Organization design and its impact on the digital innovation process and the digital innovation outcome [1st ed.] 9783658308049, 9783658308056

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Robert Eirich

Organization design and its impact on the digital innovation process and the digital innovation outcome

Schriften zur Unternehmensentwicklung Reihe herausgegeben von Max J. Ringlstetter, Wirtschaftswiss. Fakultät, Kath. Universität Eichstätt-Ingolstadt Wirtschaftswiss. Fakultät, Ingolstadt, Germany

In dieser Schriftenreihe werden aktuelle Forschungsergebnisse im Bereich der Unternehmensentwicklung präsentiert. Die einzelnen Beiträge orientieren sich an Problemen der Führungs- bzw. Managementpraxis. Im Mittelpunkt stehen dabei die Themenfelder Strategie, Organisation und Humanressourcen-Management. Herausgegeben von Prof. Dr. Max J. Ringlstetter Kathol. Universität Eichstätt-Ingolstadt

More information about this series at http://www.springer.com/series/12214

Robert Eirich

Organization Design and its Impact on the Digital Innovation Process and the Digital Innovation Outcome

Robert Eirich München, Germany Robert Eirich, Catholic University Eichstaett-Ingolstadt, Ingolstadt Supervisor: Prof. Dr. Max Ringlstetter Co-reviewed by: Prof. Dr. Harald Pechlaner

ISSN 2628-7382 ISSN 2628-7404  (electronic) Schriften zur Unternehmensentwicklung ISBN 978-3-658-30804-9 ISBN 978-3-658-30805-6  (eBook) https://doi.org/10.1007/978-3-658-30805-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Responsible Editor: Carina Reibold This Springer Gabler imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany

Introductory Remarks

Innovations have been and are still seen as the driving force of successful companies. With Clayton Christensen’s work on the innovator’s dilemma in the late 1990s, the need for disruptive innovation has been anchored in business mangers’ mind (Christensen 2013). Companies since then try to find smart ways of adopting new technology or business models to meet customers’ future needs. Through the digitalization and the speed of new products and services or completely new business models entering the market, the search for disruptive innovations has been even more intensified in recent years. Hereby, the questions of how to organize for those innovation challenges has been discussed, often with different focuses. In the light of digital innovations, those discussions have been revitalized as a lot of companies have started to organize differently for the digital innovation challenges ahead. Innovation vehicles such as digital innovation hubs, digital labs, think tanks, digital incubators popped up in the last 3–5 years across different countries and industries. Every company which claims to be innovative at least started some “new” innovation format to ensure that digital innovation will be created. Major objective of most companies in terms of digital innovation is maintaining competitive advantage. A lot of different opinions about the success of those digital innovation vehicles are discussed in the research and business world. Those discussions do not yet provide a clear picture on how to be successful from an organization design perspective. Thus, the question of whether there is a holy grail on how to best organize a company’s innovation activities to see positive results on the digital innovation outcome and the digital innovation process has fascinated me. Hence, the objective of this work is to empirically reveal different configurations which lead to a positive digital innovation outcome. For managers, this

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analysis is supposed to provide a clear perspective on the prime implicants for specific output variables and to improve decision-making for digital innovations. For research, it bridges the gap between two different fields—innovation management theories and organization (design) theories—providing insights for both streams and giving others a starting point to rethink existing frameworks when it comes to digital innovation. Like the innovation process, writing a dissertation at some point is facing resistance even if it is just self-doubt about the own approach and results. To overcome those obstacles, I could count on the support of different people to whom I would like to express my gratitude to. First, I would like to thank my PhD supervisor, professor Max J. Ringlstetter who gave me the opportunity to pursue this project and who has always been a great source for challenging my approach and my results. Second, I would like to extend my gratitude to Professor Harald Pechlaner as my co-supervisor, his interest in my topic and his support especially at the end of the project. From the LSR team, I would particularly like to thank Walburga Mosburger who has been supportive in any situation throughout my PhD journey. Moreover, Dr. Björn Schäfer holds a big share in the success of this dissertation project— thank you for always challenging my progress and providing feedback on my work. Moreover, I would especially like to thank Paul Goldmann who has been the best source for any academic work challenge which I experienced and to Martin Schmidt (my university “roommate”) who had to stand my jokes and sometimes my bad mood. I would also like to thank the rest of the LSR team (Anne-Sophie Brillinger, Florian Chitic, Elena Eberle, Ole Ibbekken, Vinzenz Krause, Michaela Lang, Martin Rademacher, Anna-Sophie Risch, Marius Schramböhmer) for always making the research seminars, Christmas parties and other team events a blast. It has always been a great time with you! A big thank you is further dedicated to Franziska Heindl. Thank you for being a great moral support and for celebrating all the highs together with me as well as handling some of the lows that go hand in hand with such a project. Finally, I would like to particularly thank my parents, my grandfather and my brother Martin who have supported me throughout my whole studies and who also did during this dissertation project. This work is dedicated to you. Robert Eirich

Preface

Digital innovation is currently an important or even the dominant topic in the discussion about the future of society. A lot of people from different disciplines and schools of thought participate in this discussion. To name some examples: Environmentalists who despair because of the increase in power consumption needed for digital innovation participate in the discussion, theologians contribute with rather philosophical remarks or computer scientists carry out interdisciplinary work to come up with new products and services (e.g. software). Hence, economists and business administration scholars must be included in that discussion. From a business administration perspective there are different forms and opportunities to define digitalization and conduct research in specific areas. The topic of digital innovation can be found along the value chain in any industry, starting from the purchasing processes and ending with the market positioning of new products or services including life-cycle-costs for the ­end-customer. Independent of the different applications of digital innovation all topics include something new, hence an emphasis must be put on the innovation part. Innovation hereby must fulfill a strict additional condition: Not only companies have to find the innovation attractive, but the customers must be enthusiastic about the new products or services. This basic business rule applies following a core mantra in business administration for decades: Innovations are innovative if the market cheers “Hooray”. This work in that context does not only focus on different opportunities to create new products or services through digitalization. The work indeed investigates how different innovations and in specific those which are related to digital technologies can be organized. Although the question of organization is only a part of the overall topic “digitalization” which covers multiple facets and disciplines,

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the topic requires further research as previous work mostly focus on other forms or a less narrow definition of innovation. Thus, the work helps to establish transparency on different options to organize for the existing digital innovation challenges. The dissertation hereby is not using a pure deductive or inductive approach to derive insights for both research and practitioners. The findings build up on a complex approach of several research building blocks. Those building blocks include a traditional literature overview, a small pre-study, a larger survey and a configurational fuzzy-set qualitative comparative analysis (fsQCA). By applying the different techniques, the work contributes to research by testing and expanding existing organization design theories and frameworks to the new conditions caused by digital innovation. It triggers a comeback of organization design theories and gives other researchers a starting point for work on the new phenomenon. Moreover, the dissertation provides insights on different organizational configurations which lead to positive digital innovation outcomes. The results give managers guidance on how to allocate scarce resources. Furthermore, it makes it easier for managers to set priorities and focus on the urgent organizational topics within the opaque context of digital innovation. Prof. Dr. Max J. Ringlstetter

Contents

1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Historic Development of Relevant Research Streams and Definition of Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1 Innovation and Innovation Management. . . . . . . . . . . . . . . . 4 1.1.2 Digital Innovation and Digital Innovation Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.1.3 Organization Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 Objectives of the Research Conducted. . . . . . . . . . . . . . . . . . . . . . . . 8 1.3 Structure of This Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Basic Theories and Concepts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1 Organization Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.1 An Overview of the Major Organizational Design Frameworks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.1.2 Galbraith Core Organization Design Framework . . . . . . . . . 23 2.1.3 Review of Galbraith’s Organization Design Framework. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.1.4 The Expanded Galbraith Organization Design Framework. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.1.5 Design Principles and Organization Design Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 2.2 Digital Innovation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 2.2.1 Digital Innovation as a New Innovation Type . . . . . . . . . . . . 49 2.2.2 The Innovation Process: Description and Developments for Digital Innovation . . . . . . . . . . . . . . . 62 2.2.3 Key Measurements for Digital Innovation. . . . . . . . . . . . . . . 65 2.2.4 Digital Innovation in Different Industries . . . . . . . . . . . . . . . 70 ix

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2.2.5 Digital Innovation and Firm Size. . . . . . . . . . . . . . . . . . . . . . 73 2.3 Organization Design for Digital Innovation. . . . . . . . . . . . . . . . . . . . 74 3 Empirical Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 3.1 Background of the Research Design . . . . . . . . . . . . . . . . . . . . . . . . . 84 3.1.1 Applied Research Principles and Methods . . . . . . . . . . . . . . 84 3.1.2 Following the Principles of Case Study Research. . . . . . . . . 86 3.1.3 Fuzzy-set Qualitative Comparative Analysis (fsQCA) . . . . . 91 3.2 Insights from the Pre-study on Organization design Parameters for Digital Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 3.3 Case Study Companies and Interviewees. . . . . . . . . . . . . . . . . . . . . . 97 3.3.1 Selection Process and Overview . . . . . . . . . . . . . . . . . . . . . . 97 3.3.2 Digital Innovation Activities Within the Case Companies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 3.4 Results from Case Studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 3.4.1 Results for Organization Design Parameters. . . . . . . . . . . . . 103 3.4.2 Results for the Outcome Variables Quantity, Quality, Speed, and Costs. . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 3.4.3 Conjoint Display for Case Study Results. . . . . . . . . . . . . . . . 172 3.5 Results from Fuzzy-set Qualitative Comparative Analysis (fsQCA). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 3.5.1 Calibration of the Causal Conditions and the Outcome Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . 190 3.5.2 Necessary Conditions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 3.5.3 Sufficient Conditions Based on the Truth Table. . . . . . . . . . . 199 3.5.4 Configurations for Positive Innovation Outcomes. . . . . . . . . 207 3.6 Discussion of Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 3.6.1 Different Strategies for Tackling the Digital Innovation Challenge. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 3.6.2 Deep-dive in the “Structure” Parameter. . . . . . . . . . . . . . . . . 222 3.6.3 The Need for a Holistic Approach. . . . . . . . . . . . . . . . . . . . . 227 4 Implications and Limitations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 4.1 Implications for Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 4.2 Implications for Practitioners. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 4.3 Limitations to the Study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Publication Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241

Abbreviations

BM Business Model B2B Business-to-Business BU Business Unit CDO Chief Digital Officer DACH Germany, Austria, Switzerland DIP Digital Innovation Performance DNA Deoxyribonucleic Acid DU Digital Unit EPO European Patent Office fsQCA Fuzzy-set Qualitative Comparative Analysis GmbH Limited company (Gesellschaft mit beschränkter Haftung) HLM Hierarchical linear modeling HQ Headquarters IRS Internal Revenue Service IT Information Technology KPI Key Performance Indicator NDA Non-Disclosure Agreements OD Organization Design OECD Organization for Economic Co-operation and Development OEM Original Equipment Manufacturer O2O Online-to-Offline PRI Proportional reduction in inconsistency R&D Research and Development SD Standard Deviation SME Small and Medium Enterprises

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Tech Technology TMT Top Management Team USPTO United States Patent and Trademarks Office VC Venture Capital

Abbreviations

List of Figures

Figure 1.1 Figure 1.2 Figure 1.3 Figure 2.1 Figure 2.2 Figure 2.3 Figure 2.4 Figure 2.5 Figure 2.6 Figure 2.7 Figure 2.8 Figure 2.9 Figure 2.10 Figure 2.11 Figure 2.12 Figure 2.13 Figure 2.14 Figure 2.15

The current “innovation dilemma” with digital innovation and organization design. . . . . . . . . . . . . . . . . . . . 3 Overview of key research streams. . . . . . . . . . . . . . . . . . . . . 9 Overview of different research formats used across this dissertation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 McKinsey’s 7S framework including hard and soft factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Weisbord Six-Box Organizational Model. . . . . . . . . . . . . . . 17 Tushman’s Congruence Model for Organization Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 The 5-STAR model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Overview of variables, measurements, and scales . . . . . . . . 29 Digital innovation performance. . . . . . . . . . . . . . . . . . . . . . . 29 Frequency distributions (ranked) for parameter importance in % . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Pearson inter-correlations, Means, and Standard Deviations (SD). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Hierarchical regression results . . . . . . . . . . . . . . . . . . . . . . . 33 Results of hierarchical linear modeling (HLM) . . . . . . . . . . 34 Adjusted STAR model including legacy as a guiding principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Functional structure for a pharmaceutical company. . . . . . . 37 Product structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Geographic structure for a beverage bottling company . . . . 38 Customer structure for the Internal Revenue Service (IRS), a U.S. government agency. . . . . . . . . . . . . . . 39

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Figure 2.16 Figure 2.17 Figure 2.18 Figure 2.19 Figure 2.20 Figure 2.21 Figure 2.22 Figure 2.23 Figure 2.24 Figure 2.25 Figure 2.26 Figure 2.27 Figure 2.28 Figure 2.29 Figure 2.30 Figure 2.31 Figure 2.32 Figure 3.1 Figure 3.2 Figure 3.3 Figure 3.4 Figure 3.5 Figure 3.6 Figure 3.7 Figure 3.8 Figure 3.9 Figure 3.10 Figure 3.11 Figure 3.12 Figure 3.13

List of Figures

Functional structure for Standard Products Co., a hypothetical company. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Matrix structure for Standard Products Co., a hypothetical company. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Range of organization design alternatives based on Galbraith. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Centralized organizational structure (schematic) . . . . . . . . . 48 Decentralized organizational structure (schematic) . . . . . . . 48 Distributed organizational structure (schematic) . . . . . . . . . 48 Innovation types and digital innovation based on Kates and Galbraith. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Digital innovation definitions per case company . . . . . . . . . 53 Digital goods classification. . . . . . . . . . . . . . . . . . . . . . . . . . 58 Development path of business model changes based on digital innovation. . . . . . . . . . . . . . . . . . . . . . . . . . 62 Schematic innovation process. . . . . . . . . . . . . . . . . . . . . . . . 63 Schematic of a five-gate stage gate process . . . . . . . . . . . . . 63 Digital innovation quality measurements. . . . . . . . . . . . . . . 68 Digital innovation speed measurements. . . . . . . . . . . . . . . . 69 Digital innovativeness of different industries. . . . . . . . . . . . 72 Range of extended organization design alternatives. . . . . . . 77 Innovation role structure for a differentiated company. . . . . 80 Relevant situations for different research methods. . . . . . . . 85 Coding process of the interviews . . . . . . . . . . . . . . . . . . . . . 89 Fuzzy-set QCA operational steps. . . . . . . . . . . . . . . . . . . . . 93 Distance of digital innovation activities in the organization design alternatives . . . . . . . . . . . . . . . . . 95 Spread of case study companies across the digital innovativeness curve . . . . . . . . . . . . . . . . . . . . . . 98 Key characteristics of case study companies . . . . . . . . . . . . 99 Overview of interviews conducted. . . . . . . . . . . . . . . . . . . . 101 Location of case companies on the development path for business model changes. . . . . . . . . . . . . . . . . . . . . . 102 Code matrix for extended STAR model parameters. . . . . . . 104 Structural paths for case company 1. . . . . . . . . . . . . . . . . . . 106 Structural path for case company 2. . . . . . . . . . . . . . . . . . . . 111 Structural path for case company 3. . . . . . . . . . . . . . . . . . . . 115 Structural path for case company 4. . . . . . . . . . . . . . . . . . . . 120

List of Figures

Figure 3.14 Figure 3.15 Figure 3.16 Figure 3.17 Figure 3.18 Figure 3.19 Figure 3.20 Figure 3.21 Figure 3.22 Figure 3.23 Figure 3.24 Figure 3.25 Figure 3.26 Figure 3.27 Figure 3.28 Figure 3.29 Figure 3.30 Figure 3.31 Figure 3.32 Figure 3.33 Figure 3.34 Figure 3.35 Figure 3.36 Figure 3.37 Figure 3.38 Figure 3.39 Figure 3.40 Figure 5.1 Figure 5.2

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Structural paths for case company 5. . . . . . . . . . . . . . . . . . . 124 Structural path for case company 6. . . . . . . . . . . . . . . . . . . . 130 Structural paths for case company 7. . . . . . . . . . . . . . . . . . . 134 Structural path for case company 8. . . . . . . . . . . . . . . . . . . . 140 Structural paths for case company 9. . . . . . . . . . . . . . . . . . . 144 Structural paths for case company 10. . . . . . . . . . . . . . . . . . 149 Structural path for case company 11. . . . . . . . . . . . . . . . . . . 155 Structural path for case company 12. . . . . . . . . . . . . . . . . . . 160 Structural path for case company 13. . . . . . . . . . . . . . . . . . . 164 Overview of output results including qualitative interview quotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 Conjoint display for qualitative results. . . . . . . . . . . . . . . . . 173 Critical decision for each organization design parameter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Transfer/calibration matrix. . . . . . . . . . . . . . . . . . . . . . . . . . 192 Fuzzy sets for the conditions and output variables per case company. . . . . . . . . . . . . . . . . . . . . . . . . . 196 Necessary conditions for the four outcome variables. . . . . . 198 Truth table with thirteen relevant sets and four output variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 Complex solution terms for quality. . . . . . . . . . . . . . . . . . . . 201 Parsimonious solution terms for quantity. . . . . . . . . . . . . . . 202 Complex solution terms for quantity. . . . . . . . . . . . . . . . . . . 203 Parsimonious solution terms for speed. . . . . . . . . . . . . . . . . 204 Complex solution terms for speed. . . . . . . . . . . . . . . . . . . . . 205 Parsimonious solution terms for costs. . . . . . . . . . . . . . . . . . 205 Complex solution terms for costs. . . . . . . . . . . . . . . . . . . . . 206 Configurations for achieving positive digital innovation quantity. . . . . . . . . . . . . . . . . . . . . . . . . . . 208 Configurations for achieving positive digital innovation speed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 Configurations for achieving a positive cost/benefit ratio for digital innovation. . . . . . . . . . . . . . . . . . . . . . . . . . . 212 Development paths to achieving a more decentralized structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226 Outlook for organization design in the context of digital innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 Organization design cycle for digital innovation . . . . . . . . . 239

1

Introduction

“Structure follows strategy” is one of the key statements that researchers and practitioners have used for decades to discuss and describe the relationship between a company’s chosen strategy and the structure of an organization. This view already dates back to the first research by Chandler, who looked into the changing strategies and structures of large industrial enterprises in the United States (Chandler 1995). Over the decades, this first view has been reviewed, challenged, and extended by various researchers within the environment of general economic developments, innovations, and product ideas. Numerous other factors that have a potential influence on the structure and a company’s strategy have been analyzed (Whittington et al. 1999, pp. 585–588).Today, research has proven that the existing (intra-organizational) structures affect the capacity of the organization to pursue specific strategies. Conversely, structures also influence the managerial interests that shape a firm’s specific strategy decisions. One core topic that requires further analysis in this context is innovations for new products and services and the innovation process; over the years, both have faced significant changes. In that context, “digital innovations” used within but also as a result of the innovation process are important topics to analyze. The increasing importance of the “digital innovation” topic is also related to companies’ realizing that to maintain a competitive advantage in the fast-moving tech world of today, they are not allowed to be suspended in terms of new digital products and services (Dereli 2015, pp. 1367–1369). Creating this competitive advantage requires well-defined and agile digital innovation management within the organization. The relationship between successful digital innovation management and the organizational setup has to date been only partially researched (Nambisan

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 R. Eirich, Organization Design and its Impact on the Digital Innovation Process and the Digital Innovation Outcome, Schriften zur Unternehmensentwicklung, https://doi.org/10.1007/978-3-658-30805-6_1

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1 Introduction

et al. 2017, pp. 1083–1086). Existing theories within innovation management or organization design theories have not yet been expanded to new conditions. This gap in existing research is also reinforced by the missing forward-looking nature of most of the research streams in organization theories during the last 50 years. An additional problem that has evolved during the last decade in both practice and theory is the focus on “silver bullet solutions” in the context of innovation, such as design thinking training, startup accelerator programs, or innovation labs. These innovation vehicles and methods can all add value and create meaningful change. Unfortunately, from my perspective, most companies and researchers have missed out on developing a holistic approach that accounts for the underlying organizational foundation and culture for these vehicles. Moreover, the interplay of the different vehicles and the first-step decision on the guiding principles, objectives, and success factors to measure the initiatives at the end are neglected by most scholars and managers. One reason for this behavior is the action bias, which can be best described using the example of a football goalkeeper facing a penalty kick in an important game. The norm for goalkeepers in penalty kicks is to act (jump to the right or left) based on the norm theory. Notably, researchers found that, given the behavior of kickers, for the goalkeeper to choose to stay in the center of the goal is optimal (which can be considered inaction), but goalkeepers almost always jump to the right or left (Bar-Eli et al. 2007, pp. 610–611). In the business world, this cognitive behavior (the action bias) translates to the manager’s attempts to show that the company is doing at least something with regard to digital innovation. This action is often undertaken independent of the chances of success and without clear coordination. In Figure 1.1, I have summarized the potential interplay of organizational strategy, organizational design, operationalization, and measurement regarding (digital) innovation. As previously described, I see that scholars and managers are currently focused on operational questions. Unfortunately, overarching organizational questions, including strategy questions, a clear organization design, and the measurement of implemented activities, are neglected. It is evident that many corporates face this new “(digital) innovation dilemma.” To overcome this problem, the theories of innovation, leadership, and specifically organization design must capture the tensions between contrasting innovation modes (Benner and Tushman 2015, pp. 509–510). In line with that view, researchers see a clear need to further develop and define theories for digital innovation management and its organization design (Henfridsson et al. 2014, p. 40; Nambisan et al. 2017, pp. 1080–1082). Therefore, this area of research seeks to provide answers and guidelines to the organizational design questions.

1 Introduction

Organizational questions

3

Operational questions

Measurement questions

Timing: One-time (revision possible)

Digital innovation as strategy pillar Objectives

Success factors

Timing: Ongoing

High-level organizational design Structure, processes, etc. Timing: One-time (revision possible)

Operational "playground" Digital innovation vehicles, e.g., hubs, labs Own budget decisions Chief Digital officer yes/no …

Measurement of Innovation success Strategic, financial, based on process/output, etc. Timing: Ongoing

Figure 1.1   The current “innovation dilemma” with digital innovation and organization design. (Source: Own visualization)

The results from the research support the importance of revisiting previous organizational design theories and adapting them for digital innovation. This dissertation contributes to research and managerial practice through the following four major topics that are described in detail in the following chapters. (1) Organizational design questions are critical for innovation success in the digital innovation environment. By defining the right organization design, companies are more likely to be more successful with digital innovation. Thus, a comeback of organization design theories is required. (2) Empirical results for complex topics such as organization design can also be arrived at without using pure regression analysis but through mixed methods that are in-between qualitative and quantitative approaches. By executing a fuzzy-set qualitative comparative analysis (fsQCA), different configurations are revealed that lead to a positive outcome. For managers, this analysis provides a clear perspective on the prime implicants for specific output variables and helps significantly with improved resource allocation and management focus. (3) Digital innovation activities must be a top management task and need clearly defined objectives before starting with any execution of activities. Companies must define the output variables on which they want to focus because these

4

1 Introduction

are often opposed to each other. Furthermore, these variables require different designs and the use of different methodologies. (4) Measurements in the field of (digital) innovation are not yet clearly defined. Most companies miss the first step of defining what success within the digital innovation activities means for them and how they want to measure performance in that area.

1.1 Historic Development of Relevant Research Streams and Definition of Key Terms As briefly stated in the introduction, two major research streams (innovation management and organization design) with several sub-streams are the core topics of this dissertation. For both streams, the first studies date back to the early days of business administration research in the beginning and middle of the last century. These streams and sub-streams overlapped considerably and experienced several development waves during the last decades. The objective of this chapter is it to provide a structured overview of the historical developments in both fields. Moreover, key terms are introduced to ensure a consistent understanding of the chosen terminologies.

1.1.1 Innovation and Innovation Management Innovation is a term used frequently in daily use and most often in an attempt to describe something new, including products and services, ways of producing organizational forms, or new channels (Burr 2017, p. 16). Hence, innovation is difficult to describe and to measure objectively. Most of the time, innovation is also permeated by subjective perception (Voßkamp and Schmidt-Ehmcke 2006, p. 15). Numerous definitions of innovation exist based on a differentiation between the content of the innovation, such as product and process innovation, goods and service innovation, or radical and incremental innovation. Different innovation types can also be categorized using several criteria, such as speed, size, or other economic-related effects, including increases in productivity or flexibility or decreases in costs (Burr 2017, pp. 18–22). In the business sector, the definition that has been used since 2005 is derived from the OECD Oslo manual (Mortensen and Bloch 2005, p. 163; Voßkamp and Schmidt-Ehmcke 2006, p. 13) and forms the basis for this dissertation. This decision has been made even

1.1  Historic Development of Relevant Research Streams …

5

though some researchers seek a broader definition that applies to different economic sectors (Gault 2018, p. 619; Mortensen and Bloch 2005, pp. 163–165): “An innovation is the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations.”

Innovation as an organizational phenomenon has a long tradition of research and has been studied in many different fields. History has also led to ambiguous definitions and measures. On the one hand, research has been done on general innovation and its impact on diverse firm-relevant factors, starting with Schumpeter in the early 20th century (Schumpeter 2017). The overall focus to date has been heavily on the correlation of innovation activities and business performance. An overview of selected key topics and examples for studies and authors working in the area of innovation can be found in the following sources: (1) Impact of innovation on organizational performance (Damanpour et al. 1989; Gopalakrishnan 2000; Damanpour et al. 2009); (2) Impact of innovation on organizations, employees, and organizational communities (Glor 2014; Park et al. 2015; Damijan et al. 2014; Maben et al. 2015); and, (3) Impact of innovation on customers and customer satisfaction (Rubera and Kirca 2017). On the other hand, innovation researchers in the last decades also looked intensively at the input factors/pre-innovation factors that are supposed to have a positive or negative influence on innovation. In that context, people-related and collaboration topics in particular have emerged in the first decade of the 21st century. Selected research topics, authors, and studies are as follows: (1) Impact of know-how and organizational learning on innovations (Darroch 2005; Alegre and Chiva 2008; Quintane et al. 2011); (2) Impact of networks on innovations (Ritter and Gemünden 2003; Fritsch and Kauffeld-Monz 2010); and, (3) Impact of regulations and laws on innovations (Qian 2007; Blind 2012; Ramanathan et al. 2010). Overall, across all different studies and authors, innovations are mainly described by picking one of the various definitions available. A clear distinction between

6

1 Introduction

different innovation types and product families is mostly missing. For that reason, this dissertation attempts to account for this gap and focuses on digital innovations as one group of innovations that are further detailed in section 2.2. In contrast to the diverse terminologies for innovation, innovation management can be described relatively clearly by the control and management of innovation in implementation (Drucker 2014, p. 35). Innovation management is a multidimensional concept that includes knowledge, technology, people, vision, leadership, and organizational structure. To succeed, all dimensions must work hand-in-hand and must be managed properly—the most significant challenge for any company. The difficulty of setting up a structure and properly managing all dimensions lies in the nature of strategic contradiction because firms and top management teams must simultaneously organize for short-term efficiency as well as long-term innovation (Smith and Tushman 2005, p. 522). Thus, within the innovation management process, developing an administrative structure that supports new ideas and innovative tendencies is also key (Dereli 2015, p. 1370). Past research on the management of innovation covered a few different directions and focused on specific types of innovations or their interplay with other areas. One emerging topic in contemporary literature has been the management of open innovations (Chesbrough 2004; Fredberg et al. 2008; Huizingh 2011). Other research streams mainly observed the interplay of innovation management with specific dimensions, such as technologies, markets, design and creativity, or organizational change (Tidd et al. 2005; Stamm 2008). In this dissertation, the focus is on the interplay of innovation management and organizational structures. I also attempt to assist in developing a specific understanding of digital innovation in that context as a specific form of innovation that has recently increased in importance.

1.1.2 Digital Innovation and Digital Innovation Management In addition to the variety of definitions of innovation in general, some researchers—particularly in the field of information research—have recently realized that digital technologies (viewed as combinations of information, computing, communication, and connectivity technologies) are fundamentally transforming business strategies, business processes, firm capabilities, products, and services. Given this environment, research on innovation must also consider this type of innovation (Bharadwaj et al. 2013, p. 471). Hereby, most researchers believe that innovation theory is at a transition point as the digital revolution shifts the locus of innovation and challenges core organizing axioms (Benner and Tushman 2015, p. 502).

1.1  Historic Development of Relevant Research Streams …

7

In line with that research stream, this dissertation starts by defining digital innovation as the use of digital technology during the process of innovating or as the description (fully or partly) of the outcome of the innovation process (Nambisan et al. 2017, p. 223). Because that field of research is still in its infancy and is core to this dissertation, an overview of the definitions and specifications can be found in chapter 2. In this chapter, pre-study results from interviews with both innovation practitioners and innovation researchers are included. The digital innovation term defined by Nambisan is still relatively broad. Hence, this dissertation attempts to contribute to a more precise and tangible definition of digital innovation. As already touched on in the description of innovation management, organizational design is critical for a successful innovation process. To also give practitioners insights into the management of the specific digital innovation process, this dissertation analyzes the process and related management methods. These methods refer to practices, processes, and principles that underlie the effective orchestration of digital innovation (Nambisan et al. 2017, p. 224).

1.1.3 Organization Design As described in the previous chapter, several organizational topics (e.g., organizational learning or networks) can affect the innovation process and outcome. The core research fields that cover the structure and processes of an organization and that affect the strategy (including innovations and digital innovations strategy) are organization design and intra-organizational innovation management. Designing organizations and structuring how people work together are major factors that determine a company’s performance (Burton and Obel 2018, p. 4), making them significant topics for sustainable, long-term success. Organization design is based on organizational theories that describe the organizational structure and processes in a relatively stable status. Hence, such design assists in explaining any existing or emerging organizational phenomena. The downside of the scientific method with its descriptive and analytic nature cannot account for qualitative novelty (Ziman 2002, pp. 42–46; Romme 2003, p. 558). Organizational theories as the foundation of organization design have developed within several research streams and even contradict each other in some parts (Daniel 2018; Hatch 2018). The study of organizations already emerged at the beginning of the 20th century with its basic theories, and it has roots that span the existence of humans (Scott 2015). The first phase of prehistorical theories

8

1 Introduction

developed by, for example, Smith, Taylor, Weber, Fayol, or Marx, dominated until the 1950s (Hatch 2018): (1) Frederick Taylor developed the scientific management theory that attempted to maximize efficiency in the organization through specialization and standardization (Waring 2016); (2) Max Weber introduced the bureaucratic theory that analyzes defining rules that state that employees work on strictly described responsibility to restrain powers (Weber 2015; Daniel 2018); and, (3) Henry Fayol’s administration theory focused more on the principles of management through its elements of planning, organizing, commanding, coordinating, and controlling. More contemporary theories in subsequent decades started to consider several factors, such as human resources leading to motivational theories (Khorasani and Almasifard 2017). In today’s research, several new streams, such as system theory and contingency theory, have emerged as leading theories. These theories also led to major contradictions with already existing schools of thought. Thus, complexity and dynamics have been added to the overall stream of research on organizational theories. Organization design questions as future-looking organizational questions were already touched on 100 years ago. At the same time, research mainly focused on line and staff roles and purely on existing organizations and their past. In this dissertation, organization design takes a more forward-looking perspective and prescribes how an organization should be structured to function and achieve its pre-set goals (Romme 2003, pp. 558–560). Thus, organization design is viewed as an agile development of organizational theories. In line with that thinking, this dissertation defines organization design as the “systematic approach to align structures, processes, leadership, culture, people, practices, and metrics to enable organizations to achieve their mission and strategy” (Galbraith 2008, pp. 325–328).

1.2 Objectives of the Research Conducted Given the described significant variety in innovation research conducted until today, all of the studies and books indicate that all innovation involves two levels: the first is an actor (e.g., an individual, a team, an organization) and second is

9

1.2  Objectives of the Research Conducted

the broader environment within which the actor is embedded (Gupta et al. 2007). To date, most innovation researchers tend to focus at one level of analysis and do not consider a combination of the levels. With digital products and infrastructure emerging rapidly, the form of innovations and the way they are and must be produced is changing. Hence, the call is for agile organizations to engage in digital innovations. These organizations or organizational setups must develop quick responses to variations in the environment or a technology. To contribute to the existing literature and to give managers tools to manage digital innovation challenges, the core objective of this dissertation is to shed light on different organizational setups and the impact on the innovation process and outcome. This dissertation combines both the actor and the environmental levels in the findings. By analyzing the different relationships, this dissertation bridges the research on innovation management and digital products, platforms, ecosystems, and infrastructure with the research stream on organization design as part of advanced organizational theories. The key goal is it to find evidence and answers to the following core research question from industry examples: Does the organization design of a company affect digital innovation results, particularly the innovation process or outcome?

The impact of firm performance, which has been the focus of a number of prior studies related to innovation, is not in the scope of and is not covered in this dissertation. The interplay of the different theories as a foundation for the core research question is visualized in Figure 1.2.

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Figure 1.2   Overview of key research streams. (Source: Own visualization)

10

1 Introduction

In addition to the contribution to the literature and to answer the research question, I want to bring the research streams of innovation and organization theories closer together. Additionally, I aim to add value to a manager’s daily decision making. By not only providing scientific proof that the organization design affects the digital innovation outcome, I provide insights into the “how”, which includes a description of clear and important areas. Furthermore, the “how” involves a call for actions that should be undertaken to reach a defined set of results.

1.3 Structure of This Dissertation To accomplish the objective of shedding light on organization design for digital innovation, I start with an overview of theories from the two major research streams in chapter 2. Within section 2.1, I provide an overview and analysis of the major organization design frameworks that emerged in the 1980s. I review the different frameworks and expand one of the core frameworks, the so-called STAR model by Galbraith. Additionally, I take this expanded model and first describe peculiarities of organization design parameters that assist with organiz­ ing for innovation activities. In section 2.2 the focus is on the emergence of a new innovation type—digital innovation. This innovation type is classified and bounded from other innovation types. Moreover, the impact of digital innovation on the innovation process and the key measurements for digital innovation are introduced. This section is complemented by an overview of digital innovation in different industries and an analysis of the relationship between digital innovation and firm size. Chapter 3 purely focuses on the empirical approach and the results of the analysis. Section 3.1 provides an introduction to the research design and the applied research principles. This section also explains the approach that follows the principles of case study research before using a fuzzy-set qualitative comparative analysis. Section 3.2 reveals insights from a pre-study that was conducted to verify the research approach and to develop the first hypotheses. Section 3.3 provides an overview of the case study companies and the interviewees that were selected. Section 3.4 covers all of the qualitative results from the thirteen case studies for both the organization design parameters and the output variables. The results are summarized in a conjoint display for easier reading in section 3.4.3. These results represented in the conjoint display are used as the basis for the fsQCA, the results of which are presented in section 3.5. That section also includes the major fsQCA steps, such as the calibration of the causal conditions

1.3  Structure of This Dissertation

11

and a necessary and sufficient conditions analysis based on a truth table. The section also includes an overview of different configurations, leading to positive digital innovation outcomes. Chapter 3 concludes with a discussion of all empirical results. Based on the results, major strategies for addressing the digital innovation task from an organization design perspective are gleaned. Chapter 4 closes this dissertation in which I highlight the implications for both researchers and practitioners. Chapter 5 provides a conclusion and a future outlook for organization design and explains my call to develop more dynamic and forward-looking theories for organization design. Because several different research formats have been used and applied to shed light on the very complex phenomenon of organization design, the results from different methods are spread across different chapters of this dissertation. Hence, the results are not solely summarized in the empirical exploration chapter—as is done in most classic research work. To navigate through the findings and the different research formats used, Figure 1.3 provides an overview of the different research formats. Figure 1.3 also includes information on the chapter in which a method and/or the results are found. The main study results, including the fsQCA, are fully comprised in chapter 3, as previously described and are not included in the overview.

Topic

Details

Research format

Galbraith's STAR model

Appropriateness and extension of the model

Pre-study with 4 interviews with 2.1.3 Review of Galbraith's organization digital experts design framework 3.2 Insights from the pre-study on organization design parameters for digital innovation

Galbraith's STAR model

Importance of the OD Survey with 120 industry parameters and extension of participants the model

2.1.3 Review of Galbraith's organization design framework

Digital innovation definition

Definition and classification of digital innovation

2.2.1 Digital innovation as a new innovation type

Digital innovation in different industries

Development of a digital innovation curve to locate different industries based on the maturity of digital innovation

Pre-study with 4 interviews with digital experts Case study results from 13 cases based on 20 interviewees Pre-study with 4 interviews with digital experts

Section

2.2.4 Digital innovation in different industries

Figure 1.3   Overview of different research formats used across this dissertation. (Source: Own visualization)

2

Basic Theories and Concepts

To bridge the gap between the two key research streams (organization design and digital innovation management), chapter 2 provides an overview of the relevant organizational design frameworks and transfers them to digital innovation. Hereby, section 2.1 builds on one research stream within the organization design literature (as shown in the historic overview in section 1.1.3) in the 1980s during which first organizational frameworks have been developed. Those frameworks were supposed to help to better investigate different organizational design decisions. Based on an analysis of four core frameworks, Galbraith’s STAR model is chosen. That model is then tested with regard to its usability to help with the digital innovation task. Section 2.2 in that context provides a clear definition for digital innovation and the innovation process and introduces the major concepts (derived from literature as well as from first empirical results) which are used throughout the dissertation. Moreover, first key measurements for digital innovation are introduced for the outcome variables quantity, quality, speed and costs and further environmental influences are discussed. Those factors include industry specifics and firm size. Section 2.3 wraps-up the chapter by providing first organization design ideas for digital innovation within the validated parameters from Galbraith’s STAR model.

2.1 Organization Design The problem of designing efficient organizations arises from the choices available among alternative bases of authority structures. This challenge has been around for companies and research for decades. Several researchers have developed models to © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 R. Eirich, Organization Design and its Impact on the Digital Innovation Process and the Digital Innovation Outcome, Schriften zur Unternehmensentwicklung, https://doi.org/10.1007/978-3-658-30805-6_2

13

14

2  Basic Theories and Concepts

frame and structure the problem of designing organizations. With digital technology changing the way business is executed, decision making and product development must accelerate, and organization design must keep up with these changes. Because most of the frameworks were developed decades ago, they must be reviewed and adjusted for the new conditions. Although questions of organization design have been investigated since the 1950s, the first frameworks for designing organizations were developed in the 1980s. Thus, this dissertation primarily analyzes at this development wave and the theoretical frameworks.

2.1.1 An Overview of the Major Organizational Design Frameworks In the literature, several different frameworks have been introduced that attempt to cover different strategies to design an organization. According to a 1999 study, the most frequently used frameworks proved to be Weisbord’s Six Box Model (25% of firms), followed by the 7S model (19%), and in third place Tushman’s Congruence model and Galbraith’s STAR model (10%) (Stegerean et al. 2010, p. 4; Jones and Brazzel 2012). In this dissertation, these four frameworks (as the leading ones based on other research) are assessed for usability in analyzing organization design decisions regarding digital innovation. In the following section, each model is briefly reviewed.

2.1.1.1 McKinsey’s 7S/8S Framework McKinsey’s 7S Model was developed in the early 1980s by the consulting company McKinsey and Company. Since then, the model has been extensively used by practitioners and academics to analyze hundreds of organizations (Bhatti 2011, p. 56; Waterman Jr et al. 1980, pp. 14–16). In its pure form, the framework identifies seven factors that are essential for strategy implementation and managing successful organization change. The framework is based on the assumption that a change in strategy requires a change in the organization’s skills and shared values by emphasizing human resources (Soft S) rather than the traditional mass production tangibles of capital, infrastructure, and equipment (Feurer and Chaharbaghi 1997, p. 67; Ravanfar 2015, p. 7). The model comprises seven elements of the company: Structure, Strategy, Skills defined as “hard S” and Staff, Style, Systems, and Shared values as “soft S.” The framework is visualized in Figure 2.1.

2.1  Organization Design

15

Structure Strategy

Systems Superordinate goals

Skills

Style

Staff Hard S

Soft S

Figure 2.1   McKinsey’s 7S framework including hard and soft factors. (Source: Ravanfar 2015, p. 8)

The shape of the diagram is significant because it has no starting point or hierarchy, making it not obvious which of the factors is the driving force in organizational change (Waterman Jr et al. 1980, p. 19). To better understand the relationship between the different factors, I provide a brief description of the parameters (Waterman Jr et al. 1980, pp. 19–25). Structure: Structure in the 7S model divides tasks and then provides coordination. Moreover, when defining the structure, organization designers attempt to find the perfect trade-off between specialization and integration as well as decentralization and recentralization (Waterman Jr et al. 1980, p. 19). Strategy: Within the framework, strategy is defined as actions that a company plans in response to or in anticipation of changes in the environment, customers, or competition. Through its strategy, the company attempts to improve its position (Waterman Jr et al. 1980, p. 20). Systems: In the 7S model, systems cover all formal and informal procedures, such as budgeting, training systems, and cost accounting (Waterman Jr et al. 1980, p. 21).

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2  Basic Theories and Concepts

Style: In the framework, a corporation’s style is the reflection of its culture and the ability to change the organization or its performance (Waterman Jr et al. 1980, p. 22). Staff: Staff is viewed as people in the framework and includes appraisal systems, formal training programs, and soft factors such as morale, attitude, motivation, and behavior (Waterman Jr et al. 1980, p. 23). Skills: In the framework, skills describe the dominating attributes or capabilities of a company (Waterman Jr et al. 1980, p. 24). Superordinate goals: Depending on the publication years and authors, the seventh dimension has different names (sometimes also called shared values). By superordinate goals, the basic paper means the guiding concepts as a set of values and attributes, often unwritten, that go beyond formal statements or rules (Waterman Jr et al. 1980, pp. 24–25). The 7S framework was further developed by Higgins to 8S, which includes an eighth factor called strategic performance. Strategic performance is viewed as a derivative of the other seven Ss. An organization possesses strategic performance in total, and it can be measured at any level based on financial performance (Higgins 2005, p. 5). The underlying principle of the 8S model is that different strategies require different types of structures, systems, style, staffing, resources, and shared values to work. If a good match does not exist among these factors, performance suffers (Higgins 2005, p. 13). Critical review: Practitioners developed the framework to solve change management problems in companies to which they have been offering consulting services. Therefore, the model is very case-focused and difficult to generalize for theoretical research. In particular, a substantial overlap among the soft S factors—style, skills, and superordinate goals—can be observed. This overlap is strongly present in the descriptions of the different parameters, such as “attributes” as core parameter descriptions can be found in “skills” and “superordinate goals”, making it difficult to derive general findings for the different factors used in the framework. In line with the criticism of most of the frameworks used for change, the 7S model is highly abstract and misses out on the details of what is truly going on in the organization (MacBryde et al. 2014, p. 159). The expanded version, 8S, does not heal the general problems of the model. I also have the opinion that, on the one hand, the framework becomes blurry because of the different factor levels (hard and soft S). On the other hand, the model does not offer the details needed in each of the factors to draw the complete picture. Hence, in this dissertation, the 7S/8S framework is not used as the guiding framework for further investigations.

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2.1  Organization Design

2.1.1.2 Weisbord Six-Box Organizational Model In 1976, Marvin R. Weisbord developed an expanded framework based on a prior framework. This new framework better fits the more complex environments in which an organization operates. The old framework mainly focused on organization diagnosis and group issues. Because Weisbord uses six labels to sort much of the activities happening within a company, he called the framework the six-box model (Weisbord 1976, p. 431). The framework is visualized in Figure 2.2.

Purpose: What businesses are we in?

Structure: How do we divide up the work?

Relationships:

How do we manage conflict among people? With technologies?

Leadership: Does someone keep the boxes in balance?

Helpful mechanisms:

Have we adequate coordinating technologies?

Rewards: Do all needed tasks have incentives?

Environment

Figure 2.2   Weisbord Six-Box Organizational Model. (Source: Weisbord 1976, p. 432)

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2  Basic Theories and Concepts

In Weisbord’s six-box model, six categories are used to perform an organizational diagnosis: purposes, structure, relationships, rewards, leadership, and helpful mechanisms. In this context, organizational diagnosis should use the six boxes to identify an organization’s problems and assist companies in improving quality (Hamid et al. 2011, p. 87). Weisbord purposefully arranged the categories on a visualized radar because he viewed them as interactive, always in flux, and each needing continual fine-tuning (Weisbord 1979, p. 21). The six factors are briefly described as follows (Weisbord 1976, pp. 436–442, 1979, p. 22): Purpose: Purpose is viewed by Weisbord as a sort of negotiation between what must be done to survive and what we want to do to grow and express ourselves. This negotiation leads to priorities, and successful companies translate these priorities in projects and products. Negotiations also assist in fulfilling customer needs (Weisbord 1976, p. 436). Additionally, the environment and what society supports complete the environmental demands (Weisbord 1979, p. 22). Structure: Weisbord views structure as a form of division of labor (Weisbord 1979, p. 22). Weisbord follows the approach of three major structures for an organization: by function, by product/program/project, or by a mix of both. Critical for Weisbord is the fit between the goal—the output—and the structure producing it. This fit should be accomplished through a formal system (Weisbord 1976, pp. 436–439). Relationships: Through this parameter, Weisbord observes how people deal with each other regarding the work that they do (Weisbord 1979, p. 22). In particular, the work relationships between people (peers), between units, and between people and their technologies are of interest. It is critical to define how much interdependence is required to get the work done. Additionally, through the relationship parameter, the degree of built-in conflict is analyzed (Weisbord 1976, pp. 438–439). Rewards: The reward system should have perfect incentives (e.g., promotion, recognition, money) for every needed task (Weisbord 1979, p. 22). Specifically, a reward system should pay off in benefits and salary and give people the sense that their work is valued. Moreover, the reward system should be equal and fair because most employees always believe that they are worth more than their supervisors judge, especially in comparison to others (Weisbord 1976, pp. 440– 441).

2.1  Organization Design

19

Helpful mechanisms: Helpful mechanisms based on Weisbord include procedures, policies, systems, forms, committees, or meetings. They can be either formal or informal and contribute to more rational purposes, structure, or relationships, or a better reward system (Weisbord 1979, p. 22). Effective organizations continually revise their mechanisms, eliminating or adding others as the need arises. Every good manager provides structured procedures, meetings, and ground rules for analysis and actions to ensure that the organization is not acting like a rudderless ship (Weisbord 1976, pp. 443–444). Leadership: Leadership is needed to keep the boxes in balance by, for example, articulating the purpose, introducing rewards, or ordering internal conflicts (Weisbord 1979, p. 22). The leaders should not know and do everything, but they should be aware of the trouble spots. Such awareness requires constantly and systematically monitoring all boxes and taking action when the radar is out of balance (Weisbord 1976, pp. 441–443). Critical review: Weisbord views his diagnostic map as a tool for practitioners and managers rather than for researchers (Weisbord 1979, p. 21). Additionally, I believe that each company must consider all of these factors, but the level on which decisions must be made regarding the parameters is different. The purpose or mission is a clear first step parameter that must be defined by the ­topmanagement team and sets guiding principles for the other parameters. In Weisbord’s model, the difference in the importance of single parameters is not taken into account. Hence, in this dissertation, the model is reviewed to derive best practices and applied categories but is not used as the core model of analysis.

2.1.1.3 Tushman’s Congruence Model Tushman’s model builds on existing research in organizational behavior and has been developed to describe more than one specific phenomenon. The model is supposed to provide a framework for thinking about the organization as a total system (Nadler and Tushman 1980, p. 36). Tushman’s model has been created following a trend in which scholars shifted their attention from external planning to internal resources. The congruence model has not been cited often but has been used frequently by both executives and researchers (Seong et al. 2015, p. 372). The model is visualized in Figure 2.3.

20

2  Basic Theories and Concepts

Transformation process

Informal organizations

Inputs

Environment Resources History

Strategy

Outputs

Formal organizational arrangements

Task

Organization Group Individual

Individual

Feedback

Figure 2.3   Tushman’s Congruence Model for Organization Analysis. (Source: Nadler and Tushman 1989, p. 195)

Tushman’s model consists of two major elements that are described in greater detail in the following (Nadler and Tushman 1989, pp. 194–195) descriptions of strategy and organization. Strategy: Strategy represents the patterns of decision that must be made over time on how resources will be deployed in response to environmental changes. Strategy hereby focuses mainly on opportunities and threats (Nadler and Tushman 1989, p. 194). Organization: Organization is the combination of mechanisms that turn the developed strategy into output. These mechanisms include four components: tasks, individuals, informal organization, and formal organizational arrangements. Tasks are the basic and inherent work to be done by the organization. Individuals include the characteristics of all individuals in the organization. Formal organizational arrangements are the structures, processes, and methods that should formally enforce individuals to perform tasks. An informal organization includes the arrangements within structures, processes, and relationships that emerge over time. Examples include leader behavior and group relations (Nadler and Tushman 1980, p. 42). The fundamental dynamic in the model is the congruence among the elements. The model emphasizes that no “one best way” to organize exists. Effective organizations should have a strategy in place that is consistent with the environment but

2.1  Organization Design

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also fits the four internal organizational components (Nadler and Tushman 1989, p. 194). Critical review: Tushman views his model more as a way to think about structure, networks, and capability as a whole. The major objective of developing the model has been to talk about the real world and to have discussions with executives (Seong et al. 2015, p. 372). Moreover, the model implies that the congruence of organizational components is a desirable state—which might be true in the short term because congruence is related to effectiveness and performance. In contrast, a system with high congruence can be resistant to change (Nadler and Tushman 1989, p. 195). Because the objective of this dissertation is it to develop an academic framework regarding the requirements of digital innovation, I believe that Tushman’s model is not the best choice. First, the model has been developed for executives and focuses more on managerial implications. Thus, it is missing an academic foundation. Second, digital innovations require speed and agility and therefore, put pressure on most organizations. With the congruence view and the potential to be resistant to change, I am of the opinion that the model should not be used as the core model.

2.1.1.4 Galbraith’s STAR Model In the 1980s, Galbraith introduced the so-called STAR model, which aimed to guide the design process and assist in building organizations. As the historical review of the different research streams involved also showed, all of the findings regarding organization design were obviously not wrong, but all of them were only partially correct (Galbraith 2008, pp. 325–327). Leavitt was the first to see and propose integrative characteristics. His model included the core blocks: problem, structure, people, and technology. Galbraith’s model is a further developed form of Leavitt’s work (Galbraith 2008, pp. 328– 329). The core principle of the STAR model is that different strategies require different organizational setups to execute them. The leader within the STAR model is tasked to design and influence the core parameters of the organization: the structure, processes, rewards, and people as the major needed capabilities (Cooper and Burke 2005, pp. 240–242; Kates and Galbraith 2010, pp. 3–5). Key in the model is that the four organizational parameters work together to support the overall strategy. As a consequence, the parameters support the organization in achieving the set goals. In rapidly changing environments, having the ability to quickly realign these parameters is also critical. The 5-STAR model is visualized in Figure 2.4.

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People

Rewards

Structure

Processes

Organization design

Strategy

Figure 2.4   The 5-STAR model. (Source: Kates and Galbraith 2010, p. 3)

Critical review: In addition to the strong theoretical foundation (the other models have been derived primarily from consulting work), the practical usefulness of the STAR model is also underlined by using parts of the contingency theory already reflected in Tushman’s model. This theory proposes that organization design choices are contingent on both the strategy selected and the environment in which the business operates. Leaders must apply their style and design choices to the right situation (Lawrence and Lorsch 1967, pp. 5–7). In contrast to the 7S, Weisbord’s box model, or Tushman’s contingency model, Galbraith clearly defines parameters that must be connected in organization design but can be separated during the design task. In each of the other models, from my perspective, too much overlap exists among the different parameters, making it difficult to assess the influence of the single parameters. Additionally, through the broad definition of the design parameters in Galbraith’s model, researchers have the opportunity to take a more detailed look at each of the parameters and add value for managers as well as researchers. Hence, because digital innovation is a new phenomenon, I decided to use the Galbraith model, which has the opportunity to develop into a model specifically valuable for digital innovation. Then, the reviewed and expanded (if necessary) model should help managers and researchers further investigate organization design choices.

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2.1.2 Galbraith Core Organization Design Framework Although strategy is part of the STAR model and is covered partially in this dissertation, the focus of the analysis is on the organization design part. This focus is also based on the fact that in terms of digital innovation or digital transformation, most companies have already defined their strategies with at least the basic objective to increase their activities in the digital context. To obtain a more precise view of the 5-STAR model, the organizational design parameters from the model are explained as follows. The first parameter to be set is “structure.” That parameter defines where formal power and authorities are located and is mostly visible in typical companies’ organizational charts. Units or departments within the structure can be formed around several different core topics, such as functions, products, geographies, or customers (Kaiser and Buxmann 2017, pp. 60–62; Kates and Galbraith 2010, pp. 8–12). The second parameter, “processes”, defines how to bridge internal boundaries and the required integration mechanisms to share information across different departments. All processes, including work or management processes and their design, affect how well units work together, either vertically or laterally (Galbraith 1974, 1971, 2008; Kates and Galbraith 2010, pp. 95–100) The other two core parameters are more strongly focused on human resources and the firm’s employees. Hereby, “rewards” with the underlying reward system motivate employees and reinforce the behavior that aligns employees’ goals and their performance with the organization’s objectives. The key question within the organization design challenge is it to create the right incentives and to reward innovative behavior that helps the firm succeed (Kaiser and Buxmann 2017, p. 59; Kates and Galbraith 2010, pp. 21–22). In line with the reward system and to also ensure that the reward system fits with employees, any organization must hire the right managers and employees with a fundamental set of competencies to work within and across organizational boundaries. These competencies include the willingness to participate in teams or share the organization’s values and commitments. Within the parameter “people”, organization design must ensure that selection, staffing, training, and development are in place to help support the chosen organizational form (Cooper and Burke 2005, pp. 67–70; Kates and Galbraith 2010, pp. 101–103). Although structure is important, I also agree that a productive organization, particularly in terms of innovation behavior, is not only a matter of structure but also of the interplay among the various variables (Waterman Jr et al. 1980, pp. 17–23).

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2.1.3 Review of Galbraith’s Organization Design Framework Galbraith’s model has been used in organization design research for decades. After digital innovation as a new innovation type (described in detail in section 2.2) gained in importance, the question has arisen as to whether the model is still valid for discussing organization design questions. Additionally, in a p­ restudy,1 the need to expand Galbraith’s model with additional parameters and increase the granularity of its broad parameters was raised. To ensure that the model is still valid for digital innovation issues, I conducted a survey to test three major hypotheses. The first hypothesis focuses on Galbraith’s core model and its reliability to the organization for digital innovation. Hypothesis 1: The four original parameters of Galbraith’s model are still valid to the organization for digital innovation. Experts in the pre-study emphasized the parameter “legacy”, which should be added to the framework as a leading parameter. Legacy is particularly important for the implementation of significant changes that have come with digital products and services. In her paper, Junginger focused on service design and noted several core points on organization design that researchers and practitioners must keep in mind (Junginger 2015, pp. 212–215): (1) Organizations are living systems and, thus, on a constant journey of developing and changing. In that context, organization design is necessary for the organization to be alive and functioning. (2) As the organization changes and reorganizes over time, it develops its design history and context and provides reasoning for why certain structures are in place and why specific procedures have been established (Junginger 2015, pp. 217–219). (3) Special areas exist, such as IT, in which replacing legacy systems is too expensive or can disrupt business operations (Markus 2004, p. 5). In these cases, the timing and urgency of organization design changes must be discussed. (4) A lot of soft legacy factors exist in the mind of employees, such as organizational identity and the knowledge of how processes and topics have been handled in the past (Walsh and Glynn 2008, p. 262). 1The

results of the pre-study are provided in greater depth in section 3.2. The pre-study contained four 30–60-minute interviews with digital experts across different industries in which current research and developments for digital innovation have been discussed.

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Typically, in every firm, one can find residues of ingrained organization design practices that can be traced to an organization’s historic development. That the legacies (if not recognized and addressed) can block needed innovation and transformation is critical for organization designers. Based on this reasoning, the second hypothesis has been developed. Hypothesis 2: Legacy must be added to the model as one core parameter. As highlighted in the previous chapter, Galbraith’s parameter “people” consists of several underlying parameters, such as selection, staffing, training, and development. For digital experts, staffing and employees, in particular, as a knowledge base have been the most important sub-parameters regarding digital innovation performance.2 As proven by several researchers, a company’s knowledge base has a positive impact on its innovation output (Ahuja and Katila 2001, pp. 215–216). In particular, in technological innovation, know-how has been researched and has been proven to provide the firm with the ability to rapidly exploit opportunities (Wiklund and Shepherd 2003, p. 1308). Overall, in innovation research, the importance of know-how and the available knowledge base have been underlined (Ahuja and Katila 2001; Grant 1996; Miller et al. 2007; Wiklund and Shepherd 2003). Hence, when designing the organization, getting the right people with the needed know-how is important. Furthermore, ensuring that the necessary ­knowhow is made available within the company is critical. Hypothesis 3: The parameter “people” must be split and analyzed in two major sub-parameters, mainly “staffing” and “knowledge.” Although the pre-study and existing literature revealed the necessity to integrate an additional leading parameter (legacy) and to split the parameter “people” on a more granular level, I wanted to further test the three hypotheses mentioned. For that reason, I conducted a survey with 123 participants3 who are active in the business world. The objective of the survey was to assess the importance of each parameter and to check the need to expand and detail Galbraith’s existing model for digital innovation.

2.1.3.1 Sample The data were collected using a self-administered electronic questionnaire and a web-page-based instrument (Unipark) (Stanton and Rogelberg 2001, pp. 213– 214). The dataset was collected over four weeks. Potential participants were 2The

importance of different parameters has been challenged and discussed in a pre-study, with the results shown in greater depth in section 3.2. 3The final N had to be reduced to N = 115 because of missing data from the participants.

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recruited based on sent emails and the use of professional (online) networks, such as LinkedIn or Xing. Potential participants were pre-selected based on their industry and an outside-in evaluation of their digital experience and day-to-day dealings with digital innovation. Before sending out the survey link, the online survey’s feasibility was tested with a small sample (n = 10) of participants. The survey participants are, on average, 31.2 years old, with 9.7% older than 40 years of age. The oldest participant in the survey was 64 years old. Regarding work experience, most participants have been active in a full-time job for between 2 and 5 years (36.5%). New job entrants (less than 2 years of work experience) are a fairly large group as well, representing 21.7% of the overall sample. Regarding digital experience, most respondents are confronted with digital innovations in their current job (>50%), indicating that a high number of survey participants should be able to provide a clear statement of digital innovation performance and the importance of the different parameters. Survey respondents are also from a variety of different industries: Aerospace (2.6%), Chemicals (0.9%), Consulting (29.6%), IT-Consulting (3.5%), Energy (1.7%), Financial Services (7.8%), Insurance (2.6%), IT (3.5%), Automotive (20.9%), Electronics (2.6%), Transportation (0.9%), Retail (2.6%), Real Estate (0.9%), Mechanical Engineering (6.1%), Multimedia (1.7%), Pharmaceuticals (1.7%), Legal Counseling (1.7%), and others (8.7%). Many studies suggest a ratio of 20 observations for each independent variable. The minimum size recommended is five observations per independent variable (Banu Goktan and Miles 2011, p. 538; Hair Jr et al. 1998). The independent variables in this study, as subsequently described, from Galbraith’s core model include structure, processes, people, and rewards. Additionally, with the proposed model extension with legacy and the split-up of the parameter people into knowledge and staffing, 6 independent variables represent the core focus of this study. Based on the literature, the sample size should be approximately 120 (6 * 20). This study meets this requirement, with N = 123 and a cleared-up dataset of N = 115.

2.1.3.2 Measures To overcome problems of conflicting measurements, especially within the existing organization design and (digital) innovation literature, the constructs of interest must be measured as accurately as possible. Thus, the existing literature and scales are used as the basis for the measured variables but have been adjusted if needed. To ensure internal validity, extraneous variance must be reduced. As observed in the sample description, the survey participants are quite diverse in terms of their demographics and the industries/companies for which they work.

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Because demographic characteristics often account for variance, I control for some variables to enhance the internal validity of the model (Banu Goktan and Miles 2011, p. 538). The parameters and the different peculiarities are described in greater detail in several sections. In the following, I provide a brief overview of the parameter descriptions in the survey before discussing the major results. Dependent Variable To engage in a detailed test of the hypothesis, innovation performance is used as the dependent variable. Most firms still tend to assess innovation performance based on core metrics, such as spend, speed to market, and number of new products. Researchers who focused only on the measurement of innovation inputs and outputs and forgot the process in between have received criticism (Adams et al. 2006, p. 22; Cordero 1990, pp. 188–189). Additionally, these variables are very subjective and limited by the field of activity of the survey participants. To avoid these problems, digital innovation performance is measured as a lateral variable through an assessment of five sub-categories. Digital innovation performance: Digital innovation performance is understood as an assessment of the capabilities to identify and exploit new digital market opportunities better than competitors can and to ensure that such digital innovations fulfill customer needs (Kyrgidou and Spyropoulou 2013, pp. 295–296). Independent Variables The independent variables contain Galbraith’s original parameters and the identified add-ons staffing, knowledge, and legacy. Structure: An organization’s structure determines where formal power and authority are located, for example, centralized or decentralized. The structure sets out the reporting relationships, power distributions, and communication channels (Kaiser and Buxmann 2017, pp. 60–62; Kates and Galbraith 2010, pp. 8–12; Morner 2013, p. 153). Processes: Processes are a series of connected activities that move information up, down, and across the organization. Such activities include, for example, production, budgeting, and conflict resolution to facilitate decision making (Kates and Galbraith 2010, pp. 95–100; Huber and McDaniel 1986, p. 572). People: People are defined as human resource policies for selecting, staffing, training, and developing an innovative knowledge base that is established to assist in forming the capabilities necessary to carry out the organization’s strategy (Cooper and Burke 2005, pp. 67–70; Kates and Galbraith 2010, pp. 101–103; Meyer 2000, pp. 330–331).

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Staffing: Staffing is the selection of individuals for specific job functions and charging them with associated responsibilities (Chen and Huang 2009, p. 109; Jimenez-Jimenez and Sanz-Valle 2008, p. 1210). Knowledge: Knowledge represents complex structured and unstructured information and knowledge available within the workforce or knowledge management systems (Chen and Huang 2009, p. 105). Rewards: The reward system motivates employees and reinforces the behaviors that add value to the organization through salaries, bonuses, stocks, recognition, and benefits. The reward system can be performance-driven or purpose-driven (Kaiser and Buxmann 2017, p. 59; Kates and Galbraith 2010, pp. 21–22). Legacy: Legacy is both tangible and intangible (e.g., IT systems, reporting structure) characteristics of an organization that have developed over time during its existence (King and Baatartogtokh 2015, p. 84). Control Variables To ensure the accuracy of the results, two major control variables have been integrated into the model: firm size and survey participants’ age. Firm size: Although organizational size is not the main antecedent of innovation, size is an important factor that must be taken into account when discussing innovation capabilities and performance (Forés and Camisón 2016, p. 845). Large firms are likely to have more resources to extend their knowledge base and devote greater efforts to innovation activities (Forés and Camisón 2016, p. 839). Age of survey participants: Because the perception for digital performance and the importance of the parameters is assumed to differ for various age groups, I also control for the age of the survey participants. Controlling age indicates controlling for the fact that survey participants’ characteristics also arise from a sample comprised of people mostly around the age of 30. Measurement of Variables Dependent and independent variables were assessed by the survey participants, and the assessment procedure is shown in Figure 2.5. The control variables were directly entered by the survey participants after starting the survey, and firm size as a log function of the number of employees was executed by the researcher. All organization design parameters were ranked by the interviewees based on a 7-point Likert-scale with 1 = not important and 7 = very important. As described, the dependent variable digital innovation performance has been developed based on five items that were also assessed by the interviewees on a 7-point Likert scale (1 = very low; 7 = very high). The slightly adapted items (Kyrgidou and Spyropoulou 2013, pp. 295–296) are represented and described in Figure 2.6.

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Figure 2.5   Overview of variables, measurements, and scales. (Source: Own visualization)

Figure 2.6    Digital innovation performance. (Source: Own visualization following Kyrgidou and Spyropoulou 2013, p. 295)

2.1.3.3 Analysis and Survey Results Before analyzing the entire dataset generated, the data were first screened for problems that may affect subsequent results. The variance inflation factor (VIF) is computed to identify whether multicollinearity has been a problem. Because the VIF values ranged from 1.0 to 1.8 and within the VIF limit of 10, I conclude that multicollinearity is not a major problem (Banu Goktan and Miles 2011, pp. 540–542; Hair Jr et al. 1998). Additionally, because no outliers were visually

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inspected, no changes were made to the data and no data points were removed. I also checked for reliability using the five items of digital innovation performance, resulting in a value of 0.9, which is desirable (Bland and Altman 1997, p. 573). The primary statistical techniques used to analyze the data in this study include descriptive statistics, correlations (Figure 2.7 and Figure 2.8), and multivariate hierarchical regression (Figure 2.9 and 2.10). Descriptive statistics include the frequency distributions regarding the importance of each parameter, and the means and standard deviations for all measures. The correlation matrix provides bivariate correlations (Pearson correlations) among all independent, dependent, and control variables.

Figure 2.7   Frequency distributions (ranked) for parameter importance in %. (Source: Own visualization)

Based on the frequency distribution of the different parameters (“people” already split into the sub-parameters “staffing” and “knowledge”), I observe that the survey participants view “staffing” and “knowledge” as the most important parameters. This importance is also reflected in the ranking as #1 with “staffing” (40.9%) and “knowledge” (20.0%). The distance between the percentages for “staffing” and “knowledge” also shows that “staffing” is viewed as the most important parameter by far. “Structure” (16.5%) and “processes” (17.4%) are both ranked #1 by the survey participants at a frequency quite similar to that of “Knowledge”. When combining #1 and #2 rankings for the different parameters, “Structure” replaces “staffing” as the second most important parameter. Notably, when looking at the pure #1 ranking, the survey participants viewed the parameter “rewards” as the least important. This view is also underlined by the #5 and #6 ranking through the participants, who ranked “rewards” mainly at #6 (41.7%).

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The results from Pearson’s correlation matrix, including means and standard deviations (SD) as represented in Figure 2.8, overall indicate a positive relationship among all organization design parameters and the digital innovation performance outcome variable. Only the firm size control variable is negatively related to digital innovation performance. If the correlation coefficient is less than 0.5, we can assume a strong relationship between the variables (Stegerean et al. 2010, p. 9), which is the case for all organization parameters and the parameters “structure,” “people,” and “knowledge”. This relationship is significant at a p