Capabilities for Data-Driven Service Innovation [1st ed.] 9783658316907, 9783658316914

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Martin Schymanietz

Capabilities for Data-Driven Service Innovation

Markt- und Unternehmensentwicklung Markets and Organisations Series Editors Arnold Picot, München, Germany Ralf Reichwald, Leipzig, Germany Egon Franck, Zürich, Switzerland Kathrin M. Möslein, Erlangen-Nürnberg, Germany

Change of institutions, technology and competition drives the interplay of markets and organisations. The scientific series ‘Markets and Organisations’ addresses a magnitude of related questions, presents theoretic and empirical findings and discusses related concepts and models. Edited by Professor Dr. Dres. h. c. Arnold Picot Ludwig-Maximilians-Universität München, Germany Professor Dr. Egon Franck Universität Zürich, Switzerland

Professor Dr. Professor h. c. Dr. h. c. Ralf Reichwald HHL Leipzig Graduate School of Management, Leipzig, Germany Professorin Dr. Kathrin M. Möslein Friedrich-Alexander-Universität Erlangen-Nürnberg & HHL Leipzig, Germany

More information about this series at http://www.springer.com/series/12561

Martin Schymanietz

Capabilities for Data-Driven Service Innovation With a foreword by Prof. Dr. Kathrin M. Möslein

Martin Schymanietz Nürnberg, Germany Dissertation Friedrich-Alexander-Universität Erlangen-Nürnberg/2019

Markt- und Unternehmensentwicklung Markets and Organisations ISBN 978-3-658-31690-7 ISBN 978-3-658-31691-4  (eBook) https://doi.org/10.1007/978-3-658-31691-4 © 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 solely and exclusively licensed 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. 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

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Foreword

Foreword

The utilization of data for service provision confronts many organizations with challenges connected to the build-up of the required capabilities to seize the associated opportunities such as increased service levels or deeper customer relationships. Especially the repercussions of data usage for service innovation processes demand for a deeper understanding from both, an academic and practical viewpoint. The work of Dr. Martin Schymanietz focusses on organizational capabilities for datadriven service innovation and aims to gain a deeper understanding of the phenomenon through its exploration together with practitioners. His dissertation investigates capabilities on both a meso-level and a micro-level. For this purpose, he connects the fields of data-driven service innovation and dynamic capabilities and takes a microfoundational view to explore specific actor roles that support organizational capability development on an individual level. The book is guided by the following main question: What organizational dynamic capabilities are required for data-driven service innovation? To answer this question, this book examines the following topics: (1) the identification of the involved actors, (2) challenges for their collaboration, and (3) the exploration of individual actor roles that are relevant and support the development of dynamic organizational capabilities in data-driven service innovation. The work motivates the reader by its approach to investigate a rather new phenomenon through a close interaction with relevant actors to derive theoretical implications and concrete guidance for practitioners. This is achieved through the application of different qualitative research methods such as case study research, expert interviews and the Delphi study method that are suitable for the generation of valuable insight on this topic. This book has been accepted as a doctoral dissertation in 2019 by the School of Business, Economics and Society at the Friedrich-Alexander-Universität Erlangen-

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Foreword

Nürnberg (FAU). The book displays the author’s knowledge on qualitative research methods and benefits from the close interaction during the research process with practitioners. It is a valuable read for everyone interested in data-driven service innovation and how this process can be fostered through organizational capability development. The reader will enjoy to see how micro-level activities can support organizational outcomes. The book deserves broad dissemination both in academia and management practice. It is especially recommended to researchers in the field of service innovation and managers in the service space who want to innovate with data. I truly recommend it as a valuable reading and resource. Prof. Dr. Kathrin M. Möslein

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Preface

Preface

The ongoing digitization opens up a variety of opportunities for organizations to provide new offerings to their customers. New organizations from the tech sector emerged, transformed whole industries and became highly successful throughout the past years. Against this background, it seems to me highly important to investigate how organizations are able to achieve competitive advantage in today’s fast changing environment. In particular, long-established organizations that formerly were able to achieve competitive advantage from the possession and exploitation of resources nowadays are confronted by novel challenges and possibilities that come along with multiple questions. Being born in the Rybnicki Okręg Węglowy (Rybnik Coal Area), an industrial region in Silesia, Poland, my life has been influenced from the beginning by organizations with a huge tradition that were focused on physical resources, since family and friends worked (e.g., my father worked as a miner around 30 years ago) or still work for them. Today, these kinds of organizations have to face criticism for harming the environment and need to find ways to reconfigure their business towards a more sustainable one. Even if I did not live there for a long time, the following 25 years were characterized by living on both edges (Cologne and Aachen) of the so called Rheinisches Braunkohlerevier (Rhenish lignite mining region). Despite the resource is slightly different and mining happens above ground, the challenges for organizations stay the same. However, these organizations are just an example (with a very personal note) for German organizations, where especially the renowned manufacturing sector is regarded as relevant, that were highly successful in the past, but now have to transform themselves under consideration of changing circumstances to be prepared for the future. Throughout this dissertation journey, I will accompany the reader as a Reiseleiter (as my friends and colleagues call me due to my passion for organizing and undertaking trips of all kinds) and guide the reader through multiple stages. We start at the outer shell of the phenomenon of dynamic capabilities for data-driven service

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Preface

innovation to get a basic understanding, before going to the core of it, thus the investigation of individual actors and how they shape these organizational capabilities. Before starting this journey, I would like to thank all the great people that supported me throughout the past years. First, Prof. Dr. Kathrin M. Möslein at FriedrichAlexander-Universität Erlangen-Nürnberg’s Chair of Information Systems, Innovation and Value Creation gave me the possibility to join her great team in 2015. She enabled me to take part in very exciting projects and to start this research on dynamic capabilities for data-driven service innovation. She encouraged and inspired me continuously during that time and helped me to improve as a researcher. My passion for the field of service innovation is strongly connected to Julia Jonas, who served as a mentor to me. Thank you for countless discussions, valuable feedback and a really great supervision that made me grow. Second, I want to thank my parents Maria and Georg that unconditionally supported me in all respects throughout my whole life. Without you, my entire academic career would not have been possible. I also want to thank my brother Matthias for so many great moments that we shared throughout the past decades. Third, I am honored to have great friends in Köln that were always there for me. Thanks to Julien Steinbach, Hanna Börder, Dennis Oczko, Patrick Schmitt and Frédéric Becker for your support during the past years and never giving me the feeling that I moved away from the Rhineland. Finally, I want to thank everyone at the Chair of Information Systems, Innovation & Value Creation. In particular, Stefan Genennig and Max Jalowski served as great companions at the chair. Thank you for being not just great colleagues, but fantastic friends as well.

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Overview of Contents

Overview of Contents

Part I: Introduction: Objective of this dissertation ...........................................1 1

Motivation .......................................................................................................................... 3

2

Theoretical background and purpose ............................................................................ 8

3

Research approach and structure ................................................................................. 13

Part II: Grounding of the dissertation: Theoretical background ................ 21 1

Objectives and structure ................................................................................................. 23

2

Service innovation in the digital age ............................................................................ 25

3

From the resource-based view to dynamic capabilities ............................................ 30

4

Dynamic capabilities for service innovation ............................................................... 35

5

Summary and implications ............................................................................................ 38

Part III: Introducing data-driven service innovation .................................... 41 1

Introduction ..................................................................................................................... 43

2

Theoretical Foundations ................................................................................................. 46

3

Methods – Systematic literature review and expert interview method .................. 49

4

Findings ............................................................................................................................ 65

5

Discussion ......................................................................................................................... 82

6

Theoretical contribution and outlook ........................................................................... 87

Part IV: Identifying actors and challenges for data-driven service innovation ............................................................................................... 91 1

Introduction ..................................................................................................................... 93

2

Theoretical background .................................................................................................. 97

3

Method – Case study research..................................................................................... 102

4

Findings .......................................................................................................................... 108

5

Discussion ....................................................................................................................... 116

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6

Overview of Contents

Summary and implications ......................................................................................... 121

Part V: Exploring actor roles and capabilities for data-driven service innovation.............................................................................................. 125 1

Introduction ................................................................................................................... 127

2

Theoretical background ............................................................................................... 130

3

Method – Delphi study ................................................................................................ 135

4

Findings .......................................................................................................................... 141

5

Discussion ...................................................................................................................... 150

6

Summary and implications ......................................................................................... 153

Part VI: Summarizing findings and implications ....................................... 157 1

Introduction ................................................................................................................... 159

2

Summary of parts I – V ................................................................................................ 161

3

Synthetization of results .............................................................................................. 170

4

Discussion, implications and further research ......................................................... 174

5

Final considerations...................................................................................................... 182

References ............................................................................................................ 185 Annexes................................................................................................................. 208

Table of Contents

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Table of Contents

Part I: Introduction: Objective of this dissertation ...........................................1 1

Motivation .......................................................................................................................... 3

2

Theoretical background and purpose ............................................................................ 8

3

Research approach and structure ................................................................................. 13 3.1 Part III: Introducing data-driven service innovation ......................................... 15 3.2 Part IV: Identifying actors and challenges for data-driven service innovation ................................................................................................................. 16 3.3 Part V: Exploring actor roles and capabilities for data-driven service innovation ................................................................................................................. 17

Part II: Grounding of the dissertation: Theoretical background ................ 21 1

Objectives and structure ................................................................................................. 23

2

Service innovation in the digital age ............................................................................ 25

3

From the resource-based view to dynamic capabilities ............................................ 30 3.1 Sensing capabilities .................................................................................................. 32 3.2 Seizing capabilities................................................................................................... 32 3.3 Reconfiguration capabilities ................................................................................... 33

4

Dynamic capabilities for service innovation ............................................................... 35 4.1 Sensing capabilities for service innovation .......................................................... 36 4.2 Seizing capabilities for service innovation ........................................................... 36 4.3 Reconfiguration capabilities for service innovation ........................................... 37

5

Summary and implications ............................................................................................ 38

Part III: Introducing data-driven service innovation .................................... 41 1 2

Introduction ..................................................................................................................... 43 Theoretical Foundations ................................................................................................. 46 2.1 Innovating data-driven service .............................................................................. 46

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Table of Contents

2.2 Dynamic capabilities for service innovation ....................................................... 46 2.3 Dynamic capabilities and digitization.................................................................. 47 3

Methods – Systematic literature review and expert interview method ................. 49 3.1 Systematic literature review .................................................................................. 49 3.1.1 Data collection .......................................................................................................50 3.1.2 Data analysis .........................................................................................................52

3.2 Expert interview study ........................................................................................... 60 3.2.1 Data collection .......................................................................................................60 3.2.2 Data analysis .........................................................................................................62

3.3 Step 3: Synthesis ...................................................................................................... 63 4

Findings ............................................................................................................................ 65 4.1 Synthetization of concepts from systematic literature review and expert interview analysis .................................................................................................... 66 4.2 Data-driven service innovation barriers, capabilities, dynamic capabilities, and their underlying characteristics ..................................................................... 66 4.2.1 Data privacy ..........................................................................................................70 4.2.2 Standardization .....................................................................................................71 4.2.3 Data access, collection, and ownership..................................................................71 4.2.4 Human IT resources ..............................................................................................72 4.2.5 Resource recombination.........................................................................................73 4.2.6 Revenue models .....................................................................................................74 4.2.7 External collaboration ...........................................................................................75 4.2.8 Internal collaboration ............................................................................................77 4.2.9 Customer-oriented culture and strategy ...............................................................78 4.2.10 Data-oriented culture and strategy .......................................................................80 4.2.11 Top management support ......................................................................................80

5

Discussion ........................................................................................................................ 82

6

Theoretical contribution and outlook .......................................................................... 87

Part IV: Identifying actors and challenges for data-driven service innovation................................................................................................ 91 1

Introduction ..................................................................................................................... 93

Table of Contents

2

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Theoretical background .................................................................................................. 97 2.1 Actor collaboration in data-driven service innovation ...................................... 97 2.2 Challenges for collaboration in service innovation in manufacturing ............. 98

3

Method – Case study research..................................................................................... 102 3.1 Case selection.......................................................................................................... 104 3.2 Data collection ........................................................................................................ 105 3.3 Data analysis ........................................................................................................... 107

4

Findings .......................................................................................................................... 108 4.1 Actors involved in data-driven service innovation .......................................... 108 4.2 Challenges of collaboration in data-driven service innovation ...................... 110 4.2.1 Challenges related to intra-organizational collaboration ................................... 111 4.2.2 Challenges related to inter-organizational collaboration .................................... 112 4.2.3 Underlying issues affecting collaboration in data-driven service innovation .... 114

5 6

Discussion ....................................................................................................................... 116 Summary and implications .......................................................................................... 121 6.1 Managerial implications ....................................................................................... 121 6.2 Theoretical contribution and outlook ................................................................. 122

Part V: Exploring actor roles and capabilities for data-driven service innovation ............................................................................................. 125 1

Introduction ................................................................................................................... 127

2

Theoretical background ................................................................................................ 130

3

Method – Delphi study ................................................................................................. 135 3.1 Data collection ........................................................................................................ 136 3.2 Data analysis ........................................................................................................... 140

4

Findings .......................................................................................................................... 141 4.1 First Delphi round – Exploration of functions and tasks ................................. 141 4.2 Second Delphi round – Identification of key activities .................................... 143 4.3 Third Delphi round – Ranking of activities by importance............................. 145 4.4 Synthetization of results ....................................................................................... 147

5

Discussion ....................................................................................................................... 150

6

Summary and implications .......................................................................................... 153

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Table of Contents

6.1 Managerial implications ....................................................................................... 153 6.2 Theoretical contribution and outlook ................................................................. 154

Part VI Summarizing findings and implications ......................................... 157 1

Introduction ................................................................................................................... 159

2

Summary of parts I – V ................................................................................................ 161

3

Synthetization of results .............................................................................................. 170 3.1 Towards a dynamic capability framework for data-driven service innovation ............................................................................................................... 172

4

Discussion, implications and further research ......................................................... 174 4.1 Overall discussion and theoretical implications ............................................... 174 4.2 Managerial implications ....................................................................................... 176 4.3 Venues for further research ................................................................................. 177

5

Final considerations...................................................................................................... 182

References ............................................................................................................ 185 Annexes................................................................................................................. 208 Annex A: Related Publications ................................................................................... 209 Annex B: Average H Index of C & D VHB ranked publications (Part III) ........... 211 Annex C: Code structure for expert interview coding (Part III) ............................ 215 Annex D: Pictures from the focus group interviews (Part IV) ............................... 215 Annex E: Code Structure under the usage of the ARA-Model (Part IV) .............. 217 Annex F: Exemplary actor map (Part IV) .................................................................. 218 Annex G: Pictures from the Delphi study (Part V) .................................................. 219

Table of Contents

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List of Figures

Figure 1: Structure of this dissertation ..................................................................................... 19 Figure 2: Structure of part II ..................................................................................................... 24 Figure 3: Structure of part III ................................................................................................... 45 Figure 4: Article selection procedure......................................................................................... 52 Figure 5: Overview on 90 codes after first coding cycle, in alphabetical order ......................... 59 Figure 6: Coding cycles and resulting codes for data analysis .................................................. 59 Figure 7: Data analysis procedure............................................................................................. 64 Figure 8: Findings from data analysis and synthesis................................................................ 65 Figure 9: Structure of part IV ................................................................................................... 96 Figure 10: Relevant actors involved in data-driven service innovation according to the focus group participants ......................................................................................... 108 Figure 11: Challenges of collaboration in data-driven service innovation .............................. 110 Figure 12: Structure of part V ................................................................................................. 129 Figure 13: Conducted Delphi method (adapted from Schmidt, 1997; Webler et al., 1991) ... 138 Figure 14: Categories and activities ........................................................................................ 139 Figure 15: Managerial category voting results ....................................................................... 143 Figure 16: Processes & Methods category voting results ....................................................... 144 Figure 17: Culture & Mindset category voting results .......................................................... 145 Figure 18: Technical category voting results .......................................................................... 145 Figure 19: Connection of roles to ordinary and dynamic capabilities ..................................... 148 Figure 20: Structure of part VI ............................................................................................... 160 Figure 21: Findings and implications contributing to the dynamic capability framework for data-driven service innovation .......................................................................... 170 Figure 22: Dynamic capability framework for data-driven service innovation ...................... 173

Table of Contents

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List of Tables

Table 1: Keyword combinations................................................................................................. 51 Table 2: Definitions referring to "smart".................................................................................. 52 Table 3: Definitions referring to "digital" ................................................................................ 53 Table 4: Definitions for concepts referring to "data" ................................................................ 55 Table 5: Definitions referring to “advanced”, “remote” and “value” ...................................... 57 Table 6: Overview of Interviewees with position and sector ..................................................... 61 Table 7: Overview on definition of data-driven service from a practical viewpoint ................. 63 Table 8: Systematic literature review concept matrix ............................................................... 67 Table 9: Data-driven service innovation dynamic resource configurations and their underlying characteristics (based on Coreynen et al., 2017) ...................................... 69 Table 10: Overview of selected cases ....................................................................................... 105 Table 11: Overview of focus group participants ..................................................................... 106 Table 12: Employees’ views about challenges related to intra-organizational collaboration .. 112 Table 13: Employees’ views about challenges related to inter-organizational collaboration .. 113 Table 14: Employees’ views about challenges related to underlying issues affecting collaboration in data-driven service innovation ..................................................... 115 Table 15. Overview of participants ......................................................................................... 137 Table 16: Organizational functions involved in data-driven service innovation ................... 141 Table 17: Overview on activities and their ranking ................................................................ 146 Table 18: Overall summary on findings and implications ...................................................... 171 Table 19: Overview on limitation and proposed further research ........................................... 179

Table of Contents

List of Abbreviations

AI

Artificial Intelligence

CEO

Chief Executive Officer

CPS

Cyber-Physical System

G-D

Goods-Dominant

ICT

Information and Communication Technology

IOT

Internet of Things

IT

Information Technology

LCD

Liquid Crystal Display

MP3

MPEG-2 Audio Layer III

PSS

Product-Service-System

RBV

Resource-based View

R&D

Research and Development

S-D

Service-Dominant

SLR

Systematic Literature Review

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Part I Introduction: Objective of this dissertation © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 M. Schymanietz, Capabilities for Data-Driven Service Innovation, Markt- und Unternehmensentwicklung Markets and Organisations, https://doi.org/10.1007/978-3-658-31691-4_1

Part I: Introduction

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3

Motivation

Everything great in science and art is simple. What can be less complicated than the greatest discoveries of humanity – gravitation, the compass, the printing press, the steam engine, the electric telegraph? (Jules Verne, 1885) During the past decades, we saw a massive change in the world economy that had to undergo a shift away from an industrial, towards a digital economy where interconnected devices generate data. This availability of huge amounts of data sets the basis for further economic activities that aim to utilize data for service innovation and provision. While around ten years ago the world’s most valuable companies (by market capitalization) were led by organizations that possessed rare resources and produced goods like ExxonMobil or PetroChina1, today tech companies such as Apple, Amazon, Alphabet or Facebook took the lead2. In contrast to the former leading organizations, their success does not result from the possession of physical resources anymore, but on the ability to connect a variety of actors for value co-creation. Even if it seems simple at a first glance to integrate resources from different actors (as it seems initially easy to stay on this planet’s surface, to navigate with a compass or to transmit messages via cables around the world in seconds), the competitive advantage of these organizations does not only arise from being able to connect these actors. In particular, their competitive advantage develops from their ability to build-up so called organizational dynamic capabilities that accommodate to the vast changes in today’s dynamic environment (Teece, 2018). Examples for the development of certain dynamic capabilities that allow organizations to cope with ongoing environmental changes are the distinctive cases of Fujifilm and Kodak, Apple as well as the German example of the Otto Group. Kodak –

https://web.archive.org/web/20091229032022/http://media.ft.com/cms/25ee9d0e-2905-11de-bc5e -00144feabdc0.pdf 2 https://fortune.com/2018/05/21/fortune-500-most-valuable-companies-2018/ 1

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Motivation

a pioneer in camera technology that introduced the first mass market camera in 1900 – presented the first digital camera in 1975 to the public. However, their photo film business was so successful that Kodak did not start to explore the opportunities that started to come along with digitization. When digital cameras became mass market compatible, Kodak was not able to seize the associated opportunities and had to witness how rival firms such as Fujifilm, Canon or Nikon outperformed them (Teece, 2018). In the same time, Fujifilm initiated to transform its business in anticipation of the disruptive power of digitization. They began to reduce their dependency on photography solutions and started to diversify their portfolio with information solutions, LCD (liquid crystal display) technology and even cosmetics and supplement products. This shift of Fujifilm’s business focus through an active exploration of opportunities as well as a reform of the organizational culture and code of conduct opened up new venues of growth (Kodama, 2018). Another example for the development of dynamic capabilities is the Otto Group, being a German online retailer that currently undergoes a transformation of its traditional business model that was primarily based on the delivery of goods that could be ordered by mail and phone from a catalogue.3 After suffering a loss for the first time in their history during the business year 2014/15, the Otto Group began to set the stage for an organizational change that aims to deal with today’s fast changing environment. During this ongoing process, the Otto Group first identified key themes such as the importance of collaboration or agility that need to be addressed during their organizational change. They recognized that their culture can only change if the behavior of employees is changed. Second, the Otto Group started to implement actions such as co-working spaces to foster behavioral change or the termination of the delivery of the printed catalogue for being able to focus on the growing e-commerce market and established a large variety of different subsidiaries – most of them acting in the e-commerce market. Finally, a new organizational culture is continuously implemented that promotes the desired cultural change through decentralization and the establishment of a failure culture.4 Today, the Otto Group is placed among the five

3 4

https://business-user.de/digitalisierung/otto-erfolg-durch-kulturwandel/ https://www.handelsblatt.com/unternehmen/handel-konsumgueter/handelskonzern-shopping

Part I: Introduction

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biggest online retailers worldwide, taking the second spot in Germany behind the market leader Amazon.5 It is characterized by a wide portfolio of different online shops (e.g., otto.de, aboutyou.com) – that meanwhile account for approximately 57% of the Otto Group’s revenue (of 13.4 billion € in 2018/19)6 – or service providers (e.g., the parcel delivery company Hermes).7 Another example for the successful development of dynamic capabilities through an organization, but with a slightly different character, is the rise of Apple during the past two decades. In course of the absence of Steve Jobs as a chief executive officer (CEO) from 1985 to 1997, Apple had to struggle with declining organizational performance. After his return, Jobs began to transform the whole organization from a simple computer producer to a company with multiple mainstays (Teece, 2012). He identified the problems of the music industry that came up through the rise of standards (such as the Franconian invention of MP3) that allowed a digitized compression of music and enabled an illegal exchange via the internet. Through the introduction of the iPod and the establishment of the surrounding iTunes ecosystem, this problem was diminished and set the basis for further market introductions such as the iPhone and the AppStore (Teece, 2018). This was enabled through Jobs’ market understanding and his focus on attractive product design and ease of use. He promoted the development of ordinary capabilities and higher-order dynamic capabilities across the whole organization through entrepreneurial and non-routine strategizing activities that fostered creativity (Teece, 2012). Since Steve Jobs’ return and through the continuous effect of his oeuvre after his passing, Apple was able to increase its sales from approximately 7 billion US-$8 in 1997 to around 266 billion US-$9 in 2018, making it a real success story.

-app-statt-kult-katalog-der-kulturwandel-des-otto-konzerns-in-vier-akten/23693306.html?ticket=ST -9561447-jIFCVkc1gZjaAxgrBh6y-ap5 5 https://www.handelsblatt.com/today/companies/bouncing-back-back-to-growth-for-online -retailer-otto/23566936.html 6 https://www.ottogroup.com/de/die-otto-group/daten-fakten/Kennzahlen.php 7 https://www.ottogroup.com/de/die-otto-group/konzernfirmen.php 8 http://www.annualreports.com/HostedData/AnnualReportArchive/a/NASDAQ_AAPL_1997.pdf 9 https://s2.q4cdn.com/470004039/files/doc_downloads/additional_reports/Net-Sales-By-CategoryQtrly-FY18.pdf

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Motivation

Even if Apple’s, Otto Group’s and Fujifilm’s stories are just a cutout of activities that led to an immense economic success, it shows the importance of actors that promote and develop dynamic capabilities throughout an organization. Both managerial and regular staff have the ability to promote change in an outstanding way and inspire this work to not only examine the development of dynamic capabilities on an organizational meso-level. This dissertation aims to take a more detailed look at actors that fill them with life through their actions, roles and how they interact with their service systems. Especially the emergence of digital technologies and the shift towards a service economy puts away the focus of innovation from product towards service and its underlying characteristics (Barret et al., 2015). This extant usage of information and communication technologies (ICT) blurs the lines between organizations, their customers and other actors during their economic activities and allows to integrate resources and knowledge through novel processes, routines and technologies (Picot et al., 2003). In particular, the multi-dimensional nature of service innovation requires the integration of multiple actors that recombine their resources (physical, knowledge, information) to co-create value mutually (Lusch & Nambisan, 2015). Even if research on service, service innovation, the involved actors and the required ordinary capabilities and dynamic capabilities (e.g., Maglio & Lim, 2016; Lusch & Nambisan, 2015; Barret et al., 2015; Jonas et al., 2016; Jonas & Roth, 2017, den Hertog et al., 2010; Kindström et al., 2013) already exists, now a new technological development is set to change aspects of service innovation: the utilization of collected data for service provision. These increasing amounts of available data and new technological developments that allow their analysis, influence almost every individual’s private and professional life. This kind of service that bases on the utilization of data as a key resource is called data-driven service (Hartmann et al., 2016). The increasing importance of data utilization for service provision sets the starting point for an investigation of this phenomenon throughout this dissertation. The research is motivated by the increasing utilization of data throughout the business environment and the connected possibilities for service provision. Especially approaches to overcome barriers and challenges during the ongoing digital

Part I: Introduction

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transformation are regarded as highly important, or as Joe Kaeser, current CEO of Siemens AG, noted: “This [i.e. digitization] will be the fate of the German economy. How quickly can it adapt in an uncertain environment that is constantly changing at high speed?”.10 Borrowing from Jules Verne’s journey to the center of the earth, this dissertation aims to take the reader on a comparable journey. It – speaking metaphorically – starts at outer shell of the phenomenon (i.e. organizational dynamic capabilities) and continuously advances to the core of it and back to the top. It aims to investigate how the flow of data impacts service innovation and the required organizational dynamic capability development. The journey starts with the definition of a data-driven service for being able to get a common understanding of the concept. This phenomenon has repercussions on the necessary dynamic capabilities that enable organizations to draw competitive advantage from their development. Then, the journey continues with the investigation of dynamic capabilities on an organizational meso-level through an overview on characteristics of data-driven service innovation that influence organizational capability development. Forth following, approaching the core of the higher-level phenomenon of dynamic capabilities, the journey experiences some challenges for collaboration activities that aim to support data-driven service innovation. Finally, this research journey considers individual actors on a micro-level that can be seen as the origin of higher-level phenomena, before going back to the top through stressing how these individual actor roles support organizational dynamic capability development.

https://www.spiegel.de/spiegel/joe-kaeser-ueber-digitalisierung-schicksalsfrage-derwirtschaft-sein-a-1169382.html 10

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Theoretical background and purpose

Theoretical background and purpose

This chapter will give an initial overview on the theoretical background of this dissertation. For this purpose, it provides a first overview on the key concepts of service innovation and dynamic capabilities (as well as on connected concepts) that will play a crucial role during the upcoming parts. Throughout the past years, service innovation received huge attention from the academic community (e.g. Barret et al., 2015; Ostrom et al., 2015). Service innovation can be seen as the process of the transformation of an existing service system or the formation of entirely new service systems (Maglio & Lim, 2016). In other words, a service innovation can be described as a new offering that demands for the modification of the applied competences from service providers and/or customers (Ordanini & Parasuraman, 2011). Service (using the term in a singular and not plural form) implies “a process of doing something for someone” (Lusch & Vargo, 2006a, p. 282), thus delivering a view on value creation rather than only focussing on market offerings. A distinctive feature of service is that a provider cannot deliver value on his own (as he could do with a tangible product), but always requires the customer for value co-creation (Vargo & Lusch, 2004; Lusch & Nambisan, 2015). Consequently, service has a customer centric perspective and relies on the value co-creation between different actors, such as service provider and customer, making it relational, processual, experiential and interactive (Edvardsson et al., 2005, p. 118).11 Nevertheless, value co-creation and the required interaction is not limited to dyadic relationships between service provider and customer, but often takes place in complex service systems. Spohrer et al. (2007, p.72) define the term service system “as a value-coproduction configuration of people, technology, other internal and external service

A more detailed view on services, service and service innovation will be provided in part 2.2 of this dissertation. 11

Part I: Introduction

9

systems, and shared information (such as language, processes, metrics, prices, policies, and laws)”. The borders of a service system are not strictly set (Spohrer et al., 2007) and all of the following can be regarded as a service system: foundations, nations, corporations, nonprofit organizations, cities and families. What they have in common is their purpose to co-create value among the involved actors through the arrangement of their resources (e.g. competences, knowledge and skills) (Maglio et al., 2009). This dissertation employs the term service system12 for being able to understand service innovation. The increased use of digital technologies in both professional and private settings during the past years is accompanied by various possibilities for data collection. Especially the use of digital technologies can foster service innovation through novel possibilities. Digital technologies allow for the transformation of existing or formation of new service systems through the application of resources from a variety of actors and new modes of interaction between them (Lusch & Nambisan, 2015). The analysis and utilization of data offers huge potential for impacting service systems through incremental innovation (i.e. transforming existing service systems) as well as radical innovation (i.e. the creation of entirely new service systems) (Demirkan et al., 2015). Data from intelligent objects can be used to improve operational processes, data from people can be utilized to deliver customized solutions that meet specific needs or to help other people e.g. through coaching applications. Finally, data from objects can assist people through the provision of additional information about these objects (Maglio & Lim, 2016).

Other concepts existing in literature with a similar meaning are the terms service network and service ecosystem. 1) A service network connects multiple of actors during value co-creation activities. Here, the actors contribute their core competencies for service provision, thus being a complex process facilitated by a variety of different service providers (Gebauer et al., 2013). 2) A service ecosystem can be defined as “as a relatively self-contained, self-adjusting system of mostly loosely coupled social and economic (resource-integrating) actors connected by shared institutional logics and mutual value creation through service exchange” (Lusch & Nambisan, 2015, p.161). Nevertheless, the employment of the term service system throughout this dissertation is based on its comprehensiveness including people, technology, other service systems and information (Spohrer et al., 2007). 12

10

Theoretical background and purpose

Altogether, research on service that relies on the utilization of data offers multiple possibilities for research from a managerial and technological perspective (Beverungen et al., 2017). The possibilities for research in this field include aspects like the impact of data analysis and utilization on value co-creation (Schüritz et al., 2017a; Lenka et al., 2017), the integration of a variety of different actors (Story et al., 2017), the required organizational change & capabilities (Lerch & Gotsch 2015) or the impact of data privacy and security on service provision (Schüritz et al., 2017a). Service innovation will strongly rely on the dynamic capabilities of the involved actors. For example, today’s successful tech corporations like Apple or Amazon were able to sense opportunities that lied outside of their original scope, seized these opportunities through a mobilization of the required resources and finally managed to transform their organization in favour of the new conditions continuously (Teece, 2018). These capabilities are developed through learning, the combination of resources or the leverage of complementary assets and can be ingrained in the top management team or through organizational routines (Teece, 2012; 2017). On an organizational level, capabilities can be separated into two categories: ordinary capabilities and dynamic capabilities. While ordinary capabilities refer to doing things right, thus being highly operational, dynamic capabilities are more strategic and explicate to do the right things (Teece, 2017). Consequently, dynamic capabilities can be defined “as the firm's ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments. Dynamic capabilities thus reflect an organization's ability to achieve new and innovative forms of competitive advantage given path dependencies and market positions” (Teece et al., 1997, p. 516). In a nutshell, dynamic capabilities enable an organization and its personnel to reach presumptions about future customer needs, business challenges and technological developments, to acknowledge them and finally to draw the necessary action through an adjustment of activities and assets. Being successful in the development of dynamic capabilities will empower organizations to outperform competitors that neglect

Part I: Introduction

11

innovation (that is required to meet alternating consumer preferences) in favour of efficiency gains (Teece, 2017). Being able to adapt new conditions through the development of the suitable dynamic capabilities can enable organizations to achieve a sustainable competitive advantage through service innovation (Lusch & Nambisan, 2015). In particular, even if data is the key resource of a certain service, the possession of this resource does not guarantee a sustainable competitive advantage. Instead, the continuous process of organizational reconfiguration as well as sensing and seizing capabilities will finally lead to a sustainable competitive advantage, especially in case of data-driven service provision which are typical for today’s digital economy (Opresnik & Taisch, 2015; Teece, 2018). To understand the higher-level phenomenon of dynamic capabilities, it seems to be fruitful to investigate individuals and their interactions on a micro-level as well. Gaining knowledge on the processes and interactions of micro-level entities (i.e. individual actors) may help to understand collective constructs such as routines and capabilities (Felin et al., 2012; Felin & Hesterly, 2007). Understanding capabilities that base on knowledge, skills, experience or abilities of single actors that exchange their resources during value co-creation activities facilitate organizational asset integration. They lead to the development of dynamic capabilities that drive the performance and behavior of organizations (Felin et al., 2012; Argote & Ren, 2012). This research aims to gain new insights on the process of data-driven service innovation, the required dynamic capabilities for organizations that pursue reconfiguration of their service system(s) and how individual actors can support this continuous process. Through research on a contemporary topic that is relevant for research and practice, especially for organizations that lack a service background and can be characterized by different organizational cultures and mental models, this dissertation intends to investigate differences and commonalities to classical approaches. The dissertation is structured into six parts. Following this introduction that presents the objectives of the research project, the grounding of the dissertation will be shown through the provision of the theoretical background throughout part II.

12

Theoretical background and purpose

Afterwards, part III contains the first study that will investigate data-driven service innovation. The second study in part IV identifies actors and challenges for data-driven service innovation. Subsequent to the exploration of actor roles and capabilities for data-driven service innovation in part V during the third study, part VI will synthesize the results. Through the usage of a combination of a systematic literature review and an expert interview study, this dissertation first aims to provide a definition for datadriven service that bases on multiple concepts that deal with the similar phenomenon (i.e. the utilization of data for service provision) and to identify characteristics of datadriven service innovation. Based on these characteristics the dissertation contemplates to derive necessary resources, barriers, ordinary capabilities and dynamic capabilities for data-driven service innovation. In the next step of the research journey, it seeks to understand which actors are involved and what challenges they experience during their collaboration, on both intraand inter-organizational level. Finally, specific actor roles and ordinary capabilities and dynamic capabilities that are supported through these roles will be identified. In sum, this dissertation intends to contribute to literature on dynamic capabilities and service innovation through the assessment and investigation of a rather new phenomenon that seems to have extensive repercussions on society and business: data-driven service innovation.

Part I: Introduction

3

13

Research approach and structure

For the investigation of capabilities for data-driven service innovation, this dissertation takes a social constructivist worldview for being able to understand service innovation and the impact of data on it. It comprehends human reality as a reality that is socially constructed through interaction across individuals and is constantly asserted through their interaction with each other (Berger & Luckmann, 1967; Wahyuni, 2012). Individuals aim to understand the world in which they live in, thus leading the researcher to study situations and the views of individuals on them. Through exploratory approaches, this dissertation wants to examine personal perceptions on the specific context of data-driven service innovation in individual (expert interviews) and group settings (focus groups and group Delphi) that foster interaction both across the participants and with the researcher (Berger & Luckmann, 1967). This is pursued through the investigation of open-ended exploratory questions that allow participants to reveal their personal views within the context of the phenomenon. The generation of meaning is based on the collected data in the field, thus being mainly inductive and highly social through constant interaction with others (Crotty, 1998; Creswell & Creswell, 2018; Wahyuni, 2012). For this purpose, this dissertation starts with a problem from practice. It intends to inductively unveil patterns of meaning and how they change through the influence of data on the behaviour of individuals and their interaction during service innovation (Creswell & Creswell, 2018; Berger & Luckmann, 1967). The social constructivist worldview is seen as a suitable approach to research service innovation due to the cocreative nature of the phenomenon. Value is co-created during service innovation activities in service systems. These are embedded in a larger social context and even value itself can be comprehended as part of a collective social context. This follows the assumption that value only unveils – for groups or individuals – in use, hence the social context that is characterized through interaction, i.e. service provision (Edvardsson & Tronvoll, 2013; Edvardsson et al., 2011).

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Research approach and structure

In line with the social constructivist view, this research aims to explore organizational capabilities that are under influence of the innovation of data-driven service through the usage of qualitative research methods. Qualitative

methods

enable the researcher to investigate how people behave in real-life situations and take account of the context of the setting. Qualitative methods are open-ended, thus being highly suitable for studying explorative settings that are denoted through social interaction between groups or individuals in social settings (Creswell & Creswell, 2018). Yin (2016, p. 9) defines five features that differentiate qualitative research from other approaches: “1. Studying the meaning of people’s lives, in their real-world roles; 2. Representing the views and perspectives of the people in a study; 3. Explicitly attending to and accounting for real-world contextual conditions; 4. Contributing insights from existing or new concepts that may help to explain social behavior and thinking; and 5. Acknowledging the potential relevance of multiple sources of evidence rather than relying on a single source alone.” The combination of these features with the characteristics of this dissertation make the conduction of a qualitative research approach sensible. It aims to investigate the impact of data utilization on organizations through researching the participants in their real-world roles through an exploration of the perspectives and views of the people involved. It captures real-world settings through the avoidance of laboratory settings and aims to explain how social behavior influences the development of organizational capabilities. Finally, this dissertation uses the access to diverse participants with various individual backgrounds and views to explain the phenomenon under investigation (Yin, 2016). In sum, the forth following parts will make use of four different methods: systematic literature review (SLR), expert interview method, multiple case study and the Delphi method. Carrying out these methods aims to investigate the overall research question “What organizational dynamic capabilities are required for data-driven service innovation?” This research question will be investigated through three studies in this dissertation that will be synthesized in a dynamic capability framework for data-driven service

Part I: Introduction

15

innovation in part IV. The first study will combine a SLR with an expert interview study to get an overview on data-driven service innovation from both a theoretical as well as a practical viewpoint (see part III) and the other two studies will conduct a multiple case study and a Delphi study in parts IV and V. This approach is chosen to start the investigation of the phenomenon with an overview on current literature. Afterwards, this dissertation aims to delve into the empirical field through the conduction of interviews with individual experts (part III), focus groups (part IV) and the inquiry of a group of experts (part V) for being able to develop the dynamic capability framework for data-driven service innovation together with practice, rather than only about them without an interaction.

3.1

Part III: Introducing data-driven service innovation

The first study of this dissertation aims to elaborate two research questions. Through the investigation of the first research question “What defines and characterizes data-driven service innovation, and what differentiates it from non-data-driven servitization and service innovation?” (RQ1), this study aims to pursue a deeper understanding of data as a key resource for service innovation and for overcoming challenges for a broader application. In particular, it aims to derive a definition of a data-driven service and to get an overview on characteristics of data-driven service innovation from a scientific and practical viewpoint. This happens through the conduction of a twofold approach that wants to set the foundations for the further investigation of the phenomenon. On the one hand, a SLR is carried out to review already existing concepts and their definitions in current academic literature (Webster & Watson, 2002) that deal with the utilization of data. On the other hand, an expert interview study with experts from industry is conducted to get a practical viewpoint on the phenomenon under investigation. The triangulation of these two research methods enables to remove limitations of the conduction of just a single method (Patton, 2002). It allows to integrate the knowledge accumulated throughout past literature with a practical view from a selection of experts in this field for being able to cope with future developments in this evolving field.

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Research approach and structure

Furthermore, this part aims to explore the question “What capabilities and dynamic capabilities are required for data-driven service innovation, and are these context-specific?" (RQ2). Based on the SLR and the analysis of the expert interview data, the results are synthesized to derive barriers, resources, ordinary capabilities, and dynamic capabilities that play a role for the data-driven service innovation. The findings indicate that the utilization of data for service provision adds additional complexity to service innovation as new characteristics evolve, including a data-oriented culture, issues of data access, ownership, privacy, standardization, and the potential for new revenue models. It shows that certain organizational dynamic capabilities gain in importance and new ones develop. This part contributes to the discussion on data-driven service innovation by providing a theoretical and practical overview on the phenomenon, the required organizational capabilities and by identifying directions for future research.

3.2

Part IV: Identifying actors and challenges for data-driven service innovation

The second study addresses two research questions: “Which internal and external actors are involved in data-driven service innovation?” (RQ3) and “What challenges influence collaboration among those actors during data-driven service innovation?” (RQ4). This is to identify new actors, resources and interfaces of interaction that are necessary to be established and managed during data-driven service innovation. To reflect the multidimensional character of data-driven service, innovation requires the collaboration of multiple actors with different backgrounds from inside and outside the innovating organization that aims to integrate their resources. This study sheds light on manufacturing organizations engaging in the innovation of data-driven service and the challenges they experience during collaboration in co-creation activities. The focus on manufacturing bases on the importance of this sector for the German economy and current efforts throughout this empirical field towards data-driven service innovation. Especially manufacturing firms have to face the challenges that come along with datadriven service innovation due to their historically product-based portfolio. Here, a qualitative multiple case study (Yin, 2018) is carried out to investigate the phenomenon in its real-life context to produce new insights. Collecting the data in the field (i.e. by

Part I: Introduction

17

studying organizations that implement innovative data-driven service) helps to understand the phenomenon under investigation through interaction with the participants and a focus on their experiences and opinions (Creswell & Creswell, 2018). Implementing an open-ended qualitative method through the implementation of an exploratory multiple case study, this part of the dissertation identifies involved actors of data-driven service innovation as well as the connected challenges for collaboration on an intra- and inter-organizational level. The analysis of three cases finds that intraorganizational collaboration is challenged by distributed data sources, unclear organizational strategies and coordinated processes, while inter-organizational collaboration is defied by a lack of knowledge sharing among partners and the absence of clear rules for partnerships. Furthermore, the empirical data shows how some underlying issues like legal provisions, data protection regulations or missing standards affect collaboration in data-driven service innovation in general. Finally, the study illustrates that data as a key resource for new service demands for the alignment of a higher number and an extended scope of actors that includes specific data-related ones like external data service providers. This part of the dissertation helps to raise awareness for upcoming key challenges during the innovation of data-driven service and extends current service research by shedding light on activities of traditional manufacturers and the importance of collaboration for data-driven service innovation.

3.3

Part V: Exploring actor roles and capabilities for data-driven service innovation

The third study addresses the question “What roles of individual actors are relevant and support the development of dynamic organizational capabilities during data-driven service innovation?” (RQ5) through the conduction of a Delphi study that aims to reach consensus within a group of experts (Schmidt, 1997). Based on qualitative information from a group of experts in a certain field, the Delphi method aims to identify relevant aspects and assigns them an importance through the execution of a series of surveys (Schmidt, 1997; Okoli & Pawlowski, 2004; Dalkey & Helmer, 1963; Linstone & Turoff,

18

Research approach and structure

1975). To ensure an interaction between the participants of the study, a modified group Delphi approach is carried out that keeps all characteristics of a traditional Delphi study (such as iterative feedback rounds, group judgements and the revision of opinions) besides of anonymity (Webler et al., 1991). This study investigates an empirical field that shall cover different actors throughout the service system for data-driven service innovation such as from information technology (IT), manufacturing or engineering. Using the Delphi method, this study aims to shed light on the roles actors take during data-driven service innovation. Prior research highlighted that roles can differ from their official description and an identification of them can help to understand the contributions from single actors to foster data-driven service innovation and organizational capability development. The analysis of the three Delphi rounds shows that a variety of different roles exist. These roles go beyond traditional organizational functions and focus rather on activities than on static functional descriptions. The derived roles from this part are: (1) the customer expert, (2) the supporting manager, (3) the innovation enabler, (4) the bridge builder, (5) the prototyper, (6) the strategic operationalizer, (7) the mindset visionary, (8) the technical expert and (9) the t-shaped expert. In addition, these roles serve as a basis for ordinary and dynamic capability development within an organization. While some of these roles are highly operational (roles 8 and 9), thus referring to ordinary capabilities, others support the development of dynamic capabilities, i.e. sensing (role 1), seizing (roles 4, 5 and 6) and transforming (role 7) or seizing and transforming in combination (roles 2 and 3). Taking these roles into action during data-driven service innovation can help organizations to successfully innovate them and to support the development of the required dynamic capabilities that are responsible for sustainable competitive advantage. Based on the findings of the three studies in part III, IV and V, a dynamic capability framework for data-driven service innovation is derived that shows the interplay of actors, resources, capabilities and dynamic capabilities. Following this, theoretical and practical implications are derived. Finally, an outlook for further research concludes this dissertation. The remainder is structured as follows (see figure 1).

Part I: Introduction

Part I Part II

19

Introduction: Objective of this dissertation Grounding of the dissertation: Theoretical background

Overall research question: What organizational dynamic capabilities are required for data-driven service innovation?

Part III

Study 1a/b

Introducing data-driven service innovation Research questions: (1) What defines and characterizes data-driven service innovation, and what differentiates it from non-data-driven servitization and service innovation? (2) What capabilities and dynamic capabilities are required for data-driven service innovation, and are these context-specific?

a: Systematic literature review

b: Expert interview study

Part IV

Study 2

Identifying actors and challenges for data-driven service innovation Research questions: (1) Which internal and external actors are involved in data-driven service innovation? (2) What challenges influence collaboration among those actors during data-driven service innovation?

Multiple case study

Part V

Study 3

Exploring actor roles and capabilities for data-driven service innovation Research question: (1) What roles of individual actors are relevant and support the development of dynamic organizational capabilities during data-driven service innovation?

Delphi study Part VI

Summary and synthetization

Figure 1: Structure of this dissertation Part II gives an overview on the theoretical foundations. Here, relevant concepts such as service innovation, the resource-based view, dynamic capabilities and the actors in service innovation are introduced. While part III aims to synthesize concepts and characteristics of data-driven service innovation as well as to identify resources, barriers, ordinary capabilities and dynamic capabilities, part IV investigates the involved actors and challenges for their collaboration during data-driven service innovation. Part V assesses the roles of actors involved in data-driven service innovation and the capabilities they have to develop to reach sustainable competitive advantage for their organization. Finally, this part provides a summary of the whole dissertation, brings the previous results together with the goal to establish a dynamic

20

Research approach and structure

capability framework for data-driven service innovation, provides the theoretical contribution and puts up limitations of this work as well as venues for further research.

Part II Grounding of the dissertation: Theoretical background © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 M. Schymanietz, Capabilities for Data-Driven Service Innovation, Markt- und Unternehmensentwicklung Markets and Organisations, https://doi.org/10.1007/978-3-658-31691-4_2

Part II: Grounding of the dissertation

1

23

Objectives and structure

It is that science is eminently perfectible, and that each existing theory is constantly replaced by a new one. (Jules Verne, 1864)

The following part II aims to introduce the theoretical background of this dissertation. The groundings of the dissertation will serve as a basis for the exploration of the phenomenon under investigation through the presentation of the relevant key concepts: service innovation and dynamic capabilities. It brings these two concepts together for a continuous further development. In conclusion, part II delivers the existing theoretical assumptions that help the reader to put the following steps and results of the upcoming parts of this dissertation into the larger context of current research and the theoretical background used. It sets the basis for the upcoming research journey and further scientific investigation of these theories with the goal to extend them throughout this dissertation. Part II is structured as follows: chapter 2 deals with the concepts of services, service and service innovation. It introduces service innovation and points out its multidimensional nature that requires the co-creation of value in the context of the digital age. Afterwards, chapter 3 emphasizes the importance of organizational (dynamic) capabilities and why the sole possession of resources is not any more sufficient for achieving sustainable competitive advantage. Following this, both service innovation and dynamic capabilities are brought together in chapter 4 to show the connection between both theoretical foundations and to display the specifics of dynamic capabilities in the context of intangible service offerings. Finally, chapter 5 concludes with a summary and the synthetization of the used key concepts throughout the dissertation. The structure of part II is displayed in figure 2.

24

Objectives and structure

Part I: Part I: Introduction Introduction

Part II: Theoretical background Part III: Systematic literature review and expert interviews

1 Objectives and structure  Objectives of part II  Structure of part II

2 Service innovation in the digital age  The multi-dimensional nature of service innovation  The influence of ICT on service innovation

3 From the resource-based view to dynamic capabilities  The resource-based view  Dynamic capabilities

Part IV: Multiple case study

Part V: Delphi study

Part VI: Synthetization and discussion

4 Dynamic capabilities for service innovation  Sensing capabilities for service innovation  Seizing capabilities for service innovation  Reconfiguration capabilities for service innovation

5 Summary and implications  Summary of part II  Implications for the rest of the dissertation

Figure 2: Structure of part II

Part II: Grounding of the dissertation

2

25

Service innovation in the digital age

Throughout the past years, service gained in importance and relevance for society, economy and research. Especially the possibilities that are accompanied by emerging digital technologies offer huge potential for research on service innovation (Barret et al., 2015; Rust & Huang, 2014; Ostrom et al., 2015). The focus on service innovation helps to put forward traditional views on innovation with a focus on the development of novel products and processes towards a view that emphasizes co-creation in dynamic market environments (Vargo et al., 2015). This chapter will introduce the concept of service innovation, emphasize its co-creative nature and set the foundation for further investigation. The concept of service innovation is strongly connected to the concepts of services and service. For being able to understand these concepts, a differentiation between these concepts will be given during the next sections of this part. First, services can be regarded as immaterial goods, thus being units of outputs (e.g. Lusch & Nambisan, 2015; Lusch & Vargo, 2006a). Under this view, services differ from goods due to four aspects: services are intangible (they are performances, not physical objects and cannot be quantified), heterogeneous (there is no common performance possible due to the necessary interaction with the customer), inseparable (production and consumption often happen simultaneously) and perishable (storing of services is not possible) (Parasuraman et al., 1985; Vargo & Akaka, 2009). This focus on the differences between services and goods proposes that the two concepts of services and goods are similar to each other and that there are no unique characteristics for services (Edvardsson et al., 2005). Furthermore, taking this view implies an internal focus during the provision of services, e.g. on the provider, neglecting the importance of the beneficiary, thus ignoring the customer and the importance of co-creation activities. As a consequence, this approach falls short of acknowledging the importance of value co-creation (Vargo & Akaka, 2009).

26

Service innovation in the digital age

An approach that takes these specifies into account is the concept of service13. Service can be defined “as the application of specialized competences (knowledge and skills) through deeds, processes, and performances for the benefit of another entity or the entity itself” (Vargo & Lusch, 2004, p. 2) and has a strong focus on co-creation of value through resource integration14 of the involved actors (Vargo & Lusch, 2004; 2008; Lusch & Nambisan, 2o15). Instead of putting a focus on differences between goods and services, the concept of service emphasizes the relationship between them and regards a good as an instrument used during service provision (Lusch & Vargo, 2006b). Edvardsson et al. (2005, p. 118) summarize the characteristics of service as follows: “- service is a perspective on value creation rather than a category of market offerings; - the focus is on value through the lens of the customer; and - co-creation of value with customers is key and the interactive, processual, experiential, and relational nature form the basis for characterizing service.” Instead of pursuing an inside view from the service provider perspective, the concept of service emphasizes the importance of the customer as a co-creator of value (Vargo & Akaka, 2009; Vargo & Lusch, 2008). Both interact in service systems that are dynamic configurations of resources (Maglio et al., 2009, p. 396). This co-creation within service systems takes place in actor-to-actor networks that go beyond the traditional dyadic actor roles (i.e. producer and consumer). Within these actor-to-actor networks, all actors become resource integrators and service providers, thus cocreators of value as well as potential innovators (Lusch & Nambisan, 2015). As a consequence, innovation of service does not take place within the borders of a single organization (Chesbrough, 2003). Value is co-created interactively from collaborative activities within actor-to-actor networks that are embedded in service systems,

13 The difference between services (plural) and service (singular) is particularly influenced by the work of Vargo and Lusch (2004; 2008). Vargo and Lusch (2004) propose a worldview that shifts away from a goods-dominant (G-D) logic towards a service-dominant (S-D) one. While G-D logic can be characterized through the primacy of operand, thus physical resources, S-D logic emphasizes the importance of intangible operant resources and regards them as primary (Vargo & Lusch, 2004). 14 Resource integration can be defined as “the incorporation of an actor’s resources into the processes of other actors. it implies a social and cultural process that enables an actor to become a member of a network. Value cocreation occurs by integrating actor resources in accordance with their expectations, needs and capabilities” (Gummesson & Mele, 2010, p.195).

Part II: Grounding of the dissertation

27

including partners, customers and/or suppliers (Piller et al., 2017; Lusch & Nambisan, 2015). This brings us to the concept of service innovation that was first introduced by Miles (1993) and opened up various opportunities for research in the past years (Ostrom et al., 2015; Barret et al., 2015). A service innovation can be defined as an “offering not previously available to the firm’s customers […] that requires modifications in the sets of competences applied by service providers and/or customers” (Ordanini & Parasuraman, 2011, p. 5). Service is not innovated by single actors, but through the co-creative recombination of resources by a variety of actors throughout their actor-to-actor network (Lusch & Nambisan, 2015) to facilitate an organization’s growth (Ordanini & Parasuraman, 2011; Barret et al., 2015; Kindström et al., 2015). Here, resource recombination refers to the combination of already existing resources within an organization with the goal to improve their application (Helfat & Peteraf, 2003). Service innovation aligns the knowledge and resources of internal as well as external actors in service systems, what makes it a multi-dimensional process that has an organization-wide impact (Kindström et al., 2013; Lusch et al., 2009; Agarwal & Selen, 2011). Consequently, the multi-dimensional nature of service innovation influences organizations due to the need for collaboration among actors from different entities (Kindström & Kowalkowski, 2014) that encompass both internal and external actors as well as their skills, resources, and knowledge (Kindström et al., 2013, Lusch et al., 2009). Another concept strongly related to service innovation is the concept of servitization (Vandermerwe & Rada, 1988). While the terminology differs, the two concepts are strongly related (Baines, 2015) in their focus on growth based on service (Kowalkowski et al., 2017) that deliver value through co-creation within networks (Lightfoot et al., 2013; Lusch & Nambisan, 2015). While servitization can be characterized through a strong focus on manufacturing firms pursuing to add service to their products, service innovation follows a cross-sector approach15. Consequently, servitization can be

15

For a deeper investigation of servitization and its connection to service, please see Posselt (2018).

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Service innovation in the digital age

understood as the “process of creating value by adding services to products” (Baines et al., 2009, p. 547), in which a manufacturer’s competitive strategy transitions from products toward an emphasis on service provision (Baines & Lightfoot, 2013), entailing organizational change and a deepening of customer relationships (Neely, 2009; Baines et al., 2009). Especially the rise of the internet and the fast development and dissemination of digital technologies supports the innovation of service in a large variety of different industries through IT-enabled service systems that facilitate the exchange and integration of knowledge and resources (Lusch & Nambisan, 2015; Yoo, 2010; Rust & Huang, 2014). The use of ICT also allows to (re-)combine and exchange resources in novel ways, thus enabling the co-creation of value among the involved actors. These inter-organizational actor-to-actor networks have the potential to radically change markets through new arising possibilities for resource recombination that add value (Barret et al., 2015). Furthermore, ICT do not only have repercussions on an inter-organizational network, but also on the intra-organizational level. From an intra-organizational viewpoint, ICT can have various effects on knowledge processes and the organization’s ability to be innovative, hence staying competitive (Barret et al., 2015; Joshi et al., 2010). The usage of ICT serves as an enabler of service innovation, nevertheless, their sole usage does not ensure successful service innovation outcomes. This is due to the multidimensionality of the process that also relies on intangible service attributes, customer interactions, the availability of skilled employees or an efficient service delivery process (Ryu & Lee, 2017). ICT enable both organizations and private users to connect artefacts of daily use to information infrastructures and ongoing further developments of computing and analysis possibilities offer a huge amount of novel opportunities for innovative service provision – sometimes even with disruptive potential (Yoo, 2010). During the predigital era, analogous data was strongly connected to physical devices that contained this data. Digitization broke up this strong connection and enables a decoupling of data and devices, thus a homogenization that allows to access, store, process, transmit and

Part II: Grounding of the dissertation

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display data from any compatible device (Yoo et al., 2010). This allows for recombination of data from a variety of sources that enables organizations to innovate service that bases on heterogeneous sources (Troilo et al., 2017; Yoo et al., 2010). Here, the utilization of data for service provision comes into the picture. Data-driven service that bases on data as a key resource (Hartmann et al., 2016) opens up various opportunities for organizations. Data-driven service provision enables organizations to co-create service that considers individual customer needs and establishes personal relationships with customers through the capitalization of personal content (Rust & Huang, 2014).

30

3

From the resource-based view to dynamic capabilities

From the resource-based view to dynamic capabilities

As pointed out in the previous chapter, the recombination and integration of resources through a variety of different actors in service systems is seen as a distinctive feature of service innovation activities (Vargo & Lusch, 2004; 2008; Lusch & Nambisan, 2o15; Maglio et al., 2009). From a service perspective, different actors recombine their operand (e.g., physical) and operant (e.g., skills, knowledge, information) resources throughout service systems (Koskela-Huotari et al., 2016; Lusch & Vargo, 2014; Barret et al., 2015). Especially the application of intangible, operant resources are regarded as a source for achieving competitive advantage (Vargo & Lusch, 2008). However, different theories and concepts on resources evolved in literature during the past decades. One of these theories is the resource-based view (RBV) that was introduced by Barney (1991). Barney (1991) argues that an organization’s sustainable competitive advantage is based on four attributes that resources have to fulfill. Resources obtained by an organization, have to be (1) valuable, (2) rare, (3) imperfectly imitable and (4) non-substitutable. In this context, valuable resources enable organizations to conceptualize and implement strategies that help to increase both effectiveness and efficiency. Even if resources possessed by organizations are valuable, this does not imply a sustainable competitive advantage (Barney, 1991). Additionally, valuable resources under an organization’s control have to be rare, what means that these rare resources are not possessed by other organizations being able to exploit them – leading to similar strategies of different organizations that neutralize any competitive advantage (Barney, 1991). Having a set of valuable and rare resources under an organization’s control requires their imperfect imitability for obtaining competitive advantage (Lippmann & Rumelt, 1982). Resources can be imperfectly imitable for three reasons. First, acquiring resources can be based on unique historical conditions like e.g. a unique organizational culture that was shaped through the organization’s history (Barney, 1986; Zucker, 1986). Second, the causal ambiguity between the possession of resources and the

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competitive advantage is not completely understood, leading to difficulties for other organizations that aim to imitate a successful strategy (Barney, 1991). Third, a social complexity arises from the competitive advantage that can arise from personal relationships between managers (Hambrick, 1987), the organization’s reputation amid customers and suppliers (Porter, 1980; Klein & Leffler, 1981) or its culture (Barney, 1986). Barney puts forward that a resource can only result in a sustainable competitive advantage, if it is non-substitutable, i.e. that there are no other resources available that can be equivalent to the possessed one (Barney, 1991). Even if the RBV became popular across innovation researchers (Kim et al., 2015), this concept was and is facing some serious criticism until now. The approach of achieving a sustainable competitive advantage through a set of multiple static resources (Connor, 2002) that is characterized through an internal view of an organization (Kim et al., 2015) is challenged by today’s fast changing environments. These circumstances require a more dynamic approach that overcomes the limitations of the RBV (Helfat et al., 2007). Especially, the concepts of service and service innovation that base on value co-creation of a variety of actors from inside and outside an organization (Vargo & Lusch, 2004; 2008; Lusch & Nambisan, 2015) cannot be covered through the static and inward directed approach of the RBV (Kim et al., 2015, Lawson & Samson, 2001). Here, the concept of dynamic capabilities offers a theoretical perspective that is able to cope with the necessary adaption of an organization’s resource base to alternating contexts (Teece et al., 1997). According to Teece (2007), in shifting and unpredictable market environments, sustainable competitive advantage does not depend anymore on the mere possession of valuable, rare, inimitable and non-substitutable resources as proposed by the RBV, but on the integration, development, and reconfiguration of competences (Barney, 1991; Eisenhardt & Martin, 2000; Teece et al., 1997). In a more detail, Teece (2007) describes dynamic capabilities as an organization’s ability to (1) sense opportunities and threats, (2) seize opportunities, and (3) manage organizational reconfiguration of an organization’s tangible and intangible assets in order to maintain sustainable competitive advantage.

32

3.1

From the resource-based view to dynamic capabilities

Sensing capabilities

Sensing opportunities and threats refers to activities carried out by an organization to scan its technological and market environment, both near and distant (March & Simon, 1958; Nelson & Winter, 1982) for being able to learn from and interpret it (Teece, 2007). These activities require organizations to invest in research activities that unveil customer needs, technological opportunities and enable the comprehension of market developments as well as competitor and supplier responses (Teece, 2007, Eisenhardt & Martin, 2000; Eisenhardt, 1989a). Additionally, organizations need to implement and establish processes that direct the internal research and development (R&D) department for being able to select new technologies and to scan the external environment for complementor and supplier innovation. Furthermore, R&D should identify recent external developments in respect to technology and science, help to understand alternating customer needs, recognize market segments that should be targeted and innovative activities by customers (Teece, 2007).

3.2

Seizing capabilities

For being able to seize opportunities, organizations should – according to Teece (2007) – not only build-up capacities to make right timed investment decisions, but also to manage technologies and complementary assets that allow to offer new products or service and the connected business models to its customers. This is accompanied by the selection of suitable technology, product and revenue architectures, the assortment of customers to target and design of mechanisms to capture the value of the offered solution. These decision-making skills demand from managers to take unbiased actions in an environment of high uncertainty that should happen simultaneously across different organizational functions to enable technology developments and market introductions at the right time (Teece, 2007). For example, through the simultaneous introduction of various products through prototyping and learning-by-doing (Eisenhardt & Martin, 2000; Eisenhardt, 1989a; Pisano, 1994). This aspect requires the development of decision-making protocols, what means that complementarities and inflexion points need to be identified to avoid decision errors and anti-cannibalization

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proclivities. Besides, the organization also has to define its boundaries for being able to control platforms and manage complements. This requires the organization to calibrate asset specifity, assess their appropriability, control bottleneck assets as well as to identify, manage and capture cospecialization economies. Finally, commitment and loyalty have to be established by the organization through leadership skills, effective communication and the recognition of non-economic values, culture and values (Teece, 2007).

3.3

Reconfiguration capabilities

After successful identification of opportunities that were seized through managerial decisions to invest in certain technologies and business models, the organization needs to reconfigure its structures and assets. This helps organizations to achieve a sustainable competitive advantage during its course for growth in a dynamically changing market and technological environment (Eisenhardt & Martin, 2000; Teece, 2007). During the ongoing reconfiguration process, the expanding organization has to face various issues like mismanagement or self-satisfaction that threat the path of sustainable growth. Especially evolving hierarchies, rules and organizational procedures have to be tackled by the top management and its leadership skills. For example, the top management has to reconfigure routines, enable asset orchestration, and promote organizational renewal semi-continuously through the establishment of decentralized structures that permit a closer connection to markets, customers and technologies (Eisenhardt & Martin, 2000; Teece, 2007; Teece et al., 1997). Ongoing reconfiguration demands for the consecutive (re-)alignment of tangible and intangible

assets and for decentralization

and near

decomposability,

cospecialization, governance and knowledge management (Teece, 2007). While decentralization and near decomposability refer to the adaptation of loosely coupled structures, the promotion of open innovation (Chesbrough, 2003) and the development of integrative and coordinative capabilities, cospecialization is characterized by the management of a strategic fit of value increasing asset combinations. Governance requires an incentive alignment, the reduction of agency issues, the examination of

34

From the resource-based view to dynamic capabilities

strategic misdemeanour and a blocking of rent dissipation (Teece, 2007). Finally, knowledge management should ensure continuous learning, knowledge transfer and integration as well as know-how and intellectual property protection (Nonaka & Takeuchi, 1995; Chesbrough, 2003; Teece, 2007).

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35

Dynamic capabilities for service innovation

For being able to develop a sustainable competitive advantage from their service innovation activities, organizations need to acquire or deploy high-order resources and capabilities that build the basis for service innovations (Lusch & Nambisan, 2015; Helfat & Raubitschek, 2018). This can happen through the establishment of digital platforms that serve as the basis for complementary asset development including products, service and technologies as well as capabilities across and throughout organizations (Gawer, 2009; Evans & Gawer, 2016; Yoo et al., 2012; Barret et al., 2015). Especially the emergence of these digital platforms demands from organizations to develop capabilities that allow them to manage these platforms. Enabling them to interact between the conflicting priorities of too much and a lack of control, both poles can lead to lower value capturing possibilities of innovation activities (Yoo et al., 2012; West & Gallagher, 2006). Organizations pursuing service innovation in the digital age require to develop certain capabilities that enable them to cope with today’s fast changing environment where possession of high-order resources does not exist over long periods of time. This excludes them from being the source of sustainable competitive advantage and demands the development of strategic capabilities for the organization. It fosters an ongoing reconfiguration process that encounters the ongoing environmental change, considers the specifics of digitization-related innovation and leads to a sustainable long-term performance (Opresnik & Taisch, 2015). While the traditional dynamic capabilities approach can be characterized by a strong focus on manufacturing and technological contexts (den Hertog et al., 2010; Lawson & Samson, 2001; Rothaermel & Hess, 2007), dynamic capabilities for service innovation have been a focus in research to reflect the characteristics of service (Kindström et al., 2013; den Hertog et al., 2010) and can be also divided into Teece’s (2007) categories sensing, seizing and reconfiguring.

36

4.1

Dynamic capabilities for service innovation

Sensing capabilities for service innovation

Organizations that aim to sense opportunities and threats during the innovation of service should be able to establish novel roles, resources and processes to identify user needs (Kindström et al., 2013; den Hertog et al., 2010; Kowalkowski et al., 2012). This includes the setup of processes that support co-creation and co-development activities with customers, suppliers and other actors in the particular service system to innovate service offerings (Kindström et al., 2013; den Hertog et al., 2010). Nevertheless, the sensing activities are not limited to activities throughout the external service system, but include an internal view as well. In particular, service that supports product sales (such as maintenance or extended warranties) are not directly identifiable in terms of financial impact or performance measurement (Gebauer & Friedli, 2005), but offer opportunities for further service provision (Kindström et al., 2013). Finally, sensing technological opportunities, especially ICT that are strongly related to service provision (Kindström et al., 2013) offer possibilities for new ways of interaction with customers or individualized service offerings (den Hertog et al., 2010).

4.2

Seizing capabilities for service innovation

After successful sensing of opportunities and threats, seizing potential service innovation opportunities represents the next step. Here, organizations should be able to deliver value to their customers that bases on ongoing mutual interactions and cocreation activities within the whole service system (Alam, 2006; Sundbo, 1997; Kindström et al., 2013). This includes also alliance building and the management of the service system (den Hertog et al., 2010). For this purpose, the establishment of structured service development processes that pay attention to service-specific challenges are discussed as relevant (Kindström et al., 2013). These processes include the application of prototyping and testing of immature innovations to facilitate the transformation of rough ideas into service (den Hertog et al., 2010). Additionally, being able to foster utilization of already existing resources can lead to innovative bundled service offerings that deliver an individualized solution to the customer. In contrast, unbundling enables the offering of highly specialized solutions

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to the customer and can build-up the basis for additional service (that consists of the remaining service elements). It can be then offered and charged separately, like e.g. basic service that can be extended with additional features at a later point of time (den Hertog et al., 2010). Besides the above mentioned aspects, seizing of service innovation opportunities also requires experimenting with novel and sometimes uncommon revenue models that go beyond an organization’s traditional approach. These shall go beyond the common approaches that usually generate revenue from a one-time sale, adapt the specifics of service and establish long-term relationships (den Hertog et al., 2010). In particular, the added value of a service does not stem from sole possession of a physical product, but mainly from the value-in-use throughout a lifecycle and comes along with a risk shift away from the customer to the provider (Baines et al., 2009). This specifity requires from organizations to adapt their revenue models to long-term revenue streams instead of one-time sales (Story et al., 2017).

4.3

Reconfiguration capabilities for service innovation

To ensure a sustainable and profitable growth of an organization, it is necessary to reconfigure organizational structures and assets (Teece, 2007). In the context of service innovation, organizations need to orchestrate the service system in an efficient way and scrutinize the whole service system that contains suppliers, customers and partners (Kindström et al., 2013; den Hertog et al., 2010). Another important aspect is the harmonization of tensions that occur during the management of product- and serviceinnovation assets, especially in organizations that have a strong product background (Jonas et al., 2016). In this context, organizations should introduce service oriented functions on both, operational and strategic level for being able to pursue successful service innovation (Kindström et al., 2013). Finally, the long-term success of a service strategy strongly relies on the development of a service-oriented mindset throughout the organization that allows to question existing organizational routines and to discard them in favour of more effective ones (Matthyssens et al., 2006; Kindström et al., 2013).

38

5

Summary and implications

Summary and implications

Part II introduced the concepts of service innovation and dynamic capabilities to the reader. This part first highlighted the concepts of services, service and service innovation as well as the repercussion of digitization on them. The usage of ICT during service provision and innovation opens up various new opportunities, but simultaneously challenges organizations pursuing it through fast changing markets. Second, this part stressed the importance of dynamic capabilities for organizations that want to achieve sustainable competitive advantage through demonstrating that the mere possession of resources with certain attributes (valuable, rare, imperfectly imitable and non-substitutable) as proposed by the RBV is not sufficient in today’s fast changing environment. The successful development of dynamic sensing, seizing and reconfiguration capabilities is regarded as a key element for organizations that occupy or aim to achieve competitive advantage. Afterwards, dynamic capabilities for service innovation were depicted. Here, the traditional set of dynamic capabilities is extended by service innovation specific ones. Sensing requires new capabilities that are able to cope with alternating user needs and support co-creation activities with other actors in the internal and external service system. Dynamic seizing capabilities should foster mutual co-creation for value delivery to the customer and the establishment of methods such as prototyping. Reconfiguration capabilities during service innovation activities are characterized by the ongoing orchestration of the whole service system, the harmonization of productand service-innovation assets (especially in case of organizations with a strong product-centric background) and the implementation of a service-oriented mindset that establishes the appropriate organizational routines. Based on these theoretical foundations, the following parts aim to answer the following overall research question: “What organizational dynamic capabilities are required for data-driven service innovation?”

Part II: Grounding of the dissertation

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For this purpose, part III will set the initial stage for further investigations: first, it will synthesize the phenomenon of data-driven service innovation and second, derive capabilities on an organizational level. Part IV will examine the phenomenon under investigation within the empirical field and aims to identify actors and their challenges for collaboration in value co-creation during data-driven service innovation. This will enable part V to put a focus on individual actors and how they support dynamic capability development while innovating data-driven service. Finally, part VI brings the prior findings and implications together and synthesizes the results in a dynamic capability framework for data-driven service innovation.

Part III Introducing data-driven service innovation

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 M. Schymanietz, Capabilities for Data-Driven Service Innovation, Markt- und Unternehmensentwicklung Markets and Organisations, https://doi.org/10.1007/978-3-658-31691-4_3

Part III: Introducing data-driven service innovation

1

43

Introduction16

We went straight ahead, forced on by a burning curiosity. What other wonders did this cavern hold, what treasures of science? (Jules Verne, 1864) Since the concept of service innovation (Miles, 1993) emerged, multiple studies have investigated its evolution, impact, and development, especially in different industrial contexts (e.g., Carlborg et al., 2014; Baines et al., 2009; Baines & Lightfoot, 2013; Baines et al., 2017; Kowalkowski et al., 2017). Rather than being innovated by single actors, service is seen to emerge through the co-creative recombination of resources by a set of actors (e.g., organizations, customers, partners, suppliers) within a service system (Lusch & Nambisan, 2015) to foster an organization’s growth (Ordanini & Parasuraman, 2011; Barret et al., 2015; Kindström et al., 2015). In the dynamic and rapidly changing environment of the digital era, heterogeneous entrepreneurial organizations co-create markets and service offerings. To compete profitably in these markets through disruptive competition, organizations should develop the requisite capabilities to reconfigure their resources and transform their business models and structures (Teece, 2017). The increasing volume of data from sensors and interconnected devices and associated analytics facilitate process improvements and co-creation of innovative service offerings (Stein et al., 2018; Porter and Heppelmann 2014; Lusch & Nambisan 2015). Service that uses data as a key resource – though data has not to be the only one – is known as data-driven service (Hartmann et al., 2016). The use of data to underpin innovative service opens up a new field of research at the intersection between service innovation, servitization, and business model development (Schüritz et al., 2017a). It

16 An earlier version of this research has been presented at the European Academy of Management (EURAM) 2018 conference in Reykjavik, Iceland and profited from valuable feedback. A further developed version is currently under review in an international management journal.

44

Introduction

pursues a common understanding of the capabilities needed to handle the associated changes that arouse curiosity to discover these throughout this dissertation. To identify characteristics of data-driven service innovation and the requisite organizational capabilities, this part aims to link insights from the existing literature to a practical perspective, combining a SLR and interviews with relevant experts. Part III presents the first study of this dissertation and sets the starting point of this research journey through an initial investigation of the phenomenon. It departs from a practical problem of high relevance and aims to set the contentual basis for the remainder of this dissertation. It intends to derive a definition of a data-driven service that bases on both a review of relevant literature that deals with the utilization of data for service provision and analysis of interviews with experts in this certain field. The definition will give an understanding of what a data-driven service is and guide the reader through the upcoming parts. Furthermore, part III contemplates to identify barriers, resources, ordinary capabilities and dynamic capabilities from the results of the SLR and the analysis of the expert interview data. This has the goal to give a first overview on what aspects can impede the innovation of data-driven service and what resources are necessary to pursue it. This part also aims to raise awareness for ordinary capabilities as well as higher-level dynamic capabilities that should be developed by organizations during data-driven service innovation. Part III is structured as follows: After this introduction in chapter 1, the theoretical background will be presented in chapter 2. Here, a focus will be set on the innovation of data-driven service and the connected dynamic capabilities an organization should develop to achieve sustainable competitive advantage under consideration of the influences of digitization. Chapter 3 describes the used research methods SLR and expert interviews. It shows the SLR procedure and how the analysis of the qualitative interview data was carried out. Finally, it will be described how the findings from this twofold approach were synthesized. Chapter 4 will give an overview on the results of the data analysis and provide the reader with a definition of a data-driven service from a theoretical and practical viewpoint. In addition, it shows a variety of barriers that need to be considered, resources to be obtained as well as ordinary capabilities and dynamic capabilities that should be developed through an organization for being able

Part III: Introducing data-driven service innovation

45

to implement data-driven service innovation. Chapter 5 will discuss the results of the study at hand and class the findings into the current literature. Finally, chapter 6 will provide a summary of this part, present the contribution and show venues for further research. The structure of part III can be obtained from figure 3.

Part I: Introduction

Part II: Theoretical background Part III: Systematic literature review and expert interviews

Part IV: Multiple case study

Part V: Delphi study

Part VI: Synthetization and discussion

1 Introduction  Concept of service innovation  Objectives of part III  Structure of part III 2 Theoretical Foundations  Innovating data-driven service  Dynamic capabilities for service innovation  Dynamic capabilities and Digitization 3   

Methods Systematic literature review Expert interview method Data collection, analysis and synthesis

4 Findings  Synthesis of data-driven service concepts  Data-driven service innovation barriers, capabilities, dynamic capabilities, and their underlying characteristics

5 Discussion  Discussion of findings

6 Theoretical contribution and outlook  Summary of part III  Implications and venues for further research

Figure 3: Structure of part III

46

Theoretical Foundations

2

Theoretical Foundations

2.1

Innovating data-driven service

Service innovation is multi-dimensional, requiring the integration of resources, capabilities, and knowledge from multiple actors from inside and outside the focal organization (Lusch & Nambisan, 2015; Carlborg et al., 2014; Jonas et al., 2016). The delivery of new service (Lusch & Vargo, 2014) depends on processes like service design to overcome traditional organizational boundaries, integrating diverse competences by means of an interdisciplinary approach (Bitner et al., 2008; Picot et al., 2003). In particular, product-centric organizations that pursue service innovation must take steps to transform the organizational culture, enhance customer relationships, and establish new revenue models and processes (Kindström & Kowalkowski, 2014). In addition, data-driven service can facilitate customized offerings of higher quality (Wünderlich et al., 2015; Ostrom et al., 2015), closer relationships within service systems (Ostrom et al., 2015), more reliable infrastructure, and better utilization of data in pursuit of new sources of competitive advantage (Porter & Heppelmann, 2014). Data-driven service innovation induces radical change, especially in organizations that were previously product-centric, requiring further transition to an availability or performance provider and the necessary scalability to reach a broad customer base (Kowalkowski et al., 2015). Sensing the opportunities of data-driven service, seizing them and adapting to continuous business reconfiguration is challenging, and there is as yet little detailed understanding of the dynamic capabilities required to foster service innovation in the digital era (Coreynen et al., 2017; Ostrom et al., 2015; Barret et al., 2015).

2.2

Dynamic capabilities for service innovation

In fast changing environments, sustainable competitive advantage depends on more than mere possession of valuable, rare, inimitable and non-substitutable resources as

Part III: Introducing data-driven service innovation

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proposed by the RBV. In markets that change rapidly and unpredictably, long-lasting competitive advantage relies on the integration, development, and reconfiguration of both internal and external competencies through the development of dynamic capabilities (Barney, 1991; Eisenhardt & Martin, 2000, Teece, 1997). For successful service innovation (Kindström et al., 2013; den Hertog et al., 2010), these must also include (1) co-creative integration of customers for innovative service provision; (2) flexible service innovation processes; (3) new revenue mechanisms; (4) orchestration of service systems involving multiple actors; and (5) organizational transformation to establish a mental model that accommodates the particularities of a service culture (Kindström et al., 2013; den Hertog et al., 2010).

2.3

Dynamic capabilities and digitization

The impact of digitization on dynamic capabilities is currently a subject of discussion in the management literature (e.g., Coreynen et al., 2017; Wamba et al., 2018; Teece, 2018; Helfat & Raubitschek, 2018). In respect to dynamic capabilities (e.g., Teece, 1997; 2007), digitization demands for the integration and reconfiguration of digital resources and capabilities17 – for example, by means of big data analytics (Wamba et al., 2018) and platforms (Teece, 2018; Helfat & Raubitschek, 2018). Big data analytics capabilities (e.g.,

infrastructure

flexibility, management

capabilities, personnel expertise

capability) are seen as key factors in organizational performance (Wamba et al., 2018; Coreynen et al., 2017). Additionally, the increasing significance of value co-creation in service systems (Teece, 2018) demands further capabilities that include (1) innovation processes that can seize opportunities and address threats through product sequencing; (2) environmental scanning and sensing capabilities; and (3) integrative orchestration of the whole service system, supporting value capture by the platform provider (Helfat & Raubitschek, 2018). To enhance their current portfolio with service, especially manufacturing organizations can take one of three paths to accommodate digitization and the required

17 Capabilites describe the application of resources (i.e. skills and knowledge) of individuals or groups of individuals (Vargo et al., 2008).

48

Theoretical Foundations

resources, capabilities, and dynamic capabilities: (1) industrial, (2) commercial, or (3) value servitization (Coreynen et al., 2017). Industrial servitization refers to the translation of internal process optimization knowledge into service that adds value for customers. Commercial servitization is characterized by the alignment of service provider value creation with the customer’s internal process through novel forms of interaction (e.g., online interface). Finally, value servitization introduces new digital products that renew the current value chain to impact on customer processes (Coreynen et al., 2017). All of these pathways entail resources (e.g., online interfaces, product data) and require new ordinary capabilities (e.g., user involvement, design-toservice capabilities) and dynamic capabilities (e.g., hybrid offering sales, data processing, interpretation) (Coreynen et al., 2017). Based on the emerging possibilities for service innovation arising from the collection, analysis, interpretation and recombination of data, the aim of the present part is to clarify the characteristics of data-driven service innovation that distinguish it from non-data-driven approaches and to specify the requisite associated ordinary capabilities and dynamic capabilities. To that end, the following research questions are addressed: RQ1: What defines and characterizes data-driven service innovation, and what differentiates it from non-data-driven servitization and service innovation? RQ2: What capabilities and dynamic capabilities are required for data-driven service innovation, and are these context-specific? These research questions will be investigated against the following background: RQ1 aims to set the basis for further inquiries through an investigation of the status quo that bases on desk research and an empirical expert interview study. After the clarification of the phenomenon under investigation, RQ2 aims examine the required (dynamic) capabilities for the implementation of data-driven service innovation.

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49

Methods – Systematic literature review and expert interview method

To shed light on data-driven service innovation characteristics and capabilities, the present part of this dissertation involves a twofold approach. First, to assess the influence of data on service offerings and service innovation, a systematic review of the extensive existing literature was considered appropriate. This enables to look beyond the specifics of single studies (Petticrew & Roberts, 2006) to more broadly investigate the characteristics of data-driven service innovation and to identify neglected issues and open problems for further research (Fink, 2013). The second component of the present study comprises interviews with industry experts, including managers and other experienced experts involved in data-driven service innovation. This qualitative approach facilitates an in-depth understanding of data-driven service innovation based on the individual perspectives and personal experiences of the interviewees (King & Horrocks, 2010; Yin, 2018). Triangulation of the two methods helps to eliminate the limitations of a single method through cross-data validity checks (Patton, 2002), enriching aggregated insights from the current academic literature with contextual qualitative data from experts.

3.1

Systematic literature review

To get an overview on the current state of the art in a specific research domain, a SLR of currently existing and relevant academic publications serves as an appropriate research approach and basis for future activities to gain additional knowledge (Webster & Watson, 2002). The SLR approach enables the researcher to remove specifics of single studies in the selected field through a consideration of a large amount of academic publications (Petticrew & Roberts, 2006). It allows to identify scarcely considered issues in research and open problems that call for further research (Fink, 2013).

50

Methods – Systematic literature review and expert interview method

To conduct a SLR, Webster and Watson (2002) propose a structured approach that consists of three steps: (1) identification of articles through search in scientific databases for both journals articles and conference proceedings. (2) The conduction of a backward search that allows the researcher to identify articles that were cited by the identified literature from step one and should be considered as well. (3) A forward search that identifies relevant articles that cited the selected articles from step one (Webster & Watson, 2002). Afterwards, the researcher can structure the review in a concept-centric manner for being able to present a synthetization of the literature on the topic under investigation (that could not be reached with an author-centric approach). It is considered in this context to setup a concept matrix that helps to present the key concepts discussed in literature and enables the handling of the unit of analysis. It synthesizes the identified literature and sets the basis for further discussion of the identified key concepts (Salipante et al., 1982; Webster & Watson, 2002). During this part, a SLR is selected as the appropriate method to investigate the current state of the art in literature of characteristics of data-driven service innovation. It provides a profound foundation for an in-depth investigation of this topic through the synthesis of different concepts (such as smart, advanced, digital or big data services) that deal with a similar phenomenon: the utilization of data for service provision. Furthermore, the SLR offers an overview on characteristics that are connected to the innovation of data-driven service and were discussed in recent literature, enabling the derivation of required resources, barriers, ordinary capabilities and dynamic capabilities in a next step. 3.1.1

Data collection

To identify the relevant literature in the field of interest, established scientific databases were searched (Scopus and Business Source Complete (EBSCO)). Adequately general and relevant multiple keywords were identified and subsequently combined as search strings. The keyword combinations used, such as data-driven AND service engineering can be seen in table 1. The keywords are based on existing literature and discussions during two expert panels that took place in the context of doctoral colloquia with renowned researchers. In addition to academic journals, sources

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included conference proceedings (e.g., European Conference on Information Systems (ECIS), International Conference on Information Systems (ICIS), Hawaii International Conference on System Sciences (HICSS))18, ensuring the inclusion of recent publications within this emerging field of research. Table 1: Keyword combinations Service innovation OR

Data-driven OR Data-based OR Data-infused OR Data-enabled OR Smart OR

Service engineering OR AND Servitization OR Servitisation

Big data The literature review was informed by criteria that included a strong connection to ICT and data use for service provision. Academic publications that discussed only the addition of services like maintenance without further offerings (e.g., predictive or preventive maintenance or other forms of analysis) or that did not discuss the provision of service based on the utilization and analysis of data were excluded. In a first step, 389 academic articles were selected as suitable for being included in the initial sample after a screening of the title. Afterwards, the abstracts of the initial sample were read for further understanding of the eligibility of the articles. The abstract reading resulted into 155 articles. These 155 were read completely and the assessment of these articles yielded 46 academic publications that were subsequently selected for inclusion in the review. From this first collection of articles, backward and forward search (Webster & Watson, 2002) yielded 13 further academic and conference papers (see Figure 4).

The publications are regarded as relevant by the German Academic Association for Business Research (VHB). Further information can be obtained from https://vhbonline.org/vhb4you/jourqual/ vhb-jourqual-3/. If a publication’s source was not included in the VHB-Ranking, its H Index according to the Scimago Journal & Country Rank was assessed. If the H Index was >36 (the average of C and Dranked VHB publications (own calculation; see annex B)), it was included into the sample. 18

52

Methods – Systematic literature review and expert interview method

389

155 After title screening

After abstract read

46

59

After full text

After fwd/bwd

Figure 4: Article selection procedure 3.1.2

Data analysis

Concepts referring to “smart” and “digital” The systematic literature review highlights the diversity of terminology currently used to describe similar underlying characteristics of data-driven service innovation. Not all authors explicitly conceptualize the provision of service that utilize data; among those that do, some of the most frequently used attributes include 1) “smart” and 2) “digital”; these are used interchangeably but can be differentiated as follows. 1) “Smartness” indicates a strong dependency on IT, as well as the use of data collected remotely and in real time (e.g., Geum et al., 2015; Grubic & Peppard, 2016). In particular, IT is seen as the basis for smartness (e.g., Demirkan et al., 2015) and for the application of analytics to data collected from interconnected devices. It connects digital as well as physical components to facilitate innovative and personalized interactions between provider and customer (e.g., Anke, 2019; Zheng et al., 2017; Beverungen et al., 2017). See table 2 for an overview on the definitions referring to “smart”. Table 2: Definitions referring to "smart" Concept

Source

Smart

Demirkan et Many types of smarter service depend on IT as a service, including big data analytics. al., 2015,

service

p. 739

Citation

Part III: Introducing data-driven service innovation

Geum et al., 2016, p. 534 Beverungen et al., 2017, p. 6

53

[…] foundations of smart services are summarized threefold: big data, cloud computing, and intelligent system. In smart services, connected devices can collect data in the everyday lives of customers. Smart service is the application of specialized competences, through deeds, processes, and performances that are enabled by smart products.

The provision of such services is based on the recording of sensors and operational data, its transmission via p. 1 digital networks, as well as its evaluation and the delivery of the analysis results, e.g. via smartphone apps. Zheng et al., An IT-driven value co-creation business strategy consisting of various stakeholders as the players, 2018, intelligent systems as the infrastructure, smart, p. 8 connected products as the media and tools, and their generated e-services as the key values delivered that continuously strives to meet individual customer needs in a sustainable manner. Anke, 2019,

2) “Digital” offerings, services, or servitization (see table 3) refer to the use of ICT to provide service that relies on digital components of interconnected physical products (e.g., Vendrell-Herrero et al., 2017; Rymaszewska et al., 2017; Lerch & Gotsch, 2015). Digital technology is used to enable fundamental changes in the dimensions of service value (Remane et al., 2017) and to influence the provider-customer relationship (Coreynen et al., 2017). Table 3: Definitions referring to "digital" Concept

Source

Digitally enabled offering

Coreynen et al., 2017, p. 10

Citation

A third pathway is to create digitally-enabled offerings that radically change customer processes and have a more disruptive impact on providercustomer relations. This form of digitization includes digitally-modified businesses combining physical and digital offerings, for example by adding online monitoring or tracking devices to products

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Methods – Systematic literature review and expert interview method

Digitally enabled service

Herterich et al., 2016, p. 1238

Digital Service

Rymaszewska et al., 2017, p. 93

Lerch & Gotsch, 2015, p. 45

Digitalized service

Remane et al., 2017, p. 41 Digital Vendrellbusiness Herrero et al., model 2017, p. 71 Digital Kowalkowski servitization et al., 2013a, p. 507

Against the backdrop of digital product innovation, the physical and digital materiality of products drives servitization and unfolds its power to incrementally improve existing service systems such as classical TCS and its generative capacity resulting in service innovation and, thus, service systems that would not have been possible without the physical and digital materiality of products. The research presented in this paper also contributes to the broader discussion on the role of digitalization in servitization that is often labelled as digital servitization which, according to can be defined in terms of providing digital services embedded in a physical product. The move toward servitization has coincided with a rising trend toward digitalization, with manufacturers equipping products with intelligent digital systems that allow the products to operate independently of human intervention and communicate with other machines. As a logical consequence of the confluence of servitization with this trend toward intelligent machines, an increasing number of manufacturers are using digital systems to support their services, creating totally new industrial product-service offerings, such as comprehensive remote services that bring digital and physical systems together to pave the way for, for instance, availability guarantees. A business model can be categorized as digital if digital technologies trigger fundamental changes in these value dimensions. This substream of research, described recently as ‘Digital Servitization’, is defined as the provision of IT- enabled (i.e. digital) services relying on digital components embedded in physical products. Furthermore, ICT enable firms to both reduce the costs for providing services (i.e. internal efficiency) and increase service revenues by infusing higher value into the customers’ value-creating processes through new services. For example, ICT can

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ICT enabled service

Kowalkowksi & Brehmer, 2008, p. 748

55

facilitate for firms dealing with high diversity of demand, and it can be a tool for information sharing and information gathering on product usage and customer needs. The collection and processing of real-time information about the condition and utilization of the installed base can in turn enable new services with a focus on value-in-use. Attention should not be limited to ICT that influence existing service processes but instead the provider should also consider using technology that enables him to provide value in new ways. Many resources and activities can be dematerialized and unbundled in terms of place (where they take place), time (when they take place), actor (who performs them) and actor constellation (with whom they are performed) and then be re-bundled into new offerings with a denser level of resource integration.

Concepts referring to “data” In the reviewed publications, diverse terms (e.g., “big data services”, “data-driven services”, “data-driven innovation”, “data-as-a-service” or “datatization”) were used to emphasize the key role of data (e.g., Herterich et al., 2015; Schüritz et al., 2017a; Demirkan & Delen, 2013). Data from connected devices is utilized to improve processes and to seize innovative opportunities for further service provision (e.g., Chen et al., 2016; Opresnik & Taisch, 2015; Herterich et al., 2015). Utilizing analytics, data from Product-Service-Systems (PSS) or Cyber-Physical-Systems (CPS) are a key resource for the provision and innovation of new data-driven service co-created within systems of diverse actors (e.g., Herterich et al., 2015; Schüritz et al., 2017a; Zolnowski et al., 2016). See table 4 for an overview. Table 4: Definitions for concepts referring to "data" Concept Big data service

Source Opresnik & Taisch,

Citation A manufacturing enterprise, in order to stay competitive, must servitize in collaboration with others within an MSE. The ICT tools and procedures that support service

56

Methods – Systematic literature review and expert interview method

engineering generate numerous additional volumes of data with a high level of variety and velocity. Due to the software and hardware that support the provision of a PS (e.g. remote maintenance), there is another source of data available – the P-S during its usage. Consequently, a manufacturing enterprise in mature markets, that is falling into the commodity trap, has new opportunities to increase its long term competitive advantage, by exploiting the Big Data that have become available due to servitization within an MSE. Chen et al., Equally important, big data is used to improve operational efficiency and disruption management that 2017, p. 5099 are essential to better customer travel experiences. Most importantly, big data is being mined to discover innovative service provision and operational optimizations that were not possible before. Data as well as insights obtained from CPSs can be used Herterich et al., 2015, as an asset to realize unexpected information and datadriven service opportunities. p. 325 Schüritz et As an advanced step of servitization, we refer to this al., 2017a, transformation as datatization and define it as the p. 4 innovation of an organization's capabilities and processes to change its value proposition by utilizing data analytics. Zolnowski For that reason, a business innovation, based on the use et al., 2016, of data and analytics to innovate for growth and wellp. 3 being, is defined as data-driven innovation. Demirkan The convergence of ICT-service-oriented, -architecture, & Delen -infrastructure and -business processes, the emergent 2013, Web applications, Web 2.0,Web 3.0, grid computing, p. 413 cloud computing, internet-enabled smartphones, RFID, and advanced sensing and data analysis—is driving the next information technology inflection point. And this technology inflection is setting the stage for business transformation. “Services” and “service platforms” are central to this evolution 2015, p. 178

Datadriven service

Datadriven innovation Data as a service

Concepts referring to “advanced”, “remote” and “value” A smaller number of publications (see table 5) mentioned advanced services that facilitate co-creation of value through advanced offerings that broaden manufacturers’

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operations (Lenka et al., 2017; Baines et al., 2009) or high-value and value-added services that utilize data analytics to deliver services integrated within products (Davenport, 2014). These often rely on remote services like predictive analytics (Golightly et al., 2017) that frees service provision from the constraints of time, location, and customer involvement (Westergren, 2011; Jonsson et al., 2008; Brax & Jonsson, 2009). Table 5: Definitions referring to “advanced”, “remote” and “value” Concept

Source

Remote Westergren, monitoring 2011, p. 224 service (RMS) Remote service

Jonsson et al., 2008, p. 229

Remote field service

Brax & Jonsson, 2009, p. 545

Predictive maintenance

Golightly et al., 2017, p. 3

Advanced service

Cenamor et al., 2017, p. 55

Citation A RMS is placed locally with a customer, but can be monitored from a distance by the service provider, who logs and analyzes machine data through the system. The continuous data flow from ubiquitous computing applications enables a seamless provision of services at any time and any place, that can even be coordinated in real time. Information technology (IT) has enabled the development of “remote field services” in which predictive maintenance is enabled through direct information exchange between technical systems, i.e. possibly without direct customer contact. With enough data, patterns of performance behaviour emerge that allow not just the identification of failure states, but of indicators of future failure and/or prognoses of the life span of an asset. This allows organisations to move from regular inspection or replacement of assets, to a condition-based approach where assets are inspected or replaced based on analysis of future degradation - for our purposes, we refer to this type of technology as ‘predictive maintenance’. […] digitalization capabilities are a key facilitator for advanced service offerings. Thus, a platform approach that leverages the value of digital technologies may be particularly beneficial in the context of advanced service implementation that facilitates both customization and efficiency.

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Methods – Systematic literature review and expert interview method

Lenka et al., 2017, p. 92

High-value service

Davenport, 2014, p. 45

This trend toward digitalization is also transforming the way manufacturing firms interact with their customers by enabling new connected product functionalities and integrating various operational processes to increase opportunities to co-create value through advanced service offerings. But big data also offers a promising new dimension: to discover new opportunities to offer customers high-value products and services.

Data analysis for characteristic identification Beside of the sole analysis of concepts used within academic literature, an inductive approach was used to derive a concept matrix synthesizing the reviewed literature (Webster & Watson, 2002). This approach has the goal to capture the characteristics of data-driven service innovation; in other words, categories were derived from the ground up, emerging from data analysis (de Ven, 2007). For this purpose, the collected data was coded. Coding comprised first and second cycle coding methods according to Saldaña (2016). First cycle coding describes the initial examination of the collected data. During this part of the dissertation, the academic literature was coded descriptively in a first cycle, summarizing the relevant passages in a short sentence or descriptive word. This descriptive coding provides a first overview of topics covered by the data and lays the groundwork for further coding, interpretation, and analysis. It allows to identify and connect similar contents (Saldaña, 2016; Wolcott, 1994). The codes help to clarify what the topic is about and not solely serve as an abbreviation of the content under analysis (Tesch, 1990). Carrying out this coding method during the first cycle of coding on the academic literature reviewed, resulted in 90 different descriptive codes, such as “monetary value of data”, “outsourcing”, ”co-creation”, “top-level support” or “user-centric perspective” (for a complete list of the descriptive codes from the first cycle see figure 5).

Part III: Introducing data-driven service innovation

     

                       

Agile processes Analytical capabilities Automated data exchange Big data expertise Business model reconfiguration Centralized vs. decentralized data analytics CEO involvement Co-creation Co-creation in value networks Co-innovation Combination of products & services Continuous data exchange Control of knowledge Control of skills Cooperative productivity improvement Cooperative value innovation Creation of analytics departments Cultural change Customer integration Customer needs Customer requirements Customer satisfaction Customer-centric value innovation Customer-oriented attitude Data access Data culture Data exchange Data interpretation capabilities Data monetization Data ownership

                              

59

Data possession Data privacy & security Data processing capabilities Data provision by customers Data security Data silo integration Data-driven mindset Data-oriented culture Decentralized structures Design of new revenue models Digital platform Digitalization capabilities Downstream collaboration Ecosystem setup Employee training Explicit strategy External collaboration Innovation of cooperation Innovation of customer interactions Innovation of resource allocation Interdisciplinary collaboration Interdisciplinary teams Interface standardization Internal collaboration Internal communication Internal coordination Internal integration Internal optimization Internal skill development Interoperability IT-skills

  

                         

Lack of employees Lack of experience Management of physical & human resources Monetary value of data Multi-actor environment New ways of customer interaction Novel revenue streams Operating risks Outcome based contracting Outsourcing Partner involvement Performance contracting Pricing of new services Recombination Resource reconfiguration Resource sharing Service-centered customer interaction Service culture Service efficiency Standardization Strategy alignment Supplier collaboration Surveillance Top-level support Top management support T-shaped data scientists T-shaped employees Upstream collaboration User-centric perspective

Figure 5: Overview on 90 codes after first coding cycle, in alphabetical order Afterwards, second cycle coding was carried out (see figure 6). During second cycle coding, the data is reanalyzed and reorganized for being able to build categories. For this purpose, the second cycle aims to merge codes from the first cycle due to their similarity with the goal to drive down the sheer amount of codes (Saldaña, 2016; Silver & Lewins, 2014). During the second coding cycle, pattern coding was used to reduce the number of topics and sentences from the first cycle of descriptive codes.

First coding cycle: 59 identified articles

Description of characteristics through descriptive coding

• • • • Consolidation • of first cycle • codes to 11 • categories • through • pattern • coding • Second coding cycle:

External collaboration Internal collaboration Human IT resources Customer-oriented culture and strategy Data-oriented culture and strategy Data access, collection and ownership Revenue models Resource recombination Standardization Data privacy Top management support

Figure 6: Coding cycles and resulting codes for data analysis

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Methods – Systematic literature review and expert interview method

Pattern codes are “explanatory or inferential codes, ones that identify an emergent theme, configuration, or explanation” (Miles et al., 2013, p. 86). They aim to synthesize major themes into a smaller number of similar themes with commonalities for further analysis. They can serve as meta-codes that help to condense the large variety of codes from the first cycle into a unit of analysis that is characterized by a higher meaningfulness (Miles et al., 2013; Saldaña, 2016). To characterize data-driven service innovation, codes from the first cycle that shared commonalities were subsumed and assigned to eleven pattern codes and that build the characteristics of data-driven service innovation.

3.2

Expert interview study

To investigate the research questions from a practical viewpoint, expert interviews were conducted. The consultation of experts in a certain field is regarded as an efficient and concentrated method for data collection during exploratory research projects. Accessing experts can help the researcher to gain important insider information on the phenomenon under investigation (Bogner et al., 2009). In this context, the expert serves as an individual that possesses knowledge that is not accessible to others, differentiating the expert from others with mundane knowledge. Experts are active participants that acquired their specific knowledge about certain topics through their participation in actions that are connected to the investigated phenomenon (Meuser & Nagel, 2009). The expert interview can be differentiated from other forms of openended interviews (such as e.g. the narrative interview) through a focus on organizational or institutional coherence and not on an individual’s context of life. The expert interview has as an ultimate goal to identify structures and their connections that can be derived from the obtained expert knowledge and a cross-check between the expert’s statements in a similar field help the researcher to generalize the results of a study (Meuser & Nagel, 1991). 3.2.1

Data collection

To explore the characteristics of data-driven service innovation from a practical perspective, qualitative, open-ended expert interviews with individuals from central

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Europe who are actively involved in the innovation of data-driven service were conducted between May and October 2018. The participants work for organizations with a product-centric background that already complement their offerings with datadriven service. To avoid contextual bias (Eloranta & Turunen, 2016), the ten organizations were drawn from different sectors such as healthcare, manufacturing, and automotive on the basis that data-driven service are seen as an important element in their current and future strategy. The ten interviewees held diverse positions (see table 6) and were selected because of their involvement in data-driven service innovation, thus giving them the possibility of being able to reflect on their experiences in this particular field. Table 6: Overview of Interviewees with position and sector Interviewee 1 2 3 4 5 6 7 8 9 10

Position Digital Innovation Manager Digital Strategy Manager Innovation Manager IT Innovation Manager Head of R&D IT Service Manager Digital Transformation and Innovation Manager Digital Factory Manager Business Development & Strategy Head of Service

Sector Manufacturing Healthcare Manufacturing Automotive Manufacturing Manufacturing Healthcare Automotive Manufacturing Manufacturing

Interviewees were asked how they understand and define data-driven service, including the characteristics and role of data-driven service innovation in their organization.19 In particular, the questions asked can be grouped as followed: (1) How can a data-driven service be defined, (2) what are characteristics of the current service innovation process and how is it influenced through the utilization of data, (3) what are the repercussions of data utilization on culture, strategy and customers, (4) what IT-specific repercussions influence data-driven service innovation and (5) do new

19 Throughout this dissertation exemplary statements of interviewees will be displayed to illustrate their opinions and to ensure transparency.

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Methods – Systematic literature review and expert interview method

revenue models need to considered. The interviews were conducted in German, either face-to-face in office or by phone. 3.2.2

Data analysis

For the purposes of data analysis, the interviews were recorded for subsequent transcription and coding using QDA software. As mentioned in section 3.1, the eleven categories that emerged from the coding cycles of the literature analysis served as a basis for the coding of the expert interview data (an overview on the list of codes can be obtained from annex C). Furthermore, it was analyzed how the experts defined a data-driven service, serving as an additional category during coding. The qualitative interview data were then coded into these categories. However, the creation of new categories was not impeded, but all relevant codes were applicable to the eleven prior categories from the extensive analysis of the literature in the previous step during the SLR. Analysis of the interview data shows that the interviewees are aware of the existence of distinct terms describing data-driven service. Interviewees remarked that this discourse is characterized by a range of buzzwords, especially among customers and colleagues who lack the necessary technical or IT knowledge to differentiate between them. Most interviewees were of the view that even among those with some knowledge of the theoretical foundations, the majority of experts does not really differentiate between the terms used in the literature (see table 7). According to Interviewee 1, for example, “I buy a service or I use a data-driven service, not because I want to buy a Big Data service. I want to buy it because it makes me some promise that improves my production, and I don't care if it's with Big Data or with Smart Data or with whatever kind of data, as long as it solves the problem for me.” In general, interviewees emphasized the added value created by the use of data as a primary resource. As Interviewee 7 put it, “For me, a data-driven service is not only based on the collection of data but also on the analysis, which together create added value for the customer.”

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Table 7: Overview on definition of data-driven service from a practical viewpoint Interviewee Definition of a data-driven service # The whole thing is data-driven if the added value that is created for 1 me, if it arises from either my own data or from external data or from the combination of both, i.e. if data contribute to the added value that arises. A data-driven service would be for me one on the basis of collected 2 data - online or offline - which then leads to a better decision either in business processes or in cooperation with the customer or in research. I would say that the value proposition is created predominantly 3 automatic, based on the available data. A service that requires data to be created. 4 The customer gives me data and I generate knowledge from this 5 data. This is a service that is primarily based on the processing of data, and 6 the prerequisite for the whole thing is remote access in order to enable us to provide service that are often not available on site. For me a data-driven service is not only based on the collection of 7 data, but also on the analysis and then creates added value for the customer based on the collection and analysis. A data-driven service is for me a service that utilizes data or 8 information as its main resource. Data-driven service is an offer to the customer that satisfy a need, that 9 are recurring, that in turn are provided or identified by either being prepared or fulfilled by collected datasets or by the knowledge that is then generated by these entire datasets. That one analyzes data, receives information from it, which gives an 10 indication for certain activities. […] A data-driven service is situation-adapted. This means that the data that changes according to a situation is included in the service and therefore one will need more or less of this service.

3.3

Step 3: Synthesis

As appropriate to the goals of the study (Saldaña, 2016), the categories that evolved from the literature review were initially used to structure coding of the qualitative interview data. During a third cycle, the findings from both SLR and expert interviews

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were synthesized with the goal to derive a definition of a data-driven service from a scientific and practical view and the identified characteristics were assigned to dynamic resource configurations such as described by Coreynen et al. (2017): barriers, resources, ordinary capabilities, and dynamic capabilities (see figure 7). These dimensions describe first the barriers that come up during service innovation and the required resources (i.e. static assets for operationalization of a strategy). Additionally, they refer to the demanded ordinary and dynamic capabilities that are necessary to overcome barriers (Coreynen et al., 2017). This approach is used due to its ability to provide a suitable description of the main characteristics to be regarded during datadriven service innovation in the context of this dissertation. The synthesis, thus the definition of a data-driven service and

data-driven

service

innovation

barriers,

ordinary capabilities, dynamic capabilities, and their underlying characteristics will be presented during the upcoming chapter 4. (1) Data collection

Desk research

Empirical field study

Step 1: Systematic Literature Review

Step 2: Expert Interview Study

First cycle coding: 90 different descriptive codes

(2) Data analysis

(3) Synthetization

Second cycle coding: 11 categories • External collaboration • Internal collaboration • Human IT resources • Customer-oriented culture and strategy • Data-oriented culture and strategy

-

• • • • • •

Data access, collection and ownership Revenue models Resource recombination Standardization Data privacy Top management support

Data-driven service definition Characteristics of data-driven service innovation Barriers, resources, capabilities and dynamic capabilities for data-driven service innovation

Figure 7: Data analysis procedure

Part III: Introducing data-driven service innovation

4

65

Findings

Based on the definitions and characteristics of data-driven service and innovation, the data analysis comes forward with three main findings: 1) a synthesized definition of data-driven service; 2) 11 characteristics of data-driven service innovation; and 3) resources, ordinary capabilities, and dynamic capabilities20 required for data-driven service innovation (see figure 8 below). Data-driven service innovation “a data-driven service uses real-time and remote data from connected devices as a key resource for digital delivery of co-created, high-value solutions to the customer”

Resources (b1) Data access, collection and ownership

(b2) Human IT-resources

Ordinary capabilities

Dynamic capabilities

(c1) Resource Recombination

(c2) Revenue Model

(d1) External Collaboration

(d2) Internal Collaboration

(d3) Customer-oriented culture and strategy

(d4) Data-oriented culture and strategy

(d5) Top management support

Barriers (a1) Data privacy

(a2) Standardization

Figure 8: Findings from data analysis and synthesis

20 Rather than just seeing dynamic capabilities as some kind of ‘dynamic resources’, Teece (2007) emphasizes that “Dynamic capabilities, by contrast, relate to high-level activities that link to management’s ability to sense and then seize opportunities, navigate threats, and combine and reconfigure specialized and cospecialized assets to meet changing customer needs, and to sustain and amplify evolutionary fitness, thereby building long-run value for investors” (p. 1344).

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4.1

Findings

Synthetization of concepts from systematic literature review and expert interview analysis

Based on the analysis of both, the data from the SLR and expert interviews, this dissertation aims to derive a definition of a data-driven service. This definition has the goal to provide a common understanding of the phenomenon under investigation throughout this dissertation. In particular, the various concepts referred to above, all address the same general phenomenon: the utilization of data to provide innovative service offerings with and for the customer. Synthesizing these concepts and building on the expert interviewees’ insights, this dissertation proposes that a data-driven service uses real-time and remote data from connected devices as a key resource for digital delivery of co-created, high-value solutions to the customer. The term datadriven is chosen to highlight the importance of data collection and analysis for the provision of service that would not otherwise be possible, and to avoid differing interpretations of broader terms like smart or digital.

4.2

Data-driven service innovation barriers, capabilities, dynamic capabilities, and their underlying characteristics

To capture the characteristics of data-driven service innovation, the articles included in the SLR sample were coded as described in chapter 3.1.2. Hence, the analysis of the SLR data that emerged from the coding cycles resulted in the following eleven characteristics of data-driven service innovation, in order of frequency, along with the relevant sources: (1) external collaboration; (2) customer-oriented culture and strategy; (3) human IT resources; (4) data access, collection, and ownership; (5) internal collaboration; (6) data-oriented culture and strategy; (7) revenue models; (8) resource recombination; (9) standardization; (10) top management support; and (11) data privacy and the concept matrix can be obtained from table 8.

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Data privacy

X X

Top management support

X X

Standardization

X

Resource recombination

Revenue models

X X X X

X X X X X

Data access, collection & ownership

X

Data-oriented culture & strategy

X X

Internal collaboration

Aho, 2015 Anke, 2019 Ardolino et al., 2018 Belvedere, 2013 Beverungen et al., 2017 Bigdeli et al., 2017 Brax & Jonsson, 2009 Brown, 2017 Cenamor et al., 2015 Chen & Zhang, 2014 Chen et al., 2016 Cohen et al., 2017 Coreynen et al., 2017 Davenport, 2014 Demirkan & Delen, 2013 Demirkan et al., 2015 Fu et al., 2018 Gebauer et al., 2017 Geum et al., 2015 Goduscheit et al., 2018 Golightly et al., 2017 Grubic & Jennions, 2017 Grubic & Peppard, 2016 Helfat & Raubitschek, 2018 Herterich et al., 2015 Herterich et al., 2016 Hou & Neely, 2018 Jonsson et al., 2008 Klein et al., 2018 Kowalkowski et al., 2013a Kowalkowski & Brehmer, 2008 Kusiak, 2009 Kusiak, 2017 Lim et al.,2018 Lenka et al., 2017

Human IT resources

External collaboration Customer-oriented culture & strategy

Table 8: Systematic literature review concept matrix

X

X

X

X

X

X X X X X X X X

X

X X X

X X X X X X X X X X X X X X X X

X X

X X X X

X

X X X X X X

X

X

X

X

X X

X

X X X X X X

X X

X X

X X

X

X

X

X X

X

X

X

X

X X

X

X X

X X

X

X X X

X

X

X

X X X

X X

X

X

X

X X

68

Lerch & Gotsch, 2015 Opresnik & Taisch, 2015 Persona et al., 2007 Pigni et al., 2016 Remane et al., 2017 Robinson et al., 2016 Rymaszewska et al., 2017 Sanders, 2016 Schüritz et al., 2017a Sorescu, 2017 Story et al., 2017 Tao et al., 2018 Teece, 2018 Troilo et al., 2017 Urbinati et al., 2018 Vendrell-Herrero et al.,2017 Wen & Zhou, 2016 Westergren, 2011 Yoo et al., 2012 Zeng & Glaister, 2018 Zheng et al., 2017 Zheng et al., 2018 Zolnowski et al., 2016 Zolnowski et al., 2017 SUM

Findings

X X X X

X

X

X

X X

X X X X X X

X

X X

X X

X X X X X X

X X X

X X

X X X X

X X X

X X X

X X X X X

X

X

X

X X X X

X

X X

X X

X X X X

X

X

X

X X

X X X

X X X

X

48

29

X X X

X X

17

13

X

X X

11

9

X X 23

21

X 13

9

8

The subsequent synthesis of the results of the SLR and the expert interview data led to underlying aspects of the individual characteristics. Table 9 shows the eleven characteristics and its underlying aspects, underpinning the synthesis of the collected data. Matching these categories to the dynamic resource configurations as used by Coreynen et al. (2017) (as mentioned in chapter 3.3), this study aims to highlight the required resources and emerging barriers coming up during data-driven service innovation. Finally, ordinary capabilities and dynamic capabilities are derived that help to overcome barriers for data-driven service innovation.

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Table 9: Data-driven service innovation dynamic resource configurations and their underlying characteristics (based on Coreynen et al., 2017)

During the innovation of data-driven service as defined here, organizations develop new service such as predictive or preventive maintenance or other new offerings that may be outcome-based (Schüritz et al., 2017b). To be able to offer service of this kind, organizations must take account of data privacy issues and access to the necessary data for collaboration with other actors. In this regard, data-driven service innovation capabilities must include customer relations processes and management (Story et al., 2017). It is also necessary to develop the required IT capabilities to analyze and interpret data to be linked to domain knowledge. To be able to transform an

70

Findings

organization and gain a sustainable competitive advantage based on innovative datadriven service, the service system must be effectively orchestrated. In particular, integrative capabilities across the service system and different organizational units enable transformation of governance structures. This shall help to encompass the whole service system including internal and external complementary asset providers, enabling value co-creation built on the use of data for service provision (Helfat & Raubitschek, 2018; Teece, 2018). The following chapters will provide a detailed view on the characteristics and their underlying aspects. 4.2.1

Data privacy

Data privacy issues (characteristic a1) are discussed to impede data-driven service innovation. To provide a) a solid foundation for data use and b) to ensure that service providers choose legally secure locations for their data servers, the introduction of appropriate privacy policies can help to increase trust in service that make use of confidential data (Chen & Zhang, 2014; Demirkan & Delen, 2013). Any such legislation should cover six aspects of data privacy. These are (1) personal data, (2) operational or (3) productivity data, (4) intellectual property, (5) commercial secrets, and (6) financial data (Chen & Zhang, 2014; Demirkan et al., 2015). Failure due to data privacy issues is shown to undermine the service provider’s brand image, reputation, customer confidence, and revenues, especially where there is sensationalist media coverage of any breach (Demirkan et al., 2015; Demirkan & Delen, 2013). These data privacy issues also emerged during the analysis of the interview data. Complementing the findings from the SLR from a practical viewpoint, Interviewee 6 noted that the German market is considered especially sensitive to data privacy issues: “In terms of data privacy, the perceived risk in other countries is far lower than in Germany, but that doesn't mean that you don't need to pay attention to it; even if the customer doesn't care … when it happens, he has also had bad luck.” While outsourcing certain activities allows service providers to concentrate on their core competencies, this approach creates additional data security risks (Sanders, 2016), “because if we can read the data, theoretically someone else can, too” (Interviewee 5). Additionally, the provision of data-driven service – especially those involving access

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to sensor data – raises sensitive issues about the potential for controlling employees as well as knowhow drain (Westergren, 2011). The analysis of the interview data shows that the interviewees perceived the use of personal data that can be linked to the individual as especially important. Works councils were seen as potentially slowing data-driven service innovation. As Interviewee 6 put it, they may even “prevent some market introductions because there's something discovered again and again. So that's an essential and influencing factor”. 4.2.2

Standardization

One distinctive characteristic of data-driven service innovation is the standardized interface for data collection, use, and exchange through machine-to-machine interaction (Demirkan et al., 2015; Lerch & Gotsch, 2015). Without this standardization (characteristic a2), the lack of interoperability of internal systems based on proprietary solutions may impede collaboration with other actors in the service system (e.g., Demirkan et al., 2015; Grubic & Jennions, 2017). In particular, the use of open platforms for standardized exchange of data can foster collaboration between different actors during value co-creation activities (Herterich et al., 2016) – even internally, when departments use disparate systems that are not compatible (Wen & Zhou, 2014). Standardization is seen to enable rapid information exchange across organizational borders, reducing lead times and increasing efficiency (Sanders, 2016). The interview data additionally revealed that standardization is an important step towards winning the trust of customers “who are still a bit skeptical and don't know if it's a sustainable solution that you're offering” (Interviewee 1). 4.2.3

Data access, collection, and ownership

While acknowledged as a key resource for data-driven service innovation, data collection and access rights as well as clarity about ownership (characteristic b1) are central issues in the reviewed literature (e.g., Demirkan et al., 2015; Rymaszewska et al., 2017). Access to data can be restricted as a result of technical issues or because actors are unwilling to share data on their problems or failures (Grubic & Jennions, 2017). Access to real-time data can be a particular problem because of outdated systems and

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routines, where data are collected only after an incident or there is no provision for automated data download and exchange (Grubic & Peppard, 2016). Information networks can also be established to ensure the continuous exchange of data between partners for data-driven service innovation and provision (Schüritz et al., 2017a), where the line of visibility between service consumers and providers blurs and new forms of interaction and data analysis emerge (Beverungen et al., 2017). Even when organizations are willing to collaborate with other actors in their service system, there is a possibility that those others may not wish to share their data because of their desire to decide for themselves who will subsequently have access and sharing rights (Pigni et al., 2016). However, analysis of the empirical data confirmed that concerns about regulation of data access, collection, and ownership can be readily overcome if the customer understands the value of the offered data-driven solution: as Interviewee 1 remarked, “that's why it's so important to identify these problems, these customer pains and gains. We deal with the pain for you here, and once you have the product, then you’ll be really happy, but what we need … is your data." Interviewee 5 refers to this “aware of the fact that data access is currently an issue; the customer is actually also aware that this is an unregulated … grey area. So, no general business terms are handed over to define exactly which data now belongs to whom, who may do what, and so on. At the moment, this is a bit experimental.” Finally, Interviewee 6 remarked that “The more industrial [the context], the less willingness there is to open the communication channels and do it yourself instead,” and that the German market in particular is not open to data sharing at present. 4.2.4

Human IT resources

The analysis of the collected data reveals that to innovate data-driven service, current staffing needs to be supplemented by employees with the multiple IT skills (characteristic b2) that perform the required analytical work and link those insights to the current business (e.g., Troilo et al., 2017; Schüritz et al., 2017a; Rymaszewska et al., 2017). Interviewee 2 supports this view, suggesting that this issue is

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“a very big one. And those are also the ones that are hard to find. So I mean, (...) that the employees themselves don't have the IT knowledge that it just needs, and you just try to counter that by building digital teams that come into the projects here and support this aspect.” To achieve the connection between analytics and business, employees must have both IT competencies and knowledge of other technical domains like engineering and mechatronics, as well as an understanding of the relationship between data analytics and their real-world application (e.g., Grubic & Jennions, 2017; Demirkan et al., 2015). As Interviewee 5 put it, “IT knowledge is something you can buy; industry-specific expertise is more important. It is more important for me to have someone who knows how parameter A can be related to parameter B. The expertise—how I can use hypothesis testing to find out from the data whether this is really the case—I can outsource this as soon as I know what I want to test.” Incorporation of the required IT skills can be achieved in two ways. On the one hand, organizations can offer special development courses or training to educate employees internally (Lerch & Gotsch, 2015; Cenamor et al., 2017). However, not all organizations are willing or able to develop and incorporate the necessary skills internally and need external partnerships to assimilate the required analytical skills (Pigni et al., 2016). Furthermore, as the literature review showed, IT and technical skills and knowledge of the business need to be complemented by social skills that support sharing of employee competences (Troilo et al., 2017; Aho, 2015). IT employees could act flexibly in a multiactor environment to implement and design the required processes for data-driven service (Story et al., 2017; Hou & Neely, 2018). In addition, the interview study reveals and emphasizes the importance of IT competencies in sales departments through “sales training for digital products” as Interviewee 1 stated—in other words, being able to show the customer the added value of data-driven service. 4.2.5

Resource recombination

Both research approaches, the SLR and the expert interview data analysis, confirmed that the collection and subsequent use of data can be improved by recombining data

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Findings

from different sources (characteristic c1). This means that organizations should consider to build up their capability to reuse, repackage, and recombine service data from one customer with other sources. These can be product, service, or information modules to improve or even innovate new service offerings to better match the customer’s needs and to create additional value (e.g., Brown, 2017; Cenamor et al., 2015). Customer needs may even differ within the organization as different units are interested in different metrics - for instance, machine operators are interested in machine availability while production planners are interested in resource consumption or productivity (Lerch & Gotsch, 2015). New offerings of this kind can move away from the micro level (i.e., specific to a use case) to deliver insights at a more general macro level that will transfer to other cases and applications (Sorescu, 2017) through recombination of data, contextual business expertise, and models to generate valuable service insights (Troilo et al., 2017). Under centralized management, a service platform can allocate and recombine data from various sources to reduce waste and operational costs and accelerate service responses (Zheng et al., 2017; Yoo et al., 2012; Beverungen et al., 2017). The analysis of the interview data shows skepticism about current organizational capabilities for recombination of data from Interviewee 3: “In a first step, we will not really be able to transfer it because the processes are too different (…). However, this may change in the long term, in the future of course. (…) And it's going to take some time until the data volumes become so large or the diversification across the customer mass becomes so great that I can learn from it and offer new service.” 4.2.6

Revenue models

It is discussed among scholars and experts that organizations pursuing data-driven service innovation should be able to extend traditional revenue models (characteristic c2) from product sales to outcome or performance-based models, in which payments depend on the achievement of certain performance goals (Aho, 2015; Zolnowski et al., 2017), and a service-for-free mentality predominates (Schüritz et al., 2017a). The price

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demanded for a service does not have to relate only to historic prices paid by former customers, but can also reflect a wide range of further (unstructured) data such as weather or competitor prices (Davenport, 2014). However, the monetary value of data remains unclear and cannot be generalized. These difficulties increase the complexity of pricing, especially for service providers with limited experience of new outcomebased revenue models (e.g., Hou & Neely, 2018; Robinson et al., 2016). These models rely on long-term relationships and require a certain degree of flexibility in reacting to environmental changes during the contract period, with increased dependency on the customer due to the service provider’s loss of control, shifting risk away from the customer (Hou & Neely, 2018; Schüritz et al., 2017a). This aspect is confirmed by the analysis of the interview study and illustrated in the following quotes. Interviewee 1 refers to a range of possible revenue models: “So whether this is ‘pay per use’ or monthly subscription or ‘I'll share your savings’ or ‘Pay once and you can use it forever,’ everything is possible. And the bandwidth should definitely be used, or ‘pay what you want,’ so, yes, what it is worth to you—everything is possible, and I am of the opinion that this should be used much more, explored much more, and experimented with.” According to Interviewee 5, however, it can be difficult to assess a solution’s benefits because of the unclear added value: “This means that if I could assure a customer that his production productivity would increase by 1% if he gave us all his data, then he would do so ... The thing is that the added value often cannot be clearly shown … in the sense of an added value that I can calculate in euro.” 4.2.7

External collaboration

In most of the reviewed literature and the interview study, collaboration among diverse external organizational actors is considered crucial for data-driven service innovation (characteristic d1). Collaboration affects the integration of other actors within the service system such as e.g. customers and suppliers. Value is co-created within a system that integrates data from multiple sources (e.g., Belvedere et al., 2013; Opresnik & Taisch, 2015; Story et al., 2017). The selection of actors and the formation

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Findings

of innovation alliances shall provide technological advantages based on existing knowledge and complementary resources. Furthermore, innovation alliances are discussed to have the ability to respond to fears of opportunism when sharing sensitive information such as technical capabilities or commercial practices that could be imitated by others (e.g., Bigdeli et al., 2017; Davenport, 2014; Grubic & Jennions, 2017). To avoid possible asymmetries within the service system, organizations should exploit their competitive advantage by retaining control over elements of the data-driven offering that are hard to imitate (Vendrell-Herrero et al., 2017). It is discussed that collaboration with third parties enables sensing of new opportunities and, in turn, innovation of additional data-driven service elements that offer more meaningful functionalities and features to the customer (e.g., Cenamor et al., 2015; Kowalkowski et al., 2013a; Lenka et al., 2017). This can be achieved by integrating customer and service provider processes to facilitate joint discovery of opportunities for the co-creation of data-driven service, especially in long-term relationships (Lenka et al., 2017; Kowalkowski et al., 2013a). For example, the use of open platforms for data-driven service within a service system can support the development of service that take account of singular requirements (Cenamor et al., 2015; Yoo et al., 2012). According to the reviewed literature, external collaboration should include outsourcing of non-core competencies to incorporate knowledge of data analyticsrelated tasks or cloud platform buildup, enabling concentration on one’s own core business while avoiding the operational risks of missing capabilities (e.g., Chen et al., 2016; Demirkan & Delen, 2013; Demirkan et al., 2015). Interviewee 5 remarked on the importance of this issue: “with certainty, because that simply requires IT knowledge or IT expertise. We have the research-specific technical knowledge, which you must integrate with IT knowledge, and that's definitely where partners are needed.” Organizations can also take advantage of third party providers’ guaranteed service levels in terms of availability and performance, as in case of cloud solutions (Demirkan et al., 2015). This is especially true for small and medium sized enterprises whose limited resources (as compared to large organizations) mean that they cannot

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undertake every activity alone (Lerch & Gotsch, 2015). As Interviewee 3 noted, “you will certainly have to implement more bilateral cooperation because you can’t do it on your own; not everything is in-house anymore, and we are forced to collaborate to this end.” At the same time, it seems clear that outsourcing can add complexity because of the need to manage multiple additional actors within the service system (Chen et al., 2016). Collaborating with experienced actors is said to unlock the full potential of datadriven service, as for example in the case of new revenue models (e.g., pay-per-use), which financial institutions can help to design (Gebauer et al., 2017), or to ensure the connectivity of products with sensors for data collection (Herterich et al., 2015). When organizations are transforming, this comes along with establishing a clear division of roles and responsibilities among diverse actors to improve value co-creation by collecting data that would not be accessible without partner commitment (Grubic, 2014; Schüritz et al., 2017a). Customer collaboration can also be seen as a prerequisite for data access and verification to ensure meaningful and effective innovation of data-driven service (Grubic & Peppard, 2016). Collaboration with partners can lead to deeper and sustainable relationships (Coreynen et al., 2017; Kowalkowski et al., 2013a; Zolnowski et al., 2016), better exploitation of the collected data (Herterich et al., 2016), and better market positioning (Zolnowski et al., 2016). 4.2.8

Internal collaboration

To prepare the organization to innovate data-driven service, both the literature and the interview study analysis suggest to foster internal collaboration (characteristic d2). This means that distributed data sources should be connected to facilitate exchange of data from individual silos in real time, with permanent access (Demirkan & Delen, 2013). In particular, centralized data analysis that provides suitable solutions for the whole organization (Troilo et al., 2017; Zheng et al., 2017) is considered beneficial as distinct from analysis at the point of origin, where for instance sufficient computing resources must be deployed (Herterich et al., 2016). To bypass the limitations of established structures and to act in more agile ways, this centralized analysis can exploit dedicated data centers or new units that act independently within the organization (Schüritz et al., 2017a). In addition, centralized data analysis can provide visibility throughout the

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Findings

organization, bridging any gaps between IT and other units such as marketing and sales by integrating key actors from these functions (Aho, 2015). A number of articles suggests that for data-driven service innovation, interdisciplinary teams should be set up (Herterich et al., 2016; Wen & Zhou, 2014) to develop trust and commitment among different units (Troilo et al., 2017), to capture new value (Robinson et al., 2016), and to avoid internal inconsistencies that might augment complexity during implementation (Hou & Neely, 2018; Sanders, 2016). This aspect is highlighted exemplarily from a practical viewpoint through Interviewee 3: “This division of labor that we currently find in many firms—where the sales department receives customer requirements and passes these on to the development department; a specification sheet is drawn up; the development is planned and processed; and then, at the end of the development process, the finished product is made—that will no longer work. In other words, all these departments simply have to move much closer together and exchange a lot more information.” Additionally, the interview data analysis confirms that the role of IT departments has to move away from being a purely internal service provider to become a solution provider for external offerings. As interviewee 3 stated, “[…] the IT department, which in many firms acts […]to satisfy its own concerns and needs, suddenly has to at least establish how to solve these future problems for externals.” 4.2.9

Customer-oriented culture and strategy

The co-creative nature of data-driven service requires a customer-oriented culture (characteristic d3) that can design offerings to meet customers’ specific demands and needs and satisfy these by fully exploiting the potential of the available data (e.g., Aho, 2015; Grubic & Peppard, 2016; Kowalkowski et al., 2013a). As the interview study shows, this interaction between service provider and customer may align the provider’s value creation process with the customer’s internal processes (Coreynen et al., 2017) to improve service quality (Demirkan & Delen, 2013), resulting in a longlasting relationship.

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Data analytics and tracking (e.g., of customer journeys or service usage) can also be used to further understand (strategic) customer needs that extend far beyond traditional paths (e.g., Davenport, 2014; Demirkan et al., 2015; Gebauer et al., 2017). The use of data for service innovation can potentially enhance existing customer-provider relationships by the introduction of novel service and optimization of existing ones (Kowalkowski et al., 2013a; Robinson et al., 2016). However, the interview study also reveals that providers using customer data face an increase in customer bargaining power. As Interviewee 7 stated, “Customers are actually more likely to increase their role and strength, as is the case for many who want to develop innovations with the customer, whose data they need.” In service systems, where data and technology are used for service delivery, customer needs and demands are especially dynamic as the service system evolves, leading to ambiguities and changing requirements (Zheng et al., 2017). Disregarding these dynamic and diverse customer needs when innovating data-driven service runs the risk of reduced customer satisfaction and glitches in service delivery (Hou & Neely, 2018). For that reason, data-driven service innovation should originate from customer requirements (Story et al., 2017). At the same time, data-driven service should not focus solely on customer requirements, as the potential for further utilization of the data through internal improvements can be a source of competitive advantage (Zolnowski et al., 2016). According to Interviewee 6, “… if I were to sum it up … for us, this is actually the key to lifelong service at the customer’s plant, with retro-fit (modernization) and the whole business—customer loyalty, yes, for the entire life cycle. That's actually what we want to achieve, and of course we now add attractive benefits for customers in the form of big data analysis … and that's actually the key for me.” As Interviewee 2 put it, “…this has consequences because you try to use the data to better understand the interaction with the customer and then adjust the service accordingly.”

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4.2.10

Findings

Data-oriented culture and strategy

Innovation of data-driven service depends on establishing a data-oriented culture within the organization to capture data’s value for business. The SLR confirms that insights from data should be seen as reliable throughout the organization and used as a basis for decision making as an alternative to gut feelings, instincts, or intuition (Troilo et al., 2017; Pigni et al., 2016). Both the SLR and the interview study analysis indicate the need for organizations to provide employees with a clear strategy for datadriven service, taking account of issues like data access and usage while relating this to the organization’s overall strategy (e.g., Schüritz et al., 2017a; Aho, 2015; Sanders, 2016;). They propose that the data strategy should ensure continuous data provision, as well as access to external data sources (Schüritz et al., 2017a) and should align with previous manufacturing or PSS strategies rather than appearing standalone (Grubic & Peppard, 2016; Opresnik & Taisch, 2015). Interviewees emphasized the establishment of agile processes with short cycles; for example, Interviewee 1 made the following observation: “Culturally, I would say, that’s another influence because, unlike physical products, a new complexity arises—not just with data-driven products but with industry 4.0 products in general. Suddenly, hardware meets software, service, data, and so on. And to master this complexity, you need new development methods, and of course, the whole matter of agility, Scrum is a very important thing.” 4.2.11

Top management support

When organizations with a conservative mindset and traditional product-based offerings look to enhance their current portfolio with data-driven service, direct top management support (characteristic d5) can help to raise the profile of IT, fostering the organizational transformation that accompanies the introduction of a new data-driven service-oriented strategy (Chen et al., 2016; Grubic & Jennions, 2017; Herterich et al., 2016; Story et al., 2017). Top management is seen as the initiator of this kind of change, in which the greatest challenge is to assess which business activities should be performed internally and which should be outsourced to other actors in the service

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system (Story et al., 2017) and, as the interview analysis shows, to set the agenda for change. Top management can further support change by setting up new organizational units to create commitment (Troilo et al., 2017), provide resources (Kowalkowski & Brehmer, 2008), and solve upcoming challenges (Grubic & Jennions, 2017). This is summarized by the following statement of Interviewee 2: “it's a decisive role from that point of view because it shapes the culture. If I have a management team that is very critical of the whole thing and tends to stir up fears, then of course I also have a culture that is correspondingly non-existent, with no projects and no budget for investment in this area. That's why it's definitely an enabler … it's also the case that you have to define a vision for your own firm.”

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5

Discussion

Discussion

To shed light on the characteristics of data-driven service innovation, this study first provides a definition of a data-driven service and identifies 11 characteristics based on a SLR and an expert interview study. These include the need for skilled IT employees, a data-driven culture and strategy, data access, collection, ownership, privacy, and standardization, and the use of new revenue models not previously addressed throughout non-data-driven service innovation literature. Now, it will be discussed how the identified characteristics add to current research and confirm prior findings. The findings of the data analysis add to current research in the field of service innovation with some specifics that emerge from the utilization of data for service. First, there is a need for skilled IT employees to perform tasks like data analytics or data interpretation. Hiring IT specialists can be expensive, and organizations require the necessary resources at their disposal. While issues such as employee training and exchange of skills have already been addressed in the literature (Alghisi & Saccani, 2015; Vargo & Lusch, 2008), data-driven service innovation adds to the complexity by introducing special requirements for interdisciplinary knowledge in IT and engineering, linked to strong social skills to facilitate sharing of that knowledge across the workforce. Second, to enable service innovation and delivery, a data-oriented culture needs to be established in parallel with a service-oriented strategy and culture (Gebauer et al., 2005). This means extending the awareness and long-term orientation required for the transition to a more service-oriented business (Gebauer et al., 2005; Kindström & Kowalkowski,

2014).

That

supports

a

data-specific

alignment

of

service,

manufacturing, and data strategy. For example, employees should be aware of the benefits of data – especially because IT induced changes may cause resistance across employees (Laumer et al., 2016) – and the development of a data strategy should foster co-creation and resource integration by deepening customer orientation across all service system actors.

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Third, data access, collection, ownership, security, privacy, and standardization play an important role in data-driven service innovation, distinguishing these from nondata-driven service innovation. This requires an appropriate infrastructure that facilitates access to and collection of network data by linking internal systems and facilitating recombination of data from different sources (Porter & Heppelmann, 2014) while ensuring data privacy and security. The exchange of information and knowledge during the innovation and provision of data-driven service means that the service provider should take these concerns seriously to ensure customer adoption of the offering (Wünderlich et al., 2015). To this end, service providers should act within the legal framework, building trust through measures such as encryption (Zissis & Lekkas, 2012). Standardization plays an important role in recombining data from different sources. Existing industrial standards for machine operation should be extended to innovative technologies like human-machine interaction to facilitate exchange, incorporate the user, and improve processes within the service system (Posada et al., 2015). In addition, different models of data ownership are discussed during co-creation of service; as one option, organizations might pursue full ownership of data accruing from the provision of data-driven service, which would simplify the discussion of data monetization. Alternatively, joint data ownership within the service system by service provider and customers (Porter & Heppelmann, 2014) might be more in line with the co-creative nature of data-driven service innovation. It could exploit the full potential of datadriven service by allowing access to data collected across the whole service system, deepening

relationships

and

encouraging

data-driven

service

innovation

(Kowalkowski et al., 2013a). Fourth, common service revenue models such as single transactions or subscriptions can be extended to multi-sided arrangements of various kinds, including the following: (1) Personalized advertising based on collected data (so-called “endure-ads”); (2) Downstream customers might allow the service provider to use their data to receive tailor-made offerings from upstream customers, who pay a brokerage fee, especially when customers of both kinds are interacting on a platform; (3) Data collected from customers can be sold to others; (4) Customers might agree to share their accrued data

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Discussion

in return for access to the data-driven service (Schüritz et al., 2017b). Long-lasting service contracts increase the provider’s risk of exposure to the service paradox, where significant investment in service provision fails to generate the expected high returns as a result of increasing costs (Gebauer et al., 2005; Neely, 2009). The present study confirms that prior findings from service innovation and servitization literatures apply to data-driven service innovation. For example, top management support, resource recombination, collaboration within and across organizational borders, and a customer-centric culture are known to play an important role during innovation of non-data-driven service (Alghisi & Saccani, 2015; Baines et al., 2009; Kowalkowski et al., 2011; Ettlie & Rosenthal, 2012; Lusch & Nambisan, 2015; Vargo et al., 2015; Jonas et al., 2016). However, some of these gain in complexity as a consequence of data utilization. The multi-dimensional nature of service innovation requires the integration of diverse actors across organizational borders (Baines et al., 2009; Lusch & Nambisan, 2015). While earlier studies have demonstrated the need to integrate service system actors during service innovation (Jonas et al., 2016), datadriven service innovation focuses in particular on new IT-related actors. For datadriven service innovation, external collaboration with IT service providers (to outsource tasks beyond an organization’s perceived core competencies) or financial institutions (to set up a suitable revenue model) seems beneficial in exploiting the full potential of such service. Small and medium-sized enterprises (SMEs) in particular should consider collaborating with external partners, as their limited human and monetary resources preclude certain tasks required by their customers (Kowalkowski et al., 2013a) such as data analytics or cloud computing. In this context, data also become a key resource for internal collaboration, as distributed data sources demand closer internal exchange to exploit the potential of data-driven service. In particular, organizations may encounter a lack of trust among different units and their members when attempting to foster the exchange of knowledge across functional borders (Diamond et al., 2004). This issue can be addressed by establishing interdisciplinary teams to connect organizational silos (Koskela-Huotari et al., 2016). It may also prove beneficial to set up centralized units

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for tasks such as data collection and analysis, operating across organizational units to prevent the emergence of data silos (Gebauer et al., 2005; Parris et al., 2016). Customer-centricity and value co-creation with customers are highlighted among the most important features of service innovation (Baines et al., 2009; Vargo & Lusch, 2008). While the analysis supports this view, it was also found that data utilization facilitates new ways of assessing customer requirements and extends current providercustomer relationships through integration with customer processes. In this way, data both enables and demands deeper integration of actors and resources within a service system that supports value co-creation (Lusch & Nambisan, 2015; Schüritz et al., 2017a). Data gathered from one customer can be recombined with data collected from the whole service system to deliver improved service and potentially offer novel solutions to customers (Vargo et al., 2015; Yoo et al., 2012). Finally, the present study extends existing dynamic resource configurations that enable organizations to offer additional value to customers through data-driven service innovation (Coreynen et al., 2017). While earlier approaches used ICTs to enable service provision, data-driven service innovation entails additional barriers, resources, capabilities, and dynamic capabilities. For example, barriers related to sales competencies (Coreynen et al., 2017) increase because of the additional need for personnel with the requisite IT knowledge. Other barriers relate to data privacy laws, works council interventions, and a lack of platforms for open data exchange. From a capability perspective, this study highlights the need to be able to recombine data from different sources and to apply findings at the meta-level, requiring data analytics skills and the ability to link analytics to specific domain knowledge (Wamba et al., 2018). Multi-sided revenue models add new pricing mechanisms beyond outcome-based ones that rely on the solution’s value-in-use (Kindström & Kowalkowski, 2014), where the determination of value requires additional pricing capabilities. To achieve sustainable competitive advantage when transforming their current business to a data-driven one, organizations should consider the management of their reconfiguration (Teece, 2007). This can be achieved by deploying dynamic dataoriented change and service system capabilities, incorporating a mindset that

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Discussion

recognizes data as a key resource for service provision (Kindström et al., 2013; den Hertog et al., 2010). Additionally, organizations should identify and strengthen their role in the service system through the integration of additional actors to capture the value of orchestrated activities (Helfat & Raubitschek, 2018). To that end, top management may develop governance procedures that support sourcing decisions and responsiveness to changes in the environment, set the organizational agenda and enabling continuous modification of the business by showing trust in the actors involved (Teece, 2007).

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87

Theoretical contribution and outlook

In summary, this part addresses two distinct questions: what defines and characterizes data-driven service and their innovation, and what ordinary capabilities and dynamic capabilities need to be developed for data-driven service innovation. While numerous academic publications deal with the utilization of data for service provision and make use of a large variety of different concepts (such as Big Data, digital or smart services), this part provides a synthesized definition of a data-driven service that helps the reader to gain a common understanding of its characteristics and the mindset pursued in this dissertation. The findings of the SLR and expert interview analysis set the basis for the further parts of this dissertation. In light of the contemporary importance of data and the enhanced potential for collecting, analyzing, and interpreting data, the study identified eleven characteristics of data-driven service innovation, and these were compared to non-data-driven service innovation and servitization to reveal commonalities and how certain aspects gain in importance through data utilization for service provision. Finally, the paper shows how data-driven service innovation can be pursued through the development of certain organizational ordinary capabilities and dynamic capabilities that help to overcome specific barriers during data-driven service innovation. Hence, this study contributes to current literature through the synthetization of a large variety of different concepts that utilize data for service provision and the investigation of data-driven service innovation characteristics. While previous studies on service innovation and servitization (e.g. Alghisi & Saccani, 2015; Baines et al., 2017) regarded data utilization as a side note, this study puts a focus on the aspect datadriven. It extends prior findings by specific data-related characteristics, thus highlighting the influence on service innovation efforts. The synthetization of concepts dealing with the utilization of data furthermore provides a solid foundation for the derived definition of a data-driven service that covers its characteristics more precisely than former ones (e.g. Hartmann et al., 2016).

88

Theoretical contribution and outlook

In addition, the study at hand does not only contribute to literature on service innovation, but to dynamic capabilities research as well. Earlier studies on dynamic capabilities in the context service innovation did not put the attention mainly on the repercussions of data-driven service innovation on dynamic capability development (Story et al., 2017; den Hertog et al., 2010; Kindström et al., 2013). This study offers a first step towards understanding what kind of dynamic capabilities should be developed to pursue competitive advantage through data-driven service innovation, however further research could use this study’s findings to put a more detailed focus on certain characteristics such as data privacy or data collection. In addition to the latter aspect, the findings point to some other interesting opportunities for future research in the evolving field of data-driven service innovation:

(1)

Additional

investigation

of

data-driven

service

innovation

characteristics; (2) Implications of data utilization on organizational change; (3) research on data privacy and security issues; And (4) examination of revenue models for data-driven service. (1) First, building on the insights of the case study analysis, a fruitful pathway for future research seems to investigate the identified characteristics of data-driven service innovation in greater depth, including the effects of data use on service systems, partnerships in complex service systems (Bigdeli et al., 2017), and the integration of resources from independent actors (Story et al., 2017). Other interesting research directions include the implications of data-driven service for value co-creation among customers and suppliers (Grubic & Peppard, 2016; Herterich et al., 2016; Lenka et al., 2017; Schüritz et al., 2017a), the long-term impact of data-rich environments on the service system (Troilo et al., 2017), and the changing roles of service system actors. These investigations should extend beyond the external service system to include intraorganizational aspects (Schüritz et al., 2017a). (2) Second, knowledge of the cultural and strategic changes associated with datadriven service innovation could equip individuals with the requisite tools and concepts for successful transition to a data-oriented business. Understanding the implications of data-rich environments would also contribute to the knowledge base on organizational

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change (Schüritz et al., 2017a) in terms of emergent opportunities and challenges (Lerch & Gotsch, 2015). Especially the investigation of mechanisms to overcome resistance across organizational actors that prevent data-driven service innovation could be of high interest. (3) Third, the present study highlights the neglected question of data privacy and security in data-driven service provision (Schüritz et al., 2017a). Disregard of data privacy and security standards, especially in countries with a strong focus on data protection, could potentially impede the implementation of data-driven service innovation. It would be furthermore of interest to investigate in greater detail how collaboration (e.g., with works councils or across multiple organizational units) can be improved and which kinds of data most often cause privacy issues. (4) Finally, as the value of data and its assessment remains poorly understood, undermining the delivery of data-driven service, future research might usefully explore models and tools that can help to develop applicable revenue models (Schüritz et al., 2017b) and pricing strategies (Vendrell-Herrero et al., 2017). It would be worthwhile to investigate the risks faced by inexperienced organizations in long-term contracts, especially when these are outcome-based (Hou & Neely, 2018). The impact of data ownership on an organization’s position within the service system and the distribution of revenues in cases of shared ownership would also be of interest.

Part IV Identifying actors and challenges for data-driven service innovation © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 M. Schymanietz, Capabilities for Data-Driven Service Innovation, Markt- und Unternehmensentwicklung Markets and Organisations, https://doi.org/10.1007/978-3-658-31691-4_4

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Introduction21

At some of the difficult parts, we had to tie ourselves together with a long rope, so that if by chance one of us happened to slip, he would be held up by his companions. This solidarity constituted a useful precaution, but did not remove all danger. (Jules Verne, 1864) An industry with high importance for the German economy (DeStatis, 2017) that is currently challenged by the availability of data and its connected possibilities that have disruptive potential, is the manufacturing industry (Perks et al., 2012; Schüritz et al., 2017a). Across this industry, service is gaining in importance with the goal to improve the competitiveness of organizations through innovative offerings that align service and products in new ways (Kowalkowski, 2016; Falk & Peng, 2013; Neely, 2008). The transition from predominantly selling products to being a provider of product-service solutions introduces the possibility of creating higher revenues, increasing profitability and deepening customer relationships (Oliva & Kallenberg, 2003; Colen & Lambrecht, 2013; Gebauer & Friedli, 2005; Forkmann et al., 2016; Kowalkowski et al., 2016; Coreynen et al., 2017; Zolnowski et al., 2016). In particular, data-driven service innovation enables manufacturers to implement radical new business opportunities together with other actors in their respective service system (Neely, 2008). However, established processes, behavior patterns and the nature of business in this particular industry are disrupted and challenged (Perks et al., 2012; Alghisi & Saccani, 2015). The potential advantages of service innovation for manufacturing organizations and the associated business opportunities are discussed in literature (Kowalkowski, 2016; Fischer et al., 2012; Baines & Lightfoot, 2013; Colen & Lambrecht, 2013). However, there remains a lack of in-depth understanding of service innovation-based growth, especially in cases where data plays a crucial role (Ostrom et al., 2015; Baines et al., 2017; Kamp & Parry, 2017; Schüritz et al., 2017a). When shifting into a more service-

21 An earlier version of this study has been presented at the European Academy of Management (EURAM) Conference 2017 in Glasgow, United Kingdom and has profited from valuable feedback.

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Introduction

oriented business, organizations must take account of a range of complex issues that include an organization’s mindset, internal processes, and the relationship among partners; further complexity is added by the use of data (Neely, 2008; Martinez et al., 2010; Baines et al., 2013; Ostrom et al., 2015; Schüritz et al., 2017a). In particular, offering integrated product-service solutions that deliver value-in-use demands collaboration within a diverse system of actors such as partners, customers, and suppliers (Neely, 2008). When actors work together, they can become part of a service system to share competencies and data (Grubic, 2014; Schüritz et al., 2017a) to implement shared practices with actors involved throughout the service system (Belvedere et al., 2013; Opresnik & Taisch, 2015; Story et al., 2017; Vendrell-Herrero et al., 2017) and to better understand customers’ needs with efficient and appropriate solutions (Cenamor et al., 2015; Kowalkowski et al., 2013a; Story et al., 2017) to overcome possible dangers. As discussed in the previous part, these challenges can be faced through the development of organizational dynamic capabilities that enable organizations to cope with fast changing market environments. To generate insights from a key industry of the German economy that can be characterized through product-centered organizations that aim to align goods with new data-driven service, this study wants to investigate actors and the challenges they face during the implementation. In particular, the influence of data on service and collaboration in value co-creation offer a starting point for such research (Ostrom et al., 2015; Baines et al., 2017; Kamp & Parry, 2017). In this context, the present study addresses two main issues: which actors are involved during innovation of data-driven service, and what challenges they experience during their data-driven service innovation activities. This part intends to shed light on actors involved in the innovation of data-driven service as well as on challenges for collaboration on both inter- and intra-organizational level. Identifying the relevant actors helps the reader to understand what organizational functions should be considered throughout the innovation process. Additionally, putting a focus of arising challenges for collaboration during the innovation activities will raise awareness on data-driven service innovation specific aspects that complement current regular service innovation ones. This points out the

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peculiarities that come along with the utilization of data for service provision on an organizational level. The structure of this part is as follows: Subsequent to this introduction, chapter 2 will give the theoretical background for this part. It introduces the importance of collaboration in co-creation activities such as service innovation, with a focus on the manufacturing sector and emphasizes challenges that occur during the innovation of regular service. This will set the basis for the further investigation of characteristic features of collaboration during data-driven service innovation. Chapter 3 will give insights into the used research method, data collection and data analysis. Here, an exploratory multiple case study is selected as appropriate and focus group interviews are carried out. Chapter 4 will present the findings and show that the innovation of data-driven service requires the integration of additional actors and is accompanied by a set of specific challenges on both intra- and inter-organizational level as well as underlying issues. Then, chapter 5 will discuss the findings in respect to the current body of literature. Conclusively, chapter 6 will provide managerial and theoretical implications and draw venues for further research. The structure of part IV can be taken from figure 9 below.

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Introduction

Part I: Introduction

Part II: Theoretical background

Part III: Systematic literature review and expert interviews

Part IV: Multiple case study

Part V: Delphi study

Part VI: Synthetization and discussion

1 Introduction  Transition of manufacturing firms  Objectives of part IV  Structure of part IV 2 Theoretical Background  Collaboration in data-driven service innovation in manufacturing  Challenges for collaboration in service innovation

3 Method  Case study research  Focus group interviews  Case selection, data collection and analysis 4 Findings  Actors involved in data-driven service innovation  Challenges of collaboration in data-driven service innovation

5 Discussion  Discussion of findings

6 Theoretical contribution and outlook  Summary of part IV  Implications and further research

Figure 9: Structure of part IV

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97

Theoretical background

2.1

Actor collaboration in data-driven service innovation

The multi-dimensional nature of service innovation affects the transition of organizations because of the need for collaboration between the different actors involved (Kindström & Kowalkowski, 2014), encompassing internal and external actors as well as their resources, skills, and knowledge (Kindström et al., 2013, Lusch et al., 2009). These actors no longer engage within dyadic relationships, but co-create value collaboratively in a service system through the integration of their available resources (Lusch & Nambisan, 2015). This collaboration is based on the lack of a single organization’s or unit’s capability of carrying out the required resource generation alone. The actors therefore become both service providers and beneficiaries in a service system, pursuing resource exchange to integrate missing ones (Goes & Park, 1997; Kindström & Kowalkowski, 2014; Lusch & Nambisan, 2015). They aim to collaboratively recombine those resources to co-create solutions for both new and existing problems (Vargo et al., 2015). Collaboration among different actors is considered as a key factor during service innovation

activities

(Chesbrough,

2011),

especially

within

fast-changing

environments. From an organizational perspective, it can be classified as intra- or interorganizational collaboration (Blomqvist & Levy, 2006). On the one hand, intraorganizational collaboration covers aspects that include willingness to work together and a common mindset, vision and goals, as well as resource sharing (Kahn & Mentzer, 1996). On the other hand, inter-organizational collaboration refers more to the exchange of knowledge and complementary capabilities across company boundaries (Blomqvist & Levy, 2006). The actors involved in service innovation may be internal (e.g., top management, sales and service personnel, local subsidiaries, other organizational units) or external (e.g., customers, users, suppliers, external service providers, universities, competitors) (Jonas et al., 2016). However, identifying and managing the appropriate actors and

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Theoretical background

their competencies is challenging, especially where multiple actors must interact (Kazadi et al., 2015; Jonas & Roth, 2017). Digitization and the consequent deployment of ICTs play a key remodeling role during service innovation activities (Barret et al., 2015; Lusch & Vargo, 2014; Vargo & Lusch, 2008; Lusch & Nambisan, 2015). Whilst a broad range of organizations now rely on data to run their business (e.g., databases of customer or supplier contacts), a service is data-driven when it uses real-time and remote data from connected devices as a key resource for digital delivery of co-created, high-value solutions to the customer, as synthesized in part III. Sensors, intelligent IT systems, Internet of Things (IoT) applications, and CPS create new possibilities for collecting, accessing or analyzing data and facilitating combinations of digital and physical objects, enabling the cocreation of innovative data-driven service offerings (Yoo et al., 2010; Cenamor et al., 2017; Demirkan et al., 2015). By these and other means, data can improve internal processes, capture value and deliver novel value propositions to the customer (Schüritz & Satzger, 2016).

2.2

Challenges for collaboration in service innovation in manufacturing

The challenges encountered during collaboration of actors in service innovation and servitization in manufacturing organizations have been extensively discussed in the recent literature by e.g. Alghisi & Saccani (2015), Schüritz et al. (2017), Baines et al. (2017) and Story et al. (2017). Building on the insights from these studies, the following challenges can be identified: (1) organizational processes and structures, (2) organizational strategy and culture, (3) design of market-oriented offerings, and (4) value co-creation among the involved actors. (1) Organizational processes and structures Manufacturing organizations pursuing service innovation are challenged by the need for coordination and collaboration processes across different internal units (e.g., production, sales, R&D) (Schüritz et al., 2017a; Porter & Heppelmann, 2015). This challenge extends to the service system, where the inter-organizational collaboration

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with suppliers, customers and other actors demands processes for service innovation and delivery that match the customer’s needs (Alghisi & Saccani, 2015; Lightfoot et al., 2013). (2) Organizational strategy and culture Manufacturing organizations are often characterized by a traditional productcentric mindset that impedes service innovation and challenges the organization’s shift to customer centricity and service orientation (Oliva & Kallenberg, 2003; Schüritz et al., 2017a; Story et al., 2017). In particular, becoming more service-oriented depends on bridging the gap between product and service elements to gain competitive advantages (Story et al., 2017). A further challenge is the persistence of staying in old-fashioned mindsets that do not support service orientation, such as resistance to a 24/7 service culture (Lightfoot et al., 2013). (3) Design of market-oriented offerings Service innovation is often accompanied by a risk shift from customer to service provider (Baines et al., 2009). While product sales are often one-time events, service delivery entails long-term contracts requiring a critical mass and more careful assessment of possible failures and financial risks, as well as of customer needs to be addressed (Neely, 2009; Martinez et al., 2010; Matthyssens & Vandenbempt, 2010; Alghisi & Saccani, 2015). In particular, communication skills must be developed to promote the service’s value and to understand customer needs with a view to creating appropriate value propositions within service systems (Alghisi & Saccani, 2015; Baines et al., 2017; Grubic, 2014). (4) Value co-creation among the involved actors The diversity of actors involved in service innovation demands new approaches to information and knowledge exchange for co-creation throughout service systems. Collaboration among different actors enables organizations to co-create targeted service and to deliver them in a way that satisfies the customer through an interaction that is relational rather than transactional (Alghisi & Saccani, 2015; Oliva & Kallenberg, 2003; Brax, 2005; Schüritz et al., 2017a; Grubic, 2014; Baines et al., 2009). In this

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Theoretical background

collaboration, service system actors depend on the integration of product and service elements from partners into their own service offerings to deliver service that go beyond their own competencies (Story et al., 2017; Sawhney et al., 2004; Windahl & Lakemond, 2006). To manage an evolving service system and to maintain a key position within it, organizations should develop the capacity to properly compose their service system – that is, to decide who should or should not be integrated (Story et al., 2017). The above challenges indicate that the process of service innovation is impacted by multiple internal and external factors. To develop value-in-use for the customer, the integration of actors from diverse internal functions of an organization and actors from its external service system (of customers and suppliers) should both be addressed (Martinez et al., 2010; Neely, 2008; Alghisi & Saccani, 2015; Schüritz et al., 2017a). To overcome organizational challenges, literature discusses the development of dynamic capabilities as a fruitful pathway (Eisenhardt & Martin, 2000). Developing dynamic capabilities can help organizations to deal with technical, organizational or interpersonal challenges (Eisenhardt & Martin, 2000) for being able to achieve sustainable competitive advantage in fast changing environments (Teece, 2007). The multi-dimensional nature of service innovation as a complex process entailing multiple challenges requires the integration of a wide range of actors in service systems with strong customer centricity (Lusch & Nambisan, 2015; Baines et al., 2009). These challenges invite research on the co-creation of value in inter-organizational service systems and on intra-organizational collaboration within co-existing product and service mindsets (Baines et al., 2017). Given the new possibilities emerging from the utilization of data, the present study seeks to identify actors and trace the repercussions for service innovation of this increased use of data as a service’s key resource by addressing the following research questions: RQ3: Which internal and external actors are involved in data-driven service innovation? RQ4: What challenges influence collaboration among those actors during data-driven service innovation?

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Building on the insights of RQ3 that aims to identify a set of actors involved in datadriven service innovation, RQ4 investigates the challenges that occur during value cocreation during service innovation. This happens through the conduction of a multiple case study that bases on focus groups that enable to research the questions together with actors involved in data-driven service innovation.

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3

Method – Case study research

Method – Case study research

To investigate the actors involved and challenges influencing collaboration in value cocreation during data-driven service innovation a multiple case study is implemented. The case study approach is regarded as an appropriate strategy, due to research on a contemporary event that is not controlled by the investigator. Furthermore, it can be characterized by an immediate observation of events and interviews with participants in them (Yin, 2018). Consequently, the case study approach helps to comprehend complex social phenomena in their real-life context (Yin, 2018) and facilitates in-depth understanding of new research fields (Miles et al., 2013). The usage of the case study method is used during this study to obtain perspectives and perceptions of actors involved in data-driven service innovation. The use of multiple cases supports the examination of differences and similarities across and between the processes and contexts (Hartley, 2004) and is considered as suitable for investigating the repercussions of data use as a key resource in the service innovation process. Yin (2018) defines a case study as “an empirical method that investigates a contemporary phenomenon (“the case”) in depth and within its real-life context, especially when the boundaries between phenomenon and context may not be clearly evident” (Yin, 2018, p. 15). A broader proposal has been given by Woodside (2010, p.1), who defines a case study as “an inquiry that focusses on describing understanding, predicting and/or controlling the individual (i.e., process, animal, person, household, organization, group industry, culture, or nationality)”. This definition does not restrict case study research to contemporary events and reallife contexts, particularly when the differences between context and phenomena are not clarified (Woodside, 2010). This unclarity between context and phenomenon in real-life situations leads to specific feature of a case study:

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“A case study copes with the technically distinctive situation in which there will be many more variables of interest than data points, and as one result benefits from the prior development of theoretical propositions to guide design, data collection and analysis, and as another result relies on multiple sources of evidence, with data needing to converge in a triangulating fashion” (Yin, 2018, p.15). The research strategy of case study research is not limited to single key aspects like experiments or surveys, but offers the investigator a portfolio of different techniques “with its own logic of design, data collection techniques, and specific approaches to data analysis” (Yin, 2018, p. 16). Thus, a case study is not only able to elaborate the outcome of certain decisions, but also their way of implementation and the reason why they were made (Schramm, 1971). To reach profound knowledge of an investigated case, it has to be observed directly within its environment, relevant documents have to be analyzed in these surroundings (Woodside, 2010) and involved persons need to be questioned for a better understanding of the steps taken by them (van Maanen, 1979). A main goal of conducting case study research is to help the investigator to gain profound knowledge of the big picture with a detailed observation of only one or a few cases by being intent on important sections and not on the whole (Eisenhardt, 1989b). The conduction of a multiple case study is regarded as delivering more convincing results (Herriot & Firestone, 1983). Researching a small number of cases has the ability to predict similar results, thus being literal replications, provide exemplary outcomes and remove peculiarities from single observations (Yin, 2018). Throughout this study, the multiple case study approach is implemented to “replicate the conditions from case to case” (Yin, 2018, p. 59) In summary, the case study research method empowers the researcher to investigate complex social phenomena through the study of analytic units and presentation of empirical evidence (Eisenhardt & Graebner, 2007; Yin, 2018). Here, this aims to gain knowledge on the views of actors involved in data-driven service innovation for being able to derive insights. This strong connection to empirical evidence implies testability, novelty and empirical validity to implications that arise from cases and makes this

104

Method – Case study research

method particularly suitable to research evolving areas (Eisenhardt, 1989b), such as data-driven service innovation.

3.1

Case selection

To investigate the repercussions of the utilization of data on service innovation and to gain profound knowledge on the phenomenon together with the involved actors, cases were included building on four selection criteria: (1) the sector’s relevance for the German economy; (2) the representativeness (i.e. being typical ones in this sector) of organizations in this sector; (3) current data-driven service innovation efforts within the organizations and (4) the possibility of access to the relevant actors. In Germany, manufacturing is the largest single sector within the domestic economy, with a 22,8% share of the German gross value added (DeStatis, 2017). Multiple organizations matching the selection criteria were approached and three of them finally agreed to participate in this research. These organizations are producing different products that include valves, drives, engines, industrial boilers, and power plants. Two of the case organizations are family-owned, the third one is a German subsidiary of an international corporation (see table 10). While the family-owned organizations are selected to represent the so called German ‘Mittelstand’ (i.e., German small and medium sized enterprises that are responsible for 35.5% of the total revenue of German organizations) (IfM, 2013), the subsidiary organization is selected to represent the experiences of a large organization. This enables the investigation of both commonalities and differences in these organizations’ experiences of data-driven service innovation to broaden their product-centric portfolios (Hartley, 2004). In selecting these organizations, other important factors were their current ambitions to innovate data-driven service and the possibility of approaching the actors involved. All of the selected organizations recognized the importance of digitization for their future and are trying to incorporate innovative data-driven service that complement their product offerings (Baines, 2015; Kowalkowski, 2016; Neely, 2008). All three selected organizations are perceiving data-driven service innovation as an element of their current and/or future strategy and have already implemented or

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planned to shortly introduce data-driven service innovations such as predictive maintenance (to prevent failures of production machines), condition monitoring, plant utilization forecasts, lifetime management of machinery and equipment, and remote diagnostic solutions. Table 10: Overview of selected cases # Organization Type

3.2

Employees in Germany (approx.)

Products

1

Family-owned mechanical engineering organization

7.000

Valves, drives, engines

2

Family-owned mechanical engineering organization

13.500

Industrial boilers

3

Dependent mechanical engineering organization

4.000

Wind turbines

Data collection

Primary data collection is based on seven focus groups, involving a total of 19 participants from the three selected organizations. These focus groups included group discussions and supporting tasks in which the participants created actor maps. The focus group participants were chosen in close consultation with the case organizations to a) represent a range of internal actors related to data-driven service innovation. Their backgrounds included innovation management, product management, engineering, and IT, and all b) had touchpoints with data projects (see table 11). The focus group approach enables data collection through informal small group discussions focused on specific topics (Wilkinson, 2004) and facilitates the discussion of problems and possible solutions (Duggleby, 2005). The researcher plays the role of a moderator, asking questions and keeping the discussion moving. Such groups encourage interaction between participants and facilitate discussion of sometimes controversial issues (Wilkinson, 2004). Additionally, focus groups allow participants to build on the previous responses of others to produce answers that may not have emerged during individual interviews (Stewart & Shamdasani, 1990). Carrying out focus groups in the context of data-driven service innovation offers the possibility to

106

Method – Case study research

gain knowledge on the phenomenon through a rich source of information that is based on the experience of the focus group participants. Focus groups were conducted in April and May 2016 and were led by three experienced researchers. Each focus group comprised two to four participants and lasted between 1h 30min and 2h 40min. To ensure high levels of openness, the focus group discussions followed a semi-structured guideline, focusing on the following aspects: (1) current activities, (2) involved actors, (3) required competencies and (4) experienced and expected challenges (see annex D for pictures of the focus group interviews). Table 11: Overview of focus group participants Organization # Focus Group # FG1

O1

FG2

Interviewee’s Position I1: Innovation Management (O1 FG1 I1) I2: Service Portfolio Management (O1 FG1 I2) I1: Internal IT (O1 FG2 I1) I2: Research & Development (O1 FG2 I2) I3: Product Management (O1 FG2 I3)

FG3

FG1

I1: Business Development (O1 FG3 I1) I2: Research & Development (O1 FG3 I2) I3: IT (O1 FG3 I3) I1: Product Management (O2 FG1 I1) I2: Product Management (O2 FG1 I2) I3: Product Management (O2 FG1 I3) I4: Product Management (O2 FG1 I4)

O2 FG2

I1: Product Management (O2 FG2 I1) I2: Customer Service (O2 FG2 I2) I3: Product Management (O2 FG2 I3)

FG1 O3 FG2

I1: Product Management (O3 FG1 I1) I2: Engineering (O3 FG1 I2) I1: Service Domestic (O3 FG2 I1) I2: Service International (O3 FG I2)

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107

Data analysis

For the purpose of data analysis, all actor maps were summarized for being able to gain an overall view on actors involved in data-driven service innovation and the focus group discussions were recorded, transcribed and coded using QDA software. The coding procedure was implemented in an inductive process – that is, categories were developed from the collected data, and then categorized using the ARA model as a frame for coding. The ARA model is a conceptual framework that captures outcomes of interaction processes across three layers: (1) actors, (2) resources, and (3) activities (Håkansson & Johanson, 1992; Håkansson & Snehota, 1995). These layers are of relevance in the present context of data-driven service innovation that involves the recombination of resources by different service system actors (Lusch & Vargo, 2014; Lusch & Nambisan, 2015). The actor layer describes the relationships between the various actors, focusing on how they influence each other and become committed. The resources layer brings up the combination of actors’ resources during their interactions; this extends beyond operand resources like machinery to include operant resources and their exchange. Finally, the activities layer refers to activity structures that can be coordinated and integrated among the relevant actors (Ford et al., 2010). The main categories derived from the coding process are (1) current and anticipated challenges, (2) environmental factors, and (3) organization wide goals and policies. These main categories were then divided into activities, resources, and actors. Finally, as subcategories, activities were divided into data analytics, generation, acquisition, processing, aggregation, visualization and distribution, and resources and actors were classified as internal or external (see annex E). In accordance with the guidelines of Stake (2006) and Yin (2018), the cases are first analyzed individually before subsequent comparison in a cross-case analysis. Based on the derived codes from the focus groups, challenges for data-driven service innovation were identified. Here, statements of the focus group participants were recognized that expressed challenges for the implementation of

data-driven service innovation. After the collection of

corresponding statements, they were summarized to intra- and inter-organizational challenges as well as underlying issues that affect data-driven service innovation in general.

108

4

Findings

Findings

4.1

Actors involved in data-driven service innovation

Analysis of the data from the three case organizations and a cross-case analysis reveals that data-driven service innovation builds on the integration of different actors. The focus group participants subdivided the actors involved in data-driven service innovation into three groups according to their importance in descending order within a prepared document for subsequent discussion (see figure 10 and annex F for an exemplary actor map).

 Controlling  National subsidiaries  Parent company

      

Trade associations Regulators Public Competitors Customers Universities Research partners

 Internal software developers  Purchasing department  Marketing department

 External data service providers  Suppliers

Low importance

        

Innovation management Service department Sales department Management Engineering department R&D department Product management Legal department Internal IT department

 Customers High importance

Legend Internal Actor

External Actor

Figure 10: Relevant actors involved in data-driven service innovation according to the focus group participants This multiple case study shows that the most important actors within the datadriven service innovation landscape (right side in figure 10) were internal personnel from the following departments: legal, innovation management, service, internal IT, sales, research & development, engineering and product management. The only external actors mentioned in this inner circle were the organization’s customers. The actors in the next layer were divided almost equally between internal actors, such as purchasing and marketing departments, internal software developers and

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external actors like data service providers for analytics, cloud solutions, platforms, data storage and suppliers. Finally, the actors assigned lowest importance included external actors like universities or research partners, competitors, customers, regulators and trade associations as well as peripheral, i.e. only slightly affected, organization internal actors such as the controlling department, other national subsidiaries and the parent organization. Customers: Across the three different cases, there is one remarkable difference. While participants from the two family-owned organizations identified their customers as among most important actors during data-driven service innovation, the focus group discussion participants from the subsidiary organization do not acknowledge the importance of customers, who, if mentioned at all, are assigned to the outer circles. In contrast, participants from the two family-owned organizations reason that a close relationship with the customer is prerequisite for the development of customized datadriven service. In a typical example, one interviewee emphasizes a close and long lasting relationships with their customers as a key to innovation: We have to understand our customer’s business considerably better! We should not only deliver components that make him say: “I need this”, but should really understand what he does and what he is selling to his customer; this process has to be understood by us pervasively. Only if we understand this process, we will be able to create tailored solutions and provide them with the data needed. To achieve this, we have to establish durable and sustainable customer loyalty. That means seeing the customer as a partner and not just as a customer, developing something together (Interviewee O1 FG2 E1). Partners: The need for deeper collaboration is not confined only to customers. In particular, the need to focus on the organization’s core competencies and the ensuing delegation of tasks to partners is seen to be crucial in data-driven service innovation. As a first step, organizations define their core competencies as a basis for future sourcing decisions. Based on these decisions, companies can determine the areas in which a partnership with other actors could be beneficial: From these data, we have to infer the competencies that we need in the future. […] What can we buy cheap and what not? (Interviewee O3 FG1 E2).

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Findings

It is highlighted that partners with IT skills (e.g., data analytics, storage, procession) are seen as potential partners in the innovation and delivery of data-driven service: “But ‘partner’ really means consulting other partners that can help us to develop and provide us with the analytics that we need for [data-driven service]” (Interviewee O1 FG2 E1). Close association to suppliers was also argued for “As a result, they [suppliers] become part of the product” (Interviewee O2 FG1 E2).

4.2

Challenges of collaboration in data-driven service innovation

The case study analysis also addresses the challenges of collaboration in data-driven service innovation that actors face. These challenges can be both, intra-organizational (e.g., resource sharing, organizational mindset) and inter-organizational (referring to interaction across organizational boundaries). The two categories are further influenced by underlying issues affecting both, intra- and inter-organizational collaboration during data-driven service innovation. Figure 11 summarizes the identified challenges that are based on the codings and analysis of the focus group interview data.

Intra-organizational collaboration:  Data silos  Corporate strategy  Corporate philosophy  Uncoordinated processes

Data-driven service innovation

Inter-organizational collaboration:  Rules for collaboration  Revenues  Data location  Data ownership

 Knowledge sharing Underlying issues:  Data Privacy  Standards  Legal Regulations  IT-Competencies

Figure 11: Challenges of collaboration in data-driven service innovation

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111

Challenges related to intra-organizational collaboration

At the intra-organizational level, the following challenges are identified: (1) data storage in organization-wide distributed silos that are inaccessible for the relevant employees; (2) a lack of established processes for data-driven service innovation; (3) lack of an organizational strategy clarifying the desired future development; and (4) an organizational culture characterized by a traditional product-centric mindset (see table 12). (1) The multiple data silos within specific areas of the case organizations are seen as an impediment for data-driven service innovation. This feature can inhibit data and knowledge exchange between different business units and/or departments when innovating data-driven service. (2) While all of the case organizations have structured processes for the development of physical products, the innovation of data-driven service is hindered by missing processes. Innovation of data-driven service depends on analytical and agile processes that can break through traditional boundaries in an interdisciplinary approach that deploys varied competences from different internal departments. (3) Analysis of the focus group discussions reveals the absence of communication of a clear organizational strategy focusing on data-driven service innovation. This lack of a strategy from the top management impedes employee innovation and creates insecurities about data usage and exchange. (4) Prevailing organizational culture is also identified as a challenge for data-driven service innovation. All three cases confirm that these organizations are still struggling with employees’ traditional, product-centered mindsets preventing agile value cocreation. In particular, older employees with a longstanding affiliation to the organization shaped an organizational culture in the past decade that does not align with data-driven service innovation and prevented the company from the transition into a more service-oriented business.

112

Findings

Table 12: Employees’ views about challenges related to intra-organizational collaboration Challenge

Illustrative quotation

Interviewee

1) Data Silos

Today, there are already loads of data sources. The O1 FG3 E1 problem is that these [data sources] are buried in sharepoints and other data-bases to which one has relatively limited access as things stand. This means that one of our tasks will now be to drill down to where the data are stored – to make these data useable on a central platform. They are not communicating with each other. O3 FG2 E2 Regularly other divisions don’t know that it [the data] exists. Aggregating these data would be worth a mint. 2) There has to be a development process for data-driven O1 FG1 E2 Uncoordinated service – but currently there is none. Processes 3) There’s no organizational big data strategy. We will O3 FG1 E2 Organizational reach this point, but at the moment there is none. Strategy There is no strategy in the background that would give O1 FG1 E1 us the security to say ‘For sure, take our data.’ 4) We are a very, very conservative business, as is O3 FG1 E1 Organizational manufacturing in general […] with regard to Culture structures and sometimes also mindsets. We have employees that have worked here for 20, 30, O1 FG2 E1 sometimes 40 years and they are not used to it. The culture has to be changed to some extent. And that’s not easy. To bring the whole organization to the frame of mind O2 FG1 E4 to say that there are not only products but products plus service. 4.2.2

Challenges related to inter-organizational collaboration

Challenges at the inter-organizational level relate to (1) the need for rules for collaboration that clarify the distribution of potential revenues; (2) data ownership and location of data storage; and (3) prevailing restrictions in respect to knowledge sharing (see Table 13).

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(1) Given the number of involved actors that may have an interest in collecting and refining data (e.g., single component suppliers, machine manufacturers, operators), it is essential to have clear rules for a valuable collaboration. In the event of a collaboratively offered service that is beneficial for all involved actors, all must be convinced to work toward a common goal with clarity about the distribution of revenues. In particular, the various service system actors need to agree terms and conditions for revenue sharing that is mutually satisfactory for all concerned, guaranteeing a fair distribution. However, this latter point is seen as non-trivial because of problems associated with the financial assessment of data as resource. Table 13: Employees’ views about challenges related to inter-organizational collaboration Challenge

Illustrative quotation

1) Rules for What is a byte worth? What price tag can you put on it? collaboration Whether it’s raw data or refined data, what happens to the price of a byte refined or visualized in a dashboard? Is it possible to a price on it? 2) Data Who owns the data then? In other words, if we set up a ownership data-driven service with a customer, data will be and location generated. Are they owned by the customer? Are they owned by the OEM in between? Or do we own them? 3) Mutual data and know-how exchange is not common in Knowledge our environment. sharing Important remark: Especially in our industrial environment, customers are often not willing to share data.

Interviewee O1 FG1 E1

O1 FG2 E1

O3 FG2 E2 O2 FG1 E2

(2) Clear rules are not only required in case of financial ambiguities, but in relation to important questions concerning data location (where the collected data will be stored), access rights and ownership (who has the right to access the collected data for analysis or further utilization). These insecurities about data location and ownership are closely connected to the earlier points: On the one hand, the data-owning actor can use its position to define the value of the data; on the other hand, this actor can prevent utilization of data within their service system and by implication, the innovation of service relying on those data.

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(3) The existing mindset in relation to knowledge sharing can also challenge collaboration in data-driven service innovation. For example, as data allowing service system partners to draw conclusions about production processes would be considered critical. The case organizations and their customers are not used to share experiences and/or data with other organizations. 4.2.3

Underlying issues affecting collaboration in data-driven service innovation

In addition, the multiple case study finds that data-driven service innovation is challenged by underlying issues that may affect intra- and inter-organizational collaboration. These include (1) missing standards for data exchange and compatibility; (2) data privacy issues that restrict the use of data; (3) unclear legal regulations for data exchange between partners; and (4) a lack of human resources with the required IT skills within organizations (see table 14). (1) Standards for data exchange should be established before being able to analyze data and to innovate data-driven service. Difficulties in connecting collected data for further utilization because of a lack of such standards are seen as a challenge for datadriven service innovation. (2) Collection and transfer of data between different actors, especially in case of personal data, have to be approved by employee organizations. They have the power to slow down the whole process and can strongly regulate or even prevent data exchange, especially in the case of personal data usage, within or among the companies in the service system. Furthermore, participants feel insecure during the discussions about data privacy and protection with other actors in their service system, as this topic is seen as an unfamiliar one that is not connected to any of their core competencies. (3) Legal regulations concerning the exchange and ownership of data could be clarified by legislators to provide involved actors with legal security and to prevent the misuse of collected data. While the export of products is regulated, the case organizations are challenged by missing provisions for data exchange (e.g., across national borders).

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(4) Finally, a lack of qualified employees with data analytics competencies can influence both inter- and intra-organizational collaboration. The current job market does not provide the case organizations with skilled IT employees with the required cross-domain knowledge of products and software. One side effect of this lack of data analytics resources is that divisions or whole organizations must change their mindset and strategy in respect of service systems for value co-creation where each actor possesses specialized competencies. Table 14: Employees’ views about challenges related to underlying issues affecting collaboration in data-driven service innovation Challenge 1) Standards

Illustrative quotation

We need these standards for data exchange. If there are different ones, it becomes difficult to tether customers again and again. We have to think about standardized communication and eventually about certain facility structures to be able to standardize these somehow and then to exchange data through these standardized interfaces. 2) Data Until now, we have talked now mainly about privacy machinery, but our sensors are of course able to identify persons and we are not yet allowed to do this. But then, there is the employee organization that takes its job seriously and says: ‘Stop! First, we need to take a closer look at it.’ – It is important to clarify what kind of data can be disseminated. 3) Legal Am I allowed to provide domestic data in China and regulations vice versa? 3) ITBased on the data, we have to conclude what kinds of Competencies competencies we need in the future. Where we don’t need it, we will outsource it to our suppliers.

Interviewee O1 FG2 E1

O3 FG2 E1

O2 FG1 E1

O1 FG2 E1

O1 FG2 E1 O3 FG1 E2

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Discussion

Discussion

This empirical study explores actors and the challenges faced in the context of datadriven service innovation in German manufacturing industry, at both intra- and interorganizational level. From an intra-organizational viewpoint, these are the prevailing, product-centric mindset within the organization that hinders recognition of service business value (Oliva & Kallenberg, 2003) and the nature of what is being sold to the customer (Neely, 2008), thus confirming prior findings from service innovation and servitization literature. Nevertheless, the case study analysis highlights that there is unawareness of challenges accompanying the provision of both regular and datadriven service. The case study emphasizes the importance of an organizational data strategy that is clearly defined and supported by management. This extends earlier findings by Gebauer et al. (2005) that service and service innovation need to be promoted not only as an add-on to classical products but as a value creation opportunity in their own right. Furthermore, the study extends current literature by the emphasis on the necessity of a suitable data strategy. Top management in particular should recognize the value of service and data for their business to raise awareness of the value added by data-driven service provision (Gebauer et al., 2005). Thus, further adjustments of the company’s service strategy in relation to the new possibilities introduced by data should be considered by the management through the development and communication of a data strategy. The study adds to the current discussion through its finding that the case organizations are characterized by a lack of the internal capabilities to deliver integrated product-service solutions exploiting the potential of data utilization. Adding upon prior findings on service innovation and servitization (Neely, 2008; Martinez et al., 2010) the use of data as a key resource is found to require agile and analytical processes that go beyond traditional boundaries and utilize competencies from different (especially IT-related) departments. In their manufacturing sector case study, Martinez et al. (2010) emphasized that the relevant internal processes remain

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product-centered and are not aligned to service innovation. This leads to the neglect of methods usage such as service blueprinting, which can help organizations to innovate new service as well as improve the customer focus of existing service (Bitner et al., 2008). The case study analysis adds to the current body of literature through revealing that data-driven service innovation entails specific IT-related challenges (Alghisi & Saccani, 2015; Schüritz et al., 2017a; Baines et al., 2017; Story et al., 2017) that add additional complexity. In particular, it finds that the existence of data silos within organizations can hinder data-driven service innovation at an intra-organizational level, where single departments know nothing about the existence, collection, possession or analysis of data that could help them to innovate data-driven service. These silos are often linked to deficits in collaboration and mutual activities among individuals within the organization, inhibiting intra-organizational collaboration across functional borders (Diamond et al., 2004). From an inter-organizational viewpoint, the present analysis confirms that the exchange of knowledge between different actors during regular service innovation applies for data-driven service innovation as well. Capturing, representing, and reusing knowledge and data among partners enables organizations to offer valuable service such as predictive maintenance to their customers (Baxter et al., 2009). However, the case study analysis supplements the current discussion through showing that the value of data and the distribution of achieved revenues from the data among contributing partners present challenges for data-driven service innovation. A lack of clarity about the value of both, raw and refined, data demands clear rules for collaboration, as does the issue of multiple actors claiming their share of the revenues (e.g., the manufacturer of single components, as well as the machine producer, and ultimately the operator). Data-driven service innovation also raises questions about data ownership, location and storage that add to prior findings in the field of service innovation. First, data ownership is strongly connected to revenue splits from data-driven service – because data ownership assigns the power to decide what happens to the data. Second, the issue

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of data storage location also encompasses data security, connectivity, and costs. Preventing partners from accessing data exacerbates them from understanding how a product works and accordingly prevents beneficial utilization of data for service provision (Porter & Heppelmann, 2014). The fear of know-how drains through data misuse or losses undermine the collaboration between partners in data-driven service innovation, especially with the emergence of decentralized cloud solutions. These external service providers rely on trust among their customers ensuring data integrity (i.e., protection of data from unauthorized access, modification, or deletion) (Zissis & Lekkas, 2012). The present findings show that the range of actors involved in data-driven service innovation is extended in comparison to regular service innovation and these actors should collaborate more closely. The results confirm that actors including the top management, other organizational units, sales and service personnel, customers, and suppliers (Jonas et al., 2016; Rusanen et al., 2014) all play central roles during datadriven service innovation. However, new actors with an IT background such as IT departments and IT service providers, become more important during data-driven service innovation, especially external IT service providers for server infrastructures, cloud solutions and/or data analytics. Although not traditionally part of the manufacturing industry, these IT service providers are now important in light of the stronger focus on interdisciplinary innovation. As a result, industry boundaries are no longer constrained by products and their systems, but are extended to a system of systems linking different product systems (Porter & Heppelmann, 2014). Some underlying issues that affect collaboration during data-driven service innovation can be seen as an extension of the challenges experienced during regular service innovation. On the one hand, there were IT-specific challenges like data privacy. In the case of personal data, organizations should operate within the framework of existing legislation to guarantee proper confidentiality and privacy protection (Zissis & Lekkas, 2012). In particular, if cloud services are used to store data on a service provider’s server or during automated decision-making, customers can express concerns about privacy and its violation (Zissis & Lekkas, 2012; Wünderlich et al., 2015; Keh & Pang, 2010). Organizations can benefit from digital interfaces that

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enable fast, effective and economic resource integration during data-driven service innovation (Lusch & Nambisan, 2015). On the other hand, organizations see a need to build up the required skills to exploit the opportunities arising from the use of data as a key resource of a service (Kindström et al., 2013). In the present context, this relates especially to IT skills. These skills can be developed internally through recruitment of new employees with the appropriate qualifications (Reinartz & Ulaga, 2008) or by outsourcing to external service providers (Kowalkowski et al., 2011; Gebauer et al., 2013). The latter option seems highly relevant to the case organizations in light of the limited availability of potential employees with the required IT-competencies and the high costs of hiring and employing these IT specialists. Additionally, outsourcing enables companies to focus on their core competencies within their service system and optimizes their own resource deployment through collaboration with partners with greater experience and competencies in the required fields. Another important aspect to be discussed are ways to face these challenges through the development of organizational dynamic capabilities (Eisenhardt & Martin, 2000; Teece, 2007). Most of the challenges that are classified as underlying issues, i.e. standardization, data privacy, and legal regulations can be faced through the development of organizational sensing capabilities (Teece, 2007). These challenges refer to the exogenous environment of an organization and need to be observed continuously by an organization. The sensing of opportunities and threats resulting from the establishment of industrial standards as well as changing legal regulations, especially in the case of data privacy, can enable organizations to react on these and to use changes in their favor. Internal challenges such as data silos and uncoordinated processes within an organization demand for the development of seizing capabilities to overcome these (Teece et al., 1997). Seizing capabilities enable an organization to establish novel processes for structured approaches as well as to foster internal collaboration through the integration of data silos. Likewise, challenges arising from external collaboration within a service system can be overcome through the establishment of new modes of

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interaction across the involved actors (den Hertog et al., 2010). This includes the implementation of processes (Teece, 2007) for the clarification of revenue distribution across partners in the service system as well as the definition of the own organization’s boundaries for being able to deal with issues concerning data location and ownership (Story et al., 2017). Finally, challenges coming from a lack of individual skills within an organization, such as human IT-competencies, should be employed throughout the organization or sourcing decisions be made to focus on an organization’s core competencies. Finally, challenges in respect to the integration of know-how and sharing of information with external sources can be faced through a continuous alignment of an organization’s culture and strategy that fits to the requirements of data-driven service innovation (Kindström et al., 2013; Teece et al., 1997; Teece, 2007). Promoting reconfiguration of an organization’s culture and strategy can help to minimize insecurities in terms of data sharing throughout the service system and support the changing towards being a service provider rather than only selling tangible goods (Matthyssens et al., 2006).

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Summary and implications

This multiple case study explored current data-driven service innovation activities within three German manufacturing organizations, highlighting the emerging intraand inter-organizational challenges of collaborating in value co-creation among manufacturers, suppliers, and/or customers and sheds light upon the involved actors. This study adds to current knowledge by describing German manufacturing organizations’ current for data-driven service innovation activities highlighting the actors and the challenges for intra- and inter-organizational collaboration. During data-driven service innovation, IT-related challenges connected to data privacy, data silos, data ownership, and data storage location add to known challenges of service innovation. In light of these challenges, new actors like internal IT departments, external IT service providers, and legal departments gain in importance during data-driven service innovation. The study captures current service research priorities and the importance of collaboration in this context, focusing on the influence of ICT on service innovation and servitization (Ostrom et al., 2015; Baines et al., 2017) illustrated by German product-centered organizations seeking to link their classical goods to data-driven service.

6.1

Managerial implications

This multiple case study provides new insights into the current data-driven service innovation activities of German manufacturing organizations and identifies key issues for decision makers that may help them to avoid or prevent potential challenges. In particular, the study revealed that new aspects such as the early clarification of data ownership, location of data storage, and issues related to revenue streams can help to ensure the success of data-driven service innovation. It also seems important that managers devise and communicate a clear data strategy that promotes the innovation of data-driven service among employees, to afford them more security during the service innovation process. It seems important that the provision of clear strategies for

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Summary and implications

data and knowledge exchange among service system actors by managers supports the establishment of service systems for data-driven service innovation. Here, especially sourcing decisions are important in strengthening an organization’s own competencies in addition to the benefits of external IT skills and knowledge. For being able to cope with the challenges revealed throughout this study, organizations could develop suitable sensing, seizing and reconfiguration capabilities. These can help an organization to overcome these challenges and reach competitive advantage The present study provides new insights on actors that are regarded to be relevant for data-driven service innovation to managers. To foster data-driven service innovation activities, managers are advised to take account of both internal and external IT functions. Finally, the findings indicate that actors such as employee organizations should be included in the early stage of data-driven service innovation processes to prevent subsequent complications.

6.2

Theoretical contribution and outlook

The multiple case study contributes to the current discussion on data-driven service innovation through the following insights. It shows that the set of actors involved (Jonas et al., 2016) in data-driven service innovation is extended by a range of actors with an IT background. The integration of these actors during data-driven service innovation increases the complexity of the whole process and brings up a variety of challenges on different levels (Holmström, 2018). The case study analysis showed that a set of inter- and intra-organizational barriers and challenges emerge that are complemented by underlying issues that can only be hardly influenced. Based on these barriers and challenges, the study puts forward current research on dynamic capabilities. Suggesting how barriers and challenges can be overcome through the development of sensing, seizing and reconfiguration capabilities serves as an extension of current research in this field (e.g. Kindström et al., 2013; den Hertog et al., 2010) through the investigation in the context of data-driven service innovation. The used multiple case study method limits the findings and range of collected data through the provision of an inside perspective of German manufacturing

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organizations. Extending the study beyond manufacturers, to include, for instance, suppliers and customers would offer additional insights into both the challenges for data-driven service innovation and on the relevant actors. Future studies should seek to validate the results in other industries and might usefully explore data-driven service innovation among organizations in other sectors. Additionally, this study only focuses on challenges for collaboration in value cocreation for data-driven service innovation. Further studies might take account of technical aspects or focus in greater depth on particular issues such as the assessment of data value or the design of appropriate revenue models which were identified as challenges for data-driven service innovation. Future studies might also investigate the risks and repercussions faced by organizations on entering service systems for datadriven service innovation. The integration of resources across multiple actors comes along with an increase of complexity that has to be managed throughout the service system. In this context, it could be also of interest to investigate how actors can support organizational dynamic capability development through roles on an individual level. Another fruitful pathway for further research could be opened up through the investigation of the importance of the establishment of data-driven service culture and the accompanied strategic change. While in particular many product-centric organizations struggle with the implementation of suitable processes and strategies for regular service innovation, the influence of data in this process adds additional complexity. Here, it would be interesting to see how organizations cope with taking both aspects in regard and pursue the shift towards becoming a service provider simultaneously with the challenges that are induced through data utilization – that alone have the ability to challenge organizations that are already service providers. Finally, it would be interesting to investigate why organizations struggle to transform themselves towards being a data-driven service provider. While the challenges and barriers are well-known throughout the organizations, the implementation of suitable strategies is still not very successful and opens up various pathways for research on resistance of employees that impede the required organizational change.

Part V Exploring actor roles and capabilities for data-driven service innovation © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 M. Schymanietz, Capabilities for Data-Driven Service Innovation, Markt- und Unternehmensentwicklung Markets and Organisations, https://doi.org/10.1007/978-3-658-31691-4_5

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Introduction22

We went straight ahead, forced on by a burning curiosity. What other wonders did this cavern hold, what treasures of science? (Jules Vernes, 1864) The utilization of data for new types of service innovation is accompanied by a set of additional barriers and challenges (as outlined in the previous parts) such as data access and ownership, the development of new revenue and business models and deeper knowledge on customer needs (Porter & Heppelmann, 2014; Schüritz et al., 2017a). As depicted in part IV, it requires the integration of multiple actors (Stein et al., 2018; Porter & Heppelmann, 2014; Lusch & Nambisan, 2015), as service is innovated across rather than within organizations, working with customers, partners and suppliers. Within these service systems, connected individual actors co-creatively integrate their resources (Lusch & Nambisan, 2015; Rothaermel & Hess, 2007) to facilitate an organization’s growth (Ordanini & Parasuraman, 2011; Barret et al., 2015; Kindström et al., 2015). To compete profitably in today’s dynamic markets, organizations need to develop the requisite capabilities to reconfigure their resources, business models and organizational structures in favour of the new circumstances (Teece, 2018). In so doing, they need dynamic capabilities to sense opportunities and threats, seize those opportunities, and reconfigure both tangible and intangible assets if they are to maintain or develop sustainable competitive advantage (Teece, 2017, p. 1319), what was shown in part III of this dissertation. Understanding these higher-level organizational dynamic capabilities still arises curiosity and the following part aims to satisfy this curiosity. This is implemented through a microfoundational account of the roles of individual actors (Felin et al., 2012; 2015; Rothaermel & Hess, 2007) who shape

22 An earlier version of this research was presented at the Hawaii International Conference on System Sciences (HICSS) 2020 and is published in the conference proceedings as Schymanietz & Jonas (2020).

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the organization and higher-level phenomena such as dynamic capabilities (Teece, 2017; Salvato, 2009; Felin et al., 2012; 2015) Data-driven service innovation provides a rich context in which to explore the nature of service innovation (Schüritz et al., 2017a; Lenka et al., 2017) alongside the development of organizational capabilities. To illuminate value co-creation activities and their importance for data-driven service innovation, this part takes a microfoundational view (Felin et al., 2012; 2015) and investigates the roles of individual actors and connected individual activities in that context. Throughout the subsequent chapters, the final study of this dissertation aims to take the reader to the core of dynamic capability development – the individual actor. The goal of this part is to draw conclusions on higher-level organizational (dynamic) capabilities from micro-level activities and capabilities of individual actors. Understanding these micro-level aspects is pivotal for understanding higher-level ones, because individuals, their activities and capabilities shape an organization (Felin et al., 2012; 2015). For this purpose, a Delphi study is carried out that aims to reach consensus across a group of experts in the field of the innovation of data-driven service offerings. Based on identified individual activities, part V aims to prioritize them in their importance to point out activities that should be carried out with a higher priority. It intends to derive actor roles that incorporate these activities with the goal to identify roles that support the development of both ordinary and dynamic organizational capabilities Part V is structured as follows: chapter 2 will provide a brief introduction to datadriven service innovation, the importance of individual actors and dynamic organizational capabilities. These topics will be deepened in the subsequent chapter 3 through a focus on the role of individual actors during organizational capability development. Chapter 4 will introduce the carried out modified Delphi method, the selection of the participants and the procedure of the three Delphi rounds that were carried out. Chapter 5 will present the results. It will delineate the functions involved during data-driven service innovation and the unique activities that were noted by the participants as well as their ranked importance. In addition, the findings will be

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synthesized and nine distinctive actor roles for data-driven service innovation derived from the data. These roles will be connected to their ability to support ordinary and dynamic capabilities. Finally, chapter 6 will discuss the findings and put it into the context of current literature on dynamic capabilities and service innovation. It will show future venues for research. The structure of part V can be seen in figure 12.

Part I: Introduction

Part II: Theoretical background

Part III: Systematic literature review and expert interviews

Part IV: Multiple case study

Part V: Delphi study

1 Introduction  The influence of digitization on organizations  Organizational capabilities  Objectives of part V  Structure of part V 2 Theoretical Background  Actors in service innovation  Individual capabilities  Organizational capabilities 3 Method  Delphi Study method  Delphi Study procedure 4 Findings  Activities during data-driven service innovation  Actor roles during data-driven service innovation  Roles supporting organizational capabilities

5 Discussion  Discussion of findings Part VI: Synthetization and discussion 6 Theoretical contribution and outlook  Summary of part IV  Implications and further research

Figure 12: Structure of part V

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2

Theoretical background

Theoretical background

During service innovation, activities, resources (e.g., physical, skills, information, knowledge) and practices are co-created or recombined through collaboration to develop solutions for new or existing problems and to deliver additional value (Vargo et al., 2015; Koskela-Huotari et al., 2016; Lusch & Vargo, 2014; Barret et al., 2015; Böhmann et al., 2014; Tuunanen et al., 2011). Service innovation is inherently multidimensional and requires the involvement of a diverse range of actors from different organizations, units and functions. As investigated during the previous parts of this dissertation, the required integration of resources cannot be carried out by a single actor. It is likely to involve both external actors (e.g., customers, users, suppliers, external service providers, competitors, universities) and internal actors (e.g., top management, sales and service personnel, local subsidiaries) (Kindström & Kowalkowski, 2014, Jonas et al., 2016; Goes & Park, 1997). Consequently, the integration of actors is seen as a key aspect for service innovation activities, where actors recombine and exchange their resources (Lusch & Nambisan, 2015; Koskela-Huotari et al., 2016). Actors are regarded as the foundational resource in service systems, thus being of high importance for service provision (Tronvoll, 2017). For this purpose, actors harness their capabilities in an innovative or creative manner that rely on their mental models (Tronvoll, 2017; Edvardsson et al., 2011) to co-create value in actor-to-actor networks and can fulfill different roles (Vargo & Lusch, 2011; Ekman et al., 2016; Tronvoll, 2017). Especially individual actors are discussed as important during service innovation, due to the connection between individual activities and organizational outcomes (Lenka et al., 2018; Jonas & Roth, 2017). Here, actors can have a radical or incremental influence on others and take expected or emerging roles, meaning that their roles lie in line with other actors’ expectations or not (Heikinnen et al., 2007; Nyström et al., 2014). These roles depend on the context, are volatile, influenced through the position of the actors and shaped through an actors’ value co-creation activities during resource integration (Tronvoll, 2017).

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However, it is important to differentiate between different actor levels: network and individual. On the network level, actors co-create value through interaction within an actor-to-actor network or across different networks that belong to a service system. On this level Ekman et al. (2016) redefined the role of the generic actor as someone who can be beneficiary and provider simultaneously and changes these roles during the interaction with the whole actor-to-actor network or with individual actors. Beside of the provider/beneficiary role, actors can also be classified as active or passive, dependent of their participation throughout innovation activities in networks (Ekman et al., 2016). During a typical innovation process (consisting of the stages idea generation, developing and testing), actors can take different roles that change during this process from being an active (i.e. mutual) co-creator over becoming reactive (acting as an information provider that responses to an impulse such as an interview or question) or just staying passive (where the actors just observe the service innovation process) (Jonas & Roth, 2017). Beside of the challenge of paying attention to changing roles, the identification and management of the appropriate actors and their capabilities is challenging, especially within actor-to-actor networks that foster interaction among them (Kazadi et al., 2015; Jonas & Roth, 2017). Here, a high complexity does not only arise for the integration of external actors (e.g. users, customers, suppliers, external service providers, universities, competitors). In addition, internal actors (such as sales and service personnel, top management, local subsidiaries or other organizational units) contribute to this complexity as well due to the necessary allocation of resources such as time and money (Jonas et al., 2016; Jonas & Roth, 2017). However, the roles of involved actors do not always fit to their official organizational one and rely on the service target (i.e. if the service is an output or a process) (Breidbach & Maglio, 2016). Every service system has an actor in its core who integrates the available resources under consideration of his norms and beliefs (Tronvoll, 2017). Tronvoll (2017) describes that actors need to face a variety of societal and individual influence factors that guide them. Societal factors can be referred to basic regulatory mechanisms that impede or permit value co-creation processes such as the actor’s position in the market and society

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(e.g. his social hierarchical position) (Lindemann, 2007) or institutions (being a social structure that provides rules for governance) (Meyer & Rowan, 2006) and institutional arrangements (i.e. the belief systems that outline the meaning of institutions) (Scott, 2001). Individual factors can be described as schemas that characterize informationprocessing and cognition mechanisms (DiMaggio, 1997) and practices that describe an actor’s routinized behavior. This enables actors to manage their actions, behaviors and intentions. Practices are often described as automated mental models that are only questioned seldom (Reckwitz, 2002; Vargo & Akaka, 2012; Kjellberg & Helgesson, 2006). For a deeper understanding of actors, their co-creation and resource integration activities, it can be beneficial to put a focus on the roles of individual actors as well. Taking this micro-foundational view helps to understand higher-level phenomena through an investigation on a level that is lower than the underlying phenomenon (Storbacka et al., 2016). Felin et al. (2012, p. 1355) define these microfoundations as a “[…] theoretical explanation, supported by empirical examination, of a phenomenon located at analytical level N at time t (Nt). In the simplest sense, a baseline micro-foundation for level Nt lies at level N-1 at time t-1, where the time dimension reflects a temporal ordering of relationships with phenomena at level N-1 predating phenomena at level N. Constituent actors, processes, and/or structures, at level N-1t-1 may interact, or operate alone, to influence phenomena at level Nt. Moreover, actors, processes, and/or structures at level N-1t-1 also may moderate or mediate influences of phenomena located at level Nt or at higher levels (e.g., N+1t+1 to N+nt+n).” In other words, this microfoundational view settles the phenomenon’s cause on a level of analysis that is lower than the phenomenon itself. An investigation of this level can deliver improved and richer explanations of higher-level phenomena (Felin et al., 2012; 2015). It supports that individual actors shape organizations and collective phenomena through their capabilities, skills, activities and interaction (Felin et al., 2012). This view is meaningful, because resource integration through individual actors and their outcomes cannot be completely planned, controlled or designed on higher

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levels and resource integration sometimes leads to surprising properties within the service system (Storbacka et al., 2016). Identifying the requisite capabilities and managing multiple actors and their interaction can be complex (Kazadi et al., 2015), and different relationships must be established to facilitate each evolving role (Story et al., 2017). These roles are assigned during resource integration for value co-creation and are established through mental models, activities (such as resource exchange) and interactions with other actors (Tronvoll, 2017). Value co-creation depends on the interaction of these different actors and their joint or independent activities in enabling the exchange and integration of resources (Breidbach & Maglio, 2016; Storbacka et al., 2016; Peters et. al., 2016; AarikkaStenroos & Jaakkola, 2012). Understanding the relevance and relative importance of these roles is central to comprehending value co-creation processes among different actors (Ekman et al., 2016; Chandler & Lusch, 2015). To foster innovation capabilities, organizations must develop the capabilities of individual actors (e.g., thinking in systems, integrating and combining, inventive thinking, networking) (van Kleef & Rome, 2007). Because individual actors contribute to innovative and co-creative interaction by applying their mental models (Tronvoll, 2017), organizational capabilities ultimately depend on an understanding of individual capabilities (Felin & Hesterly, 2007). In a nutshell, capabilities evolve on the basis of skills, knowledge, characteristics, experiences, cognitions, and abilities of individual actors that – in sum – form the whole organization (Felin et al., 2012). During value co-creation by multiple actors, understanding individual roles and connected activities (e.g., gathering knowledge and information) on a micro-level facilitates the integration of organizational assets and the development and creation of organizational capabilities (Argote & Ren, 2012). These capabilities strongly rely on the individual actors and their characteristics involved. They are shaped through the interaction of individuals and the application of processes within an organization. These processes are in turn vitalized through individuals (Felin et al., 2012).

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Theoretical background

In today’s fast changing business environment, where the sole possession of resources alone does not guarantee sustainable competitive advantage (Opresnik & Taisch, 2015), organizations must develop dynamic capabilities if they are to fully exploit their resource base (Teece et al., 1997; Eisenhardt & Martin, 2000). In this context, so-called ordinary capabilities (Teece, 2017) relate to operation, governance and administration of organizational activities, thus indicating if activities are carried out right. In addition, dynamic capabilities relate to doing the right things and are usually strategic (Teece, 2017). During service innovation, the development of dynamic sensing, seizing and reconfiguration capabilities is strongly influenced by the paradigm of value co-creation (Kindström et al., 2013; den Hertog et al., 2010). First, new modes of interaction emerge during sensing activities. Second, opportunities are seized, shifting the focus to customer value, based on continuous co-creation activities within the service system (Alam, 2006; Sundbo, 1997; Kindström et al., 2013). Finally, the service system must be orchestrated using organizational reconfiguration capabilities (Kindström et al., 2013; den Hertog et al., 2010) and sustained by establishing a service-oriented mindset within the organization (Matthyssens et al., 2006; Kindström et al., 2013). The multi-dimensional nature of data-driven service innovation results in complex processes that require the development of organizational capabilities (see parts III and IV), based on the capabilities of individual actors in multiple roles. To identify the requisite organizational capabilities through the examination of individual actors, their capabilities, activities and roles during data-driven service innovation, this study addresses the following research question: RQ 5: What roles of individual actors are relevant and support the development of dynamic organizational capabilities during data-driven service innovation?

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Method – Delphi study

To answer the research question, this study uses a modified Delphi technique. Carrying out the Delphi method elicits qualitative information from experts to identify relevant issues and their relative importance (Schmidt, 1997). Linstone & Turoff (1975, p.3) define Delphi “as a method for structuring a group communication process so that the process is effective in allowing a group of individuals, as a whole, to deal with a complex problem”. In a series of surveys, the technique seeks to establish a consensus within a group of experts from a given domain (Okoli & Pawlowski, 2004; Dalkey & Helmer, 1963; Linstone & Turoff, 1975). Data collection during a Delphi study can be distinguished into three phases: (1) the identification of issues, (2) discovery of the most important ones and finally (3) a ranking of the identified and prioritized issues (Schmidt, 1997). In detail, the first phase should enable the participants to mention a variety of different issues that should be consolidated by the researcher afterwards to a single list that groups similar issues. This list should be then fed back to the participants to verify that the list includes all relevant issues (Schmidt, 1997). The second phase prepares the list for the final ranking through a prioritization that filters out less important aspects and leaves around 20 issues on the list. Without this intermediate step, the large amount of issues would hinder a proper ranking (Schmidt, 1997). After the prioritization of the issues, the list is given back to the participants, asking them for feedback again (Dalkey, 1969). Finally, the third phase demands from the participants to rank the remaining issues from a list that presents the identified and prioritized issues in a randomized list. The group consensus can be then assessed by the calculation of Kendall’s W, the percentage of participants that place each item in the top half within their individual list or through relevant comments stated by the participants (Schmidt, 1997). Kendall’s W serves as a non-parametric indicator for the assessment of the group consensus; a value of 0 can be interpreted as complete absence of consensus within a group while a value of 1 indicates perfect consensus (Okoli et al., 2004).

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Method – Delphi study

The traditional Delphi method (Dalkey & Helmer, 1963; Linstone & Turoff, 1975) was subject to adaptions and modifications during the past years (Landeta et al., 2011). One of the developed modifications is the group Delphi method that allows for an interaction of participants in plenary sessions (Webler et al., 1991). This group Delphi method preserves all other elements such as iterative feedback rounds, group judgements, and the possibility to revise opinions of a traditional Delphi study beside of anonymity (Webler et al., 1991). Yet, the collaborative setting increases the participants’ sense of responsibility and seriousness, producing results that gain higher acceptance within the group (Landeta et al., 2011). To ensure this, these plenary sessions need to be properly moderated to prevent the undue influence of dominant personalities. To that end, the moderator must seek to balance the inputs of more and less communicative participants (Webler et al., 1991).

3.1

Data collection

To identify individual actor roles contributing to data-driven service innovation, 22 professionals with experience in that context were invited (see table 15). The experts shall cover a variety of different backgrounds that represent different organizational actors that are part of the service system for data-driven service innovation. To ensure sufficient knowledge about the phenomenon under investigation, the main selection criteria included a leadership position within their organization. All of these experts have deep knowledge of the data-driven service innovation process within their affiliated organization. To avoid cultural bias and to ensure a range of perspectives on the phenomenon in question, the selected international participants were from different industries and varied backgrounds. However, it has to be noted that the participants’ individual perceptions and beliefs lead their behavior (Webler et al., 1991; Hill & Fowles, 1975).

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Table 15. Overview of participants Participant #

Position

Industry

1

Deputy General Manager

Manufacturing

2

Digital Transformation Program Manager

Finance

3

Director

Technology

4

Lead Product Manager

IT

5

Technology Director

IT

6

Partner Development Manager

IT

7

Business Transformation Head

IT

8

General Manager

ICT

9

General Manager

Engineering

10

Director Digital Transformation

Technology

11

Chief Product Owner

Manufacturing

12

Head of Sales

IT

13

Program Manager

Engineering

14

Lead Portfolio Manager

IT

15

Senior Expert ICT

ICT

16

Senior Director

IT

17

Senior Director

IT

18

CEO

Logistics

19

Lead Project Manager

Engineering

20

Process Architect

Engineering

21

Partner Manager

IT

22

Regional Business Development Manager

ICT

The modified Delphi method was used to rank issues to develop a consensus (Schmidt, 1997) through group interaction among the selected experts (Webler et al., 1991). The first round explored the activities performed during data-driven service innovation. In the second round, the experts were asked to prioritize key activities, which were then ranked in a third and final round (see figure 13).

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Method – Delphi study

First round: Exploration Identification of organizational functions actively involved in datadriven service innovation Identification of activities to be performed during data-driven service innovation

• •

47 unique and essential activities Second round: Selection •

Selection of activities with priority 21 key activities Third round: Ranking

Referral of ranked activities to first round results

•

Ranking of activities based on their importance List of ranked key activities

Synthetization • •

Derivation of roles based on Delphi rounds Classification of roles in respect to ordinary and dynamic capabilities

Figure 13: Conducted Delphi method (adapted from Schmidt, 1997; Webler et al., 1991) First Delphi round During the first round of the Delphi study, the participants were asked about their understanding of what a data-driven service is. They were questioned to identify the actors that are actively involved in data-driven service innovation and the activities they perform. The initial questionnaire presented a range of organizational functions and external actors, as well as an open-ended option to identify other activities and free text fields for expressing further personal views without restriction. Second Delphi round Throughout the second Delphi round, the participants were asked to identify a reduced set of essential activities for data-driven service innovation. At the beginning of this round, data-driven service examples were presented to the participants to gain a common understanding in the group and to enable the participants to revise previous statements on their understanding. Afterwards, the first round results were presented to the participants and were brought up for discussion. This let the participants to extend the existing set of activities to include the following ones: leadership support

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and capability assessment (both to be added to the managerial category), process evaluation methods (processes and methods category), and failure culture (culture and mindset category). Ultimately, this process yielded a total of 51 unique activities (see figure 14; activities added by the participants during the feedback round in italic). Participants were then asked to select five priority activities from each of the four categories. (1) Managerial • Manage products & services • Decide on market launch • Prioritize activities • Analyze value of solution • Ensure funding • Analyze risks • Analyze impact • Forecasting • Motivate employees • Break structures • Research customer needs • Understand customers • Analyze customers • Integrate customers • Gain ecosystem knowledge • Leadership support • Capability assessment

(2) Processes & Methods • Plan innovation • Enable internal interaction • Remove organizational obstacles • Integrate internal actors • Understand internal processes • Create business rules • Enable feedback loops • Establish innovation lifecycle • Establish process for DDSI • Use service design methods • Use prototyping • Use piloting • Enable sandboxing • Process evaluation methods

(3) Culture & Mindset • Think lean • Challenge current tasks • Decentralize control • Ensure freedom • Be adaptable • Promote constant innovation • Think lateral • Think out of the box • Think visionary • Promote mindset change • Failure culture

(4) Technical • Apply AI • Apply Blockchain • Apply data analytics • Apply data mining • Apply machine learning • Provide knowledge on technologies • Provide data architecture knowledge • Provide chatbot knowledge • Provide domain application knowledge

Figure 14: Categories and activities

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Method – Delphi study

Third Delphi round Finally, the participants were asked to rank these 21 activities in order of importance. In this third round, 11 of the 20 second round participants responded. This low response rate and the participants’ reluctance to change their opinions indicated that further rounds would not be meaningful (see annex G for pictures of the Delphi rounds).

3.2

Data analysis

To capture the activities performed during data-driven service innovation, an inductive approach was used to derive categories from the ground up as they emerged from the data analysis (de Ven, 2007). First, the activities mentioned by the participants were coded descriptively, summarized in a short sentence or descriptive word. In a first overview of emerging topics, these descriptive codes formed the basis for further coding, analysis and interpretation (Saldaña, 2016; Wolcott, 1994). In a second cycle, pattern coding was used to reduce the number of descriptive codes. Pattern codes are “explanatory or inferential codes, ones that identify an emergent theme, configuration, or explanation” (Miles et al., 2013, p. 86), synthesizing major themes into smaller sets of commonalities (Saldaña, 2016; Miles et al., 2013). The strength of the group consensus was assessed by the calculation of Kendall’s (W) coefficient of concordance. Beside of the calculation of Kendall’s W, the amount of the top ten votes of the participants serves as another indicator for a group consensus (Schmidt, 1997). By synthetizing these results, it was possible to characterize actors’ roles in datadriven service innovation. In particular, the prioritized activities from the third round were referred back to the activities and descriptive codes initially mentioned during the first Delphi round. For example, the description of the customer expert role does not only base on the derived code “Understand customers”, but also on these exemplary statements of the participants from the first round such as the necessity of “a constant interaction with customers for reactive feedback for iterations” or a “good understanding of customers' problems and at what point in the journey” that were coded to the activities from figure 14.

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Findings

The findings of the analysis of the group Delphi data show that organizational functions such as innovation management, the R&D department, customers, management and internal IT department are regarded as important during data-driven service innovation by the participants. It emerges that the following four categories of activities are seen as important during data-driven service innovation: (1) managerial, (2) processes & methods, (3) culture & mindset and (4) technical. Based on tasks from these categories, 21 activities are prioritized and ranked with the goal to derive individual actor roles. Based on this approach, nine actor roles are derived. These are (1) the customer expert, (2) the supporting manager, (3) the innovation enabler, (4) the bridge builder, (5) the prototyper, (6) the strategic operationalizer, (7) the mindset visionary, (8) the technical expert and (9) the t-shaped expert. Finally, these individual actor roles are assigned to their potential of supporting organizational dynamic sensing, seizing and reconfiguration capabilities.

4.1

First Delphi round – Exploration of functions and tasks

The initial results (summarized in table 16) show that a majority of participants identified innovation management, R&D, customers, general management and the internal IT department as playing an active role during data-driven service innovation. Table 16: Organizational functions involved in data-driven service innovation Rank

Function

Total / %

1

Innovation management

16 / 73%

2

R&D department

14 / 64%

3

Customers

13 / 59%

4

Management

12 / 55%

5

Internal IT department

11 / 50%

6

Marketing department

9 / 41%

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7

Product management

9 / 41%

8

Service department

9 / 41%

9

Engineering department

6 / 27%

10

Purchasing department

5 / 23%

11

Sales department

5 / 23%

12

Universities & research partners

5 / 23%

13

External data service providers

3 / 14 %

14

Legal department

3 / 14 %

As 86 % (19/22) of the participants assumed that most activities could be handled internally, external actors such as universities and research partners received relatively few mentions. Additionally, the participants referred to 47 essential and unique activities that need to be performed during data-driven service innovation. These activities were coded (as described earlier) and assigned to the following four categories: (1) Managerial. The first category of activities includes decisions about market launches, risk and impact analyses, different areas of management across the organization, research on customer needs, and ecosystem analysis. (2) Processes & Methods. This category includes enablement of internal interactions, planning for innovation, formulation of business rules, and design thinking, piloting, and prototyping. (3) Culture & Mindset. This category includes promotion of lean thinking, ensuring team members’ freedom, promoting continuous innovation, and promoting mindset change. Although linked to the first category, these activities are strategic in nature, differentiating them from managerial concerns. (4) Technical. This category includes application of artificial intelligence (AI), machine learning, and data analytics or blockchain, as well as provision of knowledge in relation to technology, data architectures, chatbots, and domain-specific applications.

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Second Delphi round – Identification of key activities

The identification of key activities during the second Delphi round yielded a total of 21 activities, three of which tied for fourth place in the Culture & Mindset category and were therefore proceeded to the next round. After discussing the results, the panel made no changes to the 21 selected activities. An overview on the prioritized tasks (that were selected by a total of 20 participants) including their voting results in ascending order can be seen in figures 15-18. The activities that were processed to the next round are colored black. (1) Within the Managerial category, the participants assigned the highest importance to the 5 following activities: understand customers, leadership support, analyze value of solution, provide insights on customer needs and decide on market launch strategy. Managerial category voting results

14 12 10 8 6 4 2

Figure 15: Managerial category voting results

Forecasting

Manage products & services

Prioritize activities

Ensure funding

Analyze impact

Provide ecosystem knowledge

Break structures

Motivate employees

Analyze risks

Analyze customers

Capability assessment

Integrate customers

Decide on market launch strategy

Provide insights on customer needs

Analyze value of solution

Leadership support

Understand customers

0

144

Findings

(2) Throughout the Processes & Methods category, enable feedback loops, establish processes for data-driven service innovation, remove organizational obstacles, establish innovation lifecycle and support prototyping were the participants’ selected activities Processes & Methods category voting results 12 10 8 6 4 2

Process evaluation methods

Enable sandboxing

Support piloting

Implement service design methods

Create business rules

Plan innovation

Integrate internal actors

Enable internal interaction

Understand internal processes

Support prototyping

Establish innovation lifecycle

Remove organizational obstacles

Establish processes for data-driven service innovation

Enable feedback loops

0

Figure 16: Processes & Methods category voting results (3) In the Culture & Mindset category, three activities tied in the fourth place. This led to the subsequent six activities being transferred to the next Delphi round: promote mindset change, failure culture, promote constant innovation, think visionary, decentralize control and ensure freedom.

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Culture & Mindset category voting results

12 10 8 6 4 2

Ensure adaptability

Think lateral

Challenge current tasks

Think lean

Think out of the box

Ensure freedom

Decentralize control

Think visionary

Promote constant innovation

Failure culture

Promote mindset change

0

Figure 17: Culture & Mindset category voting results (4) Finally, the participants selected the activities provide knowledge on data analytics, AI, machine learning and data architecture and domain specific application to be proceeded to the next round. Technical category voting results

Chatbots

Data mining

Blockchain

Technologies in general

Data architecture

Machine learning

Domain specific application

AI

Data analytics

18 16 14 12 10 8 6 4 2 0

Figure 18: Technical category voting results

4.3

Third Delphi round – Ranking of activities by importance

The third round results show that the participants prioritized managerial activities such as leadership support and understanding customers, followed by activities such as removing organizational obstacles, providing insights on customers, and creating a

146

Findings

failure culture, as well as processual and methodological activities like the support of prototyping and establishing a process for data-driven service innovation. Although many technical activities were mentioned as important and discussed during the initial rounds, these occupied the five lowest positions here. Top ten activities were ranked as such by at least 64% of the participants, and technical activities were almost completely absent. Among technical activities, only domain-specific application gained a mention in the top ten (ranked 6th by a single participant) while the rest completely failed to reach high rankings. Table 17 reports these rankings, including average rank and inclusion in the top ten. In the present case, the top five activities achieved strong consensus, with a value of 0.73 and the top 10 activities were put there by a majority of the participants. However, a value of 0.41 for Kendall’s W indicated weak to moderate group consensus on all activities (Schmidt, 1997; Okoli et al., 2004). Throughout the prioritization of the tasks, at least 63.6 % of the participants voted the top 10 activities within the top half of their individual list, thus indicating a consensus across the participants (Schmidt, 1997). Table 17: Overview on activities and their ranking Rank

Activity

Cat.

1

Leadership support

(1)

Avg. Rank 4.2

Ranked in top 10 by 82.0%

2

Understand customers

(1)

4.7

90.9%

3

Remove organizational obstacles

(2)

6.4

82.0%

4

Provide insights on customers

(1)

7.3

63.6%

5

Failure culture

(3)

7.4

81.8%

6

Support prototyping

(2)

8.2

72.7%

7

(2)

8.9

72.7%

8

Establish process for data-driven service innovation Think visionary

(3)

9.0

63.6%

9

Enable feedback loops

(2)

9.7

72.7%

10

Decentralize control

(3)

10.1

63.6%

11

Promote mindset change

(3)

10.5

45.5%

12

Analyze value of solution

(1)

10.9

54.5%

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13

Ensure freedom/Think out of the box

(3)

11.1

45.5%

14

Decide on market launch strategy

(1)

12.4

36.4%

15

Promote constant innovation

(3)

13.5

36.4%

16

Establish innovation lifecycle

(2)

14.7

27.3%

17

Machine Learning

(4)

15.3

0.0%

18

Data analytics

(4)

15.7

0.0%

19

Domain specific application

(4)

16.2

9.1%

20

Data architecture

(4)

17.1

0.0%

21

AI

(4)

17.7

0.0%

4.4

Synthetization of results

The synthetization of the results, as described in 3.2, yielded nine distinct roles describing the activities of individual actors. These are: (1) The customer expert provides deep knowledge of the customer and his needs throughout the data-driven service innovation process, based on research activities and direct and continuous interaction with the customer. (2) The supporting manager ensures top management support for establishing a failure culture and the freedom of other actors to unleash their creativity and think “out of the box.” (3) The innovation enabler establishes processes that balance product and service innovation and promotes the constant pursuit of innovation to ensure adaptability throughout an appropriate lifecycle. (4) The bridge builder contributes a deep understanding of the organizational environment and removes any obstacles that might prevent collaboration at intra- and inter-organizational level. (5) The prototyper establishes and implements prototyping methods to assess the feasibility of the innovated solution(s), enabling iterative feedback loops and setting suitable timeframes for prototyping.

148

Findings

(6) The strategic operationalizer puts the innovation into action, decides how solutions are advanced to the next process step and devises market launch strategy. (7) The mindset visionary identifies current market trends for vision delivery and promotes mindset change to facilitate innovation of data-driven service. (8) The technical expert provides the required technical knowledge on AI, machine learning and other technologies across the entire process of data-driven service innovation and assesses the technical feasibility of the new solution(s). (9) The t-shaped expert links the insights delivered by the technical expert to domainspecific applications to ensure correct data interpretation for appropriate solutions that offer additional value to the customer. As a next step, these roles were classified as supporting the development of ordinary or dynamic capabilities. In the latter case, actor roles related to sensing, seizing and transforming capabilities for service innovation (see figure 19). Roles supporting the development of dynamic capabilities Sensing (1) The customer expert

Seizing

Transforming

(2) The supporting manager

(3) The innovation enabler (4) The bridge builder

(7) The mindset visionary

(5) The prototyper (6) The strategic operationalizer Roles supporting the development of ordinary capabilities

(8) The technical expert

(9) The t-shaped expert

Figure 19: Connection of roles to ordinary and dynamic capabilities Here, the technical and the t-shaped expert roles are regarded to support the development of ordinary organizational capabilities. While the activities of the technical expert refer to the provision of knowledge on IT-related technologies and their

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application, the t-shaped expert connects insights from e.g. data analysis to domainspecific knowledge of specific use cases. Both roles are highly operational and have the potential to be outsourced to external service providers rather than residing within the organization. However, not all of the identified roles should be outsourced to external service providers, but reside within the organization to support the development of organizational dynamic capabilities. The connection of roles to dynamic capability development can be characterized as follows: the customer expert senses new opportunities in the market through direct interaction with the customer and research on their needs. In seizing identified opportunities, the bridge builder, the prototyper, and the strategic operationalizer support dynamic capability development by dismantling organizational barriers to facilitate reconfiguration of existing resources, parallel prototyping of multiple solutions, and timely market introduction. The supporting manager and the innovation enabler can be assigned to a dual role of seizing and reconfiguring. They provide the freedom and structures to seize opportunities and reconfigure the organization by implementing a new culture of ongoing innovation that encourages employees to try new things. Finally, the mindset visionary is mainly responsible for reconfiguring the organization by defining a vision for the whole organization, shaping the future mindset and supporting the realignment of organizational assets to pursue sustainable competitive advantage.

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5

Discussion

Discussion

This study sheds light on the individual actors, roles, and activities involved in datadriven service innovation in relation to traditional functions. The findings indicate a strong focus on managerial activities rather than technical knowledge. Synthetization revealed nine actor roles and associated ordinary and dynamic organizational capabilities. The study at hand extends existing research on actor roles during co-creative datadriven service innovation (Breidbach & Maglio, 2016; Aarikka-Stenroos & Jaakkola, 2012; Storbacka et al., 2016) by identifying roles of actors at a micro-level and connecting these to the higher-level phenomenon of dynamic capabilities (Felin et al., 2012; Argote & Ren, 2012). The present findings consolidate earlier evidence that actors from internal departments such as innovation management, R&D, management and IT, as well as customers, play a vital role in data-driven service innovation (Kindström & Kowalkowski, 2014, Jonas et al., 2016). The findings emphasize roles that do not align completely with organizational functions or their assumed importance. For instance, the roles deemed most important relate to facilitating leadership support for a culture that allows for failure and fully exploits knowledge of customer needs and their understanding. Formal organizational functions such as management and sales were considered less important than the activities they perform in the context of data-driven service innovation – in other words, co-creative actors’ roles in data-driven service innovation are characterized by the specific activities they perform rather than by their formal organizational designation (Breidbach & Maglio, 2016). This emphasis on roles (Ekman et al., 2016) rather than formal organizational functions (Breidbach & Maglio, 2016) reflects how dynamic environments require actors to change their role to facilitate fruitful co-creation activities such as data-driven service innovation (Edvardsson et al., 2011). The roles described here are not executed by single actors alone, and individual actors can perform multiple roles as their environment changes (Breidbach & Maglio, 2016). For example, the roles of the supporting manager and mindset visionary can (but need not to) be performed by one

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actor. The relevance of actor roles that support dynamic capabilities serves to clarify how organizations can achieve sustainable competitive advantage through dynamic capabilities development that shape the organizational mindset and culture (Teece, 2007). In particular, the importance of strategic and managerial activities shows that data-driven service innovation relies on the development of dynamic capabilities. This is supported by the perceived lower importance of technical activities such as application of AI, data analytics, machine learning, or the connection to domainspecific knowledge. As ordinary capabilities that are imitable and cannot ensure sustainable competitive advantage, these operational functions can readily be outsourced to external service providers for being able to focus on an organization’s core competencies (Teece, 2017). The lesser importance of technology in exploiting new service opportunities serves as a reminder that data-driven service innovation presents partially the same challenges as service innovation in general. However, they are gaining in complexity through the utilization of data. As long as organizations do not promote a serviceoriented mindset through top management (Oliva & Kallenberg, 2003; Gebauer et al., 2005) and establish suitable internal processes for service innovation (Neely, 2008; Martinez et al., 2010), an engagement with mainly data-driven service innovation related challenges can be impeded. In such cases, the deeper focus on technological issues becomes more difficult, as does the development of appropriate dynamic capabilities that are relatively inimitable (Teece, 2017). Roles such as the technical or t-shaped expert could be furthermore regarded as ordinary due to their incremental and expected nature. Both of them just provide knowledge on an operational level. In contrast, roles that support dynamic capability development show characteristics of being more emerging and radical (Heikkinen et al., 2007; Vargo et al., 2008). This can be exemplified by the mindset visionary that has the ability to act radical and emerging due to his role to deliver visions, by the customer expert that can act unexpectedly on novel demands from customers or the strategic operationalizer that creates his role throughout data-driven service innovation which has not to be in line with the expectations of others. An explanation for the underrepresentation of roles that support sensing activities could be that the customer

152

Discussion

acts as an active innovator during data-driven service innovation, thus lowering the demand for further sensing capabilities beside of the customer expert. Finally, the study’s findings confirm the importance of integrating actors and microlevel activities in order to develop higher-level dynamic capabilities (Felin et al., 2012; 2015) for data-driven service innovation. Current research is extended through concrete descriptions of individual roles and activities to support the development of such capabilities (Argote & Ren, 2012; Felin et al., 2012; 2015) helps to achieve sustainable competitive advantage by doing the right things rather than just doing things right (Teece, 2017).

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Summary and implications

This Delphi study identified the roles of individual actors in data-driven service innovation and the capabilities required, specifying their relative importance as perceived by a panel of selected experts. This study identified nine roles and linked them to the concept of dynamic capabilities. It showed how micro-level activities help to build higher-level dynamic organizational capabilities. It showed that actor roles during the innovation of data-driven service can support both ordinary and dynamic capability development. While roles incorporating technical knowledge and their domain specific application have the potential to be outsourced to external service providers, due to their perceived lower importance, strategic and managerial roles that shape an organizations mindset support the development of dynamic capabilities. The study emphasizes that multiple roles can be taken by single actors and that the identified roles go beyond static and formal organizational roles that were perceived less important than the activities they perform.

6.1

Managerial implications

From a managerial perspective, the findings help organizations to define the roles and activities of those involved in data-driven service innovation. It supposes that managers that aim to implement data-driven service innovation should put a focus on activities shaping the organizational mindset and culture rather than on putting too much effort into technical activities that can be easily carried out by partners in the service system. By emphasizing the development of dynamic capabilities, managers can build competitive advantage through data-driven service. This study suggests to mangers that the sole consideration of organizational functions during the composition of data-driven service innovation teams could be misleading. Teams for data-driven service innovation could be constructed on the basis of the roles and activities identified in this study, rather than adhering to formal and often static organizational functions.

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6.2

Summary and implications

Theoretical contribution and outlook

This study extends current research at the intersection of data-driven service innovation and dynamic capabilities. It contributes to the current discussion on the impact of data on service provision and its innovation through the establishment of archetypal roles of individual actors during this process. The findings of this study show that mainly managerial and strategic roles are regarded as important during data-driven service innovation in contrast to technical roles that were regarded as less important. The study opens up some interesting venues for future research in the developing field of data-driven service innovation through the identified roles that can have the potential to set a starting point for further investigations on an individual level. Consequently, the identified actor roles and capabilities should be investigated and refined, using a combination of qualitative and quantitative methods to validate this study’s findings. Additional insights from in-depth exploration of the capabilities that organizations have built would help to advance our understanding of how dynamic capabilities build competitive advantage in rapidly changing environments. Beyond these timely contributions, this Delphi study has certain limitations that need to be considered. In particular, the composition of the expert panel limits the representativeness of the findings. Although diverse in terms of industry and background such as engineering, ICT, manufacturing or IT that should cover a variety of actors within the service system for data-driven service innovation, the participants provide only an internal perspective on data-driven service providers and not the customer perspective. Furthermore, the expert panel composition does not only limit the representativeness of the findings, but also effects the findings of the study. A more heterogeneous composition might have led to other roles. Different cultural context and diverse educational background might have resulted in different activities and roles for data-driven service innovation. Beyond the inside view, future research could explore the whole service system around providers of data-driven service. This may reveal additional roles of relevance to data-driven service innovation, encompassing external actors such as customers, suppliers, and research partners or others and assess if they are needed for data-driven

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service innovation. Furthermore, future research could investigate specific data utilization triggered aspects rather than taking a broad view on the phenomenon of data-driven service innovation as in the present study. Another way to extend this work could be the exploration of different contexts. This means that future studies could focus on certain industries or business-to-business or business-to-customer datadriven service innovation to gain additional knowledge on roles and required dynamic capabilities. Finally, future studies may investigate the finding that technical aspects are assigned with a relatively low priority. It would be interesting, for example, to determine whether this rests on the assumption that technical issues can be more easily mastered during data-driven service innovation or whether it reflects deficiencies in dynamic capabilities for organizational transformation.

Part VI Summarizing findings and implications © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 M. Schymanietz, Capabilities for Data-Driven Service Innovation, Markt- und Unternehmensentwicklung Markets and Organisations, https://doi.org/10.1007/978-3-658-31691-4_6

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Introduction

O what a journey, what an amazing journey! (Jules Verne, 1864) The final part summarizes the prior findings and implications. This shall support their synthetization towards a dynamic capability framework for data-driven service innovation that connects the aspects that evolved during this research journey. Furthermore, limitations and venues for further research are outlined to determine this dissertation. The remainder of part VI follows the subsequent structure: chapter 2 summarizes to findings and implications from parts I to V. After a summary of the relevance of the phenomenon under investigation (part I) and the theoretical background (part II), the recapitulation of parts III, IV, and V brings back the single building blocks that shall build a deeper understanding of dynamic capabilities for data-driven service innovation. These are (1) the definition of a data-driven service, (2) resources, (3) actors and roles, (4) barriers and challenges, (5) ordinary capabilities, and (6) dynamic capabilities. Based on these building blocks, chapter 3 synthesizes these into a dynamic capability framework for data-driven service innovation that depicts the interplay between micro- and meso-level during organizational capability development and how these can be used to overcome the identified barriers and challenges. Chapter 4 will provide an overall discussion, implications, show up the limitations of this dissertation and provide some prospective venues for further research on the topic. Ultimately, chapter six ends up this part of the research journey with final considerations on the phenomenon under investigation. The structure of this final part can be obtained from figure 20.

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Introduction

Part I: Introduction

Part II: Theoretical background Part III: Systematic literature review and expert interviews

1 Introduction  Objectives of part VI  Structure of part VI

2 Summary of parts I-V  Executive summaries of the prior parts of this dissertation

3 Synthetization of results  Establishment of a dynamic capability framework for datadriven service innovation

Part IV: Multiple case study

Part V: Delphi study

Part VI: Summary of findings and implications

4 Discussion, implications and further research  Discussion and theoretical implications  Managerial implications  Venues for further research

5 Final considerations  Final reflection on the dissertation

Figure 20: Structure of part VI

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Summary of parts I – V

Chapter 2 will briefly summarize the prior parts I to V with the goal to bring to mind what has already been found and discussed. It can be also viewed as an executive summary of the whole dissertation. Summary of part I During the first part of this dissertation, the relevance of the phenomenon under investigation was highlighted. It was emphasized that today’s digital economy – which strongly relies on the utilization of collected data from a large variety of sources – does not only require from organizations the possession of rare, valuable, imperfectly imitable and non-substitutable static resources for achieving competitive advantage, but certain ordinary and dynamic capabilities that enable them to cope with today’s fast changing environment. This phenomenon was exemplified by the description of the cases Fujifilm and Kodak, where the first case company was able to develop the required dynamic capabilities, while the second one failed, and the Otto Group. The example of Apple and the oeuvre of Steve Jobs added to this end through the call for a microfoundational view that puts a focus not only on organizations, but on individual actors as well, due to their ability to shape whole organizations. Subsequently, chapter 2 gave a first glimpse into the theoretical background of this dissertation. It gave a brief introduction into the concepts of service (Vargo & Lusch, 2004; Lusch & Nambisan, 2015), service systems (Spohrer et al., 2007), and dynamic capabilities (Teece, 2017; 2018). In a next step, the microfoundational view on higher-level phenomena such as dynamic capabilities was introduced to stress the importance of the micro-level for the development of higher-level dynamic capabilities (Felin et al., 2012; Felin & Hesterly, 2007; Felin et al., 2015). Then, chapter 3 gave an overview on the structure of this dissertation and the following research questions that were investigated during the dissertation: RQ1: What defines and characterizes data-driven service innovation, and what differentiates it from non-data-driven servitization and service innovation?

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Summary of parts I – V

RQ2: What capabilities and dynamic capabilities are required for data-driven service innovation, and are these context-specific? RQ3: Which internal and external actors are involved in data-driven service innovation? RQ4: What challenges influence collaboration among those actors during data-driven service innovation? RQ5: What roles of individual actors are relevant and support the development of dynamic organizational capabilities during data-driven service innovation? Summary of part II Subsequently, part II gave an overview on the theoretical groundings of this dissertation. After a brief summary on the structure and objectives of this part during chapter 1, the concepts of services, service and service innovation were introduced in chapter 2. In comparison to physical goods, services can be characterized as intangible, heterogeneous, inseparable and perishable (Parasuraman et al., 1985; Vargo & Akaka, 2009), and can be regarded as a unit of output. On the other hand, service requires the integration of multiple actors that integrate their resources for the benefit of themselves or others (Vargo & Lusch, 2004). Service innovation is a multi-dimensional process that can be referred to a novel offering that was prior not available to an organizations customer and demands for the reconfiguration of the applied competences by customers and/or service providers (Ordanini & Parasuraman, 2011). Afterwards, they were set in the context of the digital age, which enables a simpler resource exchange and integration through ICT during value co-creation activities across the involved actors (Barret et al., 2015). It was emphasized that the successful innovation of service requires the development of organizational capabilities (Opresnik & Taisch, 2015). Chapter 3 gave an overview on how organizations can achieve competitive advantage. While the sole possession of resources was accountable for being successful in the past, this RBV (Barney, 1991) lacks to acknowledge today’s fast changing environment. Here, the concept of dynamic capabilities was introduced that enables organizations to deal with unforeseeable market environments, not through the sole possession of valuable, rare, inimitable and non-substitutable resources as proposed by

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the RBV, but based on the integration, development, and reconfiguration of competences throughout the organization (Barney, 1991; Eisenhardt & Martin, 2000; Teece et al., 1997). These dynamic capabilities can be divided into three categories: sensing (activities to search its technological and market environment to learn from it), seizing (the management of technologies and complementary assets for being able to introduce new offerings to customers) and reconfiguration activities (the continuous restructuration of organizational assets and structures) (Teece, 1997, 2007, Eisenhardt & Martin, 2000; Eisenhardt, 1989a; Pisano, 1994). Afterwards, these categories and its microfoundations were explained. Chapter 4 connected the concept of service innovation to dynamic capabilities and showed how sensing, seizing and reconfiguring are influenced through the specifics that come along with service provision and the use of ICT. Summary of part III Part III contains the first study of this dissertation. The first chapter aimed to give an introduction into the field of service innovation and connects it to current developments that are caused by digitization. Chapter 2 introduced the theoretical background for this part. It put a focus on data-driven service innovation and connected the concepts of service innovation and dynamic capabilities under consideration of digitization. Chapter 3 gave an overview on the methods used during this study. First, it described the principles of carrying out a SLR. The objective of a SLR is to densify current academic literature in a certain research domain to provide a view on the current state-of-the-art. This approach allows the researcher to remove peculiarities of single studies through the investigation of a large number of academic publications and follows a structured approach that starts with searching of scientific databases, and concludes with the conduction of a backward and forward search (Webster & Watson, 2002; Petticrew & Roberts, 2006). Afterwards, it showed how the SLR was carried out (showing search strings, selection procedure and coding of emerging categories). Second, a focus was laid on the conduction of the expert interview study method. An expert interview study aims to investigate a phenomenon through the

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incorporation of experts in a certain field that possess some specific knowledge from an active participation in activities connected to the phenomenon (Meuser & Nagel, 2009). Here, ten experts from different industries were interviewed to investigate their views on what a data-driven service is and what characterizes its innovation. Finally, the synthesis of both approaches, that aimed to get an academic and practical view on the innovation of data-driven service, was described. Chapter 4 illuminated the findings and presented a synthesized definition of a data-driven service based on the concepts reviewed during the literature review and the insights from the expert interviews: “a data-driven service uses real-time and remote data from connected devices as a key resource for digital delivery of co-created, high-value solutions to the customer”. It furthermore, showed and explained eleven characteristics that should be considered during the innovation of data-driven service. These are (1) external collaboration; (2) internal collaboration; (3) human IT resources; (4) customeroriented culture and strategy; (5) data-oriented culture and strategy; (6) data access, collection, and ownership; (7) revenue models; (8) resource recombination; (9) standardization; (10) data privacy; and (11) top management support. In a subsequent step, these findings were linked to organizational dynamic capabilities that should be developed for being able to successfully innovate data-driven service. Then, chapter 5 put this parts’ finding into the context of current academic literature through a discussion and showed the implications of this study. Data-driven service innovation adds an additional layer of complexity on service innovation, especially through data-related aspects. These are for example the need for skilled IT-employees, the establishment of a data-oriented culture, questions about data access, collection, ownership, security, privacy, and standardization as well as the necessity of the implementation of novel revenue models that consider the specifics of service provision that bases on the utilization of data. This part also showed up prospective venues for future research that could focus on the investigation of collaboration across multiple actors during data-driven service innovation activities, the long-term repercussions of data-rich environments, the

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establishment of suitable revenue models and the roles of individual actors during this process. The final chapter concluded this part with a summary and implications. Summary of part IV Part IV of this dissertation started with a brief description of the objectives and provided an introduction to service innovation in context of the transition of manufacturing organizations that can be characterized as formerly product-centric, thus not having huge experiences in this certain field. This part of the dissertation aimed to identify both actors involved in the innovation of data-driven service and challenges that occur during inter- and intra-organizational collaboration. Following this, chapter 2 set the stage with the theoretical background that underlies this part. It first gave an overview on collaboration in data-driven service innovation in manufacturing. It highlighted the importance of collaboration for co-creation activities due to the inability of single organizations to utilize the required resources on their own (Goes & Park, 1997; Kindström & Kowalkowski, 2014; Lusch & Nambisan, 2015). This chapter divided collaboration into intra- and inter-organizational aspects to illustrate that collaboration has an impact inside and outside an organization (Blomqvist & Levy, 2006) and subsequent shows the influence of ICT on it (Schüritz & Satzger, 2016). Second, this chapter showed challenges for collaboration in service innovation that were already discussed in current literature. Challenges mainly occur in the following four fields: (1) organizational processes and structures, (2) organizational strategy and culture, (3) design of market-oriented offerings, and (4) value co-creation among the involved actors (Alghisi & Saccani, 2015; Schüritz et al., 2017a; Baines et al., 2017; Story et al., 2017). These challenges imply that service innovation asks for the governance of a variety of internal and external factors to be regarded for being able to deliver value to the customer. Chapter 3 presented the used case study method that aims to investigate a contemporary phenomenon in its real-life context (Yin, 2018). Conducting a case study enables the researcher to take a closer look on phenomena through the investigation of analytic unites and empirical evidence, what makes the conduction of this method suitable to research evolving fields such as data-driven service innovation (Eisenhardt,

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1989b; Eisenhardt & Graebner, 2007; Yin, 2018). Here, an exploratory multiple case study (Yin, 2014) with organizations from the German manufacturing industry was carried out. For this purpose, multiple focus groups interviews were conducted (Wilkinson, 2004; Duggleby, 2005). It also gave insights on the data analysis that was carried out under consideration of the ARA model, being a conceptual framework that focuses on outcomes of interaction processes across three layers: (1) actors, (2) resources, and (3) activities (Håkansson & Johanson, 1992; Håkansson & Snehota, 1995). In chapter 4, the findings of this study were described in detail. It was shown that internal organizational departments such as legal, innovation management, service, internal IT, sales, research & development, engineering and product management were assigned with a high importance. Here, the only external actor that was assumed to be equally important were customers. Afterwards, the findings show that the innovation of data-driven service is challenged by aspects on an intra- and inter-organizational level as well as by underlying aspects. On an intra-organizational level challenges occur in the following fields (1) data storage in distributed silos that are inaccessible; (2) lack of established processes; (3) lack of an organizational strategy; and (4) traditional product-centric mindsets that shape the organizational culture. On an interorganizational level, the challenges can be divided into three fields: (1) the need for rules for collaboration (especially with regard to the distribution of potential revenues); (2) data ownership and location of data storage; and (3) prevailing restrictions in respect to knowledge sharing. Finally, there were also some underlying issues discovered that affect collaboration. These are: (1) missing standards for data exchange and compatibility; (2) data privacy issues; (3) unclear legal regulations for data exchange; and (4) a lack of human resources with the required IT skills. Chapter 5 picked up the findings from the prior part and put them into the context of current literature in this field. The discussion showed that the findings extend current research on service innovation through data-related aspects such as challenges arising from data collection and utilization (Alghisi & Saccani, 2015; Schüritz et al., 2017a; Baines et al., 2017; Story et al., 2017) and how they can be overcome through dynamic capability development. Furthermore, the range of actors involved in data-

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driven service innovation extends the current set of actors that need to collaborate closer than before. Finally, the chapter closed with an overview on the theoretical and managerial implications. Summary of part V The final study of this dissertation put a focus on individual actors and their roles during data-driven service innovation and was presented in part V. After an introduction to the phenomenon under investigation, a description of the influence of ICT on service innovation and a first glimpse on the importance of individual actors for organizational capability development, the objectives and structure of this part were depicted in chapter 1. Chapter 2 offered insights on the theoretical background of this part and dealt with individual and organizational capabilities during service innovation. While the integration of a variety of different actors is important to cocreate value during service innovation activities, the investigation of individual actors’ resources and capabilities on a micro-level is a fruitful pathway for the inquiry of higher-level phenomena such as organizational dynamic capabilities (Felin et al., 2012, 2015, Storbacka et al., 2016). Individual actors shape – in sum – whole organizations and the multi-dimensional nature of service innovation requires the interaction of a variety of actors that integrate their resources. Consequently, capabilities rely on the involved individual actors in an organization that shape and vitalize the development of organizational dynamic capabilities. To support an organization’s competencies for innovation, it is required to develop the capabilities of individual actors (van Kleef & Roome, 2007). Chapter 3 put a focus on the used research method. In this case, a modified Delphi method, the group Delphi, was used to achieve a consensus across a group of experts in the field of data-driven service innovation (Webler et al., 1991). In general, the conduction of the Delphi method aims to reach consensus across a panel of experts in a specific domain. A Delphi study typically consists of a series of surveys that allow feedback in-between the survey rounds (Linstone & Turoff, 1975). The modified group Delphi method keeps all of the characteristics of a classic Delphi study (such as iterative rounds with feedback possibilities, group judgements and the possibility to revise former statements), but removes anonymity to achieve higher seriousness and

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responsibility through interaction among the participants (Webler et al., 1991; Landeta et al., 2011). This chapter showed the Delphi study’s procedure that was carried out in three rounds: (1) exploration, (2) selection and (3) ranking. Afterwards, the results from the three Delphi rounds were synthesized to derive roles of individual actors as well as to classify roles in respect to ordinary capabilities and dynamic capabilities. Chapter 4 provided an overview on the findings from the Delphi rounds and from the synthetization. During the first Delphi round, the participants were questioned to select organizational functions and external actors that play a role during data-driven service innovation. The participants mentioned innovation management, R&D, customers, general management and the internal IT department as being actively involved. However, 86 % (19/22) of the participants indicated that most activities could be carried out internally, external actors such as universities and research partners received relatively few mentions. Furthermore, the participants were asked during the first round to mention activities that should be performed during data-driven service innovation. This led to 51 essential and unique tasks that were coded into four categories: (1) Managerial, (2) Processes & Methods, (3) Culture & Mindset and (4) Technical. These set the basis for further investigation during the upcoming Delphi rounds. During the second round, the participants were asked to identify key activities from the four categories. This led to a total of 21 activities containing exemplary activities as leadership support, failure culture, the promotion of constant innovation or the provision of knowledge on machine learning, data analytics or AI. During the third Delphi round, the key activities were ranked by the participants, leading to the following five in the top spots: leadership support, understand customers, remove organizational obstacles, provide insights on customers and failure culture, while mainly technical activities were ranked on the last places. Finally, the synthetization of the results of the three Delphi rounds resulted in the derivation of nine individual actor roles that base on the activities: (1) the customer expert, (2) the supporting manager, (3) the innovation enabler, (4) the bridge builder, (5) the prototyper, (6) the strategic operationalizer, (7) the mindset visionary, (8) the technical expert and (9) the t-shaped expert. These roles were then connected to their ability to support the development of both ordinary and dynamic organizational capabilities. Here, the technical expert and the t-

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shaped expert role were assigned to ordinary capabilities due to their highly operational nature and their potential to be outsourced easily to external service providers. The other roles were assigned to support the development of dynamic capabilities as follows: the customer expert supports sensing capabilities; the bridge builder, the prototyper and the strategic operationalizer seizing capabilities; the supporting manager and the innovation enabler both seizing and transforming capabilities; the mindset visionary transforming capabilities. Chapter 5 connected the insights from the findings of the previous chapter to current literature through a discussion. The study supports prior findings that formal organizational roles do not correspond to the activities that need to be carried out and recommends a focus on activities rather than on formal structures. It was furthermore shown that especially strategic and managerial activities are regarded as highly relevant in contrast to technical activities. Throughout the final chapter 6, the implications from this study on theory and managers were presented. This study extends research on organizational capabilities through the application of a micro-level perspective, provides mangers with insights on roles that should be carried out during the innovation of data-driven service and serve as an additional layer of evidence for the derivation of capabilities for data-driven service innovation.

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Synthetization of results

Synthetization of results

This chapter has the overall goal to synthesize the findings and implications from parts III, IV and V towards an integrated framework on dynamic capabilities for data-driven service innovation. The findings and implications from the prior parts can be divided into the following categories: (1) the definition of a data-driven service (part III), (2) resources (part III), (3) actors & roles (parts IV and V), (4) barriers and challenges (parts III and IV), (5) ordinary capabilities (parts III and V), and (6) dynamic capabilities (parts III and V) (see figure 21).

Findings & implications of part III     

Data-driven service definition (1) Resources (2) Barriers (4) Ordinary capabilities (5) Dynamic capabilities (6)

Findings & implications of part IV  

Actors involved (3) Challenges (4)

Dynamic capability framework for datadriven service innovation

Findings & implications of part V   

Actor roles (3) Ordinary capabilities (5) Dynamic capabilities (6)

Figure 21: Findings and implications contributing to the dynamic capability framework for data-driven service innovation An overall summary on findings and implications from the prior parts of this dissertation can be obtained from table 18. It shows the six categories of findings, connects the categories of the findings and implications and highlights on which part(s) of the dissertation these are based on.

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Table 18: Overall summary on findings and implications Category

Findings & Implications

Based on

(1) Definition of a datadriven service

Synthesized definition of a data-driven service that provides a common understanding of the phenomenon under investigation

Part III

(2) Actors

Identified actors, both internal and external Actors with high importance: legal, innovation management, service, internal IT, sales, research & development, engineering and product management, customers Actors with medium importance: purchasing and marketing departments, internal software developers, data service providers, suppliers Actors with low importance: universities or research partners, competitors, customers, regulators, trade associations, controlling department, other national subsidiaries, parent organization Derived roles of individual actors during the innovation of data driven service (1) The customer expert, (2) The supporting manager, (3) The innovation enabler, (4) The bridge builder, (5) The prototyper, (6) The strategic operationalizer, (7) The mindset visionary, (8) The technical expert and (9) The t-shaped expert

Parts IV and V

(3) Barriers and challenges

Barriers Data privacy and standardization Challenges Challenges for internal collaboration: data silos, uncoordinated processes, organizational strategy and culture Challenges for external collaboration: rules for collaboration, data location and ownership, knowledge sharing Underlying issues: Standards, data privacy, legal regulations, IT-competencies

Parts III and IV

(4) Resources

Data and human IT-resources

Part III

(5) Ordinary capabilities

Resource recombination, revenue model

Parts III and V

(6) Dynamic capabilities

External collaboration, internal collaboration, customeroriented culture and strategy, data-oriented culture and strategy, top management support

Parts III and V

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3.1

Synthetization of results

Towards a dynamic capability framework for data-driven service innovation

The starting point to data-driven service innovation is the availability of data and its utilization as a resource. This requires from organizations to collect, store, analyze and interpret data through the application of artificial and human IT-resources. For being able to integrate data and human IT resources, organizations should develop dynamic capabilities for being able to cope with the phenomenon of data-driven service innovation. Here, it is highlighted that the development of organizational ordinary and dynamic capabilities does not only happen on an organizational meso-level. It is emphasized that individual actors and their activities – that can be described through roles on a micro-level – play an important role as well. Throughout the capability development, individual actors apply their skills, knowledge, characteristics, experiences, cognitions, and abilities 0n this micro-level. They support resource integration throughout the organization and shape the development of collective phenomena such as organizational capabilities. While ordinary capabilities are highly operational and can be outsourced to external service providers, dynamic capabilities are characterized through a strategic nature and build the essence for an organization’s desired competitive advantage. The development of organizational capabilities can help organizations to overcome barriers and challenges that occur during the process of data-driven service innovation. These barriers and challenges can be on an intra- and inter-organizational level and are influenced by underlying issues, thus covering the whole service system. Exemplarily, the development of dynamic capabilities for the reconfiguration of the organization’s strategy and culture towards a data-oriented one as well as new modes of internal collaboration in value co-creation activities can help organizations to face challenges. These can be emerging data silos, the demand for rules for collaboration, knowledge sharing and clarity about data location and ownership. Emerging challenges can be faced through the development of dynamic capabilities for external collaboration, dataand customer-oriented strategies and top management support (see figure 22).

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help to overcome

Organizational level Ordinary Dynamic capabilities capabilities

Barriers and challenges

shape

InterIntraorganizational organizational

Roles of individual actors

Underlying issues

Data-driven service innovation

Micro-level help to overcome

Figure 22: Dynamic capability framework for data-driven service innovation The synthesized dynamic capability framework for data-driven service innovation shall give an overview on both the micro-level as well as on the organizational level that empower organizations to overcome barriers and challenges during data-driven service innovation. In other words, data-driven service innovation can be seen as an outcome of the application of ordinary and dynamic capabilities through individual actors during their value co-creation activities that allow the integration of their resources. This framework can be put into life through the consideration of the involved individual (from part V) and organizational actors (from part IV) and how they can help to develop organizational dynamic capabilities (part III and V) to overcome barriers (part III) and challenges (part IV) during data-driven service innovation.

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Discussion, implications and further research

Discussion, implications and further research

4.1

Overall discussion and theoretical implications

To shed light on the phenomenon under investigation, the dynamic capability framework for data-driven service innovation provides an additional perspective to current research in this field. The framework highlights that the implementation of data-driven service innovation relies on the development of organizational capabilities that help to overcome emerging barriers and challenges on different levels (i.e. on an intra- and inter-organizational level as well as on an underlying one). Instead of just emphasizing the organizational level, the framework furthermore puts the micro-level and individual actors and their roles into the focus that set the basis for dynamic capability development on higher levels. Through the synthetization of multiple concepts that deal with the utilization of data for service provision, this dissertation offers a definition of a data-driven service and connected characteristics of its innovation. This helps to advance current research through the clarification of the specifics of a data-driven service and offers the basis for further investigations of this phenomenon. In the context of data-driven service innovation, the framework extends research on actors and their roles during service innovation through the focus on the micro-level (Breidbach & Maglio, 2016; Aarikka-Stenroos & Jaakkola, 2012; Storbacka et al., 2016; Felin et al., 2012; 2015). It helps to gain a deeper understanding of the distinction between formal organizational functions and individual roles that do not necessarily need to align (Breidbach & Maglio, 2016) and are extended through a set of actors with IT-specific knowledge (Jonas et al., 2016). The framework and its underlying assumptions add to current discussions in this field through the provision of archetypal roles of actors that are regarded as important during co-creative data-driven service innovation activities. This extends prior research (e.g. Breidbach & Maglio, 2010), through the discovery of concrete roles and an emphasis on the importance of strategic roles that support dynamic capability development rather than roles that foster

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ordinary capability development. These roles and their connected activities can help to gain a deeper understanding of the process of service innovation that is influenced by the utilization of data for service provision through a micro-level view. The framework also emphasizes barriers and challenges that occur during datadriven service innovation. Here, the framework extends former research on service innovation through the emphasis on challenges and barriers that are strongly ITrelated such as data silos (Diamond et al., 2004; Parris et al., 2016) or the deeper necessity of customer integration into service innovation processes (Lusch & Nambisan, 2015; Schüritz et al., 2017a). In contrast to former research on challenges for innovation in digital contexts on a theoretical level (Holmström, 2018), this research extends current knowledge through the investigation of challenges together with actors that take part in data-driven service innovation. It shows an increase in issues that should be considered with a focus on the required collaboration across multiple internal and external actors. Especially this aspect serves as a reminder that traditional industry boundaries are continuously blurred and are developing towards a system of systems (Porter & Heppelmann, 2014). However, this dissertation contributes to a better understanding of how to manage the increase in complexity throughout the service system (Anke, 2018). Here, the research on dynamic capabilities in the context of data-driven service innovation extends research through the provision of an approach to face these barriers and challenges through dynamic capability development on a micro-level. Consequently, the framework highlights the connection between the development of the requisite dynamic capabilities that foster internal as well as external collaboration to overcome barriers and challenges. Finally, the identified dynamic capabilities that help to overcome challenges and barriers extend current research through specific data-related aspects. In addition to a service-oriented strategy and culture (Gebauer et al., 2005), dynamic capabilities should be developed that accommodate with these specific aspects through the establishment of a data-oriented culture and strategy. This approach shall ensure an alignment of already existing service and product strategies to the requirements of a data strategy. Important aspects in this context are data access, collection, ownership,

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security, privacy, and standardization that demand for new structures and dynamic capabilities for information sharing and knowledge exchange (Wünderlich et al., 2015).

4.2

Managerial implications

The investigation of dynamic capabilities for data-driven service innovation throughout this dissertation is accompanied by implications for management. First, the synthesized framework helps to understand the phenomenon of data-driven service innovation more thorough. It displays characteristics of data-driven service innovation that extend current ones that emerge during regular service innovation. The utilization of data for service innovation adds further complexity to the whole process and demands for a deeper collaboration in co-creation activities that take account of the specifics of data such as privacy and ownership. The framework underlines that challenges and barriers occur on both intra- and inter-organizational level. Here, management should raise awareness for difficulties that can occur on both levels. Second, the dissertation derives organizational ordinary and dynamic capabilities that help to overcome barriers and challenges for data-driven service innovation. The continuous sensing of opportunities and threats from data usage such as new technologies or regulatory issues can help organizations to identify new technical applications and alternating customer needs. Seizing these opportunities has the potential to create novel possibilities for data-driven service innovation. These can be supported by the definition of core competencies, thus deciding on what activities can be carried out inside the organization and what activities are just ordinary and can be outsourced. Furthermore, the framework raises awareness for the ongoing reconfiguration of the organizational strategy in regards to the changing environment through the development of dynamic capabilities. The dissertation emphasizes that management should implement a customer-oriented culture and strategy that regards the specifics of data usage. Being aware of the required development of organizational dynamic capabilities can help decision makers to put the focus on activities that help to support this reconfiguration, especially in environments without such a mindset and strategy.

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Third, the dynamic capability framework for data-driven service innovation highlights the importance of micro-level activities for shaping meso-level activities. In particular, managers are advised to put a focus on individual actors during the development of organizational dynamic capabilities rather than solely on organizational functions. The deployment of inter-disciplinary teams for data-driven service innovation can enable organizations to focus on the performance of relevant tasks. These can be carried out by a variety of actors with certain attributes rather than organizational functions such as IT, sales or innovation management. Furthermore, a focus on strategic rather than operational activities is proposed throughout this dissertation. Management should focus on the establishment of roles during datadriven service innovation that help to reconfigure the organization through strategic activities. It is indicated that technical activities, especially the ones connected to the provision of knowledge on AI, machine learning, data analytics and architecture as well as their domain specific application, should not be in the focus of the service provider.

4.3

Venues for further research

As all research, this dissertation has some limitations that will be described during this chapter. Taking these shortcomings into account, opens up possibilities for further research in this area. These limitations and venues for further research can be split up into two categories (1) methodological and (2) contentual. An overview on limitations and proposed further research can be obtained from table 19. Methodological limitations and venues for further research Considering the SLR in part III of this dissertation, the limitations arise from the subjectivity of the initial key word determination, the article selection criteria and the coding procedure of the findings. An extension of the keywords could have presented other results that are currently not represented. The article selection process strongly relies on the subjective assessment of the literature reviewer. Including other academic literature during the selection process could have added to the definition or the

178

Discussion, implications and further research

characteristic. During the coding procedure, the categories evolved inductively from the data, thus being strongly influenced by the literature at hand. The case study research method carried out in part IV of this dissertation limits the collected data. One main limitation of case study research can be referred to the generalization of the results. In this case, it provides only an inside perspective of a small selection of German manufacturing organizations. To take up this main limitation with the goal to provide more generalizable results, an extension of this part could put a focus on a broader set of organizations with a broader background, e.g. from different industries, or from different countries. Putting a focus on organizations that solely offer service and do not connect physical products and service could enhance this part. Part V is characterized by the usage of the Delphi method, leading to some limitations as well. First, the experts included in the panel limit the generalizability of the findings of this part. Even if this dissertation took care during the selection of the participants to cover a diverse range of industries and backgrounds, more variety in the composition could enhance the findings. Second, as it is typical for Delphi studies, the dropout rate was as high as 50 %. Reducing these high number or a larger panel to compensate such a dropout rate could improve the results. Based on these limitations, this part offers some other possibilities for further research. The identified actors and their roles could be investigated on a more detailed level. Here, both qualitative and quantitative research methods could be carried out to gain additional insights and validate our findings. This could also include to put a deeper focus on the repercussions of micro-level activities on higher-level phenomena such as dynamic capabilities. Additional research on how activities and roles of actors shape an organizations dynamic capabilities would be of high interest to understand how organizations can face the rather new phenomenon of data-driven service innovation in a successful way through the suitable capability development based on individual actors.

Part VI: Summarizing findings and implications

179

Table 19: Overview on limitation and proposed further research Limitation

Venue for further research

Based on

- Identification of other relevant key words - Inclusion of other relevant literature

Part III

Generalization of case study findings

- Extension of researched cases

Part IV

Selection of Delphi study participants

- Increase of variety in panel composition - Selection of participants with various backgrounds

Part V

(1) Methodological SLR subjectivity of - initial key word determination - article selection criteria - coding procedure

Delphi dropout rate Generalization of all parts

- Extension of participants - Verification of findings through quantitative approaches

Parts III, IV and V

Scope of identified characteristics

- Investigation of characteristics in respect to service systems and the embedded value cocreation activities

Part III

Conceptuality of findings

- Research on tools and concepts for data-driven service innovation

Part III, IV and V

Missing approaches for value assessment of data

- Exploration of possibilities to assess the value of data and suitable revenue models

Part III and IV

Internal perspective

- Extension through research from a service system perspective to cover all relevant actors

Part III and IV

Assessment of importance of data privacy and security

- Further investigation of impacts of data privacy Part III and security on data-driven service innovation and IV

(2) Contentual

180

Discussion, implications and further research

Surprising low importance of technical issues

- Examination of reasons for perceived lower importance of technical issues during datadriven service innovation

Aspects covered in synthesized framework

- Research on aspects that should be additionally Part VI included

Part V

Finally, a quantitative investigation of the identified characteristics, challenges for inter- and intra-organization collaboration roles and their ability to support dynamic capability development could also have the potential to show the influence of single aspects on the whole process. This approach has the effect to consider a large amount of organizations pursuing data-driven service innovation and to gain additional insights that support the generalizability of the synthesized findings from this dissertation. Contentual limitations and venues for further research Furthermore, various options for further research arise from the contentual limitations. First, it would be highly interesting to investigate the identified characteristics of datadriven service in respect to service systems (Bigdeli et al., 2017) that are characterized through resource integration from a variety of independent actors (Story et al., 2017), especially with a focus on the long-term implications (Troilo et al., 2017). Considering other actors than just the service provider has the potential to reveal other relevant roles and activities, especially the ones carried out by external actors like suppliers or customers. In this context, it would be also interesting to investigate how the interplay of internal and external actors and roles influences an organization’s development of dynamic capabilities during data-driven service

innovation.

Especially the

consideration of challenges for collaboration from a service system perspective that does not only include the service provider, but customers, suppliers, partners or other relevant actors. In particular, the interaction across a variety of different actors could unveil further challenges that organizations have to face during the innovation of datadriven service innovation. It would be of high interest to put a more detailed focus on value co-creation for the provision of data-driven service among suppliers and customers (Lenka et al., 2017;

Part VI: Summarizing findings and implications

181

Schüritz et al., 2017). Second, research on tools and concepts that empower individuals to innovate data-driven service could be of high interest through the specifics of the transition process that many former product-centric organizations have to face (Schüritz et al., 2017). Third, research on the value of data itself or data-driven service seems to be a fruitful venue. Thus, research on concrete models and tools for monetary assessments of solutions within data-rich environments would be very useful (Schüritz et al., 2017, Vendrell-Herrero et al., 2017, Hou & Neely, 2018). Fourth, part III was not able to investigate in detail questions surrounding the complex subject of data privacy and security during data-driven service innovation. It would be exciting to investigate how data privacy and security issues could be faced (Schüritz et al., 2017a). Finally, part V brought the surprising finding to light that the execution of technical activities has a relatively low priority in comparison to strategic ones. It could be of interest to research this aspect in more detail for being able to see if mastering technical issues is just a trivial problem that can be easily solved by the provider of the service in collaboration with e.g. external service providers that are highly specialized in this field. Or if it rests upon deficiencies in the organizational transformation process that has currently a focus on other aspects such as the establishment of an appropriate mindset or the suitable strategy for data-driven service innovation. Considering the synthesized dynamic capability framework for data-driven service innovation, further research could investigate if other aspects should be taken into account throughout the framework. It could be possible that the consideration of resources, actors, roles, barriers and challenges as well ordinary capabilities and dynamic capabilities is not comprehensive and could be complemented. Additionally, the interdependencies between the single parts of the established framework could be quantified to explore the importance of e.g. dynamic capability development on an organizational level through micro-level activities of individual actors during datadriven service innovation.

182

5

Final considerations

Final considerations

The main purpose of this dissertation was to reflect on the phenomenon of dynamic capabilities and how it is influenced through data utilization for service provision and innovation. During the research journey, this dissertation started at the surface of the phenomenon and first derived a definition of a data-driven service. This was achieved through the synthetization of multiple concepts that deal with the utilization of data for service provision. Afterwards, it digged deeper towards the core of the phenomenon through an exploration of actors, barriers and challenges (with a focus on collaboration between different actors) that occur during data-driven service innovation. Finally, this dissertation moved on to the core of the phenomenon and investigated roles of individual actors on a micro-level and came back to the surface through a connection of these roles to their ability of supporting the required development of organizational capabilities. This can help organizations to select the right activities to pursue the innovation of data-driven service innovation. It supports them through putting a focus on the micro-foundations of dynamic capabilities and supports the view that higher-level phenomena can be understood through a focus on the micro-level. In this case individual actors that carry out activities that do not always correspond to their formal organizational functions. Consequently, this dissertation explored the overall research question: What organizational dynamic capabilities are required for data-driven service innovation? This dissertation adds on existing research in the fields of service innovation and dynamic capabilities through the following insights: It highlighted that the utilization of data is responsible for a further complexity. Data-driven service innovation requires the involvement of an extended set of actors, brings up data-related challenges concerning e.g. data privacy, collection or standardization. This dissertation enhances also the view on dynamic capabilities. Companies that aim to successfully innovate data-driven service have to develop dynamic capabilities that cover aspects like the development of a data-oriented mindset and strategy or need to intensify their focus on their service customers during value co-creation activities. Data and its utilization

Part VI: Summarizing findings and implications

183

for service provision require new modes of interaction with other actors within the service ecosystem. From a managerial perspective, this dissertation and the dynamic capability framework for data-driven service innovation gives guidance to managers that aim to innovate data-driven service within their organization. It helps them to anticipate barriers and challenges that can emerge during data-driven service innovation through a focus on ordinary capabilities and dynamic capabilities that need to be developed within the organization. The formulation of concrete roles, that can be carried out by individual actors, enables managers to overcome traditional and formal organizational roles to assemble teams that are defined by activities of their members and promise successful data-driven service innovation. Especially small- and medium-sized organizations from industries with lower experience in service provision (such as manufacturing) can benefit from this approach. These kinds of organizations can be often characterized as resource constrained and the distinction between ordinary capabilities and dynamic capabilities can help them to focus on the important dynamic capability development and not on pursuing ordinary ones that can be easily outsourced to specialized external service providers. For example, the findings from this dissertation suggest that the establishment of internal units that deal with the analysis of data does not seem to be necessary due to its classification of an ordinary capability that can be carried out by partners. Guiding the reader through a journey for a deeper understanding of capabilities for data-driven service innovation as a Reiseleiter was very exciting for me. The conduction of this research is connected with the hope to help organizations to overcome challenges of digitization. And of course to use the connected opportunities in their favor – as well as for the benefit of the regions where many organizations that have to deal with this topic are coming from, as I do.

References

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 M. Schymanietz, Capabilities for Data-Driven Service Innovation, Markt- und Unternehmensentwicklung Markets and Organisations, https://doi.org/10.1007/978-3-658-31691-4

References

187

Aarikka-Stenroos, L., & Jaakkola, E. (2012). Value co-creation in knowledge intensive business services: A dyadic perspective on the joint problem solving process. Industrial Marketing Management, 41 (1), 15-26. Agarwal, R., & Selen, W. (2011). Multi-dimensional nature of service innovation. International Journal of Operations & Production Management, 31 (11), 1164-1192. Aho, A. (2015). Product Data Analytics Service Model for Manufacturing Company. In: Uden L., Heričko M., Ting I.H. (eds) Knowledge Management in Organizations. KMO 2015. Lecture Notes in Business Information Processing, Vol. 224. Springer, Cham.*23 Alam, I. (2006). Removing the fuzziness from the fuzzy front-end of service innovations through customer interactions. Industrial Marketing Management, 35 (4), 468-480. Alghisi, A., & Saccani, N. (2015). Internal and external alignment in the servitization journey – Overcoming the challenges. Production Planning & Control, 26 (14-15), 1219-1232. Anke, J. (2019). Design-integrated financial assessment of smart services. Electronic Markets, 29 (1), 19-35.* Ardolino, M., Rapaccini, M., Saccani, N., Gaiardelli, P., Crespi, G., & Ruggeri, C. (2018). The role of digital technologies for the service transformation of industrial companies. International Journal of Production Research, 56 (6), 2116–2132.* Argote, L., & Ren, Y. (2012). Transactive Memory Systems. Journal of Management Studies, 49, 1375-1382. Baines, T. S. (2015). Exploring service innovation and the servitization of the manufacturing firm. Research-Technology Management, 58 (5), 9-11. Baines, T. S., Lightfoot, H. W., Benedettini, O., & Kay, J. M. (2009). The servitization of manufacturing. Journal of Manufacturing Technology Management, 20 (5), 547-567. Baines, T., & Lightfoot, H. W. (2013). Servitization of the manufacturing firm. International Journal of Operations & Production Management, 34 (1), 2-35.

23

The references marked with * were part of the systematic literature review

188

References

Baines, T., Bigdeli, A. Z., Bustinza, O., Shi, V., Baldwin, J., & Ridgway, K. (2017). Servitization: Revisiting the state-of-the-art and research priorities. International Journal of Operations & Production Management, 37 (2), 256-278. Baines, T., Lightfoot, H. W., Benedettini, O., Whitney, D., & Kay, J. (2010). The adoption of servitization strategies by UK-based manufacturers. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 224, 815-829. Barney, J. B. (1986). Strategic factor markets: expectations, luck and business strategy. Management Science, 32, 1231-1241. Barney, J. B. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17 (1), 99-120. Barrett, M., Davidson, E., Prabhu, J., & Vargo, S. L. (2015). Service innovation in the digital age: Key contributions and future directions. MIS Quarterly, 39 (1), 135154. Baxter, D., Roy, R., Doultsinou, A., Gao, J., & Kalta, M. (2009). A knowledge management framework to support product-service systems design. International Journal of Computer Integrated Manufacturing, 22 (12), 1073-1088. Belvedere, V., Grando, A., & Bielli, P. (2013). A quantitative investigation of the role of information and communication technologies in the implementation of a product-service system. International Journal of Production Research, 51 (2), 410426.* Berger, P. L., & Luckmann, T. (1967). The Social Construction of Reality: A Treatise in the Sociology of Knowledge. London: Penguin. Beverungen, D., Müller, O., Matzner, M., Mendling, J., & Vom Brocke, J. (2017). Conceptualizing smart service systems. Electronic Markets, 1-12.* Bigdeli, A. Z., Bustinza, O. F., Vendrell-Herrero, F., & Baines, T. (2018). Network positioning and risk perception in servitization: evidence from the UK road transport industry. International Journal of Production Research, 56 (6), 2169-2183.* Bitner, M., Ostrom, A. L., & Morgan, F. (2008). Service blueprinting: A practical technique for service innovation. California Management Review, 50 (3), 66-94. Blomqvist, K., & Levy, J. (2006). Collaboration capability—A focal concept in knowledge creation and collaborative innovation in networks. International Journal of Management Concepts and Philosophy, 2 (1), 31-48.

References

189

Bogner, A., Littig, B., & Menz, W. (2009). Experteninterviews: Theorien, Methoden, Anwendungsfelder. Wiesbaden: GWV Fachverlage. Böhmann, T, Leimeister, J. M., & Möslein, K. M. (2014). Service Systems Engineering. Business & Information Systems Engineering, 6 (2), 73-79. Brax, S. A. (2005). A manufacturer becoming service provider – Challenges and a paradox. Managing Service Quality: An International Journal, 15 (2), 142-155. Brax, S. A., & Jonsson, K. (2009). Developing integrated solution offerings for remote diagnostics. International Journal of Operations & Production Management, 29 (5), 539-560.* Breidbach, C. F., & Maglio, P. P. (2016). Technology-enabled value co-creation: An empirical analysis of actors, resources, and practices. Industrial Marketing Management, 56, 73-85. Brown, T. (2017). Sensor-based entrepreneurship: A framework for developing new products and services. Business Horizons, 60 (6), 819-830.* Carlborg, P., Kindström, D., & Kowalkowski, C. (2014). The evolution of service innovation research: a critical review and synthesis. Service Industries Journal, 34 (5), 373-398. Cenamor, J., Rönnberg Sjödin, D., & Parida, V. (2017). Adopting a platform approach in servitization: Leveraging the value of digitalization. International Journal of Production Economics, 192 (November 2015), 54-65.* Chandler, J. D., & Lusch, R. F. (2015). Service systems: A broadened framework and research agenda on value propositions, engagement, and service experience. Journal of Service Research, 18 (1), 6-22. Chen, H.-M., Schutz, R., Kazman, R., & Matthes, F. (2016). Amazon in the Air: Innovating with Big Data at Lufthansa. In: Proceedings of the 49th Hawaii International Conference on System Sciences (HICSS), 5096-5105.* Chen, P., & Zhang, C. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.* Chesbrough, H. W. (2003). Open Innovation. The New Imperative for Creating and Profiting from Technology. Boston, MA: Harvard Business School Press. Chesbrough, H. W. (2011). Open services innovation: Rethinking your business to grow and compete in a new era. San Francisco, CA: John Wiley & Sons.

190

References

Chester Goduscheit, R., & Faullant, R. (2018). Paths Toward Radical Service Innovation in Manufacturing Companies – A Service-Dominant Logic Perspective. Journal of Product Innovation Management, 35 (5), 701–719.* Cohen, M. C. (2018). Big Data and Service Operations. Production and Operations Management, 27 (9), 1709-1723.* Colen, P., & Lambrecht, M. (2013). Product service systems: Exploring operational practices. Service Industries Journal, 33 (5), 501-515. Connor, T. (2002). The resource-based view of strategy and its value to practising managers. Strategic Change, 11, 307-316. Coreynen, W., Matthyssens, P., & Van Bockhaven, W. (2017). Boosting servitization through digitization: Pathways and dynamic resource configurations for manufacturers. Industrial Marketing Management, 60 (June), 42-53.* Creswell, J. W., & Creswell, J. D. (2018). Research design: qualitative, quantitative, and mixed methods approaches. Fifth edition. Los Angeles: SAGE. Crotty, M. (1998). The Foundations of Social Research: Meaning and Perspective in the Research Process. London: SAGE. Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9 (3), 458-467. Dalkey, N. C. (1969). The Delphi method: An experimental study of group opinion. Santa Monica, CA: RAND Corporation. Davenport, T. (2014). How strategists use “big data” to support internal business decisions, discovery and production. Strategy & Leadership, 42 (4), 45-50.* Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems, 55 (1), 412-421.* Demirkan, H., Bess, C., Spohrer, J., Rayes, A., Allen, D., & Moghaddam, Y. (2015). Innovations with smart service systems: Analytics, big data, cognitive assistance, and the internet of everything. Communications of the Association for Information Systems, 37 (1), 733-752.* den Hertog, P., van der Aa, W., & de Jong, M. W. (2010). Capabilities for managing service innovation: towards a conceptual framework. Journal of Service Management, 21 (4), 490-514.

References

191

DeStatis (2017). Inlandsproduktsberechnung - Bruttowertschöpfung nach Wirtschaftsbereichen.https://www.destatis.de/DE/ZahlenFakten/Gesamtwirtsch aftUmwelt/VGR/Inlandsprodukt/Tabellen/BWSBereichen.html, retrieved on 30.07.2019. Diamond, M., Allcorn, S., & Stein, H. (2004). The surface of organizational boundaries: A View from psychoanalytic object relations theory. Human Relations, 57 (1), 3153. DiMaggio, P. (2003). Culture and Cognition. Annual Review of Sociology, 23, 263-287. Duggleby, W. (2005). What about focus group interaction data? Qualitative Health Research, 15, 832-840. Edvardsson, B., & Tronvoll, B. (2013). A new conceptualization of service innovation grounded in S-D logic and service systems. International Journal of Quality & Service Sciences, 5 (1), 19-31. Edvardsson, B., Gustafsson, A., & Roos, I. (2005). Service portraits in service research: a critical review. International Journal of Service Industry Management, 16 (1), 107121. Edvardsson, B., Tronvoll, B., & Gruber, T. (2011). Expanding understanding of service exchange and value co-creation: a social construction approach. Journal of the Academy of Marketing Science, 39 (2), 327-339. Eisenhardt, K. M. (1989a). Making fast strategic decisions in high-velocity environments. Academy of Management Journal, 32 (3), 543-576. Eisenhardt, K. M. (1989b). Building Theories from Case Study Research. Academy of Management Review, 14 (4), 532-550. Eisenhardt, K. M., & Graebner, M. E. (2007). Theory building from cases: Opportunities and challenges. Academy of Management Journal, 50 (1), 25-32. Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: what are they? Strategic Management Journal, 21 (10–11), 1105–1121. Ekman, P., Raggio, R. D., & Thompson, S. M. (2016). Service network value co-creation: Defining the roles of the generic actor. Industrial Marketing Management, 56, 51-62. Eloranta, V., & Turunen, T. (2016). Platforms in service-driven manufacturing: Leveraging complexity by connecting, sharing, and integrating. Industrial Marketing Management, 55, 178-186.

192

References

Ettlie, J. E., & Rosenthal, S. R. (2012). Service innovation in manufacturing. Journal of Service Management, 23 (3), 440-454. Evans, P. C., & Gawer, A. (2016). The Rise of the Platform Enterprise: A Global Survey. New York: Center for Global Enterprise. Falk, M., & Peng, F. (2013). The increasing service intensity of European manufacturing. The Service Industries Journal, 33 (15–16), 1686-1706. Felin, T., & Hesterly, W. S. (2007). The knowledge-based view, nested heterogeneity, and new value creation: philosophical considerations on the locus of knowledge. Academy of Management Review, 32, 195-218. Felin, T., Foss, N. J., & Ployhart, R. E. (2015). The Microfoundations Movement in Strategy and Organization Theory. The Academy of Management Annals, 9 (1), 575632. Felin, T., Foss, N. J., Heimeriks, K. H., & Madsen, T. L. (2012). Microfoundations of Routines and Capabilities: Individuals, Processes, and Structure. Journal of Management Studies, 49, 1351-1374. Fink, A. (2013). Conducting research literature reviews: from the Internet to paper. Los Angeles: Sage Publications. Fischer, T., Gebauer, H., & Fleisch, E. (2012). Service business development: Strategies for value creation in manufacturing firms. Cambridge, UK: Cambridge University Press. Ford, D., Gadde, L.-E., Håkansson, H., Snehota, I., & Waluszewski, A. (2010). Analysing business interaction. IMP Journal, 4 (1), 82-103. Forkmann, S., Ramos, C., Henneberg, S. C., & Naudé, P. (2017). Understanding the service infusion process as a business model reconfiguration. Industrial Marketing Management, 60, 151-166. Fu, W., Wang, Q., & Zhao, X. (2018). Platform-based service innovation and system design: research opportunities. Industrial Management & Data Systems, 118 (5), 975997.* Gawer, A. (2009). Platform dynamics and strategies: From products to services. In A. Gawer (Ed.), Platforms, markets and innovation (45–76). Cheltenham, UK: Edwards. Gebauer, H., & Friedli, T. (2005). Behavioral implications of the transition process from products to services. Journal of Business & Industrial Marketing, 20 (2), 70-78.

References

193

Gebauer, H., Fleisch, E., & Friedli, T. (2005). Overcoming the service paradox in manufacturing companies. European Management Journal, 23 (1), 14-26. Gebauer, H., Paiola, M., & Saccani, N. (2013). Characterizing service networks for moving from products to solutions. Industrial Marketing Management, 42 (1), 3146. Gebauer, H., Saul, C. J., Haldimann, M., & Gustafsson, A. (2017). Organizational capabilities for pay-per-use services in product-oriented companies. International Journal of Production Economics, 192 (November 2015), 157-168.* Geum, Y., Jeon, H., & Lee, H. (2016). Developing new smart services using integrated morphological analysis: integration of the market-pull and technology-push approach. Service Business, 10 (3), 53-555.* Goes, J., & Park, S. (1997). Interorganizational links and innovation: The case of hospital services. The Academy of Management Journal, 40 (3), 673-696. Golightly, D., Kefalidou, G., & Sharples, S. (2017). A cross-sector analysis of human and organisational factors in the deployment of data-driven predictive maintenance. Information Systems and E-Business Management, 1-22.* Grubic, T. (2014). Servitization and remote monitoring technology. Journal of Manufacturing Technology Management, 25 (1), 100-124. Grubic, T., & Jennions, I. (2017). Remote monitoring technology and servitised strategies – factors characterising the organisational application. International Journal of Production Research, 7543 (May), 1-17.* Grubic, T., & Peppard, J. (2016). Servitized manufacturing firms competing through remote monitoring technology. Journal of Manufacturing Technology Management, 27 (2), 154-184.* Gummesson, E., & Mele, C. (2010). Marketing as Value Co-creation Through Network Interaction and Resource Integration. Journal of Business Market Management, 4 (4), 181-198. Håkansson, H., & Johanson, J. (1992). A model of industrial networks. In Axelsson & Easton (Eds.), Industrial networks: A new view of reality, 28-34. London: Routledge. Håkansson, H., & Snehota, I. (1995). Developing relationships in business networks. London: International Thomson.

194

References

Hambrick, D. C. (1987). The Top Management Team: Key to Strategic Success. California Management Review, 30 (1), 88-108. Hartley, J. (2004). Case study research. In: C. Casell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research. London: SAGE. Hartmann, P. M., Zaki, M., Feldmann, N., & Neely, A. (2016). Capturing value from big data – a taxonomy of data-driven business models used by start-up firms. International Journal of Operations & Production Management, 36 (10), 1382-1406. Heikinnen, M. T., Mainela, T., Still, J., & Tähtinen, J. (2007). Roles for managing in mobile service development nets. Industrial Marketing Management, 36 (7), 909-925. Helfat, C. E., & Peteraf, M. A. (2003). The dynamic resource-based view: capability lifecycles. Strategic Management Journal, 24(10), 997-1010. Helfat, C. E., & Raubitschek, R. S. (2018). Dynamic and integrative capabilities for profiting from innovation in digital platform-based ecosystems. Research Policy, 47 (8), 1391-1399.* Helfat, C., Finkelstein, S., Mitchell, W., Peteraf, M. A., Singh, H., Teece, D. J., & Winter, S. G. (2007). Dynamic Capabilities: Understanding Strategic Change in Organizations. Oxford: Blackwell. Herriott, R. E., & Firestone, W. A. (1983). Multisite Qualitative Policy Research: Optimizing Description and Generalizability. Educational Researcher, 12 (2), 14-19. Herterich, M., Buehnen, T., Uebernickel, F., & Brenner, W. (2016). A taxonomy of industrial service systems enabled by digital product innovation. In: Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS) 2016, 12361245.* Herterich, M., Uebernickel, F., & Brenner, W. (2015). The Impact of Cyber-Physical Systems on Industrial Services in Manufacturing. Procedia CIRP, 30, 323-328.* Hill, K. Q., & Fowles, J. (1975).The Methodological Worth of the Delphi Forecasting Technique. Technological Forecasting and Social Change, 7, 179-192. Holmström, J. (2018). Recombination in digital innovation: Challenges, opportunities, and the importance of a theoretical framework. Information and Organization, 28 (2), 107-110.

References

195

Hou, J., & Neely, A. (2018). Investigating risks of outcome-based service contracts from a provider’s perspective. International Journal of Production Research, 56 (6), 21032115.* IfM (Institut für Mittelstandsforschung) (2013). Mittelstand im Überblick. Retrieved from http://www.ifm-bonn.org/statistiken/mittelstand-imueberblick/#accordion=0&tab=0 Jonas, J. M., & Roth, A. (2017). Stakeholder integration in service innovation—An exploratory case study in the healthcare industry. International Journal of Technology Management, 73 (1/2/3), 91-113. Jonas, J. M., Roth, A., & Möslein, K. M. (2016). Stakeholder integration for service innovation in German medium-sized enterprises. Service Science, 8 (3), 320-332. Jonsson, K., Westergren, U., & Holmström, J. (2008). Technologies for value creation: An exploration of remote diagnostics systems in the manufacturing industry. Information Systems Journal, 18 (3), 227-245.* Joshi, K. D., Chi, L., Datta, A., & Han, S. (2010). Changing the Competitive Landscape: Continuous Innovation Through IT- Enabled Knowledge Capabilities. Information Systems Research, 21 (3), 472-495. Kahn, K., & Mentzer J. (1996). Logistics and interdepartmental integration. International Journal of Physical Distribution & Logistics Management, 26 (8), 6-14. Kamp, B., & Parry, G. (2017). Servitization and advanced business services as levers for competitiveness. Industrial Marketing Management, 60, 11-16. Kazadi, K., Lievens, A., & Mahr, D. (2015). Stakeholder co-creation during the innovation process: Identifying capabilities for knowledge creation among multiple stakeholders. Journal of Business Research, 69(2), 525-540. Keh, H., & Pang, J. (2010). Customer reactions to service separation. Journal of Marketing, 74 (2), 55-71. Kim, M., Song, J., & Triche, J. (2015). Toward an integrated framework for innovation in service: A resource-based view and dynamic capabilities approach. Information Systems Frontiers, 17 (3), 533-546. Kindström, D., & Kowalkowski, C. (2014). Service innovation in product-centric firms: a multidimensional business model perspective. Journal of Business & Industrial Marketing, 29 (2), 96-111.

196

References

Kindström, D., Kowalkowski, C., & Alejandro, T. (2015). Adding services to productbased portfolios. Journal of Service Management, 26 (3), 372-393. Kindström, D., Kowalkowski, C., & Sandberg, E. (2013). Enabling service innovation: A dynamic capabilities approach. Journal of Business Research, 66 (8), 1063-1073. King, N., & Horrocks, C. (2010). Interviews in qualitative research. London: SAGE. Kjellberg, H., & Helgesson, C.-F (2006). Multiple versions of markets: Multiplicity and performativity in market practice. Industrial Marketing Management, 35, 839-855. Klein, B., & Leffler, K. (1981). The Role of Market Forces in Assuring Contractual Performance. Journal of Political Economy, 89 (4), 615-641. Klein, M. M., Biehl, S. S., & Friedli, T. (2018). Barriers to smart services for manufacturing companies – an exploratory study in the capital goods industry. Journal of Business & Industrial Marketing, 33 (6), 846-856.* Kodama, M. (2018). New product innovation through dynamic capabilities: the case of Fujifilm versus Kodak. In Sustainable Growth Through Strategic Innovation Driving Congruence in Capabilities. Cheltenham, UK: Edward Elgar Publishing. Koskela-Huotari, K., Edvardsson, B., Jonas, J. M., Sörhammar, D., & Witell, L. (2016). Innovation in service ecosystems—Breaking, making, and maintaining institutionalized rules of resource integration. Journal of Business Research, 69 (8), 2964-2971. Kowalkowski, C. (2016). Service innovation in industrial contexts. In: M. Toivonen (Ed.), Service innovation: Novel ways of creating value in actor systems. Tokyo: Springer. Kowalkowski, C., & Brehmer, P. (2008). Technology as a driver for changing customer‐ provider interfaces. Management Research News, 31 (10), 746-757.* Kowalkowski, C., Gebauer, H., & Oliva, R. (2017). Service growth in product firms: Past, present, and future. Industrial Marketing Management, 60, 82-88. Kowalkowski, C., Kindström, D., & Carlborg, P. (2016). Triadic value propositions: When it takes more than two to tango. Service Science, 8 (3), 282-299. Kowalkowski, C., Kindström, D., & Gebauer, H. (2013a). ICT as a catalyst for service business orientation. Journal of Business & Industrial Marketing, 28 (6), 506-513.*

References

197

Kowalkowski, C., Kindström, D., & Witell, L. (2011). Internalisation or externalisation? Examining organisational arrangements for industrial services. Managing Service Quality, 21 (4), 373-391. Kowalkowski, C., Kindström, D., Brashear, T. A., Brege, S., & Biggeman, S. (2012). Service infusion as agile incrementalism in action. Journal of Business Research, 65 (6), 765-772. Kowalkowski, C., Windahl, C., Kindström, D., & Gebauer, H. (2015). What service transition? Rethinking established assumptions about manufacturers’ service-led growth strategies. Industrial Marketing Management, 45, 59-69. Kowalkowski, C., Witell, L., & Gustafsson, A. (2013). Any way goes : Identifying value constellations for service infusion in SMEs. Industrial Marketing Management, 42 (1), 18-30. Kusiak, A. (2009). Innovation: A data-driven approach. International Journal of Production Economics, 122 (1), 440-448.* Kusiak, A. (2018). Smart manufacturing. International Journal of Production Research, 56 (1-2), 508-517.* Landeta, J., Barrutia, J., & Lertxundi, A. (2011). Hybrid Delphi: A methodology to facilitate contribution from experts in professional contexts. Technological Forecasting & Social Change, 78 (9), 1629-1641. Lawson, B., & Samson, D. (2001). Developing Innovation Capability in Organisations: A Dynamic Capabilities Approach. International Journal of Innovation Management, 5 (3), 377-400. Laumer, S., Maier, C., Eckhardt, A., & Weitzel, T. (2016). User Personality and Resistance to Mandatory Information Systems in Organizations: A Theoretical Model and Empirical Test of Dispositional Resistance to Change. Journal of Information Technology, 31 (1), 67-82. Lenka, S., Parida, V., & Wincent, J. (2017). Digitalization Capabilities as Enablers of Value Co-Creation in Servitizing Firms. Psychology & Marketing, 34 (1), 92-100.* Lerch, C., & Gotsch, M. (2015). Digitalized Product-Service Systems in Manufacturing Firms: A Case Study Analysis. Research-Technology Management, 58 (5), 45-52.* Lightfoot, H., Baines, T., & Smart, P. (2013). The servitization of manufacturing. International Journal of Operations & Production Management, 33 (11/12), 1408-1434.

198

References

Lim, C., Kim, K.-H., Kim, M.-J., Heo, J.-Y., Kim, K.-J., & Maglio, P. P. (2018). From data to value: A nine-factor framework for data-based value creation in informationintensive services. International Journal of Information Management, 39 (January 2017), 121-135.* Lindemann, K. (2007). The Impact of Objective Characteristics on Subjective Socialposition. Trames Journal of the Humanities and Social Sciences, 11, 54-68. Linstone, H. A., & Turoff, M. (1975). The Delphi Method: Techniques and Applications. London: Addison-Wesley. Lippman, S. A., & Rumelt R. P. (1982). Uncertain imitability: an analysis of interfirm differences under competition. Bell Journal of Economics, 13 (2), 418-438. Lusch R. F., Vargo S. L., & Tanniru, M. (2009). Service, value networks and learning. Journal of the Academy of Marketing Science, 38 (1), 19-31. Lusch, R.F., & Vargo, S. L. (2006a). Service-dominant logic: Reactions, reflections and refinements. Marketing Theory, 6(3), 281-288. Lusch, R. F., & Vargo, S. L. (2006b). Service-dominant logic: What it is, what it is not, what it might be. In: Vargo, S. & Lusch, R. (Eds.), The service dominant logic of marketing: Dialog, debate, and directions (43-55). New York, NY: M.E. Shape. Lusch, R. F., & Nambisan, S. (2015). Service innovation: A service-dominant-logic perspective. MIS Quarterly, 39 (1), 155-175. Lusch, R. F., & Vargo, S. L. (2014). Service-Dominant Logic: Premises, Perspectives, Possibilities. Cambridge, UK: Cambridge University Press. Maglio, P. P., & Lim, C. (2016). Innovation and Big Data in Smart Service Systems. Journal of Innovation Management, 4 (1), 11-21. Maglio, P. P., Vargo, S. L., Caswell, N., & Spohrer, J. (2009). The service system is the basic abstraction of service science. Information Systems and E-Business Management, 7 (4), 395-406. March, J. G., & Simon, H.A. (1958). Organizations. New York: Wiley. Martinez, V., Bastl, M., Kingston, J., & Evans, S. (2010). Challenges in transforming manufacturing organisations into product‐service providers. Journal of Manufacturing Technology Management, 21 (4), 449-469.

References

199

Matthyssens, P., & Vandenbempt, K. (2010). Service addition as business market strategy: Identification of transition trajectories. Journal of Service Management, 21 (5), 693-714. Matthyssens, P., Vandenbempt, K., & Berghman, L. (2006). Value innovation in business markets: Breaking the industry recipe. Industrial Marketing Management, 35 (6), 751-761. Meuser, M., & Nagel, U. (1991). ExpertInneninterviews - vielfach erprobt, wenig bedacht: ein Beitrag zur qualitativen Methodendiskussion. In: D. Garz, & K. Kraimer (Ed.), Qualitativ-empirische Sozialforschung: Konzepte, Methoden, Analysen, 441-471, Opladen. Meuser, M., & Nagel, U. (2009). Das Experteninterview — konzeptionelle Grundlagen und methodische Anlage. Wiesbaden: VS Verlag. Meyer, H.-D., & Rowan, B. (2006). The New Institutionalism in Education. Albany, NY: State University of New York Press. Miles, I. (1993). Services in the new industrial economy. Futures, 25(6), 653-672. Miles, M. B., Huberman, M. A., & Saldaña, J. (2013). Qualitative Data Analysis: A Methods Sourcebook. Los Angeles, CA: Sage Publications. Neely, A. (2009). Exploring the financial consequences of the servitization of manufacturing. Operations Management Research, 1 (2), 103-118. Nelson, R. R., & Winter, S. (1982). An Evolutionary Theory of Economic Change. Cambridge, MA: Harvard University Press. Nonaka, I., & Takeuchi, H. (1995). The Knowledge Creating Company. New York, NY: Oxford University Press. Nyström, A., Leminen, S., Westerlund, M., & Kortelainen, M. (2014). Actor roles and role patterns influencing innovation in living labs. Industrial Marketing Management, 43 (3), 483-495. Okoli, C., & Pawlowski, S. D. (2004). The Delphi method as a research tool: An example, design considerations and applications”, Information and Management, 42 (1), 1529. Oliva, R., & Kallenberg, R. (2003). Managing the transition from products to services. International Journal of Service Industry Management, 14 (2), 160-172.

200

References

Opresnik, D., & Taisch, M. (2015). The value of big data in servitization. International Journal of Production Economics, 165, 174-184.* Ordanini, A., & Parasuraman, A. (2011). Service innovation viewed through a servicedominant logic lens: A conceptual framework and empirical analysis. Journal of Service Research, 14 (1), 3-23. Ostrom, A. L., Parasuraman, A., Bowen, D. E., Patricio, L., & Voss, C. A. (2015). Service Research Priorities in a Rapidly Changing Context. Journal of Service Research, 18 (2), 127-159. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A Conceptual Model Service Its Quality and Implications for Future Research. The Journal of Marketing, 49 (4), 41-50. Parris, D. L., Bouchet, A., Welty Peachey, J., & Arnold, D. (2016). Change is hard: overcoming barriers to service innovation. Journal of Services Marketing, 30 (6), 615629. Patton, M. Q. (2002). Qualitative Research and Evaluation Methods. Thousand Oaks, CA: SAGE. Perks, H., Gruber, T., & Edvardsson, B. (2012). Co-creation in radical service innovation: A systematic analysis of microlevel processes. Journal of Product Innovation Management, 29 (6), 935-951. Persona, A., Regattieri, A., Pham, H., & Battini, D. (2007). Remote control and maintenance outsourcing networks and its applications in supply chain management. Journal of Operations Management, 25 (6), 1275-1291.* Peters, C., Maglio, P. P., Badinelli, R., Harmon, R. R., Maull, R., Spohrer, J. C., Tuunanen, T., Vargo, S. L., Welser, J. J., Demirkan, H., Griffith, T. L., & Moghaddam, Y. (2016). Emerging Digital Frontiers for Service Innovation. Communications of the Association for Information Systems, 39 (1), 2016, 136-149. Petticrew, M., & Roberts, H. (2006). Systematic Reviews in the Social Sciences. Malden, MA: Blackwell Publishing Inc. Picot, A., Reichwald, R., & Wigand, R. T. (2003). Die grenzenlose Unternehmung. Wiesbaden: Gabler Verlag. Pigni, F., Piccoli, G., & Watson, R. (2016). Digital Data Streams: Creating Value from the Real-Time Flow of Big Data. California Management Review, 58 (3), 5-25.*

References

201

Piller, F. T., Möslein, K. M., Ihl, C. & Reichwald, R. (2017). Interaktive Wertschöpfung Kompakt. Wiesbaden: Springer Gabler. Pisano, G. P. (1994). Knowledge, integration, and the locus of learning: an empirical analysis of process development. Strategic Management Journal, Winter Special Issue 15, 85-100. Porter, M., & Heppelmann, J. (2014). How Smart, Connected Products Are Transforming Competition. Harvard Business Review. November 2014. https://hbr.org/2014/11/how-smart-connected-products-are-transformingcompetition, retrieved on 30.07.2019. Porter, M. E. (1980). Competitive Strategy: Techniques for Analyzing Industries and Competitors. New York: The Free Press. Posada, J., Toro, C., Barandiaran, I., Oyarzun, D., Stricker, D., De Amicis, R., & Vallarino, I. (2015). Visual Computing as a Key Enabling Technology for Industrie 4.0 and Industrial Internet. IEEE Computer Graphics and Applications, 35 (2), 26-40. Posselt, T. (2018). Organizational Competence for Servitization. Wiesbaden: Springer Gabler. Reckwitz, A. (2002). Toward a Theory of Social Practices: A Development in Culturalist Theorizing. European Journal of Social Theory, 5 (2), 245-265. Reinartz, W., & Ulaga, W. (2008). How to sell service more profitably. Harvard Business Review, 86 (5), 90-96. Remane, G., Hanelt, A., Nickerson, R. C., & Kolbe, L. M. (2017). Discovering digital business models in traditional industries. Journal of Business Strategy, 38 (2), 4151.* Robinson, W., Chan, P., & Lau, T. (2016). Finding New Ways of Creating Value. Research-Technology Management, (June), 37-50.* Rothaermel, F. T., & Hess, A. M. (2007). Building Dynamic Capabilities: Innovation Driven by Individual-, Firm-, and Network-Level Effects. Organization Science, 18 (6), 898-921. Rusanen, H., Halinen, A., & Jaakkola, E. (2014). Accessing resources for service innovation – The critical role of network relationships. Journal of Service Management, 20 (1), 2-29.

202

References

Rust, R. T., & Huang, M.-H. (2014). The Service Revolution and the Transformation of Marketing Science. Marketing Science, 33 (2), 206-221. Rymaszewska, A., Helo, P., & Gunasekaran, A. (2017). IoT powered servitization of manufacturing – an exploratory case study. International Journal of Production Economics, 192 (March), 92-105.* Ryu, H., & Lee, J. (2018). Understanding the role of technology in service innovation: Comparison of three theoretical perspectives. Information & Management, 55 (3), 294-307. Saldaña, J. (2016). The coding manual for qualitative researchers. London: SAGE. Salipante, P., Notz, W., & Bigelow, J. (1982). A Matrix Approach to Literature Reviews. In: B.M. Staw and L.L. Cummings (eds.), Research in Organizational Behavior. JAI Press: Greenwich, CT, 321-348. Salvato, C. (2009). Capabilities Unveiled: The Role of Ordinary Activities in the Evolution of Product Development Processes, Organization Science, 20 (2), 384-409. Sanders, N. R. (2016). How to Use Big Data to Drive Your Supply Chain. California Management Review, 58 (3), 26-48.* Schmidt, R. C. (1997). Managing Delphi Surveys Using Nonparametric Statistical Techniques. Decision Sciences, 28 (3), 763-774. Schramm, W. (1971). Notes on case studies of instructional media projects. Working paper for the Academy for Educational Development. Washington, DC. Schüritz, R., & Satzger, G. (2016). Patterns of data-infused business model innovation. IEEE 18th Conference on Business Informatics (CBI), 133-142. Schüritz, R., Satzger, G., Seebacher, S., & Schwarz, L. (2017a). Datatization as the Next Frontier of Servitization – Understanding the Challenges for Transforming Organizations. In: Proceedings of the 38th International Conference on Information Systems, 1-21.* Schüritz, R., Seebacher, S., & Dorner, R. (2017b). Capturing Value from Data: Revenue Models for Data-Driven Services. In: Proceedings of the 50th Hawaii International Conference on System Sciences, 5348-5357. Scott, W. R. (2001). Institutions and organizations. Thousand Oaks, CA: SAGE. Silver, C., & Lewins, A. (2014). Using software in qualitative research. London: SAGE.

References

203

Sorescu, A. (2017). Data-Driven Business Model Innovation. Journal of Product Innovation Management, 34 (5), 691-696.* Spohrer, J. C., Maglio, P. P., Bailey, J., & Gruhl, D. (2007). Steps toward a science of service systems. Computer, 40 (1), 71-77. Stake, R. (2006). Multiple case study analysis. New York, NY: The Guilford Press. Stein, N., Meller, J., & Flath, C. M. (2018). Big data on the shop-floor: sensor-based decision-support for manual processes. Journal of Business Economics, 88 (5), 2018, 593-616. Stewart, D., & Shamdasani, P. (1990). Focus groups. Theory and practice. Newbury Park, CA: SAGE. Storbacka, K., Brodie, R. J., Böhmann, T., Maglio, P. P., & Nenonen, S. (2016). Actor engagement as a microfoundation for value co-creation. Journal of Business Research, 69 (8), 3008-3017. Story, V. M., Raddats, C., Burton, J., Zolkiewski, J., & Baines, T. (2017). Capabilities for advanced services: A multi-actor perspective. Industrial Marketing Management, 60, 54-68.* Sundbo, J. (1997). Management of innovation in services. Service Industries Journal, 17 (3), 432-455. Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48 (January), 157–169.* Teece, D. J. (2007). Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28 (13), 1319-1350. Teece, D. J. (2012). Dynamic capabilities: routines versus entrepreneurial action. Journal of Management Studies, 49 (8), 1395-1401. Teece, D. J. (2017). Towards a capability theory of (innovating) firms: implications for management and policy. Cambridge Journal of Economics, 41 (3), 693-720. Teece, D. J. (2018). Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world. Research Policy, 47 (8), 1367-1387.*

204

References

Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18 (7), 509-533. Tesch, R. (1990). Qualitative research: Analysis types and software tools. New York: Falmer. Troilo, G., De Luca, L. M., & Guenzi, P. (2017). Linking Data-Rich Environments with Service Innovation in Incumbent Firms: A Conceptual Framework and Research Propositions. Journal of Product Innovation Management, 34 (5), 617-639.* Tronvoll, B. (2017). The Actor: The Key Determinator in Service Ecosystems”, Systems, 5 (2), 2017, 1-14. Tuunanen, T., Bask, A., & Merisalo-Rantanen, H. (2012). Typology for Modular Service Design. International Journal of Service Science, Management, Engineering, and Technology, 3 (3), 99-112. Urbinati, A., Bogers, M., Chiesa, V., & Frattini, F. (2019). Creating and capturing value from Big Data: A multiple-case study analysis of provider companies. Technovation, 84-85 (July), 21-36.* van de Ven, A. (2007). Engaged scholarship: a guide for organizational and social research: a guide for organizational and social research. Oxford: Oxford University Press. van Kleef, J. A. G., & Roome, N. J. (2007). Developing capabilities and competence for sustainable business management as innovation: a research agenda. Journal of Cleaner Production, 15, 38-51. van Maanen, J. (1979). The fact of fiction in organizational ethnography. Administrative Science Quarterly, 24, 539-550. Vandermerwe, S., & Rada, J. (1988). Servitization of business: adding value by adding services. European Management Journal, 6 (4), 314-324. Vargo, S. L., & Akaka, M. A. (2009). Service-Dominant Logic as a Foundation for Service Science: Clarifications. Service Science, 1 (1), 32-41. Vargo, S. L., & Akaka, M. A. (2012). Value cocreation and service systems (re) formation: A service ecosystems view. Service Science, 4 (3), 207-217. Vargo, S. L., & Lusch, R. F. (2004). Evolving to a New Dominant Logic for Marketing. Journal of Marketing, 68(1), 1-17. Vargo, S. L., & Lusch, R. F. (2008). Service-dominant logic: Continuing the evolution. Journal of the Academy of Marketing Science, 36(1), 1-10.

References

205

Vargo, S. L., & Lusch, R. F. (2011). It's all B2B…and beyond: Toward a systems perspective of the market. Industrial Marketing Management, 40 (2), 181-187. Vargo, S. L., Wieland, H., & Akaka, M. A. (2015). Innovation through institutionalization: A service ecosystems perspective. Industrial Marketing Management, 44, 63-72. Vendrell-Herrero, F., Bustinza, O. F., Parry, G., & Georgantzis, N. (2017). Servitization, digitization and supply chain interdependency. Industrial Marketing Management, 60, 69-81.* Verne, J. (1864). Journey to the centre of the earth. Verne, J. (1885). The star of the south. Wahyuni, D. (2012). The Research Design Maze: Understanding Paradigms, Cases, Methods and Methodologies. Journal of Applied Management Accounting Research, 10 (1), 69-80. Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70 (August), 356-365. Webler, T., Levine, D., Rakel, H., & Renn, O. (1991). A novel approach to reducing uncertainty: The group Delphi. Technological Forecasting and Social Change, 39 (3), 253-263. Webster, J., & Watson, R. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26 (2), 13-23. Wen, X., & Zhou, X. (2016). Servitization of manufacturing industries based on cloudbased business model and the down-to-earth implementary path. The International Journal of Advanced Manufacturing Technology, 87 (5-8), 1491-1508.* West, J. & Gallagher, S. (2006). Challenges of open innovation: The paradox of firm investment in open-source software. R&D Management, 36 (3), 319-331. Westergren, U. H. (2011). Opening up innovation: The impact of contextual factors on the co-creation of IT-enabled value adding services within the manufacturing industry. Information Systems and E-Business Management, 9 (2), 223-245.* Wilkinson, S. (2004). Focus group research. In D. Silverman (Ed.), Qualitative research: Theory, method, and practice. Thousand Oaks, CA: Sage.

206

References

Wolcott, H. (1994). Transforming Qualitative Data - Description, Analysis, and Interpretation. Thousand Oaks, CA: Sage. Woodside, A. (2010). Case Study Research: Theory, Methods, Practice. Bingley: Emerald Group Publishing Limited. Wünderlich, N. V., Heinonen, K., Ostrom, A. L., Patricio, L., Sousa, R., Voss, C., & Lemmink, J. G. (2015). “Futurizing” smart service: implications for service researchers and managers. Journal of Services Marketing, 26 (1), 442-447. Yin, R. K. (2016). Qualitative Research from Start to Finish. New York: The Guilford Press. Yin, R. K. (2018). Case study research design and methods (6th ed.). Thousand Oaks, CA: Sage. Yoo, Y. (2010). Computing in Everyday Life: A Call for Research on Experiential Computing. MIS Quarterly, 34 (2), 213-232. Yoo, Y., Boland, R. J., Lyytinen, K., & Majchrzak, A. (2012). Organizing for Innovation in the Digitized World. Organization Science, 23 (5), 1398-1408.* Yoo, Y., Henfridsson, O., & Lyytinen, K. (2010). The new organizing logic of digital innovation: An agenda for information systems research. Information Systems Research, 21 (4), 724-735. Zeng, J., & Glaister, K. W. (2018). Value creation from big data: Looking inside the black box. Strategic Organization, 16 (2), 105–140.* Zheng, M., Ming, X., Wang, L., Yin, D., & Zhang, X. (2017). Status Review and Future Perspectives on the Framework of Smart Product Service Ecosystem. Procedia CIRP, 64, 181-186.* Zheng, P., Lin, T.-J., Chen, C.-H., & Xu, X. (2018). A systematic design approach for service innovation of smart product-service systems. Journal of Cleaner Production, 201, 657-667.* Zissis, D., & Lekkas, D. (2012). Addressing cloud computing security issues. Future Generation Computer Systems, 28 (3), 583-592. Zolnowski, A., Anke, J., & Gudat, J. (2017). Towards a Cost-Benefit-Analysis of DataDriven Business Models. In: Leimeister, J.M. & Brenner, W. (Eds.): Proceedings der 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017), St. Gallen, 181-195.*

References

207

Zolnowski, A., Christiansen, T., & Gudat, J. (2016). Business model transformation patterns of data-driven innovations. In: Proceedings of the 24th European Conference on Information Systems, ECIS, 1-16.* Zucker, L. G. (1986). Production of trust: Institutional sources of economic structure, 1840–1920. Research in Organizational Behavior, 8, 53-111.

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Annexes

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 M. Schymanietz, Capabilities for Data-Driven Service Innovation, Markt- und Unternehmensentwicklung Markets and Organisations, https://doi.org/10.1007/978-3-658-31691-4

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Annex A: Related Publications Parts of this dissertation were presented at international conferences. This applies for these articles: -

Schymanietz, M., Jonas J. M., & Möslein K. M. (2017). Collaborative data-driven service innovation – An exploration of current challenges in the German manufacturing industry. 9th Service Operations Management (SOM) Forum 2017, Copenhagen, Denmark.

-

Schymanietz, M., Jonas J. M., & Möslein K. M. (2017). Data-driven service innovation – An exploration of actors and challenges in the German manufacturing industry. European Academy of Management (EURAM) Conference 2017, Glasgow, United Kingdom.

-

Schymanietz, M., Jonas J. M., & Möslein K. M. (2018). Innovating data-driven services – What makes it special? European Academy of Management (EURAM) Conference 2018, Reykjavik, Iceland.

-

Schymanietz, M., & Jonas, J. M. (2020). The Roles of Individual Actors in DataDriven Service Innovation – A Dynamic Capabilities Perspective to Explore its Microfoundations. In: Proceedings of the 53r Hawaii International Conference on System Sciences (HICSS) 2020, 1135-1144.

These articles, co-authored by Julia M. Jonas and Kathrin M. Möslein, are mainly conceptualized by the first author. Also data collection, data analysis, and its interpretation of the results are primarily attributable to the author of this dissertation. The dissertation at hand also profited from feedback of researchers and practitioners collected at the presentation of the studies at conferences and research colloquia. Still, some parts remained unchanged and verbatim. Earlier stages of this dissertation have been presented and discussed in the following doctoral colloquia for external feedback and discussion: -

Schymanietz, M. (2016). Collaborative data-driven service innovation. 12th Research colloquium “Innovation & Value Creation”, Linz, Austria.

-

Schymanietz, M. (2017). Data-driven service innovation – A systematic literature review. 12th Research colloquium “Innovation & Value Creation”, Hamburg, Germany.

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- Schymanietz, M. (2018). Actors in data-driven service innovation. European Academy of Management (EURAM) Conference 2018 Doctoral Colloquium, Reykjavik, Iceland. -

Schymanietz, M. (2018). What is special about data-driven service innovation? 13th Research colloquium “Innovation & Value Creation”, Chemnitz, Germany.

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Annex B: Average H Index of C & D VHB ranked publications (Part III) Name 4OR Accounting and Finance Accounting and the Public Interest Accounting Forum Accounting Historians Journal Accounting History Accounting in Europe Accounting Perspectives Accounting Research Journal Administration and Society Advances in Accounting Advances in Consumer Research Advances in International Marketing Advances in Management Accounting Advances in Strategic Management American Review of Public Administration Applied Financial Economics Asia Pacific Business Review Asia Pacific Journal of Management Asia-Pacific Journal of Accounting and Economics Asia-Pacific Journal of Operational Research Australasian Marketing Journal Australian Accounting Review Australian Journal of Management Baltic Journal of Management BMC Health Services Research Business and Society Review Business Horizons Business Process Management Journal Central European Journal of Operations Research Computers in Industry Corporate Ownership and Control Corporate Reputation Review Corporate Social Responsibility and Environmental Management Creativity and Innovation Management Electronic Commerce Research European Journal of Innovation Management European Journal of Law and Economics European Journal of Marketing Financial Markets and Portfolio Management Fiscal Studies GAIA - Ökologische Perspektiven in Natur-, Geistes- und Wirtschaftswissenschaften Gesundheitsökonomie und Qualitätsmanagement greener management international (eingestellt 2012) Harvard Business Review

H Index 34 40 8 35 21 26 15 14 11 52 24 21 15 9 25 47 48 29 65 10 27 28 30 35 21 90 17 67 72 26 87 16 51 58 50 34

VHBRanking C C C C C C C C C C C C C C C C C C C C C D C C C C C C C C C C D C C C

53 26 80 19 35 23 13 36 161

C C C C C D D D C

212

Health Services Management Research Human-Computer Interaction IEEE Software Industrielle Beziehungen Informatik-Spektrum Information Systems and e-Business Management Information Systems Management Information Technology and Management International Journal of Accounting Information Systems International Journal of Accounting, Auditing and Performance Evaluation International Journal of Advertising International Journal of Automotive Technology and Management International Journal of Business Performance Management IJBPM International Journal of Business Science and Applied Management International Journal of Consumer Studies International Journal of Entrepreneurial Behaviour and Research International Journal of Entrepreneurship and Innovation International Journal of Entrepreneurship and Innovation Management International Journal of Entrepreneurship and Small Business International Journal of Forecasting International Journal of Globalisation and Small Business International Journal of Information Management International Journal of Information Technology and Decision Making International Journal of Innovation and Sustainable Development International Journal of Innovation and Technology Management International Journal of Integrated Supply Management International Journal of Knowledge Management International Journal of Knowledge Management Studies International Journal of Logistics Management International Journal of Management Practice International Journal of Managerial and Financial Accounting International Journal of Market Research International Journal of Mobile Communications International Journal of Operational Research International Journal of Product Development International Journal of Product Lifecycle Management International Journal of Productivity and Performance Management International Journal of Project Management International Journal of Public Administration International Journal of Public Sector Management International Journal of Public Sector Performance Management International Journal of Retail and Distribution Management International Journal of Revenue Management International Journal of Sports Marketing & Sponsorship International Journal of Technology Management International Journal of the Economics of Business International Review of Administrative Sciences International Review of Retail, Distribution and Consumer Research International Review on Public and Nonprofit Marketing International Small Business Journal

Annexes

29 64 99 10 15 31 53 31 44 12 37 18 18 13 56 57 11 20 26 79 14 91 36 18 15 18 19 11 44 2 8 47 38 23 22 19 48 121 28 48 6 67 10 18 51 23 45 18 11 71

C C C C D C C C C C C C D C D C C C C C C C C C C C C C C C C D C C C C C C C C C C C D C C C C C C

Annexes

International Studies of Management and Organization Issues in Accounting Education Journal for East European Management Studies Journal of Accounting Education Journal of Applied Accounting Research Journal of Applied Business Research Journal of Brand Management Journal of Business and Industrial Marketing Journal of Business Strategy Journal of Business Valuation and Economic Loss Analysis Journal of Business-to-Business Marketing Journal of Change Management Journal of Consumer Affairs Journal of Consumer Behaviour Journal of Consumer Marketing Journal of Consumer Policy Journal of Contemporary Accounting and Economics Journal of Developmental Entrepreneurship Journal of Economics and Business Journal of Economics and Finance Journal of Electronic Commerce in Organizations Journal of Electronic Commerce Research Journal of Enterprise Information Management Journal of Entrepreneurship Journal of Entrepreneurship Education Journal of Environmental Planning and Management Journal of Family Business Strategy Journal of Financial Research Journal of Financial Services Research Journal of General Management Journal of Global Marketing Journal of Healthcare Management Journal of Information Systems Journal of Intellectual Capital Journal of International Entrepreneurship Journal of International Financial Management and Accounting Journal of Knowledge Management Journal of Macromarketing Journal of Management and Governance Journal of Managerial Issues Journal of Marketing Management Journal of Marketing Theory and Practice Journal of Neuroscience, Psychology, and Economics Journal of Organizational Computing and Electronic Commerce Journal of Product and Brand Management Journal of Relationship Marketing Journal of Research in Marketing and Entrepreneurship Journal of Retailing and Consumer Services Journal of Revenue and Pricing Management Journal of Services Marketing

213

17 19 12 29 17 16 35 57 34 6 28 22 53 32 84 38 15 20 45 25 20 26 52 11 11 60 32 43 46 16 29 43 26 73 37 31 95 48 44 30 47 39 23 33 70 22 16 65 19 88

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214

Annexes

Journal of Strategic Marketing Journal of Taxation Lecture Notes in Business Information Processing Logistics Research Management Decision Managerial Auditing Journal Marketing Theory MIT Sloan Management Review Multinational Business Review Negotiation Journal Organizational Dynamics Problems and Perspectives in Management Proceedings of the Americas Conference on Information Systems (AMCIS) Proceedings of the Multikonferenz Wirtschaftsinformatik (MKWI) Production Planning and Control Project Management Journal Public Money and Management Research in Accounting Regulation Review of Accounting and Finance Review of Marketing Science Service Industries Journal Social and Environmental Accountability Journal Sport Marketing Quarterly (SMQ) Strategy and Leadership Sustainability (Switzerland) Sustainability (USA) Sustainability Accounting, Management and Policy Journal Sustainable Development Technology Analysis and Strategic Management Technovation Telecommunications Policy Thunderbird International Business Review Zeitschrift für die gesamte Versicherungswissenschaft Average: Average:

42 7 40 16 82 47 55 87 24 27 55 15 6 4 66 33 43 15 17 10 57 9 2 40 53 13 18 51 60 111 60 29 5 36,04 36,04

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Annexes

215

Annex C: Code structure for expert interview coding (Part III) Main Code

Code

Codings

Definition Characteristics

20 195

Characteristics

Customer-oriented culture & strategy

16

Characteristics

Data-oriented culture & strategy

23

Characteristics

Top management support

13

Characteristics

Data access, collection & ownership

22

Characteristics

Data privacy

18

Characteristics

Standardization

11

Characteristics

External collaboration

32

Characteristics

Internal collaboration

15

Characteristics

Resource recombination

10

Characteristics

Human IT resources

13

Characteristics

Revenue models

22

Annex D: Pictures from the focus group interviews (Part IV)

216

Annexes

Annexes

217

Annex E: Code Structure under the usage of the ARA-Model (Part IV) Main Code

Code

Codings

Current & anticipated challenges

321

Current & anticipated challenges

Activities

164

Activities

Data Generation

15

Activities

Data Acquisition

29

Activities

Data Processing

14

Activities

Data Aggregation

10

Activities

Data Analytics

45

Activities

Visualisation

6

Activities

Distribution

45

Current & anticipated challenges

Actors

98

Actors

External

32

Actors

Internal

66

Current & anticipated challenges

Resources - Data Sources

59

Resources - Data Sources

External

34

Resources - Data Sources

Internal

25

Environmental Factors

17

Environmental Factors

Actors

11

Environmental Factors

Activities

5

Environmental Factors

Resources

Organization wide goals and policies

1

178

Organization wide goals and policies Activities

57

Organization wide goals and policies Goals

41

Organization wide goals and policies Policies

80

Policies

Barriers

46

Policies

General

12

Policies

Support

22

218

Annex F: Exemplary actor map (Part IV)

Annexes

Annexes

Annex G: Pictures from the Delphi study (Part V)

219