the impact on

Hypothesis 3

Appropriately developed knowledge transfer capabilities (in terms ofchannels, infrastructure and processes) have a positive impact on theeffectiveness of knowledge transfer.


the effectiveness of knowledge transfers is not assigned adequate value.But this is seen as rather unlikely because, according to frequencies anddescriptives, the technical state of development is still low in the majorityof companies.

The impact of knowledge management infrastructure on satisfactionwas slightly higher than on the perceived benefit. This could be explainedby the fact that infrastructure also comprises learning and training facil-ities which are often seen as employees’ fringe benefits. Moreover, asthe analysis of constructs revealed, managers are least satisfied withknowledge sharing on the organization-wide level. Knowledge manage-ment infrastructure aims to enhance processes at this level.

As outlined earlier, knowledge management processes refer to thepractices of knowledge-sending and a knowledge-receiving unit. The highimpact of knowledge management processes on perceived benefit couldbe attributed to the fact that they are tied to people and sometimescontain highly personalized elements. Other than mere infrastructure,which exists in a company twenty-four hours a day, these processeshave to be applied in the right case at the right time. Moreover, asknowledge management processes always involve the sender and or thereceiver, or both simultaneously, they are perceived to be crucial for theactual encoding and decoding processes. Failures at this stage could leadto the loss of knowledge and inhibit knowledge transfer. If appliedadequately, knowledge management processes improve availability andaccessibility, and make knowledge easy to decode. However, if theseprocesses do not work adequately, recapturing of knowledge mightbecome a time-intensive and strenuous task.

The effect on satisfaction linked to the development of knowledge pro-cesses was also significantly positive (0.636). Again, as employees engagepersonally in these processes cooperative behaviour and the exercise ofprocesses are strongly linked to the perceived outcome and associatedwith opportunity cost. As knowledge sharing is perceived as a time-consuming task, people could as well dedicate their time to other duties –which often result in more direct and better visible outcomes. It mightalso be because of the more direct personal involvement that satisfactionwith knowledge management is augmented.

Hypotheses 4 and 5

The lower the organizational and cultural distance between units thehigher the effectiveness of knowledge transfer.


The effectiveness of knowledge transfer was hypothesized to be mod-erated by the organizational and cultural distance between the sendingand the receiving unit. The impact of organizational similarity (the counter-part of organizational distance) on the perceived benefit of knowledgetransfers was significantly positive, scoring 0.967. Satisfaction wasequally positively influenced, ranking 0.583. This points towardsthe fact that similarity in organizational structure and style enhancesthe connectivity and knowledge can thus be easily transferred. Sucheffects have often been suggested in the literature and many studieshave come to similar conclusions. This notion can be confirmed by thissample.

For cultural distance, on the contrary, all results were insignificant.This may be attributed to the problematical nature of the construct inthe measurement model. Such a tendency could already be assumed,looking at the descriptives of that construct. Although the impact ofcultural differences has long been highlighted in the knowledge manage-ment literature (Barkema and Vermeulen 1997; Tenkasi 2000; Bhagatet al. 2002), managers surveyed did not see cultural distance as animpediment to effective knowledge transfer. The issues most often citedin the literature – language problems and misunderstandings based oncultural artifacts – did not seem to be an issue – at least at the topmanagement level. However, results could be different if employees char-acterized by lower international exposure or a lower level of educationwere surveyed.

From the above, it could also be concluded that in MNCs corporateculture becomes more important than country culture. Many authors(Adler 1983; Nohria and Ghoshal 1994) have pointed towards the factthat in large companies represented by many diverse national cultures,corporate culture has a considerable cohesive power which may alsoact as coordination and control instrument. It could be possible that,especially at the top management level, the power of corporate cultureoverrides the conflicting effects of different origins and socializations.

Hierarchical relationships and culturally distant subsidiaries

As mentioned earlier, the models analyzed in the previous section includeheadquarters’ and subsidiaries’ data. Headquarters’ managers answeredquestions referring to two different subsidiaries, one being culturallyclose, one culturally distant. Subsidiary managers referred to hierarchical(i.e. headquarters) and lateral (i.e. another subsidiary) relationships. In themodels discussed so far, the data on headquarters referring to culturally


close subsidiaries and subsidiaries referring to their headquarters wasmerged. Cross-validation was done by comparing the results of a secondand a third set of data, as shown in Exhibit 6.37. While the second datasetis a slightly modified version of the first, including headquarters whichrelate to culturally distant subsidiaries and subsidiaries to headquarters,the third dataset includes subsidiary data only. In this case, subsidiarymanagers refered to their lateral relations with other subsidiaries. Thus,the third dataset has a smaller sample size, including only 124 cases.The analysis of the two additional sets of data is now performed. As theprocedure has already been described in the previous section, the explan-ations are limited to the major steps.

As the same model was used with slightly modified data, results arevery similar to the first analysis. When it comes to the testing ofhypotheses, more differences can be observed. In contrast to the firstdataset, all hypotheses derived from knowledge inflow are insignificant.The hypothesis ‘A high development of formal transmission channels affectsthe satisfaction with knowledge management positively’ (T1 ------> EFF2),which is also rejected in the first analysis, has now turned insignificant(Exhibit 6.38).

Discussion of results

It has to be emphasized that the 124 cases of the first analysis (i.e. sub-sidiaries that relate to their headquarters) are also included in the second

38Headquarters


124Subsidiaries



38Headquarters

Exhibit 6.37 2nd Dataset

126

Exhibit 6.38 Hypotheses and direct effects (2nd Dataset)

HYPOTHESES DIRECT EFFECT

HYPOTHESIS ACCEPTED


0.992** √


0.964*** √

OUT ---> T3 High levels of knowledge outflow positively affect the development of knowledge transfer processes

0.964*** √


not sig. X


not sig. X

IN ---> T3 High levels of knowledge inflow positively affectthe development of knowledge transfer processes

not sig. X

ABIL1---> T1 A unit’s high value of knowledge stock positivelyaffects the development of formal transmission channels

not sig. X


–0.251*** X

ABIL1 ---> T3 A unit’s high value of knowledge stock positively affects the development of knowledge transfer proc-esses

–0.236*** X


0.729** √

127



not sig. X


0.164** √


0.400*** √

T3 ---> EFF1 A high development of knowledge management processes positively affects the perceived benefit of knowledge transfers

0.664*** √

T3 ---> EFF2 A high development of knowledge management processes positively affects the satisfaction with knowledge management positively.

0.816*** √

Model 3ORG ---> EFF1 High organizational similarity between the units –i.e. low organizational distance – positively affects the perceived benefit of knowledge transfers

0.999*** √

ORG ---> EFF2 High organizational similarity between the units –i.e. low organizational distance – positively affects the satisfaction with knowledge management

0.397** √


not sig. X


not sig. X


dataset. Essentially, only forty-eight cases vary compared to the initialanalysis. Some interesting divergences were nevertheless found whichaccount for the difference between headquarters’ relations to culturallyclose and culturally distant subsidiaries.

As far as the hypotheses tested in model 1 are concerned, results corres-ponded to those of the first dataset. The impact of knowledge outflows onthe development of all knowledge transfer capabilities was significantlypositive. In all these cases, the effects scored slightly higher than in thefirst dataset, but generated only a negligible difference. The hypothesesregarding the effects of knowledge inflows were all rejected, though allrespective results have turned insignificant.

The unit’s ability to transfer knowledge – i.e. its knowledge stock –showed negative effects on the development of knowledge manage-ment infrastructure and processes – instead of positive – and nostatistically significant impact on the development of formal trans-mission channels was found. Again these results reflected the abovefindings, but the negative empirical direction was higher for thesecond dataset.

Model 2 essentially showed the same pattern as before. The impact offormal transmission channels on the perceived benefit of knowledgetransfers reached a coefficient of 0.729, which was higher than in theprevious analysis. This suggests that in a sample where headquartersand relations to their culturally distant (instead of culturally close),units are included the development of formal transmission channelshas an even higher effect on the perceived benefit. In this case, how-ever, the relation to the satisfaction with knowledge managementturned statistically insignificant. While the effect of a sophisticatedknowledge management infrastructure on the perceived benefits stayedcomparatively low, the impact of knowledge management processesreached slightly lower levels for this data. The development of formaltransmission channels thus has a higher impact on the perceived benefitthan the development of knowledge management processes in thissample. From these results, it can be concluded that, when relations toculturally distant units are surveyed, formal transmission channelswork more effectively, as they account for tighter control and can becommunicated easily throughout the organization. Nevertheless, theimpact of knowledge management processes was still considerable,reaching a coefficient of 0.664.

The impact on satisfaction achieved by the development of knowledgemanagement processes was the highest, generating a coefficient of 0.816.But the development of infrastructure also led to more satisfaction with


knowledge management in the company. In this case, the value of0.400 was about the same as in the first analysis.

As expected, model 3 led to very similar results. The impact of organi-zational similarity on the perceived benefit of knowledge transfers wasslightly higher, whereas the impact on satisfaction ranked slightlylower. The hypotheses that cultural distance reduces the effectiveness ofknowledge transfer again turned insignificant.

Lateral relationships of subsidiaries

As a next step, the analysis was performed with a dataset that differedmore from the first one. Only subsidiaries were included in the modelsand were tested with regard to their lateral relationships with their peersubsidiaries. As a consequence, the sample size was reduced to 124 casesas shown in Exhibit 6.39.

Overall, a good model fit was achieved by all partial models. The patterngenerated by the measures of fit, again, corresponded to the previousanalyses. In this respect, the subsidiary dataset including lateral relation-ships seemed to fit as well as the ‘hierarchical’ datasets.

Next, the hypotheses were tested. Owing the lower sample size of124, different cut-off values were used to test for significance. For a 0.01significance level, a minimum value of 2.358 had to be reached, for 0.05a value of 1.658, and for 0.10 a value of 1.289 was required (Backhauset al. 1996, p. 573). In contrast to the first and second datasets, only two

38Headquarters


124Subsidiaries



38Headquarters

Exhibit 6.39 3rd Dataset


hypotheses turned insignificant, ‘High value of knowledge stock affects thedevelopment of formal transmission channels positively’ (ABIL1 ------------->T1)and ‘Cultural distance between units affects the perceived benefit of knowledgetransfers negatively’ (CULT -------------> EFF1). As both did not reachedsignificance in the above analysis, these problematical cases werealready expected. Important findings were that the same hypothesesthat were rejected earlier showed the same directions as in the‘hierarchical samples’. More inflow from other subsidiaries does notseem to support the development of knowledge transfer channels;a higher level of knowledge stock does not enhance development ofthese channels either. From this perspective, the same tendencies canbe found in all three datasets. However, it is important to note that asignificant negative impact of cultural distance on knowledge transfereffectiveness was found. This hypothesis never reached significant levelsbefore. To illustrate these findings, Exhibit 6.40 shows which hypotheseshave to be rejected.

Discussion of results

Obviously, the third dataset contained some new information. Instead offocusing on hierarchical relationships, lateral knowledge flows betweensubsidiaries were now in the centre of study. Accordingly, some notabledifferences were found compared to the two prior analyses.

Generally, model 1 produced the same results. The hypotheses con-cerning high knowledge outflow were all confirmed. Coefficients weresignificantly positive and ranked slightly lower, but still reached levels of0.807–0.848. All other hypotheses had to be rejected. The only differencewith regard to the first dataset was that high knowledge inflows hada significant but negative effect on the development of formal transmissionchannels. (Results were insignificant earlier.)

An important issue, however, arose in model 2. The effect of formaltransmission channels on the perceived benefit of knowledge flows, whichwas significantly positive in the previous analysis and reached coefficientsof 0.729 and 0.511 respectively, now showed a negative empirical rela-tionship. Therefore, the hypothesis that ‘A high development of formaltransmission channels affects the perceived benefit of knowledge transferspositively’ had to be rejected. Even more amazing, the relation of thedevelopment of knowledge management infrastructure and the satisfaction,which had always been rejected before, was significant, generating a coef-ficient of 0.366. This results in the notion that the development offormal transmission channels does not affect the perceived benefit ofknowledge transfers positively, but has a positive impact on the satisfaction

131

Exhibit 6.40 Hypotheses and direct effects (3rd Dataset)


HYPOTHESISACCEPTED


0.807*** √


0.848*** √

OUT ---> T3 High levels of knowledge outflow positively affect the development of knowledge transfer processes

0.827*** √


–0.590*** X


–0.518*** X

IN ---> T3 High levels of knowledge inflow positively affect the development of knowledge transfer processes

–0.542*** X

ABIL1---> T1 A unit’s high value of knowledge stock positively affects the development of formal transmission channels

not sig. X


–0.114* X

ABIL1 ---> T3 A unit’s high value of knowledge stock positively affects the development of knowledge transfer processes

–0.151* X


–0.886** X


0.336* √

132




HYPOTHESISACCEPTED


0.264** √


0.436*** √

T3 ---> EFF1 A high development of knowledge management processes positively affects the perceived benefitof knowledge transfers

0.381** √

T3 ---> EFF2 A high development of knowledge management processes positively affects the satisfaction with knowledge management

0.822** √

Model 3ORG ---> EFF1 High organizational similarity between the units –i.e. low organizational distance – positively affects the perceived benefit of knowledge transfers

0.980*** √

ORG ---> EFF2 High organizational similarity between the units –i.e. low organizational distance – positively affects the satisfaction with knowledge management

0.391* √


not sig. X


–0.921** √


with knowledge management. Such a finding supports the explanationof the previous cases that headquarters tend to enforce the establish-ment of formal channels and use them as control instrument – a fact thatdoes not exist in the lateral view. In spite of this, formal transmissionchannels give subsidiaries the opportunity to build lateral ties that do notnecessarily have to use the headquarters as a hub. This results in highercollaboration and the exchange of knowledge. Such autonomy-grantingfactors could improve the overall satisfaction with knowledge manage-ment in the company.

When it came to the effect of knowledge management infrastructure,the impact on the perceived benefit was slightly higher than previously,but still low (0.264). Still, the results for the satisfaction were compar-able. So was the effect of the development of knowledge managementprocesses on satisfaction. The impact on the perceived benefit, how-ever, was considerably lower – 0.381 instead of 0.847 and 0.664. Itcould be concluded that the effective exercise of knowledge manage-ment processes required close cooperation. Assuming that subsidiariesinteract less on the lateral level than on the hierarchical level, thedeclining order of coefficients from culturally close and culturallydistant to lateral flows could be explained. Knowledge managementprocesses thus have the potential to lead to an increase in perceived benefit,but only when they are executed in a relatively close and controlledenvironment.

Model 3 generated approximately the same values for the impactof organizational similarity on knowledge transfer effectiveness as theprevious analyses – 0.980 for perceived benefit and 0.391 for satisfaction.Astonishingly, the negative impact of cultural distance was confirmedby this dataset. While the effect on the perceived benefit stayed insig-nificant, a highly negative impact on the satisfaction with knowledgemanagement could be confirmed (−0.921). It could be that in hierarchicalrelationships, the roles are clearly defined and less cultural problemsemerge because corporate culture and the experience of operations provideclear guidelines. When it comes to lateral relationships, it may not beclear to other subsidiaries which position in the network is assigned totheir counterpart and cultural issues might play a more important role –or, at least, are thought to account for misunderstandings and failuresin knowledge transfer.

To sum up, Exhibit 6.41 shows an overall assessment of the hypothesesby the different sets of data.

134

Exhibit 6.41 General assessment of hypotheses

Dataset 1 Dataset 2 Dataset 3

Headquarters Headquarters Subsidiaries

Close Subs Distant Subs Subsidiaries

Model 1

OUT---> T1

High levels of knowledge outflow positively affect the development of formal transmission channels

√ √ √

OUT ---> T2

High levels of knowledge outflow positively affect the development of knowledge management infrastructure

√ √ √

OUT ---> T3

High levels of knowledge outflow positively affect the development of know-ledge transfer processes

√ √ √

IN ---> T1

High levels of knowledge inflow positively affect the development of formal transmission channels

X X X

IN ---> T2

High levels of knowledge inflow positively affect the development of knowledge management infrastructure

X X X

IN ---> T3

High levels of knowledge inflow positively affect the development of knowledge transfer processes

X X X

135

ABIL1---> T1

A unit’s high value of knowledge stock positively affects the development of formal transmission channels

X X X

ABIL1 ---> T2

A unit’s high value of knowledge stock positively affects the development of knowledge management infrastructure

X X X

ABIL1 ---> T3

A unit’s high value of knowledge stock positively affects the development of knowledge transfer processes

X X X

Model 2

T1 ---> EFF1

A high development of formal transmission channels positively affects the perceived benefit of knowledge transfers

√ √ X

T1 ---> EFF2

A high development of formal transmission channels positively affects the satisfactionwith knowledge management

X X √

136


Dataset 1 Dataset 2 Dataset 3

Headquarters Headquarters Subsidiaries

Close Subs Distant Subs Subsidiaries

T2 ---> EFF1

A high development of knowledge management infrastructure positively affects the perceived benefit of knowledge transfers

√ √ √

T2 ---> EFF2

A high development of knowledge management infrastructure positively affects the satisfaction with knowledge management

√ √ √

T3 ---> EFF1

A high development of knowledge management processes positively affects the perceived benefit of knowledge transfers

√ √ √

T3 ---> EFF2

A high development of knowledge management processes positively affects the satisfactionwith knowledge manage-ment

√ √ √

137

Notes:Hypothesis accepted √.Hypothesis rejected X.

Model 3

High organizational similarity between the units – i.e. low organizational distance –positively affects the perceived benefit ofknowledge transfers

√ √ √

ORG ---> EFF2

High organizational simi-larity between the units –i.e. low organizational dis-tance – positively affects the satisfaction with know-ledge management

√ √ √

CULT---> EFF1

High cultural similarity between the units –i.e. low cultural distance – positively affects the perceived benefit of knowledge transfers

X X X

CULT ---> EFF2

High cultural similarity between the units –i.e. low cultural distance – positively affects the satisfaction withknowledge management

X X √

138

7Conclusion, Limitations and Implications

Conclusion

This study has investigated knowledge transfers between dispersed MNCunits. The aim was to shed more light on the effectiveness of thesetransfers. As shown in the literature review, the importance of knowledgemanagement in the MNC is indisputable and management of dispersedknowledge is not only quite important but critically affects organizationaldesign as well as the MNC’s strategic configuration. Many authors haverecently addressed the topic of knowledge management in variousareas, but only a few have systematically investigated intra-MNC knowledgetransfers. Even though the works cited in the ‘state-of-the-art’ reviewadd much to our knowledge, it was also shown that research is largelyfragmented and takes different perspectives. Transfer is mostly seenas beneficial per se and the effectiveness of cost-intensive intra-MNCknowledge transfers is hardly ever questioned. In an attempt to combinesome of these perspectives while concentrating on the effectiveness ofknowledge transfer, a comprehensive model of knowledge transferwithin MNCs was developed. It was argued that effective knowledgetransfer is contingent on the development of several knowledge transfercapabilities which, in turn, are developed in response to the unit’s strategicposition in the network and the unit’s value of knowledge stock. More-over, two contingency factors, organizational and cultural distance, wereseen as decisive moderators to effective transfer.

Pursuing a nodal level of analysis, the determinants of effectiveknowledge transfer were tested empirically. The study focused on lateraland hierarchical knowledge transfers of 162 MNC units (thirty-eightheadquarters and 124 subsidiaries). While most of the measures appliedwere drawn from major earlier studies, the remaining were developed

Conclusion, Limitations and Implications 139

specifically for this survey. They reached a high level of quality in termsof reliability and validity and were found appropriate to be entered intothe structural equation model. The results of the general models wereaffirmative, supporting many of the hypotheses proposed.

The results can be extended to the research fields of organizationalnetworks, coordination mechanisms, capabilities, knowledge managementtools and infrastructure and context. Nine of the major findings are that:

• The network perspective is applicable to the sampled firms. • The majority of units engage either in low inflows and low outflows or

high flows in both directions.• The formal transmission channels used in hierarchical relations add

considerably to beneficial knowledge transfer, but they do not havea positive impact on the satisfaction with knowledge management.

• In lateral relationships, formal channels have a negative impact on thebenefit of knowledge transfers but a positive impact on satisfaction.

• While units showing high levels of knowledge outflows were found todevelop knowledge management capabilities at a high level, unitsexhibiting a high level of inflow did not show a positive development.

• No evidence was found that a high value of knowledge stock supportsthe development of transfer capabilities.

• ‘Conservative’ knowledge management tools such as databases orface-to-face meetings are dominant.

• Culture was found to be insignificant in all datasets including hier-archical relationships, but showed a positive impact in the lateral sample.

• Organizational similarity has a highly positive impact on knowledgetransfer effectiveness.

The issues mentioned above will now be discussed in more detail.

The network perspective

The theoretical basis of this study is the network model of the MNC.According to this perspective, the strategic tasks of organizational unitsshould differ according to their relative level of specialization. Head-quarters are not necessarily assigned a central, hierarchical position; allsubsidiaries and the headquarters are part of an interdependent network.This implies that headquarters do not have to play a dominant rolein the organization and thus the firm’s ability to react flexibly to fastchanging market conditions is increased. Among the sampled firms,centralized functions, such as technology and purchasing, seem to liewith the headquarters whereas local market analysis is largely done by


subsidiaries. This points towards a rather traditional, hierarchical positionof headquarters.

As far as knowledge flows are concerned, most headquarters arecharacterized by high knowledge outflows and high knowledge inflows.Only the smallest group engages in high knowledge outflows and lowknowledge inflows – which reflects a traditional position. The underlyingrationale is that firms usually expanded by so-called ‘forward’ transfers –i.e. from the centre to the periphery – and were not supplied with muchknowledge from their subsidiaries (Vernon 1966). Traditionally, the flowsback to the headquarters included only standardized reports or financialdata. Only recently has the phenomenon of transfers of knowledge fromthe subsidiary to the headquarters, so-called ‘reverse flows’, been investigated(Hakanson and Nobel 2000, 2001; Frost 2001; Chini, Ambos andSchlegelmilch 2003; Zhou and Frost 2003). In this research, the head-quarters surveyed seem to receive a lot of these ‘reverse’ flows and alsobenefit from them.

Compared to regional headquarters, a larger percentage of globalheadquarters receives high inflows. Asakawa and Lehrer (2003), however,found that regional headquarters manage local innovation relays andare thought to be more involved in ‘reverse’ transfers than globalheadquarters. Overall, regional headquarters in this sample follow thesame pattern as most other units – exhibiting either high inflows and highoutflows or low inflows and low outflows. In the literature, the role ofregional headquarters has been investigated, if at all, from the perspectiveof coordination and control than from a knowledge transfer point ofview. Only recently, has research on regional headquarters focused onthe identification and mobilization of knowledge at that level (Asakawaand Lehrer 2003). Regional offices are supposed to function as innovationrelays, linking local knowledge to the MNCs’ global operations. The fewregional headquarters surveyed in this study predominantly engageeither in high knowledge flows in both directions or show a low level ofinflows and outflows. Whereas the high inflow/high outflow type can beseen as such a relay of innovation, the existence of the other type cannotbe explained. Arbitrary clustering of different national units may haveled to such patterns; alternatively, the sampled firms are showing only‘trace elements’ of a network organization, but have not yet reached thefully developed form (see also Wolf 1997).

As far as the differences between individual subsidiaries were con-cerned, it became obvious that not all of them were assigned the samestrategic mandate. However, in contrast to the four mandates suggestedby Gupta and Govindarajan (1991), only two different types prevailed.


Subsidiaries seemed to be characterized either by high inflows and highoutflows (Integrated Players) or low inflows and low outflows (LocalInnovators). The most interesting finding in this regard was that know-ledge inflows and outflows seemed to develop simultaneously. Anotherremarkable aspect was that high knowledge outflows had a positiveeffect on the development of transfer capabilities, whereas high levelsof inflow showed no significant effect on capability development.

In sum, the network perspective provides a useful theoretical frameworkfor analysing this sample. Because of the lack of a dynamic perspective,the network cannot be investigated in depth, but it is important toshow that the average company follows a heterarchical rather thana hierarchical model. It seems that firms are developing towards net-works but in many cases the headquarters is still the centre of gravity.To investigate this issue in more depth, longitudinal studies may be needed.

Coordination mechanisms

As far as coordination between units is concerned, formal and informalmechanisms, which serve as knowledge transmission channels, have beensurveyed. In both lateral and vertical relations, liaison personnel are mostfrequently employed, followed by temporary task forces and permanentteams. This accounts for the fact that connectivity between units isreinforced rather by integrative mechanisms than on a temporary basis or inthe form of specialized teams. Among informal channels, executive devel-opment programmes across subsidiaries and the use of mentors dominate.

Gupta and Govindarajan (2000) found that units tightly controlled inthe form of formal and informal socialization generate high knowledgeoutflows. This study suggests that the effectiveness of such flows can becalled into question. As shown in the results section, the formal trans-mission channels used in hierarchical relations add considerably tobeneficial knowledge transfer. But they do not have a positive impact onsatisfaction with knowledge management. However, when culturallyclose subsidiaries are included in the analysis instead of culturally distantsubsidiaries, formal channels add more to the benefit, though still withouta positive influence on satisfaction. This can be explained by the factthat coordination and control mechanisms are seen as coercive as they areimposed by headquarters for control reasons in hierarchical relationships.However, they seem to work effectively, as they contribute significantlyto the perceived benefit. The hierarchical point of view thus confirmsGupta and Govindarajan’s (2000) findings that knowledge flowsthrough formal channels are beneficial, but they do not augment thesatisfaction with knowledge transfers.


On the subsidiary level, where Gupta and Govindarajan (2000) alsofound support for their hypothesis that tightly controlled units exhibithigh knowledge outflows, this research generates different results.Astonishingly, the impact totally changes when lateral relationships areconsidered. Formal channels then have a negative impact on the benefitof knowledge transfers but a positive one on satisfaction. In this case, thelateral ties seem to be voluntarily built and not enforced by headquarters.Lacking a direct link, such relationships cannot be managed from head-quarters. Therefore more autonomy is granted, augmenting satisfaction.The other side of the coin is that the perceived benefit from knowledgetransfers is not coupled to lateral formal coordination. No positiverelationship could be found. More research applying more complexmultirespondent designs and accounting for perception gaps might behelpful to investigate these issues further.

As increased interaction between subsidiaries can be seen as a way ofbuilding social capital, this finding does not support the theory ofNahapiet and Ghoshal (1998). The authors suggest that social capitalenhances the firm’s combinative capability and ultimately adds to valuecreation. Gold, Malhotra and Segars (2001) also focus on the role ofsocial capital and find that technology, structure and culture forma definitional basis for the theoretical framework of social capital andpositively impact key aspects of organizational effectiveness. In thestudy at hand, such creation of social capital was found to have a positiveimpact on satisfaction with knowledge management. However, the effecton additional benefit to operations – which is comparable to valuecreation – could not be confirmed.

Capabilities

Several knowledge management studies have researched the transfer ofcapabilities – e.g. production capabilities – (Zander and Kogut 1995;Mowery, Oxley and Silverman 1996; Subramaniam and Venkatraman2001). To avoid confusion, it needs to be emphasized once again that inthis study the specific capabilities to transfer knowledge were at thecentre of analysis. This approach is in line with Gold, Malhotra and Segars(2001, p. 206) whose results suggest that theories of knowledge capabilitiesprovide a rich resource for developing empirically based studies. Theauthors also stress that capabilities can provide a useful benchmark forknowledge management within the firm, but are complex not onlyin definition but also in operationalization. Notwithstanding this com-plexity, integrating the concept of mutually reinforcing capabilitiesinto the model of knowledge transfer is a vital task. The development of


transmission channels, infrastructure and processes was used as an additiveconstruct to mirror knowledge transfer capabilities.

While a high value of knowledge stock residing in a unit wasfound to have a positive influence on knowledge outflows in Guptaand Govindarajan’s (2000) study, it does not support the developmentof knowledge transfer capabilities in this study. It has to be recognizedthat the underlying operationalization is different. While Gupta andGovindarajan (2000) use secondary data – namely mode of entry andsubsidiary size – a perceptional measure was applied in this study. Thehypothesis that a high value of knowledge stock – which symbolizes theunits’ potentiality to engage in knowledge transfer – positively affects thedevelopment of knowledge transfer capabilities was clearly rejected. Thisis a rather surprising fact that might be explained only by motivationalfactors. To the author’s best knowledge, however, motivational issueshave not been found to play a dominant role in any major studies(Szulanski 1996; Gupta and Govindarajan 2000; Foss and Pedersen 2002).

From a network perspective, some interesting results were received.While units showing high levels of knowledge outflows were found todevelop knowledge management capabilities at a high level, unitsexhibiting high inflow did not show a positive development. This mightbe explained by the fact that the ‘receivers’ of knowledge are not cognizantof the channels, the infrastructure nor the processes they use. Furtherinvestigation of these phenomena must be left to future research.

Knowledge management tools and infrastructure

The most utilized knowledge management tools are face-to-face meetings,learning-by-doing and on-the-job training – all knowledge managementtools closely tied to personal contacts. Overall results indicate a balanceduse of ‘codified’ and ‘personalized’ (Hansen, Nohria and Tierney 1999)knowledge management tools. Despite the prevalence of ‘conservative’tools, such as databases or face-to-face meetings, which existed longbefore the trend towards a knowledge economy was established, nearlyevery company has established sophisticated tools such as web-basedgroupware or decision support systems. But the infrequent use of thesetools demonstrates that most technology-intensive instruments are notyet accepted by employees.

No matter what relationships are included into the analysis, there isevidence that infrastructure adds to overall knowledge transfer effectiveness.However, the impact of highly developed infrastructure on the perceivedbenefit is lower than the effect of the other knowledge transfer capabil-ities, but has a high bearing on satisfaction with knowledge management.


One possible explanation is that infrastructure, comprising featuressuch as learning tools, is seen as a fringe benefit or a corporate incen-tive. On the other hand, its small impact on perceived benefits canbe attributed only to lack of skills and resistance to technical features.The importance of the other two capabilities, formal transmissionchannels and processes, may outweigh the impact of infrastructure oneffectiveness.

Context

Empirical observations suggest a consistent pattern of results for con-tingency factors. As in many other studies, context dissimilaritieswere expected to be obstacles to effective knowledge transfer – despiterecognizing the innovation potential of loose ties (see Chapter 3).

The gap between theory and empirical findings in this area could also beconfirmed by this study. Generally, theoretical studies tend to argue thatcultural differences impact the process of knowledge transfer. However,many studies clearly failed to find empirical support (Lyles and Salk 1996;Simonin 1999b; Zhou and Frost 2003), while others confirmed theirhypotheses (cf. Kogut and Singh 1988; Ambos 2002). This study ranksamong the first category, which is well represented in the research oninternational knowledge transfers. In his study about the transfer ofmarketing know-how across strategic alliances Simonin (1999b), forexample, did not confirm the hypothesis that cultural distance impedesknowledge transfer. Lyles and Salk (1996) did not find any significanteffects of cultural misunderstandings/differences and knowledge acquisi-tion. Zhou and Frost (2003) had to reject their hypothesis that culturaldistance alters reverse knowledge transfers. A way out of this dilemma isproposed by Subramaniam and Venkatraman (2001), who expect to getmore insight when focusing on tacit differences among countries – despitethe intrinsic difficulties in doing so. What could also explain thesenon-findings is that contrary forces are at work: On the one hand, culturaldifferences cause misunderstandings and complicate the knowledgetransfer while, on the other, new knowledge – perceived as beneficial –is likely to originate in unfamiliar contexts.

The results found in this study are somewhat unique. Culture was foundto be insignificant in all datasets including hierarchical relationships,but cultural distance had a negative impact on knowledge transfereffectiveness in the lateral sample. However, the result is significant onlyfor satisfaction with knowledge management. This means that culturaldistance has a negative effect on satisfaction only when knowledgetransfers between peer subsidiaries are concerned. One could go as far as


to assume that lateral exchange is per se more spontaneous and informaland, thus, involves more personal relations and social capital, wherecultural differences are addressed directly. However, to draw firm con-clusions, more empirical work is needed.

As far as organizational isomorphism is concerned, the results illustratethat organizational similarity highly impacts knowledge transfer effect-iveness. It seems that in this matter – which is less researched thancultural distance in the field of knowledge management – not muchempirical support has been found either. Simonin (1999b) for example,did not find an impact of organizational distance and concludes that(marketing) functions are relatively homogeneous across contexts. Teigland,Fey and Birkinshaw (2000) conducted a qualitative study about know-ledge dissemination in R&D and found that a one-company culture isa key facilitator for knowledge transfer.

The conclusion on contingency factors’ impact on knowledge transfereffectiveness is that the importance of organizational similarities prevailsin MNCs which are trying to become a one-company culture (Teigland,Fey and Birkinshaw 2000) or even a ‘knowledge culture’ (De Long 1997;Hauschild, Licht and Stein 2001).

Effectiveness

The main rationale underlying this study is that not only the existence ofknowledge transfers but also the effectiveness of these transfers is criticalfor the continuous (re-)creation of the MNC’s competitive advantage.It was convincingly shown that effective transfer is, indeed, contingenton many factors – predominantly on the development of knowledgetransfer capabilities and on organizational similarity. By far the highestimpact among the capabilities across all models was reached by theknowledge process capabilities.

As effectiveness is difficult to measure directly, perceived benefit andsatisfaction were used as proxies. As illustrated, these two constructsproduce divergent results and it has to be concluded that differentfactors influenced perceived benefit and satisfaction. This is one of themajor findings of this study, as the distinction between perceived bene-fit and satisfaction will allow managers to adopt appropriate strategicactions.

Limitations

As with all social science models aiming to depict complex relationships,some important limitations need to be considered. One corollary already


mentioned earlier is that of a stable perspective. To survey networkrelationships, the development of knowledge transfers and theirintegration over time a dynamic perspective would have been advan-tageous. However, such a research design is difficult when a large sampleof MNCs is targeted. One-firm studies (cf. Dyer and Nobeoka 2000) arebetter suited to investigate such network characteristics but general-izability of results is limited. Alternatively, longitudinal studies couldshed more light on these issues. The main constraint in this specific casewas the time frame for the research. Several studies solve that problemby focusing on secondary data – primarily patent citations (Cantwelland Janne 1999; Frost 2001; Zhou and Frost 2003). But given themethodological and conceptual limitations of patent citations (see alsoCriscuolo 2003), it was considered important to gather primary data.

Another issue is that findings are based on self-reported data, entailingpotential respondent bias or general method variance. However, multipleassessments (e.g. reliability, exploratory factor analysis, confirmatoryfactor analysis) reported good properties. This supports the validity ofthe results. Moreover, like most social science models, that developed inthis study excludes some potentially important factors. Although severaltheoretical perspectives were addressed, including the main measuresand controlling for side effects, other forces – not included in thismodel – might be at work. It would have gone beyond the scope of thisstudy to include diverse sources of knowledge (Foss and Pedersen 2002)as well as its re-use and application. Although only a limited spectrum wasinvestigated, a unit’s external knowledge access is also characterized byits network position (Tsai 2001) and thus was surveyed indirectly.

Last but not least, this study pioneered the exploration of knowledgetransfer effectiveness where the development of a viable measure is critical.It was shown that perceived benefit and satisfaction are two facets ofeffectiveness, which are sometimes influenced by different factors.

Despite these limitations, the study makes some valuable contributionsto practice and identifies some potentially important directions for futureresearch.

Implications for future research

Given the usual limitations of perceptual measures, objective measureson knowledge transfer effectiveness would be useful in future research;a multifacet construct probably is needed. The measures developed inthis research exhibit good quality of reliability and validity and shouldprovide a useful tool for further investigation into this topic.


A promising avenue for further research could also centre more onthe motivations of knowledge transfers and on the actual outcomes ofeffective transfer. It would be valuable to find out in which situationsknowledge is transferred – e.g. if the unit decides to distribute the know-ledge or if other units request this knowledge. In contrast to findings ofearlier research, motivational factors and protectiveness could playa role. On the other side of the spectrum, the integration of knowledge isvital. Some studies, especially from the international JV literature (cf. Lylesand Salk 1996; Griffith, Zeybek and O’Brien 2001), have alreadyprovided some useful insights in this regard. However, researchers haveto go further and question the overall contribution of knowledge flows tothe firm’s operations. At the moment, authors unanimously call for anincrease of knowledge flows (the reasons are discussed in this study), buta critical and systematic investigation of the cost-benefit relation of suchan increase would be welcome. Acknowledging the methodologicallimitations of research questions concerning the ultimate benefit ofknowledge flows, this study has made only a small first step developinga critical view on knowledge transfers by arguing that effectiveness – notmere transfer – is the decisive dimension.

More light needs also to be shed on the issue of context similarities.Although the topic is present in almost every study in the internationalarena, researchers find divergent results without a clear trend in eitherdirection. Of course the conceptualization of culture is an unsolvedproblem in social science, the issue often being naively circumventedby using Hofstede-based measures. Looking at other disciplines, such ascultural studies, could add to a more realistic approach – which willundoubtedly be more complex and, thus, harder to integrate. The specificsuggestion of this research is to approach the topic of knowledge flowsacross different contexts from two perspectives. First, with regard tonovelty, innovation potential and usefulness. Secondly, centring on thetransmission problems which arise from de- and re-contextualization.

Managerial implications

Although the results of this study cannot address all the potential obs-tacles that managers may face in their quest to create knowledge-based organizations, it implies that certain factors have an impact onthe effectiveness of knowledge transfers, and might help managers tocope with this topic.

Clearly, the results suggest that managers must first assess the unit’sstrategic position in the network. Less important, however, is the unit’s


value of knowledge stock. It would thus be advisable to stay wellinformed about the network configuration of the firm and to acquire asmuch network knowledge as possible. Knowledge about the networkconfiguration is a potential source of power. If the unit’s value of know-ledge stock has no effect on the development of transfer capabilitiesit suggests that units holding attractive knowledge are not likely todistribute this knowledge generously. Managers have then to search forthis knowledge actively in order to make it available for the wholeorganization.

The empirical observations suggest that satisfaction with knowledgemanagement and the perceived benefit from knowledge transfers areonly partly influenced by the same factors. While formal coordinationmechanisms clearly augment the benefit from inflows, they do notincrease satisfaction. In contrast, highly developed infrastructure ratherincreases satisfaction but has only a small impact on perceived benefit.In this respect, this study provides some insight and potential means toinfluence these dimensions.

Knowledge process capabilities are responsible for the highest increasein benefit. Although a systematic development of these capabilities issuggested, such a step has to be undertaken cautiously. All knowledgetransfer capabilities can be regarded as mutually reinforcing, and neglectingone is likely to alter the whole mechanism. A detailed balancing of specificcapabilities would go beyond the scope of this research, however.

What seems to be a particularly relevant finding for managers in chargeof international transfers is the effect of organizational and culturaldissimilarities. This study clearly suggests that in hierarchical relationships,organizational homogeneity is vital. However, no impact of culture couldbe found. Advice to managers would be that continuous investment ina one-company culture pays off in knowledge transfer effectiveness.When focusing on lateral relationships between subsidiaries, nationalculture does have a detrimental effect; probably more ‘mentality-building’has to be carried out in this field.

149

Appendix 1 Specification of the Measurement Model

The specification of the measurement model includes the choice of variablesincluded in the model. Latent and manifest variables have to meet several pre-requisites, such as unidimensionality and reliability, to achieve a good model fitin the final calculations. These criteria are now explained and the exclusion ofseveral items described for the dataset ‘Hierarchical Relationships and CulturallyDistant Subsidiaries’.

Unidimensionality of the constructs

Prior to the assessment of the measurement model’s reliability and validity, theconstructs’ unidimensional characteristics have to be confirmed. Unidimensionalityof a measurement model is given if different sets of indictors identify onefactor, preferably the underlying construct (Andres 2003, p. 182). If a measure isunidimensional, then all the items measure the same trait. However, the amountof error of measurement in those items may vary (Anderson and Gerbing 1982).

Exhibit A1.1 shows the variables that were eventually included in the models. The CFA (confirmatory factor analysis) procedure basically follows the same

model as causal analysis but includes only a single construct in place of thewhole model. The aim is a close reproduction of the empirical matrix ofcovariances.

Exhibit A1.1 Unidimensionality of constructs

CONSTRUCT/INDICATOR

CRONBACHALPHA

ITEM-TO-TOTAL CORRELATION

FACTOR LOADING VEA

Out: Strategic Mandate 0.8775 73.15%out_h_ma 0.7365 0.881 out_h_di 0.7712 0.860 out_h_te 0.7095 0.844 out_h_pu 0.7227 0.836

In: Strategic Mandate 0.8542 69.60%In_h_ma 0.7195 0.853 In_h_di 0.7133 0.848 In_h_te 0.6610 0.829 In_h_pu 0.6898 0.806

150

Exhibit A1.1 (continued)

CONSTRUCT/INDICATOR

CRONBACH ALPHA

ITEM-TO-TOTAL CORRELATION

FACTOR LOADING VEA

Abil: Knowledge Stock 0.8176 64.67%sto_ma 0.6368 0.834 sto_di 0.5960 0.806 sto_te 0.6388 0.800 sto_pu 0.6808 0.776

T1: Formal Channels 0.5197 51.79%co_li 0.3539 0.827 co_tf 0.4426 0.772 co_pt 0.211 0.523

T2: Infrastructure 0.8700 60.91%infra_10 0.7779 0.866 infra_9 0.7326 0.831 infra_11 0.6986 0.806 infra_6 0.5875 0.710 infra_8 0.6220 0.747 infra_12 0.5871 0.710

T3: Processes 0.840 cap_1 0.3074 cap_2 0.4880 cap_3 0.3291 cap_4 0.5844 cap_5 0.6228 cap_6 0.5101 cap_7 0.4101 cap_8 0.3725 cap_9 0.4894 cap_10 0.5305 cap_11 0.6140 cap_12 0.4769 cap_13 0.3588 cap_14 0.5526 cap_15 0.3564 cap_16 0.3265 cap_17 0.2542 cap_18 0.1687 cap_19 0.3373

C: Cultural Distance 0.679 60.90%cult_3 0.5152 0.688 cult_2 0.4986 0.651 cult_1 0.4655 0.591

Eff1: Benefit 0.8368 67.22%ben_h_di 0.7214 0.859 ben_h_ma 0.6967 0.844

Appendix 1 151

CFA is used to test the following constructs of the model which have beenintroduced as reflective variables:

• Knowledge Transfer Infrastructure (T2) • Knowledge Processes (T3) • Satisfaction (Eff2)

As Exhibit A1.2 shows, results are good and the constructs can be includedwithout further modification.

Reliability of the measurement model

Having investigated the unidimensionality of constructs for a suitable measure-ment model, the reliabilty of the underlying variables has to be examined. Indi-cator reliabilty (IR) tells us how much of the variance is explained by theunderlying factor. To assess the reliability of a factor, factor reliability (FR) andaverage variance extracted (AVE) are used.

Indicator reliability is calculated on a normed [0;1] interval and is referred to asSquared Multiple Correlation in the AMOS module. The desirable cut-off valuesdepend on the size of the sample. A sample size of n <100 demands values of 0.6–0.9while samples of n 100 < n < 400 require values of 0.4–0.6 (Andres 2003, p. 192).

The average variance explained (AVE) indicates how much of the total vari-ance is explained by the construct (Fornell and Larcker 1981). Values higherthan 0.5 are required in this respect. As factor reliability and average varianceexplained are not calculated by the AMOS module, the formulas underlying thecalculations are shown in Exhibit A1.3 and the results afterwards.

ben_h_pu 0.6357 0.795 ben_h_te 0.6182 0.779

Eff2: Satisfaction 0.8923 69.981%sato_2 0.8040 0.885 sato_4 0.7602 0.856 sat_1 0.6920 0.802 sati_3 0.6948 0.804 sato_1 0.7323 0.833

Exhibit A1.2 CFA results: hierarchical relationships

Modell Chi2 df p-Value RMSEA Chi2/df NFI CFI

Infrastructure (T2) 70.50 9 0.000 0.150 4.611 0.985 0.988Processes (T3) 317.09 146 0.000 0.085 2.172 0.955 0.975Satisfaction (Eff2) 15.85 5 0.000 0.116 3.170 0.993 0.995

152

Exhibit A1.3 Indicator reliability, factor reliability and average variance explained

IR

Factor Score Weights ^2 S.E. FR AVE

T2: Infrastructure infra_6 0.415 0.082 0.006724 0.105 infra_8 0.441 0.098 0.009604 0.093 infra_9 0.652 0.197 0.038809 0.084 infra_10 0.759 0.336 0.112896 infra_11 0.555 0.154 0.023716 0.08 infra_12 0.375 0.087 0.007569 0.089 0.306 0.669

T3: Processes Ext 0.278 0.084 0.007056 Int 0.183 0.069 0.004761 0.242 Soc 0.618 0.213 0.045369 0.496 Com 0.571 0.183 0.033489 0.469 0.070 0.200

Eff2: Satisfaction sato_2 0.785 0.339 0.114921 sato_4 0.704 0.215 0.046225 0.076 sat_1 0.504 0.11 0.0121 0.083 sati_3 0.532 0.137 0.018769 0.071 sato_1 0.618 0.166 0.027556 0.075 0.419 0.754

153

Appendix 2 Model Identification and Assessment

Model identification

The refined measurements are now entered into the structural equation analyses.To estimate parameters, the maximum likelihood (ML) method is used. The latteris based on the following four assumptions (Byrne 2001, p. 70):

• The sample is very large (asymptotic) • The distribution of the observed variables is multivariate normal• The hypothesized model is valid• The scale of the observed variables is continuous (recent discussions also admit

Likert-type scales).

‘If a unique solution for the values of the structural equation parameters can befound, the model is considered to be identified. As a consequence, the parametersare considered to be estimable and the model therefore testable’ (Byrne 2001, p. 35).The identification of a model depends on whether a matrix of covariances definesthe collectivity of the estimated parameter unambiguously or whether thereexist other matrices of covariances which lead to the same parameter estimation.The identification of a model can be solved through a nonlinear equation modelwith q*(q + 1)/2 equations and t variables (q being the number of indicators andt the number of parameters to be estimated). The number of parameters to beestimated (t) must not be higher than the number of empirical variances andcovariances (q*(q + 1)/2). The difference of these tells us the model’s degrees offreedom (Homburg and Pflesser 1999b, p. 645).

The aim of SEM is to obtain an overidentified model – i.e. a model in whichthe number of estimable parameters is less than the number of data points(variances, covariances of the observed variables). In case of a just-identifiedmodel, there is a one-to-one correspondence between the data and the structuralparameters, in case of an underidentified model, the number of estimableparameter exceeds the number of data points. Thus, only overidentified modelsshow positive degrees of freedom and allow for a rejection of the model (Byrne2001, p. 35).

Models 1–3 show the descriptive information in Exhibit A2.1.

Assessment of the model

After the specification of the model and the parameter estimation, the model hasto be assessed. If this step leads to negative assessment, a modification of themodel is necessary. In case of positive assessment, a detailed interpretation of

154 Appendix 2

results has to follow. According to Homburg and Baumgartner (1995), thefollowing five steps need to be taken to assess a model:

1. Assessment of formal aspects: strange estimates and high standard errors pointtowards a misspecification of the model.

2. Assessment of the measurement model’s goodness of fit: local measures of fit. 3. Assessment of the whole model’s goodness of fit: global measures of fit. 4. Assessment of the structural equation model’s goodness of fit. 5. Cross-validation/comparison with alternative model structures: does the model

explain the structures – i.e. the covariances of observed variables – of a secondset of data:

Step 1 refers to the fact that indicators for unidentified models are high standarderrors and incomprehensible or strange estimates (Homburg and Pflesser 1999b,p. 645). Parameter estimates should exhibit the correct sign and size, and beconsistent with the underlying theory. ‘Examples of parameters exhibitingunreasonable estimates are correlations > 1.00, negative variances, and covarianceor correlation matrices that are not positive definite’ (Byrne 2001). Moreover,standard errors which are excessively large or small indicate a poor model fit. Forthe statistical significance of parameter estimates, the AMOS Module providesthe critical ratio (c.r.), which represents the parameter estimate divided by itsstandard error. ‘Based on a level of 0.05, the test statistic needs to be > 1.96 beforethe hypothesis (that the estimate equals 0.0) can be rejected. Nonsignificantparameters, with the exception of error variances, can be considered unimportant tothe model’ (Byrne 2001, p. 76).

Steps 2 and 3 refer to the more specific model assessment which is based onseveral measures of fit. Generally, global and local measures of fit have to bedistinguished. While global measures of fit assess the whole model, localmeasures of fit relate to specific parts of the model. Based on Homburg andPflesser (1999b, p. 648), Exhibit A2.2 gives an overview.

As shown in Exhibit A2.2, local measures of fit relate either to the measurementmodel or to the structural model. The reliability of indicators (IR) and factors(FR) was discussed earlier in the course of CFA and found adequate for both

Exhibit A2.1 Model descriptives

MODEL 1 MODEL 2 MODEL 3

Number of variables in the model 56 49 32Number of observed variables 25 22 14Number of unobserved variables 31 27 18Number of exogenous variables 28 25 16Number of endogenous variables 28 24 16Sample size 162 162 162Number of distinct sample moments 350 275 119Number of distinct parameters to be estimated

81 70 44

Degrees of freedom 269 205 75

Appendix 2 155

measures. To assess particular equations of the structural model, squared multiplecorrelation is measured. On the construct level, the average amount of varianceextracted is critical. All these values are normed on the [0;1] interval, where highvalues point towards a high quality of measurement (Homburg and Pflesser 1999b,p. 649). ‘Values less than 0.05 are indicative of good fit, between 0.05 and under0.08 of reasonable fit, between 0.08 and 0.10 of mediocre fit and > 0.10 of poor fit’(Diamantopoulos and Siguaw 2000, p. 85). Moreover, each factor loading’s signifi-cance can be tested in a t-test. Given a significance level of 0.01 for a one-tailed solu-tion, t-values should meet a minimum value of 2.345 (Backhaus etal. 1996, p. 573).

Global measures of fit are based on the comparison of the empirical matrix ofcovariances and the matrix of covariances produced by the model. Stand-aloneand incremental measures of fit can be distinguished. The latter assess the relevantmodel on the basis of a null model. Values close to 1.0 point towards a goodmodel fit. In these models, GFI (goodness of fit index) and AGFI (adjusted goodnessof fit index) are not calculated because cases with ‘missing values’ are included inthe analysis (Arbuckle and Wothke 1995, p. 331). Both relevant measures used inthe analysis, normed fit index (NFI) and comparative fit index (CFI), comparethe chi-square values of the model and the null model in different ways.

Stand-alone measures can be seen from inferential statistics’ and from adescriptive point of view. Chi-square test statistics test the absolute ‘reproduc-tion’ of reality through the model. ‘The more the implied and sample covari-ances differ, the bigger the chi-square statistic, and the stronger the evidenceagainst the null hypothesis’ (Arbuckle and Wothke 1995, p. 97). This can be seenambiguously, as a good approximation of reality is more important than an

Measures of Fit

Local Measures of FitGlobal Measures of Fit

Stand-Alone

Measuresof Fit

IncrementalMeasures

of Fit

Measures ofthe

MeasurementModel

Measures ofthe Structural

Model

Inferential-Statistic

Measures ofFit

DescriptiveMeasures of

Fit

accounting fordegrees offreedom

not accountingfor degrees of

freedom


Squared multiplecorrelation

– IR– FR– AVE– t-value of factorloading

NFI CFI– AGFI– Chi-Square/df

GFI


freedom

Source: Based on Homburg and Pflesser (1999b).

Exhibit A2.2 Overview of measures of fit

Measures of Fit

Local Measures of FitGlobal Measures of Fit

Stand-Alone

Measuresof Fit

IncrementalMeasures

of Fit

Measures ofthe

MeasurementModel

Measures ofthe Structural

Model

Inferential-Statistic

Measures ofFit

DescriptiveMeasures of

Fit



freedom


Squared multiplecorrelation

– IR– FR– AVE– t-value of factorloading

NFI CFI– AGFI– Chi-Square/df

GFI


freedom

156 Appendix 2

exact reproduction (Homburg and Pflesser 1999a, p. 427). Therefore, RMSEA (rootmean squared error of approximation) is often seen as a better measure. Forboth measures, a low value is ideal. RMSEA values less than 0.05 indicate goodfit, and values of high as 0.08 represent reasonable errors of approximation inthe population. RMSEA values ranging from 0.08 to 0.10 indicate mediocre fitand those greater than 0.10 poor fit (Buckley and Casson 1976; Byrne 2001).

Descriptive measures of fit, which also account for degrees of freedom are – givenequal approximation – more meaningful as models with fewer parameters wouldbe assessed worse (Homburg and Pflesser 1999a, p.428). Many authors stress thatcritical cut-off values of measures of fit are problematic in the sense that theyshould not be regarded as isolated (cf. Homburg and Pflesser 1999b; Diaman-topoulos and Siguaw 2000). The sample size and the model’s complexity inparticular influence the value of fit measures. A basic guidance is given byrecommending the values in Exhibit A2.3 (Homburg and Pflesser 1999b, p. 651;Byrne 2001, p. 85).

Exhibit A2.3 Suggested assessment of measures of fit

Measures of Fit

Measurement ModelIndicator Reliability (IR) ≥ 0.4 Factor Reliability (FR) ≥ 0.6 Average Variance Extracted (AVE) ≥ 0.5

Whole ModelRMSEA ≤ 0.05 (≥ 0.08; ≤ 0.10)Chi-Square/df ≥ 2.5 NFI ≥ 0.9 CFI ≥ 0.9

157

Notes and References

2 Knowledge and the MNC

1 Sveiby calls it ‘competence’ in this context.

4 A Model of Knowledge Transfers in MNCs

1 Szulanski (1996) finds that the major barriers to internal knowledge transferare not motivational factors but knowledge-related factors, such as the recipient’slack of absorptive capacity. Based on these findings and the subsequentdiscussion of Foss and Pedersen (2002), motivational factors are excludedfrom this model of knowledge transfer in order to limit complexity.

2 A more detailed argument was presented in the conceptualization of theknowledge transfer process (p. 14).

3 Higher than all other strategic mandates. 4 Higher than Local Innovators but less than Integrated Players. 5 Higher than Local Innovators but less than Integrated Players. 6 Lower than all other strategic mandates.

5 Research Design and Methodology

1 A single-informant approach is a common weakness in international businessresearch, which is shared with prominent studies such as Ghoshal and Nohria(1989) or Harzing (1999).

2 http://www.top500.de/g0030800.htm, 2003/06/27. 3 As only the strategic mandates of subsidiaries are involved, headquarters’ data

would not be necessary for this construct. Nevertheless, it was decided togather this data because the intensity and the direction of knowledge flowsare vital for this study and will be analysed in the structural equation model.

6 Analyses and Results

1 The detailed characteristics of cultural and organizational distance are discussedlater (p. 99).

2 Gupta and Govindarajan (2000) entered the variables into regression togetherwith several other variables. Thus, the original method cannot be replicated.

3 The assumption of normal distribution is met by all variables. 4 The assumption of normal distribution is met by all variables. 5 The assumption of normal distribution is met by all variables. 6 The assumption of normal distribution is met by all variables. 7 The assumption of normal distribution is met by all variables. 8 The assumption of normal distribution is met by all variables.

158 Notes and References

9 The calculation of one whole model was possible only by aggregating allscales to the level of indicators. The summated-scales model’s measures of fitare similar to what is reported in the following chapters. However, factorreliability and average variance explained reach only low levels.

10 Higher than all other strategic mandates. 11 Higher than Local Innovators but less than Integrated Players. 12 Higher than Local Innovators but less than Integrated Players. 13 Lower than all other strategic mandates.

159

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170

Index

Notes: ch=chapter; e=exhibit; n=note; bold=extended discussion or heading/wordemphasized in main text.

absorptive capacity (Cohen and Levinthal) 33e, 51, 60–1, 77,157(n1 to ch4)

Active Subsidiary 43adjusted goodness of fit index

(AGFI) 155e, 155 Adler, N. J. 124 agriculture, forestry, fishing 73e Ambos, B. 33e, 35e, 35,

40, 144 AMOS software module 115,

151, 154 Analysis of Variance (ANOVA) 98,

119, 157(n7) Andersson, U. 38 Argote, L. 30e Arvidsson, N. 160 Asakawa, K. 21, 25, 65, 69, 140 Asia 74e, 75e, 102 Au, K. Y. 102 Australia 74e, 75e Austria 72, 73–5 autonomy 38, 46, 133, 142 average variance extracted

(AVE) 151, 152e, 155e, 156e

Barney, J. B. 25 Bartlett, C. A. 2, 20, 21, 22, 25,

40, 41e, 42, 43, 46 Baskerville, R. 161 Baumgartner, H. 154 Becerra-Fernandez, I. 8, 32e, 35e,

35, 54, 66, 78, 96, 108 Beers, M. C. 13e behaviour 44, 66, 123 Belgium 72, 75e best practice 27, 30e, 31e, 96e Bhagat, R. S., et al. (2002) 33e,

34, 55, 160 Harveston, P. D. 160 Keida, B. L. 160 Triandis, H. C. 160

Birkinshaw, J. 32e, 38, 40, 41e, 41, 43, 46, 86, 145

categorization of knowledge management systems 12

definition of knowledge management 11e

Birkinshaw, J., et al. 69, 160 Arvidsson, N. 160 Holm, U. 160 Thilenius, P. 160

Black, S. J. 102 ‘Black Hole’ 43Bloodgood, J. M. 12 Brain, C. 20 Buckley, P. J. 11e, 21, 50, 65 Bulgaria 72 business intelligence 51, 62 business practice 78, 99, 99e, 100, 100e Byrne, B. B. 111, 156

capabilities 22, 23, 25, 32e, 35e, 37, 40, 47, 53, 54

combinative 49, 50, 51 human 2 knowledge management 50–2, 139 learning 50organizational 3, 49–50, 67see also knowledge transfer

capabilities capture and transfer of experts’

knowledge 95, 96e, 97e Carter, M. J. 11e, 65 Casson, M. C. 21, 50 centralization 37, 139 Centres of Excellence (CoEs) 47 CFA (confirmatory factor

analysis) 149, 151e, 154 Chandler, A. 50 chat groups 96e, 96, 97e Chi squares 114–15, 115e, 155e, 156e China 73 Chini, T. C. 33e, 35e, 35

Index 171

cognition 66 ‘cognitive knowledge’ 8Cohen, W. M. 51, 61 combination (knowledge conversion

process) 18e, 19, 62, 63, 64e, 78, 97–8

common method variance 69, 119 communication 86

in the company 95informal channels 102 lateral 24

communication model (Shannon and Weaver, 1957) 15–18

‘noise’ 15e, 52 companies/firms

domestic (contrasted with MNCs) 20

evolutionary theory 50 comparative fit index (CFI) 114,

115e, 155e, 155, 156e competence 17, 157(n1 to ch2) competitive advantage 2, 9, 10, 14,

24, 25, 29, 46, 54, 64, 145 competitors 76, 95

see also market data on competitors Conner, K. R. 25 construction 73e constructionism 12 consulting companies 72 context 15, 19, 27, 31e, 34, 35e,

37, 38, 68–70, 144–5, 147 structural 41

context factors 99–103cultural distance 101–3 organizational distance 99–101

context similarities 147 contingency factors 3, 52–7, 145

‘eventful’ knowledge management 53–4

institutional isomorphism 53, 54, 65

national culture 53, 54–7 control 3, 35e, 35, 37, 43–5, 46–7,

86, 122, 124, 140 bureaucratic formalized 44, 45e, 46 formal and informal 44, 49 means to achieve an end called

‘coordination’ 44 mechanisms (four categories)

44, 45e personalized centralized 44, 45e by socialization and networks 45e,

45, 46 two aspects 44

control instruments/mechanisms 39, 49, 133, 139, 141–2

determinants 47 formal 92, 148

coordination 3, 21, 35e, 37, 39, 43–5, 46–7, 51, 64, 92, 122, 124, 140

formal 49 informal 49, 77–8 ‘hypothesis 1c’ 63, 116 lacking 121 lateral and hierarchical 77

Cordey-Hayes, M. 30e covariance 108, 149, 153, 154 creativity 55, 56 Criscuolo, P. 146 critical ratios (c.r.) 115, 154 Croatia 72 Cronbach alpha 149–51e cross-border exchanges 20, 23 cultural distance 3, 45, 55–6, 57, 59e,

66, 67, 68, 79, 86, 93e, 94, 100, 100e, 101–3, 108, 110e, 110, 111e, 114e, 124–9, 133, 138, 139, 144, 145, 148, 150e, 157(n1 to ch6)

hidden assumptions hypothesis 66, 102, 118e, 123–4, 127e, 132e, 137e

impact on knowledge transfer 101e, 102

micro and macro level 56 resource-based view 56 second dataset 127e subjective perceptions 102 third dataset 130, 131e transaction cost perspective 56

cultural distance index (Kogut and Singh) 101

cultural mandates 45 culture 2, 31e, 33e, 34, 139, 142, 147

company-centric 47corporate 78, 99, 100, 124, 133 local 53 national/country 53, 54–7, 101e,

102, 124, 148 one-company 145, 148 operational 100 organizational 71

customers 13, 62, 76 see also market data on customers

Czech Republic 72

D’Cruz, J. R. 41e Daal, B. V. 13e

172 Index

Darr, E. D. 30e data 5, 7e, 7, 11e, 103

codification 6 definition 6different areas 76 primary 146 secondary 146

data collection 3, 68, 70–6controlling for industry-specific

results 71 final sample 72–6, 73e informant selection 71–2 target sample 68, 70–1, 73e two levels (headquarters and

subsidiaries) 71 databases 7, 95, 96e, 96, 97e,

139, 143 Davenport, T. H. 13e Davenport, T. H., et al. 11e, 161

De Long, D. W. 161 Harris, J. G. 161 Jacobson, A. L. 161

De Long, D. W. 13e, 161 decision support systems 95, 96e, 96,

97e, 143 decision-making 21, 22, 48, 65

formalization 37 Demarest, M. 11e, 13e df (degrees of freedom) scores 114–15,

115e, 155e, 156e Diamantopoulos, A. 109n, 112n Dinur, A. 15, 31e, 35e, 62 distribution know-how 88e, 89e,

103, 103e, 104e, 105, 105e documents 7, 19 Doz, Y. L. 22, 25, 29, 31e, 34,

53, 55, 62, 66 Dyer, J. H. 31e, 146

Earl, M. 12 Ebrahimpur, G. 28, 71 economies of scale 2, 23, 25, 42 embeddedness 32e

external 38 twofold (subsidiaries) 38

empiricism 1, 21, 34, 35, 35e, 50, 51, 61, 66, 68, 70, 78, 81, 120, 121, 128, 130, 138, 142, 144, 145, 148, 149, 153, 155

employee rotation 95, 96e, 97e employees 17, 28, 78, 80, 81–2, 82e,

85, 122, 123, 124, 143 environment (business) 40,

41, 46

epistemic communities 26 epistemology 12, 18, 27, 32e Epple, D. 30e Europe

Central and Eastern (CEE) 73, 74e, 75e, 75–6

Central/Western 72, 73, 74e, 75e, 75–6, 81

Northern 72, 73, 74e, 75e Southern 72, 74e, 75e

executive development programmes 78, 93e, 141

externalization (knowledge conversion process) 18–19, 18e, 62, 64e, 78, 97, 97e, 98e, 98

‘hypothesis 1b’ 63, 116

face-to-face meetings 19, 95, 96e, 96, 97e, 139, 143

factor reliability (FR) 151, 152e, 154, 155e, 156e

Fey, C. F. 32e, 145 field of interaction 63finance and insurance 72, 73e, 81 foreign direct investment (FDI) 20Forsgren, M. 38 Foss, N. J. 28, 33e, 35e, 35, 61,

157(n1 to ch4) France 72 Frost, T. 144

Galbraith, J. R. 23 Gallupe, B. 10, 12 George, G. 51 German-speaking countries 71 Germany 72, 75e Ghoshal, S. 2, 20, 22, 23, 25, 31e, 34,

37, 40, 41e, 42, 43, 46, 124, 142, 157(n1 to ch5)

Gilbert, M. 30e global exchange 49Global Innovators 41e, 42, 48, 59e,

60, 64e, 77, 84, 84e, 85e ‘hypothesis 1b’ 63, 116, 157(n4 to

ch4), 158(n11) globalization 21 Gold, A. H. 32e, 35e, 35, 50, 66, 78,

108, 142 goodness of fit index (GFI) 155e, 155 Govindarajan, V. 8, 21, 27, 28, 29,

30e, 32e, 34, 35e, 35, 40, 41e, 42, 44, 47, 61, 76–7, 84, 86, 88, 90, 91, 140–1, 142, 143, 157(n2 to ch6)

Index 173

Granovetter, M. 53 Grant, R. M. 2, 25, 28 Grayson, J. C. 31e Griffith, D. A. 57, 147 Grosse, R. 30e groupware 70, 96, 143 Gupta, A. K. 8, 21, 27, 28, 29, 30e,

32e, 34, 35e, 35, 40, 41e, 42, 44, 47, 61, 76–7, 84, 86, 88, 90, 91, 140–1, 142, 143, 157(n2 to ch6)

Hakanson, L. 9, 28, 32e, 35e, 35 Hamel, G. 25 Hansen, M. T. 12, 31e, 34, 35e, 47, 51 Harris, J. G. 161 Harveston, P. D. 160 Harzing, A.-W. 44, 45, 69, 72, 157(n1

to ch5) Hass, M. 13e headquarters 20, 22, 29, 33e, 39–44,

46, 58, 60, 68, 69, 72, 79, 83–4, 85–6, 88, 89, 92, 95, 97, 106, 107, 113, 114e, 116, 138, 139–40, 141, 142

benefit from knowledge transfers 103

benefits of knowledge transfers from culturally close

subsidiaries 105, 105e benefits of knowledge transfers from

culturally distant subsidiaries 105, 105e

functions 21 global 87e, 87, 140 hierarchical knowledge flows 27 informal transmission

channels 93e, 94 knowledge flows 76e, 157(n3 to ch5) location 73–6 managers 70 perceptions of similarity vis-à-vis

culturally close and distant subsidiaries 100e

personnel-intensive 81–2 regional 87e, 87, 140 strategic orientation scales 100–1 strategic positions 87e strategic tasks 90 value of knowledge stock 90–1

headquarters–subsidiary relationships 37–9, 56, 61–2, 63, 65, 78, 102

central coordination 37 culturally close subsidiaries 107–24,

134–7e

culturally distant subsidiaries 124–9,134–7e

data 107 dyadic 69, 71 ‘hypotheses 1a–1d’ 63 local responsiveness 37 principal–agent relationship 37 results 125–9, 134–7e second dataset 124–9see also subsidiary–subsidiary

relationships Hedlund, G. 9, 10, 13, 22,

23, 24 Heenan, D. A. 2, 21, 25 ‘hermeneutic circle’ 27 Hierarchical Relationships and

Culturally Distant Subsidiaries (dataset) 149

Hofstede, G. 79, 102 Hofstede-based measures 147 Holden, T. 8, 13, 13e Holm, U. 160 Holsapple, C. W. 12 Holzner, B. 13e Homburg, C. 111, 154, 156 homophily 56Hong, J. 12 Hood, N. 38 Hoopes, D. G. 31e Horizontal Organization (White and

Poynter) 22 host country (economic level) 77,

90–1 Hungary 72

imitation 30e Implementers see Local

Implementers indicator reliability (IR) 151, 152e,

154, 155e, 156e individuals 16e, 17, 18 industries 71, 72, 73e information 5, 7e, 7, 11e, 46

definition 6information distribution 12 information exchange 45 information interpretation 13 information processing 21 information technology (IT) 6, 11e information-processing capacity 48 infrastructure 51, 54, 59e, 113e

reflective variable 111e Inkpen, A. C. 15, 31e, 35e, 62 innovation 22, 57, 140, 147

174 Index

institutional isomorphism/organizational isomorphism 53, 54, 65, 145

Integrated Network Model (Bartlett and Ghoshal) 22, 23, 24

Integrated Players 41e, 43, 48, 59e, 60, 64e, 77, 84–6, 141

‘hypothesis 1a’ 63, 116, 157(n3–5 to ch4), 158(n10–12)

integrating mechanisms (Hedlund) 24

integration 21, 22, 34, 37, 146 intellectual capital 31e interactions across distance 53internalization (knowledge

conversion process) 18e, 19, 50, 62, 64e, 78, 97–8

‘hypothesis 1c’ 63, 116 internet 95, 96e, 97e interviews 68 intranet 95, 96e, 97e Ireland 72 Italy 72

Jacob, M. 28, 71 Jacobson, A. L. 161 Japan 73 Jarillo, J. C. 40, 41e, 44, 46 Jemison, D. B. 56 Johanson, J. L. 55 joint ventures ( JVs) 27, 29 Joshi, K. D. 12

K-means cluster analysis 85 Katsikeas, C. S. 65 Keida, B. L. 160 key informant approach 69, 82,

157(n1 to ch5) know-how 103, 106 knowing how 8 knowing what 8 knowledge

characteristics‘context-bound’ 27 ‘application-related process’ 8‘complexity’ 28‘has to be adapted to

recipient culture’s specifications’ 55

‘not culture-free’ 55classification/types 7–8, 30–3e

cognitive 8declarative 27, 30e, 32e, 33e, 47

developmental 30eexplicit 5, 6, 8–10, 18, 18e, 19,

24, 26, 28, 62, 98 explicit (separate) 62 explicit (systemic) 62, 63, 98highly complex 34idiosyncratic 28–9individual 5, 10, 31e, 32e, 62instrumental 30e internal or external 13managerial and administrative

29new 51, 58, 62, 94e, 95, 144organizational 5, 10, 31e, 32e,

33e, 62 procedural 27, 30e, 31e, 32e,

33e, 47, 76 social 10 tacit 5, 6, 8–10, 18, 18e, 24, 26,

28, 32e, 51, 62, 71, 98, 144technical 29, 32e

generalacquisition 12, 26, 47, 144aggregation 28, 51application 13e, 14asymmetries 25availability 106 codification 14conceptualization 5–10data–information–knowledge

continuum 7edefinitions 5, 6–7diffusion/dissemination 13e, 14,

27, 50, 51 dispersed assets 25distribution 13e, 14identification and

mobilization 140identification of source 13neoclassical view 29 ‘object’ 8, 12process 12‘resource’ 10sources (subsidiary

companies) 35‘stickiness’ 29 storage 13–14, 13e, 17, 26subjective measures 69transferability 27–9, 35e, 37transformation 13, 14 type transferred 28 use 13e, 14

knowledge conversion processes18–19, 78, 96–8

Index 175

knowledge creation 13, 13e, 18, 39, 42, 43, 50, 55

individual 26 role of subsidiaries 38, 39

knowledge culture 145 knowledge discovery 51, 62 knowledge extensity 24knowledge flows 44, 77, 87, 110, 140

as control or administrative mechanisms 35

hierarchical 83, 86, 116 intensity and direction 76internalization 20 lateral 83–4, 86 optimization 47–9overall contribution to firm’s

operations 147 perceived benefit 130

knowledge inflows 32e, 40, 42,43, 48, 58, 59e, 60, 76e, 77, 84, 84e, 86, 87e, 87, 98, 99, 103, 105, 110, 111e, 113e, 125, 128, 139, 140, 141, 143

benefit from 148 first dataset 130 ‘hypotheses’ 63, 116, 117e, 119,

120e, 126e, 131e, 134e perceived benefits 79 second dataset 126e third dataset 130, 131e

knowledge integration 32e, 51, 147 knowledge intensity 24knowledge management 3, 57, 61,

101, 103, 129, 138, 145, 148 capabilities 50–2, 139 challenge 1–2conceptual background 5–19definitions 10–12effectiveness 54 ‘eventful’ 53–4human resources-oriented

approach 10 hypothesis 117e, 118e, 127e, 131e,

132e, 135e, 136e, 137e ‘integrative’ approach 10 ‘organization-wide activity’ 12organizational levels 80 overall satisfaction 67perceived benefit 66–7, 117e relevance 19–36satisfaction with 106–7, 107e,

117e, 118e, 122, 125, 127e, 130, 131–2e, 133, 135e, 136e, 137e, 139, 141–2, 143–4, 157(n8)

second dataset 127e structure of MNC 23–4technology-oriented approach 10 theoretical concepts 10–19third dataset 131e value chain 12–14vision and strategy 13e, 14

knowledge management infrastructure 78, 94–5,117e–18e, 119, 120e, 122, 128, 130, 133, 143–4, 148, 157(n4 to ch6)

‘hypotheses’ 63, 64e, 116, 117e, 118e, 119, 126e, 127e, 131e, 132e, 134e, 135e, 136e

impact on satisfaction 123 means and medians 94 perceived benefit of knowledge

transfers 122–3 second dataset 126e, 127e third dataset 131e, 132e variables 120e

knowledge management processes 123, 128, 133

effect on satisfaction 123 encoding and decoding 123 hypothesis accepted 118e, 127e,

132e, 136e perceived benefit 123 second dataset 127e third dataset 132e

knowledge management systems 12 knowledge management tools 70,

78, 95, 139, 143–4‘conservative’ 139, 143 use 96e

knowledge mapping 51, 62 knowledge outflows 32e, 40, 42,

43, 48, 58, 59e, 60, 76e, 77,84, 84e, 86, 87e, 87, 99, 110, 111e, 113e, 119, 120e, 120, 130, 139–43

hypothesis 63, 116, 117e, 126e, 134e

second dataset 126e third dataset 131e

‘knowledge is power’ 28 knowledge process capabilities 59e,

62, 78, 113e, 145, 148 hypothesis 1 60, 115 reflective variable 111e, 113e

knowledge processes 11e, 94e, 95, 120e, 122, 150e, 151e, 151, 152e

input, throughout, output 47

176 Index

knowledge sharing 13e, 14, 27, 123

internal 31e willingness 28

knowledge stock 128, 150e maintaining valuable 51 perceived 91 see also value of knowledge stock

knowledge transfer 13, 14, 51, 52, 68, 139, 148

antecedents 83–91 barriers 30e, 157(n1 to ch4) beneficial 141 benefit 103, 139 between individuals 16e, 17comprehensive model 37 conceptual studies 29, 31e, 32e,

33e, 34 contextualization problems 147 decoding and adaptation 106 decontextualization and

recontextualization 57 dyadic relationship 52, 55 effectiveness 32e, 35–6, 49 empirical studies 29ex post analysis 79–80 external 27findings 30–3e‘forward’ 140 headquarters–subsidiary 3 hierarchical 27, 138, 139, 141 hypotheses accepted 117e,

118e, 122, 126e, 127e, 132e, 136e, 137e

hypothesis rejected 118e, 127e, 130, 131e, 132e, 135e, 137e

improvement of effectiveness 106 from individual competence to

internal structure 16e, 17individual satisfaction 80 integrative model 35 internal 27from internal structure to individual

competence 16e, 17intra-MNC 29, 30–3eintra-organizational 27, 29 lateral 27, 138, 139, 141, 142,

145, 148 methodology 30–3emicro perspective 34 MNCs 26–36model 58–67, 157motivations 147 nine types (Sveiby) 16e

not context-free 19 organizational and cultural

barriers 53 phases 34 potentiality to engage in 88 qualitative studies 31e, 32e quality 80 quantitative studies 30e, 31e, 32e,

33e reverse 35‘reverse flows’ 140 speed 30e, 106 state-of-the-art (literature

review) 29–36, 138 technological dimension 62terminology 27 third dataset 130 varieties 16e, 26–7within internal structure 16e, 17see also perceived benefit;

satisfaction knowledge transfer capabilities 27,

52, 59e, 61–4, 64e, 77–8, 79, 91–9, 108, 110e, 111e, 113e, 116, 119–21, 128, 138, 139, 141, 142–3, 144, 145, 148, 157(n2–6 to ch4)

further research 121 hypothesis 61, 64, 117e, 119,

121, 122 knowledge management

infrastructure 94–5, 157(n4 to ch6)

knowledge transfer processes 95–9, 157(n5–7)

transmission channels 91–4 knowledge transfer effectiveness 59e,

64–7, 79–80, 103–7, 108, 110e, 111e, 129, 138, 139, 143, 144, 145, 146, 147, 148

aim of research 2–3benefit from knowledge

transfers 103–6 hypotheses 64, 66, 122–4 outcomes 147 perceived benefit 111e, 113e, 114e positioning 1–4satisfaction 106–7, 107e, 111e,

113e, 114e third dataset 130

knowledge transfer infrastructure 143, 150e, 151e, 151, 152e

reflective variable 111e, 113e

Index 177

knowledge transfer in MNCs: conceptual model 3, 58–67, 157

analysis and results 4, 81–137,157–8

avenues for future research 4, 141, 142, 143, 145, 146–7

conclusion 4, 138–45constructs 82–107context 144–5cultural distance (hypothesis 5) 66 data collection 3, 68, 70–6descriptives of unit of analysis 81–2 gap between theory and empirical

findings 144 hierarchical relationships and

culturally close subsidiaries 107–24, 134–7e

hierarchical relationships and culturally distant subsidiaries 124–9, 134–7e

knowledge transfer capabilities (hypothesis 3) 61–4, 122, 157(n2–6 to ch4)

knowledge transfer effectiveness (hypotheses 4–5) 64–7

lateral relationships 129–33, 134–7e limitations 145–6managerial implications 4, 147–8organizational distance

(hypothesis 4) 65–6 position of respondents 83e results 115–37 stable perspective 146 strategic mandate (hypothesis 1)

58–60, 63, 64e, 115, 116 value of knowledge stock

(hypothesis 2) 60–1, 157(n1 to ch4)

see also Model of Intra-MNC Knowledge Transfer; research design and methodology; structural equation modelling

knowledge transfer processes 14–19,52, 61, 95–9, 120, 121, 122, 143, 144, 157(n5–7 to ch6)

adaptation 15communication model 15–18composition 97e conceptualization 3, 157(n2 to ch4) context 15 decoding/encoding 15 four stages 15–16 hypotheses 116, 117e, 126e, 131e,

134e, 135e

implementation 16initiation 15media 8 ‘noise’ 15e, 52 second dataset 126e, 127e sender–recipient 15, 15e, 16, 17 spiral of knowledge 18–19, 62 storage devices 8 third dataset 131e, 132e time-lag 16translation 16variables 120e

knowledge transmission channels 39, 47, 48, 54, 59e, 77, 91–4, 103, 122, 143

corporate socialization 44 formal 61–2, 92, 92e, 113e, 117e,

119, 121, 122, 125, 126–7e, 128, 130, 131e, 133, 134e, 135e, 139, 141, 142, 144, 150e

formal integrative mechanisms 44 hierarchical/vertical 62, 92, 92e ‘hypotheses 1a–1d’ 63, 64e, 116 hypothesis accepted 117e, 119,

126e, 131e, 134e hypothesis rejected 117e, 119,

126–7e, 130, 131e, 134–5e informal 61, 62, 63, 93e, 116, 141 lateral 62, 92, 92e second dataset 126e third dataset 130, 131e

Kogut, B. 8, 23, 25, 28, 29, 30e, 35e, 49, 50, 79, 86, 101, 102, 144

Köhne, M. 31e, 34 Korine, H. 23 Krogh, G. von 12, 31e, 34

language 2, 79, 101e, 102, 124 systematic 9

learning 22, 46, 51, 94e, 95, 120e, 123 learning culture 61 learning-by-doing 95, 96e, 97e, 143 Lehrer, M. 140 Leonard-Barton, D. 11e Leonidou, L. C. 65 Levinthal, D. A. 51, 61 liaison personnel 48, 77, 91, 92,

92e, 141 Liebowitz, J. 13e Likert-type scales 70, 77, 79, 80,

110, 153 literature

business 1, 102 control mechanisms 45

178 Index

literature – continuedcoordination and control 47 cross-cultural knowledge

transfer 55 embeddedness of MNC units 69 international business 19, 157

(n1 to ch5) international JV 147international strategy 2, 25 knowledge integration 147 knowledge management 1–2, 15,

52, 108, 124 knowledge transfer 29–36multinational corporations 21, 43 product innovation 53 role of regional headquarters 140 strategic mandates (of

subsidiaries) 39–40 strategic value disciplines 79

Local Implementers (autonomous subsidiaries) 41e, 42, 46, 48, 59e, 60, 64e, 77, 84, 84e, 85e

‘hypothesis 1c’ 63, 116, 157(n5 to ch4), 158(n12)

Local Innovators 41e, 48, 59e, 60, 64e, 77, 84, 84e, 85, 85e, 86, 141

‘hypothesis 1d’ 63, 116, 157(n4–6 to ch4), 158(n11–13)

location 2, 17, 25, 70 Long, C. 161 long-term orientation (concept) 102 longitudinal studies 146 Lyles, M. A. 144, 147

mail 71, 72 Malhotra, A. 32e, 35e, 35, 50, 66,

78, 108, 142 Malhotra, Y. 11e management 43

active 20management practices 54 management style 38, 78, 99,

99e, 100, 100e management systems 41 managers 56, 70, 77, 96, 102, 106, 123

expatriate 48 headquarters 86, 93e, 94, 95,

100, 124 implications of research

findings 147–8major finding of study 145 subsidiary companies 48, 93e, 94,

95, 99, 124 working experience 93e, 94

Manev, I. M. 56 manuals 44 manufacturing 69, 72, 73e, 81 market analysis 90, 139 market data on competitors/

customers 88–90, 103–6 market knowledge 95 market mechanisms 66 market opportunities 66 marketing 23, 28, 29, 42, 69, 72,

76, 145 marketing know-how 88–90, 103,

103e, 104e, 104, 105, 105e, 106, 144

marketing managers 83e markets 50

inefficiency as means of knowledge transfer 26

national 23 Martinez, J. I. 40, 41e, 44, 46 Marx, J. 13e maximum likelihood (ML)

method 153 McAdam, R. M. 12 McCreedy, S. 12 McDermott, R. 8 measurement error 69 measurement models 156e measures of fit 154, 155e, 155, 156

descriptive 156 incremental 155stand-alone 155suggested assessment 156e

Mendenhall, M. 102 mentors 78, 93e, 94, 96e, 141 mergers and acquisitions (M & A) 29,

82, 85, 90 message transmission 15Metanational Organization (Doz,

Santos and Williamson) 22 misunderstandings (cultural) 101e,

102, 124, 133, 144 MNCs see multinational corporations mode of entry 77, 90, 143 mode of set-up 82, 90, 91 Model of Intra-MNC Knowledge

Transfer 107 monitoring 44 Morgan, N. A. 65 Morosini, P. 55 Morrison, A. J. 40, 41e, 41, 43, 46, 86 motivation 28, 121, 143, 147,

157(n1 to ch4) Mudambi, R. 33e, 34

Index 179

Multifocal Organization (Doz) 22 multinational corporations

(MNCs) 2, 81 ability continuously to combine and

recombine knowledge 24Austrian 73–4 business environment 23 combinative capability 142 competitive information 23 conceputalization 19–23coordination and control 43–5European Top 500 70–1,

157(n2 to ch5) evolutionary theory 26 foreign added-value activities 19functional areas 69 heterarchical model 21, 22, 141 hierarchy model 22, 24 home-based model 21integrated network model (Bartlett

and Ghoshal) 22, 23, 24 integrative mechanisms 141Japanese 18 knowledge and 3, 5–36knowledge transfers 26–36knowledge-based view 19, 23,

25–6, 34, 49 ‘knowledge-integrating

institution’ 50 learning organization 21, 23–4 modes of entry 55 N-Form 22 network perspective 139–41‘network of units’ 21–2relevance of knowledge

management 19–36requirements (Bartlett and

Ghoshal) 20 resource-based view 19, 25–6, 49 role assigned to subsidiaries 46shift from hierarchical to

heterarchical conceptualization 37–8

‘social community’ 50strategic and organizational

integration 20 structure 23–4see also organizational units

multinational corporations: knowledge-based determinants of strategic configuration 3,37–57

capability perspective 49–52 contingency factors 52–7

coordination and control (different strategic mandates) 46–7

coordination and control (within MNC) 43–5

headquarters–subsidiary relationships 37–9

knowledge flow optimization 47–9 strategic mandates 39–43, 46–7,

47–9

Nahapiet, J. 31e, 34, 142 Netherlands 72 network effects 71 network knowledge 148 network perspective 139–41, 143, 146networks 21–2, 31e, 33e, 34, 35e, 35,

37, 38, 44, 45e, 45, 46, 47, 53, 56, 58, 60, 133

global 86, 99 interdependent 22strategic position in 116

Nobel, R. 32e, 35e Nobeoka, K. 31e, 146 Nohria, N. 12, 51, 37, 124,

157(n1 to ch5) Nonaka, I. 6, 9e, 9, 13e, 18, 18e,

62, 78, 95, 96 normed fit index (NFI) 114, 115e,

155e, 155, 156e norms 7 North America 74e, 75e North American Industry

Classification System (NAICS) codes 71

Norway 72

objectivity 6 O’Brien, M. 57, 147 O’Dell, C. 8, 31e one-firm studies 146 operational distance 78–9

self-perception 78operational mechanisms 99, 100 opportunity cost 123 opportunity generation 51, 62 organizational

capabilities 3, 49–50, 67effectiveness 142 networks 139 memory 13 processes 7 skills and routines 49structure 68 see also institutional isomorphism

180 Index

organizational distance 54, 59e, 65–6, 67, 86, 99–101, 103, 108, 110e, 110, 111e, 114e, 133, 138, 139, 145, 148, 157(n1 to ch6)

hypothesis 66, 118e, 123–4, 127e, 132e, 137e

second dataset 127e third dataset 131e

organizational units (of MNCs) 63, 68, 80, 108

dual task 60 extent of integration in global

network 82 hypotheses 60, 115, 116 orientation 79 potentiality to transfer

knowledge 60 size (number of employees) 81–2,

82e, 85, 85e ‘source’ and ‘target’ 60 strategic position, 67, 147–8 unit of analysis 69–70,

157(n1 to ch5) working experience in other, 78

output control 45e, 45 outputs 44

p-value 115e patent citations 146 Pearson correlation coefficient 91, 101 Pedersen, T. 28, 33e, 35e, 35, 61,

157(n1 to ch4) peer subsidiaries see

subsidiary–subsidiary relationshipsPentland, B. T. 13e people/humans 17, 52 perceived benefit 103–6, 113–14e,

123–4, 129, 130, 139, 143, 144, 146, 150–1e

hypotheses 117e, 118e, 122, 126e, 127e, 130, 131e, 132e, 135e, 136e, 137e

proxy for knowledge transfer effectiveness 145

second dataset 126e, 127e third dataset 130, 131e, 132e

perception 6, 79 cultural distance 102 headquarters–subsidiary

relationships 37 perception gaps 69, 91 perception measure 101 performance 44, 54, 55, 57, 65 Perlmutter, H. V. 2, 21, 25

permanent teams 77, 91, 92, 92e personal contacts 95, 143 Pflesser, C. 111, 154, 156 ‘piece of knowledge’ 27 Plato 6 Poland 72 Polanyi, M. 8, 9 Porter, M. E. 2, 21, 25 Postrel, S. 31e power 14, 28, 38, 68, 148 Poynter, T. A. 22, 37, 41e Prahalad, C. K. 25 Probst, G. 7e, 7 product

development 31e, 32e flows 43 knowledge 120e

products 11e, 42, 43, 86, 94e, 95 ‘psychic’ distance 55 purchasing 42, 76, 139 purchasing know-how 88–90, 103,

103e, 104e, 105e, 106

questionnaires 68, 71, 79, 119 design and pre-test 70pre-contacts (effect on

response rate) 72 response rate 73–4

Raub, S. 7e, 7 Raven, A. 161 real estate 73e regression equations 108 research design and methodology 3,

68–80, 157challenges in knowledge

management research in MNCs 68–9

countries 72–6, 81 country of origin effects

(insignificant) 74 data collection 3, 68, 70–6 final sample 68 operationalization and

measures 68, 76–80 questionnaire design and pre-test 70 research context 68–70 sampling process 3, 68 unit of analysis 69–70

research and development 23, 32e, 69, 145

resource dependence 38, 44 resource flows 46 resources 22, 23, 25, 40, 49

Index 181

responsiveness 21, 22 ‘role’ 40 Romania 72 Romhardt, K. 7e, 7, 12 root mean squared error of

approximation (RMSEA) 115e, 115, 156e

Rosenzweig, P. M. 56 Roth, K. 41e routines 7, 9, 49Rugman, A. M. 20 Russia 72

Sabherwal, R. 8, 32e, 35e, 35, 54, 66, 78, 96, 108

sales 42, 43, 69 Salisbury, D. 12 Salk, J. E. 144, 147 Salzberger, T. 72 Santos, J. F. P. 22, 29, 31e, 34, 53,

55, 62, 66 satisfaction 106–7, 107e, 122, 124,

125, 128–33, 139, 141–6, 148, 151e, 151, 152e

hypothesis 117e, 118e, 127e, 131–2e, 135–7e

proxy for knowledge transfer effectiveness 145

reflective variable 111e, 113e, 114e second dataset 127e third dataset 131–2e

Schlegelmilch, B. B. 33e, 35e, 35, 72

Schmidt, R. A. 8, 13, 13e security 51, 62 Segars, A. H. 32e, 35e, 35, 50, 66, 78,

108, 142 service industries 30e service sector 72, 73e Shannon, C. E. 15, 15e, 16, 52 Shenkar, O. 79, 102 Shin, M. 8, 13, 13e Siguaw, J. A. 109n, 112n Simonin, B. L. 8, 28, 29, 65, 78, 108,

144, 145 Singapore 73 Singh, H. 79, 86, 101, 102, 144 Singh, J. V. 56 Sinkovics, R. 72 Sitkins, S. B. 56 skills 8, 22, 47, 49, 50

lacking 144 Slovakia 72 Slovenia 72

social capital 31e, 32e, 34, 142, 145

‘social knowledge’ 10 socialization (knowledge conversion

process) 18, 18e, 45e, 45, 46, 48, 61, 62–3, 64e, 78, 97, 97e, 98e, 98, 124

formal and informal 141 ‘hypothesis 1b’ 63, 116

Sölvell, O. 21 ‘South East Asia’ 73 space 2Spain 72 Specialized Contributors (receptive

subsidiaries) 41e, 42, 43, 46 spiral of knowledge 18–19, 62 Stevenson, W. B. 56 Stewart, K. A., et al. (2000) 11e, 161

Baskerville, R. 161 Long, C. 161 Raven, A. 161 Storey, V. C. 161

Storey, V. C. 161 strategic alliances 27, 144 Strategic Leaders 41e, 43, 46, 47 strategic mandates (of subsidiaries) 3,

21, 39–43, 45, 58–60, 64, 83–7, 98, 99, 108, 110e, 110, 111e, 113, 113e, 116, 140, 149e, 157(n3, n6 to ch4), 158(n10, n13)

coordination and control 46–7cross-tabulation with number of

employees 85, 85e embedded 85 ‘global’ and ‘local’ 86 hypothesis one 60 independent 85 means and medians 84–5 measurement 76–7, 79,

157(n3 to ch5) medians 86 nature of operations 58 optimization of knowledge

flows 47–9perspectives 41 subsidiaries 39–43‘world’ 86 see also Global Innovators;

Integrated Players; Local Implementers; Local Innovators

strategic value disciplines (Treacy and Wiersema) 79

strategy 40

182 Index

structural equation modelling (SEM) 4, 81, 86, 98–9, 107–24,139, 157(n3 to ch5), 158(n9–13)

assessment of model 114–15, 153–6average variance extracted

(AVE) 151, 152e, 155e, 156e causal relationships 115 construct-to-indicator

relationships 110 constructs 109e, 111, 112e, 149,

149e–51e content validity 110 ‘data = model + residual’ 111 dataset one 114e, 125 dataset two 125e, 125 dataset three 125, 129e datasets 107, 139, 144 degrees of freedom 153, 154e,

155e, 156 description 108–13, 158(n9) descriptives 154e design 108, 109e factor reliability (FR) 151, 152e,

154, 155e, 156e formative variables 111e measures of fit 154, 155e, 155 ‘has to be overidentified’ 112 hierarchical (headquarters–

subsidiary) data 107 hierarchical relationships and

culturally close subsidiaries 107–24, 158(n9–13)

hypotheses 115 ‘hypothesis-testing approach’ 108 identification 153indicator reliability (IR) 151, 152e,

154, 155e, 156e indicators (formative and

reflective) 110 just-identified 153latent variables (factors) 108, 109e,

110e, 110, 111, 149 latent variables (endogenous/

exogenous) 108, 109e, 154e lateral (peer subsidiaries) data 107 maximum likelihood (ML)

method 153 multivariate methods 108 null hypothesis 155 observable (manifest) variables

(indicators) 108, 110, 115, 149, 149e–51e, 153, 154e, 158(n9)

overidentified 153

parameter estimation 114, 153, 154e, 154

recursive and non-recursive 108 reflective variables 111, 111e rejection 153 reliability of measurement

model 151–2results 115–24 sample size 154e, 156 selection of variables 112e specification of model 114, 149–52Squared Multiple Correlation 151 structural model v. measurement

model 111 three partial models 113 unidimensionality of

constructs 149–51 unstandardized results 115 variables 151, 154e

structural equation modelling (SEM): Model One 81, 113e, 114, 115, 117e

descriptives 154e first dataset 134–5e global measures of fit 115e second dataset 126e, 128, 134–5e third dataset 130, 131e, 134–5e

structural equation modelling (SEM): Model Two 81, 113e, 114, 115, 117e–18e

descriptives 154e first dataset 135–6e global measures of fit 115e second dataset 126–7e, 128–9,

135–6e third dataset 130, 131–2e, 133,

135–6e structural equation modelling (SEM):

Model Three 81, 114e, 114, 115, 118e

descriptives 154e first dataset 135–6e global measures of fit 115e second dataset 127e, 129, 136–7e third dataset 132e, 133, 136–7e

structure–conduct–performance (SCP) paradigm 25

Subramaniam, M. 29, 32e, 35e, 55, 144 subsidiaries 13, 20, 22, 24, 29, 30e, 33e,

35e, 35, 41, 68, 69, 72, 79, 81–90, 92, 95, 97, 138, 139–40, 141, 142

autonomous initiative 47, 48 benefit from knowledge

transfers 103

Index 183

benefits of knowledge transfers from culturally close subsidiaries 104e, 104, 106

benefits of knowledge transfers from culturally distant subsidiaries 104e, 104, 106

benefits of knowledge transfers from headquarters 103e, 104, 105–6

culturally close 100, 100e, 103, 114e, 107–24

culturally distant 100, 100e, 103, 114e, 124–9

focal 76e, 99, 100 global responsibility and

authority 47, 48 greenfield investment 82, 85, 90 hierarchical relationships 107–29‘hypotheses 1a–1d’ 63, 116 informal transmission

channels 93e, 94 integration/interdependence/

coordination 40 knowledge flows 76e knowledge stock 88e lateral interdependence 39, 47, 48 lateral knowledge flows 27 local adaptation 90 location 75e, 75–6 managers 70 mode of set-up 85 perceptions of similarity vis-à-vis

headquarters and peer subsidiaries 99e

responsiveness/autonomy 40 role 38, 39, 46 self-sufficiency 43 size 77, 82, 90, 91, 143 strategic mandates 39–43strategic orientation scales 100–1 twofold embeddedness 38 via merger or acquisition 82, 85 see also headquarters–subsidiary

relationships; strategic mandates

subsidiary groups 99 subsidiary–subsidiary relationships

(‘lateral relationships’/‘peer subsidiaries’) 46, 62, 65, 71, 83–4, 89, 90, 99, 113, 125e, 125, 144–5

‘hypotheses 1a–1d’ 63 knowledge flows 76e knowledge stock 88e results 130–3, 134–7e

third dataset 129–3, 134–7e transmission channels 92 see also headquarters–subsidiary

relationships summated-scales model 158(n9) Sveiby, K-E. 6, 16, 16e, 17, 32e, 34,

52, 62, 157(n1 to ch2) Sweden 72 Switzerland 72, 75e systems theory 6, 12 Szulanski, G. 15, 23, 28, 29, 30e, 35e,

157(n1 to ch4)

t-tests 95, 97, 107, 115, 155 Takeuchi, H. 6, 9e, 9, 13e, 18, 18e,

62, 78, 95, 96 technology 28, 30e, 45, 52, 76, 139,

142 to monitor competition and

business partners 95 problem-solving 95, 96e, 97e to retrieve, use and search for

knowledge 95 technology know-how 88, 88e, 89e,

89, 90, 103, 103e, 104e, 104, 105e technology transfer 29, 30e Teigland, R. 32e, 145 telephone contacts 19, 71, 72 temporary task forces 77, 91, 92, 92e Tenkasi, R. V. 8 Thilenius, P. 160 Tierney, T. 12, 51 ties and network perspective 34,

35e, 37 time 2, 17, 120e, 123, 146 top/senior management 46, 51, 82,

102, 124 trade (wholesale and retail) 73e training 123

on-the-job 95, 96e, 97e, 143 transaction costs 50, 56 transmission channels see knowledge

transmission channels transnational organizations 22, 24

heterarchical 24 transportation 73e Treacy, M. 79 Triandis, H. C. 160 Tsai, W. 21, 33e, 34, 35e Tsoukas, H. 11e, 12

uncertainty 38, 44, 45 United Kingdom 72, 75e Uppsala Model 55

184 Index

Vahlne, J.-E. 55 value added/added value 19, 42 value chain 42, 46

‘control circuit’ 14 four stages (Hong) 12–13 knowledge management 12–14strategically driven 14

value creation 39, 142 value of knowledge stock 59e, 60–1,

67, 77, 83, 88–91, 110e, 111e, 113e, 138, 143, 148, 157(n1 to ch4)

further research 121 headquarters compared with

subsidiaries 89e hypothesis 61, 117e, 121, 126e,

131e, 135e perception 77 second dataset 126e subsidiaries compared with

headquarters 89e subsidiaries compared with peer

subsidiaries 88e third dataset 130, 131e variables 90, 157(n2 to ch6)

variances 153, 154, 155 Venkatraman, N. 29, 32e, 35e,

55, 144 Venzin, M. 12 Vernon, R. 20 Vladimirou, E. 11e, 12 Von Hippel, E. 29

Weaver, W. 15, 15e, 16, 52 Weggeman, M. 13e Wernerfelt, B. 25 White, R. E. 22, 37, 41e Wiersema, F. 79 Williamson, P. J. 22 Wolf, J. 140 World Mandate 41e, 43, 46

Zack, M. H. 8, 12 Zahra, S. A. 51 Zander, I. 21 Zander, U. 8, 23, 25, 28, 29, 30e,

35e, 49, 50 Zeybek, A. Y. 57, 147 Zhou, C. 144

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Effective Knowledge Transfer in Multinational Corporations