Release external expertise in collaborative innovation

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Release External Expertise in Collaborative Innovation

Jelle de Vries

Eindhoven University of Technology

Department of Industrial Engineering & Innovation Sciences

Supplier-manufacturer collaborations have become crucial for the innovation of products and services

of high-tech manufacturers. Supplier experts (SEs) interface between supplier and manufacturer

and thus personify the supplier’s knowledge contributions to high-tech manufacturers’ innovation

programs. With the aim to develop the best possible customer offerings, manufacturers want SEs to

share their most valuable knowhow and spur innovation when interfacing with their employees. While

manufacturers expect SEs to take the interests of the manufacturer’s innovation project to heart,

many innovation managers still apply a short-term engagement perspective (i.e., a focus on getting

the project done) and treat SEs as “the external person”. The human factor at play can, however, not

be underestimated: manufacturers’ need external experts with vested interest in the manufacturer’s

success to benefit from their expertise. This dissertation shows how contractual, relational and

psychological governance mechanisms influence external experts to share their expertise in support of

manufacturers’ innovation aims.

Keywords: Knowledge sharing, innovative work behavior, R&D collaborations, inter-organizational

innovation, supplier expertise, relational governance, contractual governance, psychological

governance

Release External Expertise in Collaborative InnovationJelle de Vries

Release External Expertise in Collaborative Innovation

I N V I T A T I O N

to the public defense of my dissertation

The public defense will take place on Thursday December 14th 201716.00 hrs, at the ‘Senaatszaal’ of the Auditorium at Eindhoven University of Technology. Address: De Groene Loper 1, Eindhoven

After the public defense there will be a reception.

ParanimfenFemke Huijbregts-de VriesAukje Janssen-de Vries

Jelle de VriesTorenweg 145731 CJ Mierlojelle.de.vries@philips.com

Release External Expertisein Collaborative Innovation

Jelle de Vries

ISBN/EAN 9789462957473

Layout cover: OAK-Duurzaam.nl – Ellen VereijkenLayout inside: ProefschriftOntwerp.nlPrint: ProefschriftMaken | Proefschriftmaken.nl

Release External Expertisein Collaborative Innovation

PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven,

op gezag van de rector magnificus prof.dr.ir. F.P.T. Baaijens, voor een commissie aangewezen door het College voor Promoties,

in het openbaar te verdedigen op donderdag 14 december 2017

om 16:00 uur.

door

Jelle Johannes Antonius Petrus de Vriesgeboren te Mierlo

Dit proefschrift is goedgekeurd door de promotoren en de samenstelling van de promotiecommissie is als volgt:

voorzitter: prof.dr. I.E.J. Heynderickx1e promotor: prof.dr. A.J. van Weele2e promotor: prof.dr. F. Langerakcopromotor(en): dr. J.J.L. Schepersleden: prof.dr. H.J. Hultink (Technische Universiteit Delft) prof.dr.ir. E.W. van Raaij (Rotterdam School of Management,

Erasmus Universiteit Rotterdam) prof.dr. F.A. Rozemeijer (Universiteit Maastricht) prof.dr. C.C.P. Snijders (Technische Universiteit Eindhoven)

Het onderzoek of ontwerp dat in dit proefschrift wordt beschreven is uitgevoerd in overeenstemming met de TU/e Gedragscode Wetenschapsbeoefening.

Dedicated to Nienke, Tijn and Lieve,Mom and Dad.

Acknowledgements

A journey that started late 2011 has come to fruition. I want to take this opportunity to thank those who have supported, coached, inspired and enabled me to arrive at this ‘destination’.

First, this journey was enabled by Prof. Dr. Arjan van Weele. Arjan, you shared your enthusiasm for my research interest already during my master thesis project. It was you who launched the idea to continue with this work and helped transform it into a PhD project. I thank you for your warm approach throughout, keeping me on track, setting deadlines as well as allowing me to explore and “lose time” when needed. Thank you for your trust, support, enthusiasm, and taking good care of your PhDs. Your passion for procurements’ role in enabling high-quality innovative collaborations between buyers and suppliers is a true inspiration for me.

Additionally, I want to thank my second promotor, Fred Langerak, for his support and ever constructive feedback. When I realized that you were to join as the second promotor I felt honored. Today, I am even more honored and grateful, simply because your sharp reviews and constructive feedback helped me learn a great deal and made my work better. I further thank you for making me feel welcome in the ITEM community, positive mindset and professionalism. The ITEM group is blessed with your leadership and people management skills.

During the whole project, one person in particular could be counted on at all times: Jeroen Schepers, my co-promotor. During the ‘flight’ you were my true navigator, teacher and friend in the world of academia. What an incredible person you are! Words hardly can cover the work and energy you have put in helping me to arrive at this point. Thank you for the many moments of fun and laughter but in particular thank you for providing me the feedback I needed to dig deeper, take nothing for granted and to deliver. You taught me to never stop asking questions, to being critical of own work and to continuously reflect on lines of reasoning. You give me confidence, helped me develop my academic skills, stopped me when I needed the handbrake and motivated me to continue. Without you, I would have never been able to get where I am now. I will never forget the Istanbul trip where we laid the foundation for the second and third study, together with Gielis. I am certain that, one day, I will have to say “dear professor” to you, something you truly deserve. Your stock of theoretical knowledge, academic writing and reasoning skills simply amaze me every time we speak.

Next, I want to say thank you to all the ITEM colleagues from the TU/e Connector building. I always enjoyed entering this magic room called the “PhD-garden” (a.k.a. “AIO

tuin”). Thank you to my (ex-)PhD colleagues for the moral support, fun talks, music on ‘turn-table Fridays’ and making me feel welcome. In particular I want to thank Regien and Gielis for the epic conference visits (IPSERA and EMAC), their friendship, jokes and help. I also want to thank the secretaries for making sure things work within the ITEM group. Thank you for making sure that workplaces, tools and material were available at all times.

I also want to acknowledge the many fine colleagues from Philips. First the (former) IMS IT procurement colleagues. You are a warm bunch of top IT commodity managers from who I’ve learned a great deal. Second, I want to recognize the great team I am privileged to lead today of the commodity cluster Embedded Computing and User Interfacing. In random order: Jim, Ray, Godfried, Tiny, Stella, Yehuda, Geert, William, Lix, Yuliya, Jan, Xuan, Jack, Leon, Genevieve, Kelly, Mohit and Philip, thank you for your professionalism that allowed me as well to devote time to this project off-work. A special appreciation goes to Ria Krops. I am highly appreciative for your support; always covering my back, managing my agenda, and creating failsafe travel schedules. Third and finally, I want to thank four inspirational leaders in Philips that shaped me as a professional: Reinoud Selbeck, Jeroen Tas, Richard Crane and Scott Schwartz. Jeroen, thank you for your vision, confidence and support. Reinoud, I will never forget the first day we met at Philips when you were assigned as my personal coach. You not only supported and helped enable this project but you also coached me along the way during numerous occasions. Your skills as a coach are unparalleled and I am grateful for all the support you are, still today, giving me. Richard and Scott, thank you for leadership and the confidence you place in me every day.

A warm ‘thank you’ goes to the entire “Club Kerkuil” (including kids). Thank you for the ever-available barbeque and evening drinks and being a pleasant distraction at all times. I also want to acknowledge Joost van Wijngaarde for the countless times we discussed work, life, and family, especially during the soccer games of the club in our heart: PSV. A heartfelt appreciation goes to all of my family who have always backed me up during good and rough times. Femke and Aukje, not only are you my paranimfs during the public defense, above all I am proud that you are my sisters. I am equally proud of your husbands and kids - Seppe, Milou, Juul, Finn, Ruben, and Jade. They all bring an enormous deal of extra love, joy and energy in my life. Furthermore, I want to thank Henny for being a great second mother. You have always been there to support Nienke and myself, helping to alleviate the ever full schedule. Similarly, a warm hug to Jojanneke, my sister-in-law, and my niece Jipp.

Most of my appreciation goes to the love of my life: Nienke. Words can hardly describe what you have done for me over the past years. Giving me a break from home-duties, being the mother to our children, being there and backing us up at all times, without

a single complaint. At times where this project was tough, you pulled me through by showing your unconditional love, enthusiasm, and appreciation. I love you and our kids, Tijn and Lieve, and am extremely proud of us as a family. In similar vein, I want to show gratitude to my parents. Mom, dad, you have raised and supported me all my live, making me the person I am today. You created a close family as a solid base for us all to thrive. The energy you have put in us has motivated me throughout to continuously raise the bar and work just as hard. Thank you for your unconditional love and support.

Jelle, November 2017

Table of Contents

Chapter 1: Introduction 1.1 The innovation challenge in high-tech and dynamic markets 1.2 Perspectives on governing the influx of supplier expertise 1.3 Research positioning and aim 1.4 Focal supplier expert behavior 1.5 Dissertation outline 1.5.1 Overall research design 1.5.2 Research objectives study 1 (chapter 2) 1.5.3 Research objectives study 2 (chapter 3) 1.5.4 Research objectives study 3 (chapter 4) 1.6 Author & co-author contributions

Chapter 2: When do they care to share? How manufacturers make contracted services partners share knowledge

2.1 Introduction 2.2 Theoretical background 2.2.1 Managing service partner relationships 2.2.2 Exploitative and exploratory knowledge sharing 2.3 Conceptual framework and hypotheses 2.3.1 The influence of contract characteristics on knowledge sharing 2.3.2 The influence of relationship characteristics on knowledge sharing 2.4 Method 2.4.1 Sample and data collection procedure 2.4.2 Sample characteristics 2.4.3 Measures 2.4.4 Control Variables 2.5 Results 2.6 Conclusions and implications 2.7 Managerial Implications 2.8 Limitations and further research

Chapter 3: Aligning Interests or Aligning Rewards? Exploring the Drivers of Knowledge Sharing of External Technology Experts in Collaborative R&D Projects

3.1 Introduction 3.2 Theoretical Background 3.2.1 Knowledge sharing 3.2.2 Knowledge sharing in R&D collaborations

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2931333334353538414141424343485051

5557595959

3.3 Conceptual framework & hypotheses 3.3.1 Knowledge sharing process. 3.3.2 Effect of customer stewardship. 3.3.3 Effect of distributive fairness. 3.3.4 Formal coordination as a moderator of the knowledge sharing

process. 3.4 Methodology 3.4.1 Research setting and data collection 3.4.2 Sample characteristics 3.4.3 Measures 3.5 Data Analysis & Results 3.5.1 Measurement model 3.5.2 Analyses 3.5.3 Results 3.5.4 Post-hoc analyses: floodlight analysis of full moderated mediation

model 3.6 Discussion & Conclusions 3.6.1 Effects of customer stewardship 3.6.2 Effect of distributive fairness 3.6.3 Aligned rewards or interest? 3.6.4 Managerial Implications 3.6.5 Strengths, limitations & future research

Chapter 4: Mind the Gap: How Identification Gaps Influence External Team Members’ Innovative Work Behavior in New Product Development Projects

4.1 Introduction 4.2 Theoretical Background 4.2.1 Social Identity, Self-Categorization, and Identification Theory 4.2.2 Multiple Identities and Identification Gaps 4.2.3 Innovative Work Behavior 4.3 Hypotheses 4.3.1 Inter-Organizational Identification Incongruence 4.3.2 Intra-Organizational Identification Congruence 4.3.3 Managing Identification (In)Congruence 4.4 Methodology 4.4.1 Research Setting and Data Collection 4.4.2 Sample Characteristics 4.4.3 Measure 4.5 Data Analysis and Results

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636464656669697073

73767677787879

838587878788898990909292939495

4.5.1 Measurement Model 4.5.2 Analytical Procedure and Results 4.6 Discussion & Conclusions 4.6.1 Theoretical Implications 4.7 Managerial Implications 4.8 Limitations and Further Research

Chapter 5: Discussion and conclusions 5.1 Synopsis 5.2 Main conclusions and insights from the chapters 5.2.1 Conclusions and insights from study 5.2.2 Conclusions & insights from study 2 5.2.3 Conclusions & insights from study 3 5.3 Overall conclusions 5.3.1 Contractual governance 5.3.2 Relational governance 5.3.3 Psychological governance 5.4 Practical implications 5.5 Limitations & avenues for future research

References

Summary

About the author

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1Introduction

INTRODUCTION

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This introductory chapter explains the importance of external supplier expertise for high-tech manufacturing firms. It highlights the increasingly important role of suppliers in manufacturers’ innovation processes. The supplier’s employees, to which we refer as supplier experts (SEs), represent an important knowledge source that can boost a manufacturer’s product and service innovation. This chapter highlights relevant research and theories connected to this topic. Following this review, the aim and research questions of this dissertation are derived. Finally, this chapter provides an introduction to the three studies which have been conducted and thus gives a high-level overview of the dissertation’s structure.

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1.1 The innovation challenge in high-tech and dynamic markets

High-tech manufacturers face intense competitive pressures in dynamic and globalized markets. Factors, such as technological change, demand uncertainty, declining margins and increased globalization have raised one clear challenge for Western high-tech manufacturers: “innovate or die” (Quinn 2000, p. 13). Successfully investing in product and service innovation is, however, not simple. It requires a conscious, structured and focused decision-making process to redesign research and development (R&D) processes (Berchicci 2013). A recent European Union innovation survey (European Commission 2016) reveals that manufacturers increasingly suffer from scarce resources to come up with and realize innovations. In response, they increasingly focus on those assets and capabilities that provide them with a competitive edge (manufacturers’ core competences) and augment their innovation resources with those of external specialists, i.e. suppliers (manufacturers’ non-core competences). Through collaboration with suppliers, manufacturers gain access to skills, abilities, knowledge, solutions, and technologies for which they lack internal resources. Through investments in the development of their core competences, suppliers develop specific content expertise, technologies, and solutions. Because of continuously working with a wide range of business partners and customers, suppliers increasingly become experts in the application of their expertise and can contribute significantly to manufacturers’ innovation processes and product development programs (Eisingerich, Rubera, and Seifert 2009). For example, a call-center services supplier that is responsible for a manufacturer’s customer service processes continuously learns about customer problems and experiences. Knowledge of and acting on such insights allows the manufacturer to improve existing products and perhaps even to create successful new products and services. Another example is a supplier which specialized components and technologies may help the manufacturer’s become more innovative but need much solution-specific tweaking and adjustment. This supplier’s employees act as engineers to integrate the components in the manufacturer’s solution. High-tech manufacturers may thus benefit in many ways from the expertise of specialist suppliers. We define supplier expertise as specific tacit knowledge, skills, ideas, and insights from experience, held by a supplier employee that enables their customers (i.e., manufacturers) to create competitive and novel market offerings. Supplier expertise is thus strategic in nature and different from knowledge that is generally available in an industry. The ability to effectively access, adopt, absorb and integrate supplier expertise for the creation of novel products and services allows high-tech manufacturers to speed up and improve the innovation process and thus beat competition (Dangelico, Pontrandolfo, and Pujari 2013; Verona 1999). Of crucial importance in this process is managing the supplier employees who span the organizational boundary between supplier

INTRODUCTION

18

and manufacturer and are on the frontline of sharing the supplier expertise. These so-called supplier experts (SEs) represent the focus of this dissertation.

1.2 Perspectives on governing the influx of supplier expertise

Manufacturers struggle to get their suppliers and their employees to disclose their expertise (Van Burg, Berends, and Van Raaij 2014). An important reason for this problem is that suppliers fear knowledge misappropriation, loss of competitive advantage to get a fair return for their contributions (Miozzo et al. 2016). Often business interests (i.e. goals, division of labor, and risk sharing) between supplier and manufacturer also seem to be misaligned or even conflicting (Wang, Kayande, and Jap 2010). In an effort to solve some of these challenges, extant research describes multiple governance mechanisms to motivate suppliers to share their expertise in supplier-manufacturer collaborations. Three categories of governance mechanisms can be defined: contractual, relational and psychological governance. First, contractual governance comprises all mutually agreed upon, written and unwritten management-initiated activities that are designed to influence the probability that either party behaves in ways that support agreed objectives (Jaworski, Stathakopoulos, and Krishnan 1993). For instance, formal contracts are generally created before the actual supplier-manufacturer interactions starts and used throughout the collaboration to coordinate supplier behavior. In literature, the application of contractual governance to safeguard parties from mutual opportunism is prominently discussed by agency theory (Eisenhardt 1989). The theory describes the problems encountered when one party (the principal; here: manufacturer) delegates work to another party (the agent; here: supplier) (Bergen, Dutta, and Walker 1992). The basic tenet is that the agent puts self-interest first and that the diverging interests and the difficulty of the principal to verify the agent’s behavior lead to ‘agency problems’ (Albanese, Dacin, and Harris 1997). Formal agreements can address this problem in supplier-manufacturer relationships by solving information asymmetries and limiting self-serving behavior. Contractual governance thus controls supplier behavior through formal agreements on legal and financial terms (Hagedoorn and Hesen 2007). This dissertation explores the role of formal agreements in supplier-manufacturer exchanges, specifically with a focus on sharing supplier expertise by SEs. Second, relational governance structures supplier-manufacturer interactions through reliance on trust, social norms, and resultant self-control of each party (Cao and Lumineau 2015). When trust and norms govern a relationship parties are less motivated to exploit their position because this would jeopardize the future value of the relationship (Baker, Gibbons, and Murphy 2002). Relational governance operates on multiple levels such as the organizational level (Blomqvist, Hurmelinna, and Seppänen 2005; Carson, Madhok, and Wu 2006), departmental or team level (Gittell 2002; Rousseau 1998a), and

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the individual level (Konovsky and Pugh 1994). For supplier-manufacturer collaboration in innovation projects, relational governance on the inter-organizational level is relevant. This dissertation specifically zooms in on how such inter-organizational relational governance structures affect the individual SE’s behavior. It specifically conceptualizes relationships using stewardship theory, which describes that supplier employees can act as stewards, motivated to act in favor of the manufacturer, when their interests are aligned and/or vested in the problems and interests of the manufacturer (Schepers et al. 2012). The relational governance then becomes a motivational mechanism. Third, psychological governance operates at the individual-level and is based on an individual’s commitment and attachment to honoring the obligations of the exchange relationship (Hui, Lee, and Rousseau 2004). Psychological governance is particularly important because the act of sharing expertise across organizational boundaries does not occur at an organizational level, but is an employee-to-employee process. The personal beliefs and motivations of SEs determine how individuals regulate their behavior (Riketta and Van Dick 2005). Psychological governance beliefs are inherently perceptual (Epitropaki 2013) and may overlap with terms outlined in a formal written contracts (Bingham et al. 2013). Still, individuals interpret such formal terms through their own perceptual lens. That lens influences the SEs’ personal commitment to contribute, and personally attach, to the aims and objectives of the manufacturer and its employees. Theories that explain the effects of personal commitment and attachment are the theory of reasoned action (Fishbein and Ajzen 1975) and social identification theory (Mitchell, Agle, and Wood 1997). Reasoned action theory argues that subjective and normative beliefs held by the SE actually influence commitment to engage in expertise sharing behavior (Bock et al. 2005). Identification theory argues that SEs can identify (i.e., feel a sense of “oneness”) with, and commit and attach themselves to a manufacturer’s interest, or not (George and Chattopadhyay 2005). This dissertation builds on both theories to explore how SEs share supplier expertise within supplier-manufacturer collaborations.

1.3 Research positioning and aim

While formal, relational, and psychological governance and their related theories have been widely applied to date, there are three major areas that require a deeper understanding of SEs’ motives to share expertise for the purpose of product and service innovation. First, existing literature that has taken a manufacturer-to-supplier control perspective to understand the exchange of information in supplier-manufacturer collaborations (Van Burg, Berends, and Van Raaij 2014; Zsidisin and Ellram 2003). In contrast, this research takes a supplier perspective, seeking an understanding of the formal, relational and psychological pressures that (de)motivate suppliers, and their

INTRODUCTION

20

employees, to disclose their expertise. A prime reason for taking this perspective is that supplier expertise represents strategic value: a core competence of the supplier that is built up over time through investments (Grant 1996). Once the supplier shares its expertise it can be used freely by the manufacturer without giving the supplier a return (Mascitelli 2000). Given the growing dependence of manufacturers on suppliers sharing their expertise (Yeniyurt, Henke, and Yalcinkaya 2014), there is a clear need to research under what conditions or circumstances suppliers are willing to share their expertise. This dissertation therefore focuses on the supplier sharing expertise and not whether the manufacturer shares expertise in return, absorbs the shared expertise, or recognizes its strategic value. Second, existing works focus on organization-level expertise sharing (Potter and Lawson 2013). These works do not investigate how (individual-level) SEs decide to share supplier knowledge with manufacturers’ employees. As these SE contributions are increasingly important, a better understanding of how formal, relational, and psychological governance influence SEs’ expertise sharing behavior is needed. Agency, stewardship, reasoned-action and social identification theories can all be used to understand the behavioral decision process that SEs go through when the interests of multiple parties in the work environment compete. Third, most prior academic research takes either a formal (i.e., Miozzo et al. 2016; Tate et al. 2010), a relational (i.e., Jeffries and Reed 2000; Liu et al. 2008) or a psychological governance perspective (i.e., Bock et al. 2005; George and Chattopadhyay 2005) to analyze behavior in inter-organizational exchange contexts. However, the three perspectives are not mutually exclusive, they all operate at the same time in a given supplier-manufacturer constellation (Ferguson, Paulin, and Bergeron 2005; Zhang and Zhou 2013). In an effort to address this shortcoming of extant literature, the current research project investigates the role of the three governance forms in three distinct studies, but in the same domain of supplier-manufacturer collaboration. Although the focus and positioning of the studies did not allow to integrate all three governance types in one study, the first two studies feature multiple governance mechanisms and the general discussion is geared towards integrating the insights from the three different studies. We return to introducing the studies’ focus and contents shortly.To summarize, this research aims to better understand SEs’ sharing of supplier expertise to support a manufacturer’s innovation process. To do so, it investigates how individual motivations and cognitions, together with contractual, relational and psychological governance factors, explain SEs’ behavioral choices. The main research question is therefore defined as:

What contractual, relational and psychological mechanisms motivate or discourage supplier experts to share their expertise and innovative insights with a manufacturer?

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In order to answer this question focal SE behaviors must be defined first. Which SE behavior supports a manufacturers’ innovation aims?

1.4 Focal supplier expert behavior

SEs span the organizational boundaries and may transfer expertise from supplier to manufacturer. Expertise is here defined as knowledge, which is tacit, residing within an individual (here: SE) who has acquired and stored insights during social and content-related interactions in a variety of contexts (e.g., working with customers, participating in R&D efforts) (Easterby-Smith, Lyles, and Tsang 2008). Knowledge comprises individual schemas, skills, habits, and abstract information that apply to a specific context (Mascitelli 2000). Knowledge is therefore unique and varies in transferability (Grant 1996). It is also strategic in nature because knowledge is owned by a supplier but desired by a manufacturer. Because of the tacit nature of knowledge in supplier-manufacturer exchanges, the exact and complete prescription in contracts of must-be-shared knowledge is virtually impossible (O’Neill and Adya 2007). The decision to share knowledge during work interactions is at the discretion of the SE: his or her personal choice to engage in knowledge sharing behavior or not. Knowledge sharing behavior is defined as all the efforts that relate to the transfer or dissemination of knowhow and insights from one person to another (Lee 2001). Applied to this research context, knowledge sharing involves the act of SEs sharing their expertise such that it can be appropriated to create innovations. Knowledge sharing behavior is a crucial antecedent of new knowledge creation within the manufacturer’s organizational boundaries (Foss, Husted, and Michailova 2010). The resultant generation, promotion, discussion, modification and transformation of new knowledge during interpersonal interactions allows for developing new valuable and marketable ideas (Janssen, Van de Vliert, and West 2004). Knowledge sharing behavior therefore is an antecedent of product and service innovation. Another behavior that can clearly be linked to product and service innovation is innovative work behavior, which is defined as “the intentional creation, introduction and application of new ideas within a work role, group or organization, in order to benefit role performance, the group, or the organization … restricting innovative behavior to intentional efforts to provide beneficially novel outcomes” (Janssen 2000, p. 288). SEs’ innovative work behavior is considered a broad construct that describes the intentional efforts of SEs to come to new outcomes beneficial for the individual, group or manufacturer (De Jong and Kemp 2003). Both knowledge sharing and innovative work behavior of SEs in supplier-manufacturer contexts are relevant to investigate because they focus on joint value creation. They are distinctly different from behaviors to uphold relationship quality

INTRODUCTION

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(Chiaburu et al. 2015), pro-social behaviors (Bettencourt, Brown, and MacKenzie 2005) and helping behaviors (Vigoda-Gadot 2007), because they directly enable the manufacturer to refi ne existing or create new products and/or services using the expertise held by SEs.

1.5 Dissertation outline

This dissertation focuses on formal, relational and psychological infl uences on SEs, who have to decide whether to share knowledge and work innovatively in favor of a manufacturer. Three distinct studies address the main research question and aim in separate chapters. Each chapter will describe relevant literature, methodologies used, just as theoretical and managerial implications. The next paragraphs in this subsection describe the research aims and questions of the different chapters.

Figure 1-1: Supplier expertise sharing context, SE roles and focal behaviors

1.5.1 Overall research design

In the research design, two specifi c SE roles are distinguished: SEs as frontline service employees (FSEs) and as external technology experts (ETEs) (see Figure 1-1). The fi rst, FSEs operate at the end of a manufacturers’ value chain, where they engage in performing outsourced customer facing services. FSEs work, for example, in call center operations where they interact with customers of the manufacturer on its behalf. Supplier expertise at this side of the manufacturers’ value chain enables to improve products and services

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using customer feedback and insights (Eisingerich, Rubera, and Seifert 2009). Second, the influx of external expertise is critical in collaborative R&D settings where ETEs should integrate supplier technology into the manufacturers’ product/services in the front end of the manufacturers’ value chain (Handfield et al. 1999). By focusing on FSEs and ETEs this research thus investigates the effects of contractual, relational and psychological governance at both ends of the manufacturer value chain. This is done in three separate studies. Table 1-1 provides an overview of the studies conducted, indicating for each study what SE role the study focuses on and the theoretical perspective that is employed. Other high-level study details are provided too. Study 1 focuses on exploitative and exploratory knowledge sharing as the dependent variable. Both exploitative and exploratory knowledge enable a manufacturer to innovate its products and services (Lakemond and Detterfelt 2013). Study 2 integrates both knowledge types into an overarching knowledge sharing behavior concept. Study 3 focuses on SEs’ commitment to behave innovatively in favor of a manufacturer, that is, not just sharing knowledge but also to champion ideas that stem from interactions with manufacturers’ R&D team. In the final chapter, perspectives and insights derived from the distinct three studies are combined to derive a holistic overview of research findings, to arrive at implications for scholars and practitioners, and to provide directions for future research. The next three sections further describe the specific research objectives per study.

1.5.2 Research objectives study 1 (chapter 2)The first study focuses on knowledge sharing by FSEs (see Figure 1-1). The primary goal of this study is to investigate whether contractual and relational governance factors influence FSEs’ knowledge sharing behavior. Knowledge transfer theory discusses the use of formal contracts as a means to limit opportunistic behavior in the process of developing new products (Zhao and Lavin 2012). However, the effects of contract comprehensiveness in outsourced services arrangements (Gainey and Klaas 2003), and the effects of relational factors remain virtually unexplored drivers of knowledge sharing behavior. The chapter applies an agency perspective (Eisenhardt 1989) and distinguishes two types of knowledge sharing: exploitative and exploratory knowledge sharing. The first relates to sharing knowhow that can ultimately lead to the refinement of existing products and services; the second to the creation of completely new products and services that augment the existing portfolio of a manufacturer. This distinction is important since the sharing of exploitative knowledge is more common in outsourced services arrangements and often regarded as in-role behavior (Bettencourt, Brown, and MacKenzie 2005). The objectives of this chapter are to: (1) provide clarity if and to what extent contractual and relational governance mechanisms stimulate FSEs knowledge sharing, and; (2) determine if and to what extent contractual and relational governance mechanisms explain the different types of knowledge being shared.

INTRODUCTION

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Table 1-1: Overview of studies

Chapter 2/STUDY 1 Chapter 3/STUDY 2 Chapter 4/STUDY 3

Unit of AnalysisService supplier-

manufacturer interface (see 1 in figure 1-1)

Technology expert-manufacturer R&D team

interface (see 2 in Figure 1-1)

Technology expert-manufacturer R&D team interface (see 3a, 3b in

Figure 1-1)

Core theories usedAgency &

Knowledge transfer theory

Stewardship, Reasoned-action &

Fairness theory

Identification & Organizational

socialization theory

Focal expertise inflow

Service knowhow, customer feedback & insights

Technology knowhow & insights

Technology knowhow & insights

“Sender”Frontline service employee

(supplier employee)External technology expert

(supplier employee)External technology expert

(supplier employee)

“Receiver”Relationship manager

(manufacturer employee)R&D project team

(manufacturer employees)R&D project team

(manufacturer employees)

Dependent variable

Exploitative / exploratory knowledge sharing

Knowledge sharing behavior

Innovative work behavior

Antecedents Contract characteristics:Contractual incentives

Specification levelRelationship

characteristics:Experience relationship

manager Relationship quality

Customer stewardship

Distributive fairness

ETE onboardingETE goal socialization

Mediator -Knowledge sharing

intention

(3a) Inter-organizational identification incongruence(3b) Intra-organizational identification congruence

Moderator - Formal coordination -

Study method survey survey survey

Sample size N = 70 N = 187 N = 187

1.5.3 Research objectives study 2 (chapter 3)Study 2 continues with investigating knowledge sharing behavior within the context of collaborative R&D (see 2; Figure 1-1). This chapter specifically focuses on external technology experts’ (ETE) cognitive processes through which they are motivated to share knowledge, using a motivation-intention-behavior framework (Ajzen and Fishbein 1977). The research examines how customer stewardship and distributive fairness motivate ETEs’ to share knowledge, testing the effects on their intention and behavior. This study also investigates whether efforts by the manufacturer to formalize the cooperation with ETEs influences the efficacy of their motivations and therefore limit or enhance ETE knowledge sharing behavior. The objectives of study 2 are to: (1) determine whether perceptions of distributive fairness and customer stewardship are important drivers of ETEs’ intention

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to share knowledge; (2) whether knowledge sharing intentions lead to knowledge sharing behaviors; (3) whether formal coordination influences the relationship between intention and behavior, and; (4) investigate the complex interplay between formal coordination, distributive fairness and customer stewardship.

1.5.4 Research objectives study 3 (chapter 4)Study 3 examines the role of psychological attachment in collaborative R&D settings, i.e. the ETEs identification with supplier, manufacturer and R&D team (see Figure 1-1). Building on social identification theory (Ashforth and Mael 1989), this study investigates how ETEs’ identification with the manufacturer relative to the supplier, and their identification with the manufacturer relative to the R&D project team, influence ETEs’ innovative work behavior. Identification scholars posit that individuals can identify with multiple ‘social entities’, such as departments and organizations at the same time (Bartel 2001; Cooper and Thatcher 2010). When identifying with such an entity, the individual aligns his/her behavior to advance its cause. It is however unclear how ETEs behave in case of identification with multiple entities at the same time. Therefore the objectives are to: (1) test whether a stronger identification with the manufacturer relative to identification with the supplier (i.e., identification incongruence) is associated with higher levels of ETEs’ innovative work behavior; (2) whether identification with the manufacturer and the R&D team (i.e., intra-organizational identification congruence) influences ETEs’ innovative work behavior; (3) whether identification (in)congruence can is influenced by goal socialization, and: (4) whether identification (in)congruence is influenced by onboarding practices.

1.6 Author & co-author contributions

This dissertation has been created by the PhD candidate (Jelle de Vries) under supervision of and in collaboration with his promotors (Prof. Dr. Arjan van Weele, Prof. Dr. Fred Langerak), co-promotor and daily supervisor (Dr. Jeroen Schepers) and co-author (Dr. Wendy van der Valk). The overview below (Table 1-2) highlights the contributions by each of the supervisors and/or authors to each article that is presented in chapters 2, 3 and 4.

INTRODUCTION

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Table 1-2: Author contribution overview

Dissertation section Jelle

de

Vrie

s

Jero

en S

chep

ers

Arja

n va

n W

eele

Fred

Lan

gera

k

Wen

dy v

an d

er V

alk

Chapter 1

Writing main text √

Copy editing √

Corrections and feedback √ √ √

Chapter 2

Conceptual development √ √ √

Design of study √ √ √ √

Literature review √

Data collection √

Data analysis √

Interpretation of results √ √ √ √

Writing main text √

Corrections and feedback √ √ √ √

Chapter 3

Conceptual development v √

Design of study √ √ √ √

Literature review √

Data collection √

Data analysis √ √

Interpretation of results √ √ √ √

Writing main text √

Corrections and feedback √ √ √ √

Chapter 4

Conceptual development √ √

Design of study √ √ v √

Literature review √

Data collection √

Data analysis √

Interpretation of results √ √ √ √

Writing main text √

Corrections and feedback √ v √ √

Chapter 5

Writing main text √

Copy editing √

Corrections and feedback √ √ √

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1

2When do they care to share?

How manufacturers make contracted services partners share knowledge

2When do they care to share?

How manufacturers make contracted services partners share knowledge

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Manufacturing firms that outsource customer-facing services, risk losing touch with their customers and thereby forfeit valuable market and customer-related knowledge. To maintain informed and competitive, the manufacturer’s customer-facing service partners should engage in knowledge sharing and transfer their market knowledge and insight to the firm. Building on knowledge transfer and organizational learning theory, this study investigates how contractual and non-contractual (i.e., relationship) characteristics influence knowledge sharing behavior by service partners. The authors specifically distinguish between sharing exploitative knowledge (insights that help the manufacturing firm to refine current skills and procedures) and exploratory knowledge (insights that help the manufacturing firm to challenge prior approaches to interfacing with the market). Based on survey data from 70 relationship managers from a large multinational firm and partial least squares path modeling, results show that contractual incentives had a negative effect on exploratory knowledge sharing, but not on exploitative knowledge sharing. The level of contract specification and relationship quality positively related to both types of knowledge sharing. Relationship manager experience related positively to exploratory knowledge sharing, but not to exploitative knowledge sharing. These findings provide valuable insights on how (non-)contractual mechanisms can be used to manage knowledge sharing in outsourced services.

Earlier versions of this chapter have been presented at the 2012 IPSERA conference (Naples, Italy). The article has been published in 2014 in Industrial Marketing Management, volume 43, issue 7, page 1225-1235 and currently has 3 citations. The version of the article presented in this chapter is as published, unmodified. The authors of this chapter are: Jelle de Vries, Jeroen Schepers, Arjan van Weele and Wendy van der Valk.

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2.1 Introduction

In today’s networked economy, manufacturers increasingly outsource activities that do not constitute the core process of the firm. A prime example is customer-facing services, which represent pre- or post-sales activities that require direct contact with (business) customers, such as interactions through call centers, logistic operations, and maintenance staff (Davies 2004). Such outsourcing arrangements imply a service triad (Li and Choi 2009; Rossetti and Choi 2005, 2008), in which the service partner interfaces between the manufacturing firm and its customers. In fact, it now “owns” the customer (Brown and Chin 2004) in this triad, a situation reminiscent of the tertius gaudens (“rejoicing third”) described by George Simmel (1902). Although this arrangement allows a manufacturer to focus on and optimize its core activities, it is undesirable—maybe even dangerous—from a marketing and innovation perspective (Chen, Tsou, and Huang 2009). Giving up direct contact with customers means that a manufacturer misses out on important market information, which typically develops over time on the basis of many customer contacts (Kohli and Jaworski 1990). This blocks innovation and endangers continuity, because external knowledge residing at service partners cannot immediately be used to improve a firm’s current expertise and/or create new knowledge, skills, products or services (Baker and Sinkula 1999; Kyriakopoulos and Moorman 2004; Marinova 2004). To prevent a manufacturer from losing ground with its customer base and endangering its competitiveness in the market, its service partners should engage in knowledge sharing; a partner’s discretionary behavior to disseminate to and discuss with a manufacturing firm salient insights in up-to-date customer needs and market trends (Panayides 2007). Literature on organizational knowledge transfer provides important cues on how what factors drive partner knowledge sharing. Broadly, knowledge transfer is dependent on knowledge characteristics (e.g., tacitness, ambiguity), organizational characteristics (e.g., size, absorptive capacity), and network characteristics (e.g., trust, shared vision, culture) (Meier 2011; Van Wijk, Jansen, and Lyles 2008). Less attention has been given to the applicability of the knowledge transferred; once acquired, how can the knowledge be used? This appropriation process has been described by organizational learning theory (Baker and Sinkula 1999; Bell, Whitwell, and Lukas 2002; Slater and Narver 1995). It proposes that knowledge can be used in a marketing exploitation strategy, such that knowledge is acquired, interpreted, and applied to improve and refine current skills and procedures associated with existing marketing mix strategies, including segments, positioning, and distribution. Alternatively, knowledge may be appropriated to challenge prior approaches to interfacing with the market, such as new segmentation, new positioning, new products, or new channels (Atuahene-Gima 2005; Kyriakopoulos and Moorman 2004; Vorhies, Orr, and Bush 2011). While organizational learning literature thus distinguishes between exploitative and exploratory knowledge (Im and Rai 2008), little is known on what factors lead service

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partners to share which type of knowledge. Knowledge transfer literature has not provided much insight in this issue. In addition, both knowledge transfer and organizational learning literature largely concentrate on horizontal relationships: knowledge transfer in vertical relationships has received far less attention. As vertical relationships are governed by contracts (or, alternatively: formal control; cf., Kohli and Jaworski 1990) and relational factors (or: informal control), these factors are of paramount importance to explain knowledge transfer becomes pertinent and should hence be investigated. The aim of this study is thus to bridge organizational knowledge transfer and organizational learning literature by answering the research question: “How do contractual and relationship characteristics enhance exploitative and exploratory knowledge sharing by service partners to whom manufacturers have outsourced customer-facing services?” In finding an answer to this question, we make the following contributions to literature. First, we extend knowledge transfer literature, which has focused on horizontal forms of governance such as strategic alliances and joint ventures. Insights in knowledge transfer in buyer-supplier relationships are limited to new product development (NPD) projects (Zhao and Lavin 2012). Compared to such projects, service partner and manufacturer goals in outsourcing arrangements tend to be more divergent, and hence, the role of contracts tends to be more pronounced (Gainey and Klaas 2003). Second, we add to outsourcing studies, which have focused on what contractual factors limit opportunistic behavior in the relationship (Aubert, Rivard, and Patry 1996; Heide, Wathne, and Rokkan 2007; Wathne and Heide 2004), but have left the factors that drive knowledge sharing in outsourcing relationships virtually unexplored. Knowledge sharing is hard to enforce contractually and managers should know which elements in an outsourcing contract and the informal relationship are more or less conducive to knowledge sharing. This enables them to design a mode of governance that balances service partners’ efficient and effective service delivery with the manufacturers’ implicit need for a continuous inflow of external knowledge over time (Chen, Tsou, and Huang 2009). We therefore consider which contract and relationship characteristics are conducive to knowledge sharing. Third, we make a distinction between exploitative and exploratory knowledge sharing. Although knowledge transfer processes are affected by the type of knowledge shared, research has been preoccupied with tacitness as a knowledge characteristic (Meier 2011) and has left other categorizations of knowledge unexplored. We define exploitative knowledge sharing as the service partner disseminating and discussing with a manufacturing firm salient insights that help the firm to refine current skills and procedures. For instance, such knowledge may point out slack in existing service scripts or production processes (Benner and Tushman 2003; Brown and Eisenhardt 1998). With exploratory knowledge sharing we refer to shared insights that help the manufacturing firm to challenge prior approaches to interfacing with the market (cf., Kyriakopoulos and Moorman 2004). Such knowledge may lead to long-term rewards, such as ideas for a

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completely new product or service (Im and Rai 2008). We relate contract and relationship characteristics to exploitative and exploratory knowledge sharing and investigate their differential effects to help firms achieve a healthy balance in their focus on exploitation or exploration (cf., Gupta, Smith, and Shalley 2006).

2.2 Theoretical background

2.2.1 Managing service partner relationshipsService partners that conduct service activities on behalf of a manufacturer gather unique market knowledge during the interaction with customers (Gebauer et al. 2010). For example, a call center employee or field service worker may have a service encounter with a customer who complained about a manufacturer’s product. The complaint may hold detailed insights on the product that malfunctioned, such as what events preceded this failure and how the customer discovered the failure in the first place. This information may be used to improve the product and make it fit customer demands better. Therefore, both theorists and managers increasingly recognize the importance of getting external knowledge inside the firm (Chesbrough 2003; Im and Rai 2008). Trends of open innovation and crowd sourcing are implemented by firms who have experienced the advantages of operating in closely connected networks that jointly provide value propositions to customers (Chesbrough 2003; Vargo and Lusch 2004). Outsourcing customer-facing services lead to triadic relationship structures, in which the service partner usually has better access to customer information than the manufacturer (Li and Choi 2009). Manufacturers nevertheless need customer information for product and process enhancement, yet are dependent on their service partners for disclosing this information. Still, the contracts that formally govern relationships with service partners are often not geared to fostering information exchange. Rather, performance goals are set (e.g., reflected in service targets, price arrangements, payment details, and extension/termination clauses) to prevent a service partner from self-enrichment, shirking responsibilities, or cutting corners (Handley and Benton Jr. 2009; Kotabe, Mol, and Murray 2008; Lee and Kim 2010). Whether contractual characteristics relate positively or negatively to a service partner’s intentions to share knowledge, is to date unknown. Previous studies do hold that a good relationship between two parties is essential to facilitate the flow of information. The quality of social relationships, as informal governance, can be a strong deterrent against opportunistic behavior in dyads or networks (Chesbrough 2003; Granovetter 1985). Literature on organizational knowledge transfer theory further notes that close relationships between partners in strategic alliances and joint ventures leads them to expend effort ensuring that knowledge seekers or receivers understand sufficiently the newly acquired knowledge (Hansen 1999). Also trust, or:

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the belief that a partner’s word or promise is reliable and that a partner will fulfill its obligations in the relationship, enables knowledge transfer because it increases the willingness to commit to helping partners understand new insights (Inkpen 2000; Van Wijk, Jansen, and Lyles 2008). In sum, these studies hold that a good relationship is key to knowledge sharing in horizontal forms of governance. In vertical forms of governance though, outsourcing and supply chain management considered relationship quality predominantly as an antecedent to customer satisfaction (Fynes, Voss, and De Búrca 2005) or requirements obtainment (Lee and Kim 1999). A knowledge perspective is lacking. Still other studies regard knowledge sharing as a dimension of the relationship quality between two partners (rather than its outcome) and do not break down the information shared into different types (Yang and Chen 2007). We hold that knowledge sharing is a behavior and should therefore be modeled as a consequence of a state (relationship quality), rather than be an underlying part of that same state (Lee and Kim 1999). This is in line with distinctions made between these constructs in knowledge transfer theory. In addition, we feel that with the continued trend toward outsourcing customer-facing services, a more in-depth insight is needed into how contract- or relationship characteristics influence different types of knowledge sharing.

2.2.2 Exploitative and exploratory knowledge sharingOrganizational learning theory provides a sound theoretical lens through which we can examine how firms acquire, disseminate, integrate and/or update, and act on knowledge (Bell, Whitwell, and Lukas 2002; Selnes and Sallis 2003; Ye, Marinova, and Singh 2012). First, information may be acquired from direct experiences, the experiences of others, or organizational memory. Then, through organizational dissemination of the information in the organization, multiple organizational actors are able to ask questions, provide feedback, or modify the interpretation of the information (Slater and Narver 1995). Thereafter, the information may be codified based on a shared organizational vision and integrated in the firm’s knowledge stores; reservoirs of collective insights, beliefs, behavioral routines, procedures, and policies (Johnson, Sohi, and Grewal 2004; Ye, Marinova, and Singh 2012). When the new knowledge contradicts previously held knowledge, a decision should be made which of the pieces of information to retain in the knowledge store. The updated knowledge store now provides better information on what a firm can and should do in its operations, which should result in a better performance (Atuahene-Gima 2005; Johnson, Sohi, and Grewal 2004). Successful engagement in these learning processes takes up organizational resources and firms have to balance the exploitation of current knowledge and skills and the exploration of new knowledge and skills. Learning theorists have shown that strategies to develop new knowledge about the firm’s existing markets, products, and capabilities in an effort to refine them tends to limit the amount of effort that goes in to developing new knowledge that goes beyond what is currently known about markets,

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products, technologies and capabilities (Kyriakopoulos and Moorman 2004; Vorhies, Orr, and Bush 2011). In this paper, we do not investigate how firms can balance exploration and exploitation learning strategies. Neither do we investigate how managers can make learning processes more effective. In contrast, we focus on the first stage of the learning process: how can a manufacturer shape the contract and relationship with customer-facing service partners to ensure the inflow of external knowledge that can ultimately lead to the refinement of existing practices (i.e., exploitative knowledge) or to the development of new market propositions (i.e., exploratory knowledge)? Where previous organizational learning studies have focused on market orientation (Baker and Sinkula 1999; Kyriakopoulos and Moorman 2004), market knowledge development (Vorhies, Orr, and Bush 2011), or customer and competitor orientation (Atuahene-Gima 2005), we focus not so much on a firm’s awareness of or capability to absorb knowledge, but rather on how a firm can govern its service partners to share knowledge.

2.3 Conceptual framework and hypotheses

2.3.1 The influence of contract characteristics on knowledge sharingA contract provides a legal structure for a relationship and formally describes the cooperation between the two parties. The elements of the contract thus define the interactional environment during the contractually agreed term. In this study, we are specifically interested in the role of two contract characteristics: incentive schemes and level of contract specification. We pick these two elements because they are essential in agency theory (Eisenhardt 1989), which has been used as the theoretical backdrop in previous outsourcing studies (Logan 2000; Tate et al. 2010). Agency theory suggests that contractual incentives reward service partners for their behavior, whereas contractual specifications communicate distrust by setting the boundaries and preconditions of the relationship. Moreover, contractual incentives can exist only with clear specifications of what is rewarded or punished (Bergen, Dutta, and Walker 1992; Eisenhardt 1989). We now hypothesize the relationships between these contract characteristics and service partner knowledge sharing.

Contractual incentivesContractual incentives refer to elements in the contract that describe when a service partner may expect additional payments in return to its performance, for instance when it has (over)achieved pre-specified performance levels (Axelsson and Wynstra 2002). For instance, call centers regularly receive a bonus for reaching performance levels for a set of productivity and customer satisfaction related metrics. This bonus scheme may extrinsically motivate the service partner to achieve the best possible performance to improve the firm’s bottom-line results.

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We posit that contractual incentives have a counterproductive effect on knowledge sharing. When confronted with extrinsic motivators, service partners are likely to focus on prescribed tasks realizing the contractual incentives provided (Atuahene-Gima 2005; Ryan and Deci 2000). This leaves less room for behavior outside specifications, such as knowledge sharing. Indeed, Osterloh and Frey (2000) study the effects of extrinsic motivators on knowledge transfer and conclude that contractual incentives focus on result achievement and do not motivate service partner participative behavior. Alternatively, goal setting theory predicts that clearly specified goals focus people’s attention on achieving these goals, even when this does not have financial implications (Dekker 2004). Because contractual incentives generally do not focus on knowledge sharing due to the inherent difficulties in objectifying this activity, they focus the service partner’s attention away from exchanging their customer and market insights. We expect that contractual incentives more negatively relate to exploratory knowledge sharing than to exploitative knowledge sharing, because exploratory knowledge sharing carries more risk and uncertainty than exploitative knowledge sharing (Lavie, Stettner, and Tushman 2010). In the latter activity, a clear connection can be observed between the content of the information and the conducted activities. For example, a logistics service provider observes that customers (e.g., retailers) are downsizing their warehouse facilities and shares this trend with the manufacturer. This allows the manufacturer to adjust product packaging to reduce its own storage costs and increase efficiency. The service partner will understand how its business may benefit from sharing this knowledge with the manufacturer: with more frequent logistics on smaller packages, the return of sharing this knowledge is evident. Sharing exploratory knowledge has a less clear future return. For example, a call center service provider may sense that many customers demand a top-end version of a product currently on the market. While sharing this knowledge with the manufacturer would allow the latter to increase market share, the service provider may not directly see the return-on-investment for its own business. Hence, from an economic perspective, the return on investment is more easily identifiable for exploitative than for exploratory knowledge sharing. Moreover, when a service partner would see the future value of explorative knowledge, it may prefer to keep the knowledge to itself because it can be a powerful asset to win future contracts or to integrate backward in the supply chain and take over some of the manufacturer’s activities. Contractual incentives may then be interpreted as a manufacturer’s intention to prevent a service partner extending its business (Pisano 1991). This makes it less likely the partner will share exploratory knowledge. As a result we hypothesize:

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H1a: Contractual incentives negatively relate to exploitative knowledge sharing.

H1b: Contractual incentives negatively relate to exploratory knowledge sharing.

H1c: Contractual incentives more negatively relate to exploratory knowledge sharing than to exploitative knowledge sharing.

Level of Contract SpecificationContractual incentives can exist only with clear specifications of acceptable or expected behavior (Bergen, Dutta, and Walker 1992; Eisenhardt 1989). The level of contract specification is the extent to which behavioral guidelines, outcome specifications, and contract extension or termination clauses are indicated in the contract that underlies the relationship between manufacturer and service partner. Manufacturers specify the contract based on the desire to control risks and outcomes. While knowledge sharing itself is unlikely to be mandated because of its intangibility and non-routine nature (Lee 2001), the extent to which service partners share their insights may depend on the specification of other guidelines. Knowledge transfer theorists have indicated the importance of clear communication of expectations on the efficiency and effectiveness of knowledge transfer between parties (Joshi 2009; Mascitelli 2000). The contract is the initial instrument a company can use to express its expectations and give insight in what it regards as valuable information. We posit that under highly specified contracts the manufacturer-service partner relationship will suffer less from unforeseen disturbances. As the rules of engagement are well known to both partners, the collaborative environment is conducive to creating and sharing knowledge. Fewer escalations make the contract partner feel more secure and likely to invest more in the relationship (Ling-yee 2010). In contrast, with a lower level of contract specification, service partners experience more autonomy to make decisions and plan work activities. This may lead to negotiating the right modus operandi between the parties, rather than knowledge build up and exchange. Based on earlier work (Joshi 2009; Mascitelli 2000), we expect the level of contract specification to relate more strongly to exploitative than to exploratory knowledge sharing. Exploitative knowledge is more closely related to existing activities and hence more likely to be shared as a “by-product” in performing the contracted service. In sum, we hypothesize:

H2a: The level of contract specification positively relates to exploitative knowledge sharing.

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H2b: The level of contract specification positively relates to exploratory knowledge sharing.

H2c: The level of contract specification more positively relates to exploitative knowledge sharing behaviors than to exploratory knowledge sharing.

2.3.2 The influence of relationship characteristics on knowledge sharingWhen the contract has been drawn up, negotiated and agreed, contract parties enter into a relationship. This stage is characterized by offline and online interactions, such as periodic meetings to discuss performance, telephone calls or emails with specific operational questions, etcetera. In these interactions, a service partner may choose to share its knowledge with the manufacturer. We posit that two major relationship characteristics facilitate the knowledge transfer process: relationship quality and relationship manager experience. Relationship quality has been considered as a key determinant of supply chain performance (Fynes, Voss, and De Búrca 2005), outsourcing success (Lee and Kim 1999), relationship learning (Selnes and Sallis 2003) and interfirm knowledge transfer (Inkpen 2000; Johnson, Sohi, and Grewal 2004). We extend existing findings by investigating how relationship quality relates to the type of knowledge sharing by service partners. In addition, service partners usually do not share information with a random representative of a manufacturer; instead, a relationship manager acts as a single point-of-contact. He or she holds a key position as the boundary-spanner between external information and internal processes. An experienced relationship manager may more easily breed the trustful environment needed for knowledge sharing activities (Dahl and Pedersen 2004). Next, we relate these two characteristics to knowledge sharing.

Relationship qualityWe define relationship quality as the state, strength and climate of the inter-firm relationship (Johnson 1999; Smith 1998). Previous literature has shown that state-like constructs such as relationship quality are potent drivers of acts such as knowledge sharing (Ling-yee 2010; Selnes and Sallis 2003; Zhao and Lavin 2012). Various scholars have argued that relationship quality includes elements such as cooperation, responsiveness, empathy, assurance and trust (Deepen et al. 2008; Johnson, Sohi, and Grewal 2004; Langfield-Smith and Smith 2003). When a business-to-business relationship is characterized by these elements, each partner in the relationship feels that the other is less likely to behave opportunistically (Eisingerich, Rubera, and Seifert 2009). Also, partners are willing to take more risk and go the extra-mile when their counterpart is reliable, empathic, and provides support when necessary (Inkpen 2000; Zhao and Lavin 2012). Contractual boundaries between the firms blur as they value each other’s needs and abilities; knowledge sharing will consequently emerge more naturally as the

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relationship has more depth and the partner is less concerned with information sharing vulnerabilities (Zhao and Lavin 2012). We also expect that relationship quality will have a stronger effect on exploratory than on exploitative knowledge sharing. As exploratory knowledge sharing requires a larger and more risky investment of the service partner, true cooperation and a trustworthy environment are required for a service partner to engage in such activities (Eisingerich, Rubera, and Seifert 2009). Exploitative knowledge sharing consumes less resources and its payoff is easier to understand. It may therefore exist even in less-than-optimal relationships; the marginal effect of a higher relationship quality therefore will be lower. In sum, we hypothesize:

H3a: Relationship quality positively relates to exploitative knowledge sharing.

H3b: Relationship quality positively relates to exploratory knowledge sharing.

H3c: Relationship quality more positively relates to exploratory knowledge sharing behaviors than to exploitative knowledge sharing.

Relationship manager experienceRecent inter-organizational knowledge transfer research (Meier 2011)has indicated the importance for organizational knowledge transfer of ongoing face-to-face interactions between partners. The relationship manager monitors, evaluates, and documents service partner performance. He/she also acts as a communication interface and should be a true representative of the manufacturer to create a positive image for the service partner (Lin, Pervan, and McDermid 2007). The relationship manager thus influences the communicative pattern between the parties and thereby influences knowledge transfer practices on the interface (Easterby-Smith, Lyles, and Tsang 2008; Im and Rai 2008; Meier 2011). Relationship managers become more competent and effective in managing relationships with experience (Lee 2001). Experienced managers possess a wide set of decision making heuristics, because they have been involved in many different outsourcing relationships (Nonaka 1994). This allows them to more easily assess whether a service partner could hold interesting knowledge and allows him to more effectively communicate needs (Cohen and Levinthal 1990; Easterby-Smith, Lyles, and Tsang 2008). They are able to emphasize what is valued by the manufacturing firm and focus the attention of the service partner on these issues (Barthélemy 2003; Meier 2011). Periodic meetings will consequently be centered on this topic, increasing the chance of information exchange. In addition, more experienced relationship managers create an environment conducive to information sharing because their reputation is more likely to be known, and their expertise makes it more likely that they are able to fulfill their

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promises in a buyer-seller relationship. Any past interpersonal and relational investments made by a relationship manager would be jeopardized by untrustworthy behavior (Doney and Cannon 1997), which makes a service partner less hesitant to share knowledge. Also, experienced relationship managers hold their position over time, which prevents service partners from dealing with a different boundary spanner on every interaction. This is important as individuals share more information with people they know than those they do not know (Van Wijk, Jansen, and Lyles 2008; Zhao and Lavin 2012). We thus expect that experienced relationship managers can leverage their position in the relationship between manufacturer and service partner to stimulate knowledge sharing. We posit that an experienced relationship manager is more important for a service partner to engage in exploratory knowledge sharing than in exploitative knowledge sharing. Again, we argue that the former is more risky, takes more effort and its returns-on-investment are more difficult to identify. Moreover, the experience of a relationship manager allows him/her to more clearly express what knowledge is considered valuable and what is regarded as common knowledge (Easterby-Smith, Lyles, and Tsang 2008). Exploratory knowledge sharing goes beyond what is routinely expected from a service partner and only an experienced relationship manager may convince the service partner of the relevance of sharing. Hence, we hypothesize:

H4a: Relationship manager experience positively relates to exploitative knowledge sharing.

H4b: Relationship manager experience positively relates to exploratory knowledge sharing.

H4c: Relationship manager experience more positively relates to exploratory knowledge sharing than to exploitative knowledge sharing.

Figure 2-1 outlines our conceptual model.

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Figure 2-1: Conceptual research model

2.4 Method

2.4.1 Sample and data collection procedureTo empirically test our framework we collected data from a large multinational fi rm. The focal fi rm operates on a global scale in several industries (medical, automotive, electronics, industrial goods & services) and produces a large variety of products ranging from simple consumer products to technologically advanced professional products. The fi rm’s product divisions operate in more than 100 countries and employ around 120.000 employees. Most of its markets can be characterized as highly competitive and uncertain with global players competing with high-tech startups. Furthermore, there is an ongoing process of technological change in many of the fi rms’ traditional markets. The fi rm has a centralized global purchasing unit, which sources customer-facing services on behalf of decentralized business units. The contracts that result from centralized sourcing activities are operationally managed by relationship managers in the decentralized departments. Preliminary interviews were held within the fi rm to build our conceptual model and to identify those measures that are regarded as adequate in our setting. Thereafter, we targeted 122 relationship managers who managed relationships with customer-facing service partners and asked them to refl ect on one specifi c relationship.

2.4.2 Sample characteristicsWe collected our data using an online survey tool and received 70 completed surveys (58.3% response rate). The main reason for pursuing a quantitative strategy has its roots in the fact that the constructs used in this study are well understood in existing works and the aim of this research to provide rigorous tests of relationships (Edmondson and Mcmanus 2007). A secondary reason for this strategy is that it allowed us to test our model across multiple industries and across different types of customer facing services.

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The mean tenure of the respondents was eight and a half years, 29% had up to four years of experience in managing outsourced relationships, 20% reported four to eight years of experience, 24% had eight to twelve years of experience, and 27% had more than twelve years of experience. Of all respondents, 64% managed logistics services, 14% managed call center services, 12% managed repair services, 8% managed marketing services and the rest related to sales services (2%). The average contract duration in the sample is 3.48 years; 25.8% of the contract terms were up to two years, 51.8% of the contract terms were between two and four years, 17.6% of the contracts were between four and six years, the rest of the contracts had a longer term. The average relationship duration (including past extensions) in the sample is 4.87 years; 54% of the relationships were up to four years old, 26% between four and eight years, 11% between eight and twelve years and 9% of the relationships in the sample originated twelve years back (longest being twenty years). Despite the relatively high response rate, we tested for non-response bias by comparing the answers of early respondents to those of late respondents (Armstrong and Overton 1977) T-tests did not reveal any significant difference in the means and standard deviations of focal variables.

2.4.3 MeasuresWe used measurement scales from existing works to operationalize our constructs. We pre-tested the survey questions to secure measurement adequacy. Unless indicated otherwise, responses were recorded on 7-point Likert scales tapping the extent to which a respondent agreed to each statement. Exploitative knowledge sharing and exploratory knowledge sharing were measured with three items each, adapted from He and Wong (2004). Contractual incentives were measured with an instrument specifically designed for this research, assessing the extent to which exceeding contractual performance targets resulted in a bonus. Level of specification was measured with four items from Lusch and Brown (1996). Relationship quality was modeled as a reflective second-order construct (cf., Burke Jarvis, MacKenzie, and Podsakoff 2003) and consisted of the first-order constructs cooperation, responsiveness, empathy, assurance, and trust (Deepen et al. 2008; Sako 1992). Four items adapted from Powell, et al. (1996) measured cooperation and indicated whether the parties truly cared about each other’s interests. Responsiveness was measured with five items from Deepen, et al. (2008) that reflected the willingness and ability of the partners to quickly respond to each other’s requests and suggestions. Empathy was measured with seven items from Lahiri and Kedia (2009) that tapped to what extent the relationship parties sympathized with each other’s view. Assurance was measured with four items from Deepen, et al. (2008) that assessed the extent to which it is safe to take risks within the relationship. Trust was conceptualized using five items from Sako (1992) and tapped whether the parties could rely on each other’s capabilities and intentions.

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Relationship manager experience reflected the tenure of the relationship manager in managing outsourcing relationships and was measured in months; logarithmic transformation was applied to create a normal distribution.

2.4.4 Control VariablesWe included additional measures in the survey to control for alternative explanations of our hypothesized effects. Service complexity measured the extent to which the outsourced service was knowledge intensive. This accounts for lock-in effects associated with services that require specific knowledge (Cannon and Perreault 1999). Face-to-face interaction was measured as a binary variable to control for the fact that knowledge is more likely to emerge from face-to-face interactions between service provider and customer. Varying contract duration is a salient technique used by contractors to control service partner actions. Short contract durations are thought to increase the perception of pressure to perform and hence reduce the risk of opportunistic behavior (Cook 1999). Contract duration reflected the duration of the current contract with the partner in months; a logarithmic transformation was applied to achieve a normal distribution. Finally, relationship duration indicates the duration of the overall relation between the parties, accounting for all former contracts. Longer relationships create a trusted environment and make the parties involved more aware of each other’s needs (Liu et al. 2008). This simplifies mutual understanding of what information would be valuable to share with the other party.

2.5 Results

We examined our data in a two-step approach, using SPSS 19.0 and SmartPLS 2.0 (Ringle, Wende, and Will 2005). Partial least squares path (PLS) modeling was used as this technique is suitable for relatively small sample sizes (Hair et al. 2006). In addition, PLS allows the simultaneous modeling of relationships among multiple independent and dependent constructs. It allows to construct unobservable variables measured by indicators. It thereby overcomes several limitations of regression-based approaches, such as the postulation of a simple model structure, the assumption that all variables can be considered as observable, and the conjecture that all variables are measured without error (Haenlein and Kaplan 2004). In the first step we examined descriptive statistics and performed confirmatory factor analysis to check validity and reliability. Psychometric properties of the focal constructs are presented in Table 2-1. The Fornell and Larcker (1981) procedure was used to test for discriminant validity, i.e. whether the constructs could be meaningfully separated from an empirical point of view. This procedure states that the square root of the average variance extracted (AVE) for each construct should exceed the correlation shared between any two constructs. Table 2-2 shows that all constructs passed this test. Notably, we conclude knowledge sharing and relationship quality to be two separate constructs.

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Table 2-1: Summary of measurement scales

Variable FL CR AVE

Contractual incentives .83 .72

The payment to our service partner increases based on the achieved performance. .70

We pay our service partner performance bonuses when performance goals are over-achieved.

.98

Level of Specification .86 .61

The contract precisely states the legal remedies for a service partner’s failure to perform.

.77

The contract precisely states what will happen in the case of service events occurring that were not planned.

.76

The contract precisely states how service disagreements will be resolved. .89

The contract is highly customized and required considerable legal work. .66

Cooperation .94 .79

Each party typically keeps its promises because it genuinely cares about the other party’s interests.

.84

Both parties have a clear intention to cooperate closely because of mutual positive feelings.

.90

Both parties believe they should cooperate well because they share the same values and interests.

.93

Each party takes into account the other party when making important decisions, because they share a strong feeling of loyalty to one another.

.89

Responsiveness .85 .54

Both parties in the relationship can adapt operations quickly to environmental changes.

.75

We are able to make adjustments in the partnership to cope with changing circumstances.

.71

Whenever some unexpected situation arises, the joint management of this relation is capable of modifying the existing structure and strategies of the partnership.

.72

The personnel of both parties give prompt service to each other. .78

The personnel of both parties are never too busy to respond to each other requests. .70

Empathy .93 .65

In the relationship with the service partner…We always see things from each other’s view. .76

We know how the other feels about our joint business. .81

We understand each other’s values and goals. .79

We care about each other’s welfare. .86

We understand each other’s specific needs. .86

We work with each other’s best interests at heart. .80

We give each other due attention. .77

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Assurance1 .74 .43

In the relationship with the service partner…we often hold mistakes against each other [r] .62

We are able to discuss problems and tough issues. .62

It is safe to take a risk. .76

No party would deliberately act in a way that undermines the other party’s efforts. .60

Trust .86 .57

The relationship with the service partner is characterized by high levels of trust .83

Both parties generally trust that each will stay within the terms of the contract. .78

Both parties are generally skeptical of the information provided to each other [(r]. .57

Both parties are trusted to have the right resources of capital and labor. .62

Both parties acknowledge each other’s reputation and abilities. .76

Exploitative Knowledge Sharing .91 .78

Our service partner has shared knowledge and suggestions that could have led to…

…higher efficiency levels. .95

…cost savings. .88

…reduced consumption of materials or resources. .82

Exploratory Knowledge Sharing .92 .80

Our service partner has shared knowledge and suggestions that could have led to…

…a new generation of services. .91

…the opening up of new markets. .87

…the entrance and/ or application of new technological fields. .92

FL = standardized factor loading, CR = composite reliability, AVE = average variance extracted, [r] = reversed scale1 Construct and items were removed due to not meeting Average Variance Extracted criterion >.5, (Hair et al. 2006)

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Table 2-2: Discriminant and convergent validity of the constructs

Construct 1 2 3 4 5 6 7 8

1 Contractual incentives 0.722

2 Specification Level 0.034 0.605

3 Relationship Quality 0.011 0.363** 0.633

4 Exploitative Knowledge Sharing 0.141 0.375** 0.318** 0.781

5 Exploratory Knowledge Sharing 0.291** 0.328** 0.344** 0.510** 0.806

6 Relationship Manager Experience1 0.046 0.167 0.027 0.174 0.149 -

7 Relationship Duration1 0.102 0.190 0.219 0.116 0.039 0.518** -

8 Contract Duration1 0.106 0.065 0.203* 0.079 0.116 0.111 0.102 -

Average Variance Extracted plotted on the diagonal 1 observed variable (AVE extraction not applicable)* Correlation is significant at the 0.051 level (2-tailed)** Correlation is significant at the 0.01 level (2-tailed)

The procedure of Chin (1998) was used to test multidimensionality of the second-order construct relationship quality. All path estimates for the first-order dimensions were above .7 and highly significant (p<.01), indicative of convergent validity. Nevertheless, we removed the assurance dimension because its AVE was <.50 (Hair et al. 2006). To further validate the relationship quality construct, a goodness-of-fit index (GoF) was computed for a model that solely consisted of this construct. The GoF score of 0.55 exceeds the baseline cut-over values of 0.36 proposed by Wetzels, et al. (2009); we conclude that our first-order latent variables are distinct and capture different information, but are tied to a common higher-order relationship quality construct (Table 2-3).

Table 2-3: Validity test of second-order construct relationship quality

Construct Path T-value Correlations

estimate Cooperation Responsiveness Empathy

Cooperation 0.882 40.407**

Responsiveness 0.788 25.774** 0.594**

Empathy 0.919 58.762** 0.742** 0.652**

Trust 0.766 15.887** 0.367** 0.444** 0.376**

** Significant at the 0.01 level (2-tailed).* Significant at the 0.05 level (2-tailed).

The second step in our approach comprised hypothesis and model testing. The model was tested with 300 iterations; t-values were obtained through a bootstrap procedure with 500 samples (Chin 1998). Table 2-4 presents the results from PLS estimation and indicates that our antecedents explained 25.2% of the variance in exploitative knowledge sharing and 33.2% in exploratory knowledge sharing. We applied procedural recommendations by Conway and Lance (2010) to prevent common method variance. In addition, formal

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tests proposed by Lindell and Whitney (2001) and Podsakoff, et al. (2003) showed that common method bias did not affect our results.

Table 2-4: Results

Dependent

Exploitativeknowledge sharing

(H..a)

Exploratoryknowledge sharing

(H..b) (H..c)

(β) t-value (β) t-value BF

Independent constructs

H1: Contractual incentives -0.133 1.329 -0.293*** 3.194 0.02 1

H2: Specification level0.297*** 3.219 0.227*** 2.911

3.73 2

H3: Relationship quality 0.206** 1.995 0.274*** 3.443 3.80 3

H4: Relationship manager experience 0.149 1.559 0.218** 2.039 2.84 4

Control Variables

Face-to-face interaction 0.085 0.794 -0.113 1.087 -

Service complexity 0.080 0.440 -0.002 0.127 -

Contract duration -0.130 1.159 -0.087 1.335 -

Relationship duration -0.053 1.085 -0.172 1.419 -

R2 25.2% 33.2%

N=70; *p≤0.05 (two-tailed), **p≤0.01 (one-tailed), *** p≤0.01 (two-tailed)1 H1c: β (H1a) > β (H1b)

2 H2c: β (H2a) > β (H2b)3 H3c: β (H3a) < β (H3b)

4 H4c: β (H4a) > β (H4b)

Figure 2-2 outlines our results. Contractual incentives had a negative effect on exploratory knowledge sharing, but not on exploitative knowledge sharing (β=-.293, p<.01; β=-.133, n.s. respectively). We thus found support for H1b, but not H1a. To test H1c, a significance test on the differences between these two effects, we calculated the Bayes Factor (BF) using state-of-the-art BIEMS software (Kass and Raftery 1995; Mulder et al. 2009; Mulder, Hoijtink, and De Leeuw 2012; Mulder, Hoijtink, and Klugkist 2010). No significant effect was found (BF = 0.02); hence, H1c is rejected. PLS results confirm the positive effect of contract specification level on both exploitative (β = .297, p<.01) and exploratory knowledge sharing (β = .227, p<.01). The pattern of effect sizes also supports H2c (BF = 3.73); a stronger effect on exploitative knowledge sharing. We found significant positive effects of relationship quality on exploitative knowledge sharing (β = .206, p<.05) and exploratory knowledge sharing (β = .274, p<.01), supporting H3a and H3b respectively. H3c was also confirmed by the results (BF = 3.80); a stronger effect of relationship quality on exploratory knowledge. Relationship manager

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WHEN DO THEY CARE TO SHARE?

experience related positively to exploratory knowledge sharing (β = .218, p<.05), but not to exploitative knowledge sharing (β = .149, n.s.). This supported H4b, but not H4a. The Bayes factor calculation confi rms the larger effect of experience on exploratory knowledge sharing, supporting H4c. We found no signifi cant effects for our control variables (face-to-face interaction, service complexity, contract duration and relationship duration) on exploitative and exploratory knowledge sharing.

Figure 2-2: Final Research Model Results

N=70; *p≤0.05 (two-tailed), **p≤0.01 (one-tailed), *** p≤0.01 (two-tailed)

2.6 Conclusions and im plications

The aim of this research was to identify how contractual and relationship characteristics enhance exploitative and exploratory knowledge sharing by service partners to whom manufacturers have outsourced customer-facing services. We outline the major takeaways below. Our fi rst key fi nding is that outsourcing relationships with clearly specifi ed boundaries seem to be characterized by higher levels of knowledge sharing. We found positive relationships between the level of contract specifi cation and knowledge sharing. This corroborates with expectations from knowledge transfer theorists who state that unclear specifi cations hinder knowledge transfer (Joshi 2009; Mascitelli 2000; Zhao and Lavin 2012), and argues against scholars who show that discretionary behavior can be limited by highly, or over-specifi ed contracts (Tate, Ellram, and Brown 2009) because of distrust signals (Wuyts 2007) and lock-in effects (Aubert, Rivard, and Patry 1996). We posit that unarticulated expectations leave service partners guessing for desired performance levels, resulting in disappointing service performance. We speculate that contract specifi cations aid a service partner to get an overview of which information is valued by a manufacturer through which the partner is enabled to update the fi rm’s knowledge store. Not all information shared by a service partner may be interpreted by

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the manufacturer as knowledge. Only when a service partner shares insights that are built around an area of interest and that can be regarded as new to the knowledge stores (as reflected in specifications), a manufacturer may value the communication and recognize the insights shared as knowledge. Hence, we hold that contractual specifications provide a frame of reference that makes a service partner share those insights that provide value to a manufacturer. A second important finding is that there is, at least in our data, a strong negative relationship between contractual incentives and exploratory knowledge sharing. This finding is in line with earlier work indicating negative effects of output control on knowledge sharing (Atuahene-Gima 2005; Hitt et al. 1996). Contractual incentives drive a service partner to maximize its short-term profit, rather than optimize the long-term relationship with the supply chain partner (Osterloh and Frey 2000; Quinn 2000). Although knowledge-supporting contractual incentives are very rare in outsourced customer-facing service contracts, it is clear from an organizational learning perspective that knowledge is a vital asset to firms (Baker and Sinkula 1999). Our results suggest that exploitative knowledge sharing is not affected by the presence of contractual incentives, which may indicate that service partners are hard to incentivize on sharing knowledge on perfecting extant operational processes. Yet, such knowledge may either be shared by default, or in an effort to (re)gain a positive image to ensure contract renewal (Kim and Mauborgne 1996). To increase knowledge sharing, we suggest contractual incentives should emphasize knowledge sharing in supply chain relationships, rather than performance goals (Dyer and Nobeoka 2000). A suggested solution is to contractually put emphasis on knowledge management practices and articulate related goals (Meier 2011) which implicitly motivate knowledge transfer. Third, knowledge sharing from service partners may also be enhanced through a well maintained relationship. We found that relationship quality is strongly and positively associated with sharing both types of knowledge. This is in line with extant works that conclude that relationship quality enables successful knowledge transfer in organizational relations (Eisingerich, Rubera, and Seifert 2009; Johnson, Sohi, and Grewal 2004; Ko, Kirsch, and King 2005; Van Wijk, Jansen, and Lyles 2008; Zhao and Lavin 2012). It builds a long term commitment between two parties, and as a result, both are willing to make idiosyncratic investments into the relationship. Experienced relationship managers may also trigger explorative knowledge sharing as they manage the contact with the service partner. Experience enables the relationship manager to absorb new insights and pose the right questions for further exploration. Experience further allows the relationship manager to more clearly communicate what knowledge is to be transferred thereby influencing the efficiency and effectiveness of knowledge transfer (Atuahene-Gima 2005; Joshi 2009). However, no effect was found of his/her experience on exploitative knowledge sharing. It could be that more tenured relationship managers are not really excited to hear information that incrementally improves services

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or products. They are therefore not probing service partners to share such information. Only insights that radically deviate from existing scripts may trigger their interest. Finally, we conclude that firms may more effectively control exploratory knowledge sharing than exploitative knowledge sharing by varying contractual and relationship characteristics. The study shows that firms can secure the sustained buildup and revitalization of their knowledge stores at vertical partners and secure adaptive or generative learning (Baker and Sinkula 1999) with contract and relationship characteristics. Apparently, exploitative knowledge sharing may be explained by elements that we did not assess in our study, such as the presence of a technology that facilitates information sharing.

2.7 Managerial Implications

Manufacturing firms increasingly outsource customer-facing services to external service partners. In the resulting triadic relationship structures, manufacturers risk losing touch with their customers, as their service partners are the ones with customer contact. Service partners may now possess valuable market and customer-related knowledge that, when shared, could help the manufacturing firms sustain and enhance the value of its market offers. Based on our findings, we give advice to managers responsible for such outsourcing arrangements. First, we advise firms to see outsourcing activities in the light of the broader marketing focus on exploitation or exploration. A firm with an exploitation strategy focuses its activities on refining and strengthening its current propositions through e.g. optimizing their existing marketing mix strategies. Alternatively, a firm with an exploration strategy focuses on challenging prior approaches to interface with the market, such as new segmentation, new positioning, new products, or new channels. Both strategies are dependent on the influx of information from the environment. We show that careful management of service partners can ensure them sharing information that may benefit either an exploitation or exploration strategy. As such, outsourcing should not (only) be seen as a “money saver”, but rather as a part of the marketing strategy of a firm. Remarkably though, many contracts lack any references to the importance of knowledge sharing. We specifically advice to define goals that put emphasis on knowledge transfer. For instance, a manufacturer may set key performance indicators on knowledge management sessions and periodical revisions of knowledge documents (e.g. written procedures or posts on knowledge community blogs). Additionally, predefined fair share policies related to rents coming from improvements in costs or profits as a result of service partner knowledge transfer may further motivate these discretionary behaviors. Second, if a firm is looking to boost their exploitation strategy, they should design contracts with customer-facing service partners in such a way that these entities

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likely share exploitative knowledge. We advise managers to design the contract such that it is clear for a service partner what the manufacturer’s expectations are toward knowledge management practices. This may be done by bilaterally scheduling periodical meetings to review current practices, apart from meetings to officially review service performance. Alternatively, the manufacturer may require the service partner to log inefficiencies following a service encounter. Another advisable option is to conduct a relationship quality survey wherein both parties rate each other, next to a self-rating, making use of the relationship quality measures in our study. Such survey provides a clear structure for any resulting gap-analysis discussion but moreover provides clarity to the parties what the aims are for the manufacturer on a relationship management level. In contrast, if a firm strives to receive knowledge to help their exploration strategy, we advise to hold back on installing contractual incentives, but to be clear in contractual specifications, and nurture a high-quality relationship, for instance through experienced relationship managers. Clearly specified contracts create a shared vision of the future which motivates the service partner to take a long-term perspective to a relationship with the manufacturer. Service partners become less concerned with their competitive position on the market, because their future is sustained in the contract with the manufacturer. This motivates service partners to go beyond the transactional nature of the relation and share any available knowledge. Finally, regardless of a firm’s marketing strategy, we advise that following partner selection and contracting process, sufficient emphasis should be placed on crafting a high quality relationship. To prevent service partners from post-contractually capitalizing on the knowledge gathered during the relationship with the manufacturer, service partners should be allowed to act as a vertically integrated department of the manufacturer. They may then use their autonomy to expand the manufacturer’s business. However, providing services to multiple manufacturers may stop service partners from sharing knowledge, because their existence has become less dependent on a single manufacturer.

2.8 Limitations and further research

As with every study, our research carries a few limitations that at the same time provide a fruitful ground for future research. First, our sample size was limited. We employed PLS’s bootstrapping procedures to circumvent this problem, but future studies should replicate our findings with a larger sample size. Furthermore, the generalizability of the results could be further tested by studying firms active in a single industry, to see whether results hold across domains. Second, knowledge sharing in supplier-buyer relationships is a complex phenomenon to understand; many factors may determine or affect service partner behaviors in these situations. The relationships among the core constructs used in this

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WHEN DO THEY CARE TO SHARE?

study may therefore be more complicated than hypothesized. For example, although we find contract specification to influence knowledge sharing, there are many factors that potentially play a role before knowledge is shared or not, ranging from partner and market characteristics (such as competitive overlap) to cultural differences (Meier 2011). Future research may try to identify mediators and/or moderators that further strengthen and explain the effects found. This is especially true for non-significant paths. An example of such effect is the relative importance of the manufacturer to the service provider in terms of fit with business goals and models. Service partners may serve multiple customers giving rise to conflicts in making customer-specific investments. A single manufacturer competes with others to get relation-specific efforts from the service provider that could influence the effects found.Third, in our study the relationship between contract specification and knowledge sharing was researched. Investigating which types of specifications drive knowledge sharing could further enrich the results presented. Practitioners would value insights in how to specify knowledge sharing in a contract; scholars may consequently study the most effective options, for instance through experiments. Finally, more research can be devoted to understand the role of personality traits of relationship managers. Their experience could also have long-term detrimental effects due to the decreasing ability to absorb external knowledge (not-invented-here-syndrome). However, it is unknown whether rotation of relationship managers prevents lock-in effects.

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3.Aligning Interests or Aligning Rewards?

Exploring the Drivers of Knowledge Sharing of External Technology Experts in

Collaborative R&D Projects

3Aligning Interests or Aligning Rewards?

Exploring the Drivers of Knowledge Sharing of External Technology Experts in

Collaborative R&D Projects

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High-tech manufacturers increasingly rely on the knowledge contributions of external technology experts (ETEs). These employees represent the manufacturers’ suppliers and contribute to collaborative R&D projects. However, many ETEs do not fully share their knowledge and insights because the interests of suppliers, ETEs, and the manufacturer diverge. This study investigates whether the alignment of the interests in collaborative R&D (i.e., a sense of stewardship for the manufacturer held by the ETE) or the alignment of economic rewards flowing from the R&D project (i.e., an ETE’s sense of distributive fairness of project outcomes) drives ETEs knowledge sharing. We build a conceptual model based on stewardship theory and fairness theory and account for the fact that innovation managers often try to control the uncertainty and risk in R&D projects by instructing their in-house employees to practice a high degree of formal coordination in their cooperation with ETEs. We surveyed 187 ETEs participating in R&D projects at seven global, high-tech manufacturers. A regression-based floodlight analysis of direct and indirect effects in a full moderated mediation model shows that customer stewardship indirectly influences knowledge sharing behavior (through knowledge sharing intention) when formal coordination is low. In contrast, distributive fairness directly influences knowledge sharing behavior when formal coordination is high. Together, the results provide valuable insights for R&D project managers to manage external knowledge contributions.

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3.1 Introduction

High-tech manufacturers realize that collaborative research and development (R&D) with their suppliers can help them stay ahead of competition, as they get quicker access to know-how they would normally have to build up themselves (Yeniyurt, Henke, and Yalcinkaya 2014). Suppliers however increasingly struggle with the misappropriation of know-how produced and shared by their employees during the interactions in the manufacturers’ R&D team. A well-known example is the case of Goodyear; its tire engineers imitated a technological innovation of a supplier, whose employees had been involved in a R&D project. The supplier’s technology was pushed out of the new product and the supplier did not get a fair yield on its contribution. Similar situations have occurred in cases such as Compuware vs. IBM (Cowley and Larson 2005) and Lexar Media vs. Toshiba (Thomas 2003). Supplier employees, in these settings referred to as external technology experts (ETEs), thus have to consider the responsibility to advance the manufacturer’s business as well as the fair distribution of rewards from the manufacturers’ new product or service to advance the supplier’s business. Given the many examples of knowledge misappropriation and unfair distribution of rewards in collaborative R&D settings, manufacturers and suppliers increasingly expend effort to track ownership of the derivatives that result from their collaborations (Ghelfi 2005). However, constant monitoring is costly (Langfield-Smith and Smith 2003), emphasizes the risk rather than the benefit of knowledge sharing for both parties (Giarratana and Mariani 2014), and may thus limit ETEs’ motivation to contribute knowledge (Hirst et al. 2011) to the R&D project. Although current business practice focuses on formalization, scholarly theorizing about motivating individuals in contexts with conflicting interests suggests that ETEs’ should instead be provided with a sense of responsibility and accountability (Davis, Schoorman, and Donaldson 1997). Stewardship theory postulates that when ETE’s individual motives are aligned with those of the manufacturer, self-actualization motivates ETEs to exhibit behavior that helps the manufacturer reach its goals and at the same time drive ETE individual well-being. Although alignment of interests may thus be important to drive ETE knowledge sharing in collaborative R&D, business practice suggests that alignment of economic rewards flowing from the collaborative project is important too. Fairness theory explains that individuals balance their invested efforts against expected outcomes. When the balance is assessed as fair, self-regulation motivates individuals to expend effort to pursue these joint outcomes (Franke, Keinz, and Klausberger 2012). In other words: both parties get pieces from a bigger pie. Thus, a fair distribution of rewards between supplier and manufacturer should stimulate ETEs to share their knowledge. This raises the question which type of alignment is more important to foster ETE knowledge sharing: alignment of interests or alignment of economic rewards. The aim of this study is to empirically

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answer this question by surveying 187 ETEs participating in R&D projects at seven global, high-tech manufacturers. We aim to make the following research contributions. First, we extend stewardship theory by applying stewardship theory in an R&D project setting where, apart from individual and customer (i.e., manufacturer) interests, also supplier interests play a role. We specifically focus on the concept of ETE stewardship, which we define as felt ownership of and moral responsibility for the manufacturers’ overall welfare. Our findings indicate that customer stewardship enhances ETEs’ knowledge sharing intention rather than behavior. This finding contrasts earlier findings that stewardship can directly regulate behavior and suggests limits to the applicability of the theory. We suggest that stewardship has considerable value in stable, enduring relationships (between employees and the organization), but has less explanatory power when economic interests of multiple parties are involved (Nahapiet and Ghoshal 1998; Putnam 2000). Second, we extend the knowledge sharing literature by accounting for the differences in intention and behavior. Previous studies have focused on the antecedents of knowledge sharing behavior (Auh and Menguc 2013; Wasko and Faraj 2005) and intention (e.g., Kleijnen et al. 2009), but have not combined them in one framework. This is important though, as in open innovation and outsourcing settings ETEs may be willing to share knowledge, but may refrain from doing so because of misappropriation concerns or cost-benefit considerations (Luria, Gal, and Yagil 2009). Using this integrative perspective we find that in collaborative R&D projects customer stewardship influences knowledge sharing intention, but that distributive fairness drives knowledge sharing behavior. Finally, we shed light on the role of formal coordination in knowledge sharing processes. Evidence from court cases and the emphasis put on tracking knowledge ownership (European IPR helpdesk 2015) provide support for the potentially important role of formal coordination in collaborative R&D projects. Although prior studies have focused on knowledge sharing climate and culture (Davenport, De Long, and Beers 1998; Van den Hooff and De Ridder 2004), the role of formal coordination has largely been overlooked. We find that the potency of customer stewardship and distributive fairness to drive ETE knowledge sharing behavior indeed depends on the level of formal coordination exerted on the collaborative R&D project. Next we outline the theoretical foundation of our conceptual model and develop our hypotheses. We empirically test our hypotheses using survey data gathered from 187 ETEs involved in R&D projects at seven global, high-tech manufacturers in a variety of industries. After outlining and discussing our results, we provide important implications for R&D managers.

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3.2 Theoretical Background

3.2.1 Knowledge sharingETEs span the boundary between suppliers and manufacturers and enrich R&D projects with their unique tacit knowledge that they built up over time across a range of projects (Huang, Hsieh, and He 2014). ETEs thus enable quicker and more novel innovations (Storey and Kahn 2010). To study the process of how knowledge flows from ETEs to their beneficiaries, previous literature has introduced the concepts of knowledge sharing, knowledge transfer and knowledge exchange. Although sometimes used interchangeably, clear differences can be outlined. First, knowledge transfer occurs when the knowledge shared by the source has affected the knowledge of the recipient (Argote, McEvily, and Reagans 2003). Knowledge exchange occurs when both source and recipient engage in knowledge sharing behaviors with the aim to enhance each other’s knowledge (Yeniyurt, Henke, and Yalcinkaya 2014). Knowledge sharing requires neither a “learning effect” nor reciprocal engagement of the receiver; it simply indicates that an individual displays an effort to communicate his/her knowledge to another party. Second, these conceptual differences imply that knowledge sharing behavior is incorporated in the process of knowledge transfer and exchange (Van Burg, Berends, and Van Raaij 2014). Finally, knowledge transfer and exchange have been studied at the organizational and individual level (Hutzschenreuter and Horstkotte 2010), while knowledge sharing is an individual-level phenomenon (Bock et al. 2005). We define knowledge sharing behavior as the actual disclosure of past experiences, learnings, expert insights and contextual information with the intent to have it acquired and processed by another unit or person to create new knowledge (Golden and Raghuram 2010). Such behavior is discretionary, voluntary and prosocial (Lee, Yoo, and Yun 2015; Reychav and Weisberg 2010) and typically cannot be enforced through hierarchical authority or contractual obligations (Flynn 2003). Indeed, Golden and Raghuram (2010, p. 1063) typify knowledge sharing as “knowhow relayed to others on an impromptu basis, whereby individuals feel comfortable spontaneously disclosing personal experiences” rather than a planned or programmed activity. Where knowledge sharing behavior is the actual disclosure of information, knowledge sharing intention reflects the willingness to engage in knowledge sharing behavior in the (near) future.

3.2.2 Knowledge sharing in R&D collaborationsKnowledge protects or enhances an individuals’ status and can thus be used to exert influence on others. Disclosing knowledge alters existing power balances, such that individuals are likely to hoard rather than share knowledge (Lee, Yoo, and Yun 2015), especially when knowledge is tacit, unique, and can be used freely without any contribution in return (Bock et al. 2005). This complicates the decision of an individual to share knowledge with colleagues within their organization (e.g., think about salespeople

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that compete for the same bonus). A collaborative R&D environment where interests of supplier and manufacturer may conflict further complicates one’s decision as the results of knowledge sharing behavior transcend the interpersonal and organizational level. Literature suggests that in such situations of conflicting interests, three modes of governance may motivate individuals to share knowledge in R&D projects. First, agency theory assumes that individuals are self-interested, prone to opportunism, and thus only share knowledge when it benefits their individual utility (Eisenhardt 1989; Sharma 1997). Such considerations can be suppressed by formalizing the work relationship (Gopal and Gosain 2010), such that knowledge sharing behavior is bound to goals and targets and then rewarded to benefit one’s utility. Second, a different perspective is to recalibrate the motivational function of ETEs. Inextricably linked to agency theory, stewardship theory describes that individuals are motivated to display discretionary behavior by a moral responsibility to act in the best interests of the other(s) in the relationship when they perceive that such behaviors have a greater utility than individualistic, self-serving behaviors (Davis, Schoorman, and Donaldson 1997). This greater utility can be achieved by aligning the interests of the different actors involved in the work relationship. Alignment operates through ETEs taking psychological ownership of and responsibility for the work outcomes they realize. This creates a moral obligation to advance joint welfare, rather than a focus on self-interest. Third, where agency and stewardship theory focus on the relationship between principal and agent or steward, the typical problem of ETEs operating in R&D collaborations is that they have to serve not one, but two principals: their manager at the supplier and the R&D project leader at the manufacturer. This means that the motivational function they rely on in knowledge sharing decisions is not only determined by formal coordination or self-regulation through moral obligations, but also by the relationship between the two principals. An ETE’s knowledge contribution could lead to the development of a product or service that sparks great economic returns. The way these payoffs are distributed over supplier and manufacturer influences the willingness of ETEs to share their knowledge. Specifically, fairness theory postulates that individuals compare the expected outcomes with their invested efforts and expect this ratio to be fair (Cropanzano et al. 2001). Individuals try to alter unfair ratios by changing their behavior or by leaving the field (Husted and Folger 2004). Literature suggests the governance modes of stewardship and fairness interact with formal rules and guidelines in the work context (Schepers et al. 2012). For instance, proponents of stewardship theory have emphasized that stewardship outcomes may be contingent on specific organizational structures (Hernandez 2008, p. 122). In a similar fashion, fairness scholars have highlighted that justice perceptions interact with governing structures such as monitoring and enforcement in people’s perception of work relationships (Husted and Folger 2004). Hence, we argue that emphasizing contractual

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rules and regulations in the work context acts as a contingency factor that determines the true impact of stewardship and fairness considerations. Figure 3-1 summarizes our conceptual model of moderated mediation. Below we develop the underlying hypotheses. Figure 3-1: Conceptual model

3.3 Conceptual framework & hypotheses

3.3.1 Knowledge sharing process. It is well documented in cognitive psychology literature that human behavior is preceded by the intention to perform that specifi c behavior (Ajzen and Fishbein 1977; Sheeran 2002). This fi nding has been replicated for a wide variety of behaviors including helping behaviors (Vallerand et al. 1992), innovation adoption behaviors (Arts, Frambach, and Bijmolt 2011) and organizational citizenship behaviors (Dalal 2005). Also for knowledge sharing behavior, Bock et al. (2005) conclude that intention to share knowledge is the prime determinant of knowledge sharing behavior. In line with these works, we posit:

H1: ETE’s knowledge sharing intention positively associates with his/her knowledge sharing behavior.

3.3.2 Effect of customer stewardship. The creation of individual knowledge typically involves acquiring and processing information, integrating it with one’s extant knowledge base, and testing or applying the updated knowledge in the fi eld (Smith, Collins, and Clark 2005). Individuals need time to refl ect on whether and how new pieces of information are useful additions to their knowledge base. To help others create knowledge, individuals need to transform or “gear” their knowledge toward an application domain before involved others can derive value from the knowledge shared (Liyanage et al. 2009). In such work situations, it is imperative that the person who considers to share knowledge is able to picture him- or herself in the

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ALIGNING INTERESTS OR ALIGNING REWARDS?

position of the receiving person or collective. Not being able to take the perspective of the receiver complicates successful knowledge sharing and thus limits the willingness to do so. When individuals have a sense of responsibility for and ownership of customer problems, they differentiate less between the interests of the collective and the self (Hernandez 2012). This makes the knowledge sender better able to see how his/her knowledge benefits the receiver and thus increases the intention to share knowledge. We thus posit that an ETE’s perceptions of customer stewardship make the ETE more committed to share his/her knowledge in the (near) future. Formally:

H2: ETE’s customer stewardship positively associates with his/her knowledge sharing intention.

3.3.3 Effect of distributive fairness. Distributive fairness is defined as the perceived favorability of the benefit-to-burden ratio of being involved in the collaborative R&D project for the supplier compared to the manufacturer’s benefit-to-burden ratio (Franke, Keinz, and Klausberger 2012; Netemeyer et al. 1997). ETEs may feel that they (and the supplier they represent) have invested much time and effort in gathering expert knowledge and that the receiving party does not have to invest the same set of resources to gather similar knowledge. ETEs thus expect something in return and cognitively balance the (mental) costs of sharing knowledge with the expected long-term returns. Fairness theory posits that individuals are inclined to engage in behaviors that serve long-term relational goals when they believe that their efforts invested increase the chance of a future reward that is fairly distributed (Laurin, Fitzsimons, and Kay 2011). For suppliers, the benefit of ETEs sharing knowledge in collaborative R&D projects is that their solutions or technological features are designed into the manufacturers’ product or service. When ETEs believe that solutions or product features that result from sharing their (and their supplier’s) knowhow will not be illegally copied or otherwise misappropriated, the economic returns resulting from the collaboration will be perceived as fair. This increases their willingness to share proprietary expertise such that it can be embedded into the product (Zaefarian et al. 2016). In contrast, when ETEs believe the distribution of future economic returns to be unfair, they will lower their intention to share knowledge to, at least cognitively, restore the contribution-return ratio (Holten et al. 2016). We hence hypothesize:

H3: ETE’s perceived distributive fairness positively associates with his/her knowledge sharing intention.

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3.3.4 Formal coordination as a moderator of the knowledge sharing process. In R&D projects, manufacturers often want to keep the relationships with ETEs formal rather than trying to govern the cooperation using informal, social means (Zhang and Zhou 2013). For instance, medical manufacturers instruct their employees to stick to agreed terms, corporate policies, or R&D project-specific instructions. Since formal coordination is a regular practice in R&D projects, it important to understand how it influences the knowledge sharing process. We define formal coordination as the extent to which manufacturer employees take a formal approach in working with ETEs in the R&D project. Examples of high formal coordination include employees referring an ETE to contractual obligations (rather than engaging in informal talks) when work progress is not as expected, and employees immediately requesting to update or review the governing contract in case the project runs into unforeseen circumstances. We first consider the moderating role of formal coordination on the relationship between knowledge sharing intention and behavior. Formal coordination prescribes behaviors and leaves less room for discretionary efforts (Wünderlich, Wangenheim, and Bitner 2013). Two underlying mechanisms have been argued in literature. First, formal coordination constrains an individual’s cognitive resources because time and effort are needed to weigh actions against formal expectations (Hirst et al. 2011). Under such conditions, ETEs who intend to support the R&D project team with their knowhow explicitly consider whether their intended input is in line with the clearly communicated formal guidelines and expectations. Second, formal coordination signals the ETE that the manufacturer suspects that the he/she is unwilling to cooperate, or worse, cannot be trusted (Giarratana and Mariani 2014). An individual’s perceptions of distrust and the associated surveillance undermine acting on good intentions (Deci, Koestner, and Ryan 1999). We therefore hypothesize:

H4: Formal coordination moderates the positive relationship between knowledge sharing intention and knowledge sharing behavior such that this relationship is weaker when an ETE perceives a higher level of formal coordination.

We also hypothesize formal coordination to affect the relationships between knowledge sharing antecedents and intention. First, we posit that an ETE who acts as a steward of the customer but is constantly confronted with formal coordination perceives reduced freedom to share knowledge. Enhanced levels of formal coordination focus an ETE’s attention on delivering on communicated expectations to a greater extent and reduce the salience of other ways in which he/she is willing to help a customer (Vecchio, Justin, and Pearce 2008), such as providing customer solutions that include state of the art supplier intelligence and technology. Because of their felt responsibility for customer welfare, ETEs understand and “see through” that sticking to the formally communicated rules is ultimately not in the customer’s best interest (Schepers et al., 2012). However, with

64

ALIGNING INTERESTS OR ALIGNING REWARDS?

increasing formal coordination, their perceived customer stewardship translates less into intention to share knowledge because they see that advancing customer welfare through knowledge sharing would not fully be appreciated. We therefore hypothesize:

H5: Formal coordination moderates the positive relationship between customer stewardship and knowledge sharing intention such that this relationship becomes weaker when an ETE perceives a higher level of formal coordination.

Second, we expect formal coordination to influence the effect of distributive fairness on knowledge sharing intentions. Formal coordination provides a clear frame of reference to ETEs on what is agreed and expected in terms of knowledge contributions to further the project (Hirst et al. 2011). It thus reduces the uncertainty in the work environment and builds a shared understanding on how the project should be run (Lawson et al. 2009). Under such conditions ETEs’ are less concerned with the distribution of future rewards but rather focus on the collaborative nature of the relationship (Bstieler 2006). Fairness perceptions consequently become a less important determinant of intentions to share knowledge. On the other hand, if there is no formal coordination, the work environment is more uncertain. Other factors hence have to compensate. Distributive fairness then becomes a more important determinant of an ETE’s intention to share knowledge. We therefore hypothesize:

H6: Formal coordination moderates the positive relationship between ETE perceived distributive fairness and his/her knowledge sharing intention such that this relationship is weaker when an ETE perceives a higher level of formal coordination.

3.4 Methodology

3.4.1 Research setting and data collectionTo empirically test our conceptual framework, we collected survey data from ETEs involved in R&D projects at seven global manufacturers in high-tech industries. We sampled these manufacturers as they integrate a wide range of components from specialist suppliers in their R&D projects. Suppliers are involved in the R&D project to secure seamless integration of parts and develop solutions for specific modules, which requires a great deal of information exchange. ETEs’ knowledge sharing is thus crucial for the manufacturers to be able to successfully develop new products or services. In cooperation with R&D managers from the seven manufacturers, we selected one or more R&D projects that required large resource investments and depend heavily on know-how and solutions from ETEs. We excluded temporary hires (e.g., from consulting firms) from our sample. These projects require larger investments in terms of time and

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3

resources (Hernandez-Espallardo, Molina-Castillo, and Rodriguez-Orejuela 2012) and are full of risk and uncertainties (Calantone, Chan, and Cui 2006). Furthermore, such projects require new external knowhow to be integrated during the project (Chai et al. 2012). We selected only those projects that were in an advanced phase of development (nearing first release) or that were recently completed (within the last 6 months). In cooperation with the manufacturers we contacted the management of the suppliers involved and asked for their cooperation. After the explicit agreement of the suppliers, we identified the ETEs involved based on human resource and R&D project records. We excluded temporary hires (e.g., from consulting firms) because they may lack experience and the ability to understand the conflicts of interest among the parties. The remaining 536 ETEs, who participated in 72 projects, were sent an email preannouncement that they would soon receive an invitation to participate in our survey. The announcement indicated that both manufacturer and supplier supported their participation. We also guaranteed confidentiality and anonymity. A next email contained the survey link and a specific reference to the name of the project, which the respondent should take in mind throughout answering the survey.

3.4.2 Sample characteristicsIn total 236 surveys were returned, resulting in a 44.0% response rate. We had to discard 39 surveys because they were largely incomplete. Eyeballing the data and screening means and standard deviations identified a further 10 surveys that had to be excluded from further data analysis due to response patterns indicative for response bias (e.g., straight-lining), leaving 187 for final analysis.

Table 3-1: Sample descriptives

Gender % Tenure in function %

Male 81 <2 35

Female 19 2 – 6 years 24

6 – 10 years 32

Age % >10 years 29

Less than 30 years 23

30 – 35 years 27 Tenure at manufacturer %

36 – 45 years 36 Less than 10 months 29.4

Over 45 years 14 10 months to 4 years 60.4

Over 4 years 10.2

Of our respondents 81% were men (see Table 3-1). The mean age of our sample was 36.4 years; 23% had an age up to 29 years, 27% of the respondents was 30 to 35 years, 36% was 36 to 45 years, 14% was over 45. The mean experience in an ETE position was 6.4 years; 35% operated

66

ALIGNING INTERESTS OR ALIGNING REWARDS?

in this role about 2 years, 24% between 2 and 6 years, 32% between 6 and 10 years, the other respondents had over 10 years of experience. 29.4% of our respondents indicated that their supplier company has worked with the manufacturer for 10 months or less, while 10.2% of our respondents indicated that the cooperation extended to over 4 years. The average duration of cooperation was 19.74 months. The average R&D project team size in the sample was 18.4 employees; on average 42% of the team members were ETEs. On average, 2 ETEs per project responded to our survey, which limited the opportunity to identify multi-level structures in our data. To check the representativeness of our sample, we compared the mean values of age, gender and job tenure of our sample to those of relevant studies conducted in the R&D domain. The mean age reported varies from 30 (e.g., Chi, Huang, and Lin 2009) to 45 (e.g., Petroni 2000), with multiple studies reporting a mean of 39 years (Cordero, Ditomaso, and Farris 1996; Faraj and Sproull 2000; Zenger and Lawrence 1989). The mean in our sample (36.4 years), including standard deviation (8.8 years) corresponds to these samples. Similarly, a meta-analysis conducted by Ng and Feldman (2013) investigates the relationship between age and innovative behavior. Their sample comprises 6153 individuals with a mean age of 36. This again corresponds to the mean age of our sample. The relatively high percentage of men in our dataset (81%) corresponds to percentages found in other recent studies, e.g. Chi et al. (2009;78%), Garcia Martinez et al. (2017; 74,2%), Cordero et al. (1996; 75%), and Tortoriello and Krackhardt (2010; 90%). Therefore, based on sample comparisons we concluded that our sample is representative for collaborative R&D projects. We tested for non-response bias by comparing the answers of early respondents to those of late respondents but did not find significant differences in means and standard deviations on our focal constructs (Armstrong and Overton 1977). Non-response therefore does not seem to pose a threat to the validity of our results. Furthermore, we tested for manufacturer or project bias by comparing the means of our focal constructs across manufacturers or projects; we did not find significant differences.

3.4.3 MeasuresWe used validated scales from existing works to operationalize the constructs from our conceptual model. The survey was pre-tested by 7 practitioners and 8 academic researchers prior to the actual data collection and minor wording changes were made in response to their feedback. Unless indicated otherwise, responses were recorded on 7-point Likert scales tapping the extent to which a respondent agreed to each statement. All constructs including their item wordings, loadings, and psychometric properties are listed in Table 3-2. Knowledge sharing behavior was measured using 6 items inspired by He and Wong (2004) and Im and Rai (2008). The items capture the extent to which respondents shared expertise to help a manufacturer’s R&D department gain novel insights that may positively influence the technical, cost, and efficiency capability of the product or service

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under development. Knowledge sharing intention was measured using a 4-item scale. We transformed original items used by Siemsen et al. (2008) that measured “knowledge sharing attempt” (3-items) and “motivation to share” (adopted 1-item; dropped in their study) after careful analysis. The items focus on the willingness to share and teach tacit knowledge, expert insights, and internalized information with other members of the project. Knowledge sharing behavior was measured using 6 items inspired by He and Wong (2004) and Im and Rai (2008). The items capture the extent to which respondents shared expertise to help a manufacturer’s R&D department gain novel insights that may positively influence the technical, cost, and efficiency capability of the product or service under development. Knowledge sharing intention was measured using a 4-item scale. We transformed original items used by Siemsen et al. (2008) that measured “knowledge sharing attempt” (3-items) and “motivation to share” (adopted 1-item; dropped in their study) after careful analysis. The items were transformed such that they measure an intrinsic willingness to share and teach tacit knowledge, expert insights, and internalized information with other members of the project. We assessed Customer Stewardship using 3 out of 5-items from the customer stewardship control scale developed by Schepers et al. (2012). The items measure feelings of accountability and responsibility for a customers’ welfare and taking ownership for customer problems. To measure Distributive Fairness we adopted the scale used across extant literature (Franke, Keinz, and Klausberger 2012). The 3 items tap the extent to which an ETE perceives future benefits to be fair and balanced just as whether the investment-benefit ratio was perceived as being fair. From preliminary interviews with seasoned practitioners we learned that the organization of work in R&D projects can be typified on a continuum ranging from very formal, to having some formal and informal elements, to very informal. We studied existing scales from Moenaert et al. (1994), Song and Thieme (2006), Schulz et al. (2013), Steinicke, Wallenburg, and Schmoltzi (2012), and Walter, Walter, and Müller (2015), but none of these scales adequately discussed the formal-informal continuum. Practitioners further indicated (in)formality to occur in three domains: governance structures, process coordination, and the flexibility to deal with unplanned events in the R&D project. We looked for elements in the previously validated scales that captured these domains and adapted these statements to fit our context. As a result, formal coordination was measured with three items to which ETEs responded on semantic differential scales, anchored by two polar adjectives to describe the formality of the R&D project in which they participated. The items were pre-tested using our panel of 7 practitioners and 8 academic researchers to confirm their reliability and validity. Finally, we included control variables to account for alternative explanations for knowledge sharing intention and behavior. Gender and age were to control for the possible impact of individual characteristics and we included a dummy variable to model whether the R&D project reflected a new service (i.e., 0) or new product (i.e., 1). The nature of services (e.g., intangibility and inseparability) may complicate knowledge

68

ALIGNING INTERESTS OR ALIGNING REWARDS?

Table 3-2: Summary of measurement scales

Variable FL CR AVE

CUSTOMER STEWARDSHIP .847 .649

I feel accountability for results of my customers. .754

I feel owner of problems my customers face. .847

I feel a sense of responsibility for results of my customers. .814

DISTRIBUTIVE FAIRNESS .818 .602

The benefits of the product/ project development for both companies feel balanced. .796

The contribution in amount of resources against the expected payoff feel fairly split (e.g., people. tools. and investments).

.834

In my eyes, there is a balance between what my company contributes and receives from this project. .690

FORMAL COORDINATION .767 .525

I had the feeling this R&D project team is mostly governed by: mutual understanding – contractual agreements

.794

The work processes in this R&D project are influenced by: informal coordination – formalities .545

In case of disagreement or unplanned events. it was solved by: mutual understanding – referring to the contract

.728

KNOWLEDGE SHARING INTENTION .926 .758

I intended to teach knowledge to my coworkers of the R&D team. .829

I felt I had to make an effort to transfer my expertise to my coworkers of the R&D team. .871

I wanted to share my experience and knowhow with my coworkers of the R&D team. .903

I meant to share knowledge with my coworkers of the R&D team. .878

KNOWLEDGE SHARING BEHAVIOR .863 .558

I shared knowledge with the project team to increase efficiency levels. a .474

I shared knowledge with the project team to realize cost savings. .705

I shared knowledge with the project team to reduce consumption of materials or resources. .818

I shared knowledge with the project team to generate new services. .743

I shared knowledge with the project team to open up new markets. .794

I shared knowledge with the project team to enter and/ or apply new technologies. .664

RISKTAKING PROPENSITY .938 .792

I like to take risk by venturing into the unknown. .695

I am willing to invest a lot of time and/or money on something that might yield a high return. .760

I tend to act “boldly” in situations where risk is involved. .630

EXTRAVERSION .867 .566

I am sociable. .717

I am very communicative and like talking to others. .810

I am assertive and active. .742

I am enthusiastic. .737

I am full of power and keen on action. .751

FL = standardized factor loading, CR = composite reliability, AVE = average variance extracted a Construct and items were removed due to Average Variance Extracted criterion >.5 (Hair et al. 2006).

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3

sharing and was should be controlled for. We further controlled for ETE involvement density, reflecting the degree of cooperation in R&D projects and measured by asking respondents to indicate the number of R&D stages they were involved in (Potter and Lawson 2013). Finally, we controlled for the ETEs’ personality traits Extraversion and Risk-Taking Propensity because behavioral theorists have linked cooperativeness explicitly to these two traits (Yilmaz and Hunt 2001).

3.5 Data Analysis & Results

3.5.1 Measurement modelWe conducted a confirmatory factor analysis in AMOS to examine the psychometric properties of our measures and fit of the measurement model. Table 3-2 shows that the measures exceed the recommended thresholds for reliability and convergent validity (Bagozzi and Yi 1988): factor loadings and average variance extracted (AVE) were above .50 and composite reliabilities above .70. Table 3-3 shows that the discriminant validity criterion (Fornell and Larcker 1981) was met for all constructs, as for any construct its AVE exceeded the squared correlation (i.e., shared variance) with any other construct. We further analyzed the variance inflation factors (VIF) and concluded that multicollinearity is not a concern in our data either (highest VIF: 1.419). Overall, model tests reveal a good fit of the measurement model: χ2 (303) = 419.04, CFI = .97, TLI = .96, RMSEA = .045.

We also tested for potential common method bias (CMB), next to the applied a priori measures to reduce the potential for CMB (survey design separating measurement, variability in response and randomization of item order; Podsakoff et al. 2003). First, a principal component analysis with varimax rotation revealed the presence of seven distinct constructs, rather than a single factor. The seven factors together accounted for 76.2% of the total variance; the first (largest) factor did not account for a majority of the variance (14.3%). Thus, no general factor was apparent; this provided a first relief for CMB concerns. Second, in an alternate CFA we modeled a common latent factor to load on all manifest variables. The single factor revealed a common variance of 17.8%, confirming that CMB is not of concern and unlikely to confound the interpretations of results (Podsakoff et al. 2003).

70

ALIGNING INTERESTS OR ALIGNING REWARDS?

Table 3-3: Correlation matrix

1 2 3 4 5 6 7 8 9 10 11

1. Customer Stewardship .806

2. Distributive Fairness .292** .776

3. Formal Coordination .023 .061 .725

4. Knowledge sharing intention

.332** .157* .043 .871

5. Knowledge sharing behavior

.342** .293** -.011 .404** .747

6. Gender (1=male) a .136 -.061 .098 .044 .155* -

7. Age a -.103 -.279** .008 .026 -.124 .200** -

8. Development type (1=service) a -.268** -.350** .062 -.031 -.150* .109 .376** -

9. Risk taking propensity .459** .191** .000 .251** .382** .124 -.093 -.113 .890

10. Extraversion .582** .369** .021 .311** .357** .011 -.240** -.304** .451** .752

11. Involvement density a .171* .016 -.184* .175* .195** .223** -.005** -.030 .076 .129 -

Mean 5.709 4.474 4.050 4.013 3.278 - 36.40 - 5.273 5.749 3.572

Standard deviation 1.149 1.314 1.483 .774 .894 - 8.810 - 1.055 .889 1.629

Average variance extracted plotted on the diagonala Continuous Variable* Correlation is significant at the 0.05 level (2-tailed).** Correlation is significant at the 0.01 level (2-tailed).

3.5.2 AnalysesTo test our hypotheses we estimated our moderated mediation model using the PROCESS macro in SPSS 22 (Hayes 2013). PROCESS is specifically designed to handle multiple moderators and/or mediators to operate in a single model. Furthermore, the software enables a floodlight analysis rather a spotlight analysis for the effect of X on Y and gives a detailed insight into the conditional effects at the 10th, 25th, 50th, 75th, and 90th percentiles of the moderator. To test our model, we first averaged the items of each construct to create our latent variables and then standardized these variables. To start we instructed PROCESS to estimate a mediation model without moderating effects. Template 4 (Hayes 2015) represents this model. We entered our control variables as covariates for both the mediator (knowledge sharing intentions) as the dependent variable (knowledge sharing behavior). More specifically, our set of equations takes the following form:

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3

KSI = iKSI + a1CSTW + a2DFAIR + a3AGE + a4GND + a5DEV + a6RISK + a7EXT + a8INVOL + eKSI

KSB = iKSB + c1’CSTW + c2’DFAIR + b1KSI + b2AGE + b3GND + b4DEV + b5RISK + b6EXT + b7INVOL + eKSB

CSTW denotes customer stewardship, DFAIR distributive fairness, FORMC denotes formal coordination. Further, AGE denotes an ETE’s age, GND denotes an ETE’s gender (dummy, male coded as 1), DEV denotes the R&D development type (dummy, service projects coded as 1, products as 0), RISK denotes an ETE’s risk taking propensity, EXT denotes an ETE’s level of extraversion, and INVOL denotes an ETE’s involvement density. Of the coefficients, ai indicates the estimations of the effects of the respective variables on the mediator, the bi indicates the estimations of the effects of the mediator and covariates on the dependent variable, c’ represents the direct effects of the independent variables, i’s are the intercepts, and the e’s are the error terms. Following the estimation of our core model, we included our interaction effects. We now instructed PROCESS to estimate model 58 (Hayes 2015). Because PROCESS does not allow specification of multiple independent variables in the same estimation, we ran one model where customer stewardship was defined as independent variable and where formal coordination was defined as a covariate, and one model where we reversed the role of these variables. Our set of equations takes the following form:

KSIj = iKSIj + a1jCSTW + a2jDFAIR + a3jFORMC + a4jINTj

+ a5jAGE + a6jGND + a7jDEV + a8jRISK + a9jEXT + a10jINVOL + eKSIj

KSB = iKSB + c1’CSTW + c2’DFAIR + b1KSI + b2FORMC + b3KSI×FORMC + b4AGE + b5GND + b6DEV + b7RISK + b8EXT + b9INVOL + eKSBj

j INTj

We used 5000 bootstrap samples to estimate the 95% asymmetric bias-corrected confidence intervals (CIs) for inferences about the conditional indirect effects of our independent variables on KSB through KSI. Table 3-4 displays the results of our estimations.

0=CSTW model1=DFAIR model

INT0 = CSTW x FORMCINT1 = DFAIR x FORMC

72

ALIGNING INTERESTS OR ALIGNING REWARDS?

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3.5.3 ResultsThe hypothesized model explained 33.5% of the variance in knowledge sharing behavior and 18.7% in knowledge sharing intention. In support of H1, the results confirm the expected positive effect of knowledge sharing intention on knowledge sharing behavior (β= .288, t = 3.348, p < .001). We also found support for H2 because customer stewardship related significantly to knowledge sharing intention (β = .165, t = 2.047, p < .05). Interestingly, we did not find support for H3 which relates to the effect of distributive fairness on knowledge sharing intention (β= .090, t = 1.140, n.s.). Next we test whether intentions have a (full) mediation role in our model. Therefore, although not formally hypothesized, we included in the model the direct effects of customer stewardship and distributive fairness on knowledge sharing behavior. The direct effect of customer stewardship on knowledge sharing behavior was not significant, while the unconditional indirect effect was significant as the 95% CI did not include zero: [.009; .130]. Knowledge sharing intentions thus fully mediate between customer stewardship and knowledge sharing behavior. In addition, we found a significant positive direct effect of distributive fairness on knowledge sharing behavior (β= .156, t = 2.293, p < .05), but the unconditional indirect effect was not significant as the 95% CI included zero: [-.019; .079]. Therefore, intentions did not mediate between distributive fairness and knowledge sharing behavior. Regarding our hypothesized moderating effects, we found a significant effect of formal coordination on the relationship between knowledge sharing intention and knowledge sharing behavior (β= -.154, t = 1.987, p < .05). This significant negative effect provides support for H4. We do not find support for H5 because the relationship between customer stewardship and knowledge sharing intentions was unaffected by formal coordination (β= -.057, t = -.707, n.s.). In contrast, the relationship between distributive fairness on knowledge sharing intention did depend on formal coordination (β= -.118, t = -2.065, p < .05). This supports H6. Regarding the control variables, we found a significant effect of risk taking propensity (β= .220, t = 2.983, p < .01) and extraversion (β= .194, t = 2.261, p < .05) related positively to knowledge sharing behavior and intention, respectively. These findings are in line with earlier work, as risk averse individuals tend not to engage in risky behavior such as knowledge sharing (Brockhaus 1980), while extraverts have a natural tendency towards prosocial behaviors (Mechinda and Patterson 2011). Other control variables did not display any significant effects.

3.5.4 Post-hoc analyses: floodlight analysis of full moderated mediation modelWe were surprised not to find evidence for an effect of distributive fairness on knowledge sharing intentions. This also caused the indirect effect of distributive fairness on knowledge sharing behavior through intentions to be non-significant. However, the moderating

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effect of formal coordination on the relationship between fairness and intention did turn out to be significant. It could be that the indirect effect of distributive fairness on knowledge sharing behavior is significant for certain values of formal coordination, but not for others. At the same time, it is important to know whether the direct effect of distributive fairness on knowledge sharing remains significant under these conditions. We therefore set out to explore the total effects of distributive fairness in a full moderated mediation model. This was tested using PROCESS model 59 (see Hayes 2015) which takes the following form:

KSIj = iKSIj + a1jDFAIR + a2jCSTW + a3jFORMC + a4jINTj

+ a5jAGE + a6jGND + a7jDEV + a8jRISK + a9jEXT + a10jINVOL + eKSIj

KSBj = iKSBj + c1j’CSTW + c2j’DFAIR + c3j’INTj

+ b1jKSI + b2jFORMC + b3jKSI×FORMC + b4jAGE + b5jGND + b6jDEV + b7jRISK + b8jEXT + b9jINVOL + eKSBj

j INTj

We ran the model for j = 1 with 5000 bootstrap samples to obtain the bias corrected CIs; Table 3-5 shows the results of this additional analysis. We find that for low values of formal coordination (10th and 25th percentile), distributive fairness leads to knowledge sharing behaviors through knowledge sharing intentions. Under these circumstances, manufacturer employees do not refer much to contractual terms, which makes the stakes of the manufacturer less salient to ETEs, which makes their fairness perceptions a more powerful antecedent of intentions to share knowledge. For higher values of formal coordination (75th and 90th percentile), distributive fairness has a direct effect on knowledge sharing behavior. In such cases it could be high formal control signals to ETEs that their R&D colleagues are well-informed on the project’s characteristics and between-firm agreements. This alleviates any potential concern that fairness perceptions are only held by this individual ETE, which directly drives behavior. We return to this effect in our discussion section.

0=CSTW model1=DFAIR model

INT0 = CSTW x FORMCINT1 = DFAIR x FORMC

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Table 3-5: Floodlight Analysis of Total Effect of Distributive Fairness on Knowledge Sharing Behavior

Direct effect Indirect effect Total effect

Value of Formal coordination

Percentile Effect LLCI ULCI Effect LLCI ULCI

-1.382 10th -.017 -.243 .208 .141 .029 .316 .141

-.708 25th .048 -.129 .226 .076 .001 .180 .076

-.034 50th .114 -.034 .263 .029 -.014 .093 n.s.

.640 75th .180 .034 .329 .003 -.031 .045 .180

1.315 90th .246 .007 .424 -.005 -.075 .013 .248

Driven by the strong conditional effects in the relationship between fairness and knowledge sharing behavior, we also conducted a floodlight analysis to examine the contingencies in the relationships between customer stewardship, knowledge sharing intentions, and knowledge sharing behavior. We again relied on PROCESS model 59, now running the model for j = 0. Table 3-6 shows the results of this additional analysis. Similar to the results for distributive fairness, we find that for low values of formal coordination (10th to 50th percentile), customer stewardship leads to knowledge sharing behaviors through intention. Interestingly, the direct effect of customer stewardship on knowledge sharing behavior is not significant for any moderator value. Perhaps ETEs who feel accountable for the customer’s welfare think twice before actively sharing their knowledge because some creative thoughts may need more sharpening, or some to-be-shared ideas may pose a too large financial or performance risk for the customer if the R&D project team were to implement these ideas into the new product.

Table 3-6: Floodlight Analysis of Total Effect of Customer Stewardship on Knowledge Sharing Behavior

Direct effect Indirect effect Total effect

Value of Formal coordination

Percentile Effect LLCI ULCI Effect LLCI ULCI

-1.382 10th .122 -.119 .364 .117 .016 .295 .117

-.708 25th .065 -.108 .237 .081 .017 .182 .081

-.034 50th .007 -.137 .151 .052 .005 .125 .052

.640 75th -.051 -.227 .126 .029 -.010 .118 n.s.

1.315 90th -.108 -.356 .139 .012 -.020 .146 n.s.

Figure 3-2 graphically summarizes our analyses of the total effects of customer stewardship and distributive fairness on knowledge sharing behavior. This plot serves as a dashboard for R&D project managers to control the levels of ETE knowledge sharing behavior in R&D collaborations. We will discuss the implications of these findings next.

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Figure 3-2: Total effects distributive fairness and customer stewardship on KSB

3.6 Discussion & Conclusions

The aim of this research was to understand whether aligned interests or aligned rewards drive an ETE’s knowledge sharing process in collaborative R&D projects. We presented an integrated model to understand how and when ETEs share their knowhow and identifi ed a complex pattern of effects in our moderated mediation model. When ETEs are often referred to contractual obligations by their colleagues in the R&D project, their perception of a fair distribution of rewards directly drives their knowledge sharing behavior. When there are no such referrals (i.e., formal coordination is low), perceptions of customer stewardship and distributive fairness enhance knowledge sharing intentions which then infl uence behavior. Coordinating ETEs’ knowledge contributions thus requires a careful alignment of the R&D project’s future rewards, the interests of the parties involved, and formal coordination. Next, we discuss our theoretical implications.

3.6.1 Effects of customer stewardship First, our study adds to extant insights about customer stewardship in multiple ways. For instance, where stewardship has predominantly been considered to regulate behavior in stable long-term relationships (Hernandez 2012; Le Breton-Miller, Miller, and Lester 2010), we show that stewardship’s governing principles can also be effective in shorter-term, uncertain and volatile R&D projects. However, we also speculate on the theory’s limits for explaining individual ETE’s behavior across work contexts. Existing works focused on the direct effects of stewardship on behavior (cf. Schepers et al. 2012; Walumbwa, Hartnell, and Oke 2010), but we fi nd that customer stewardship only infl uences knowledge sharing behavior through intentions. Stewardship’s effect on knowledge sharing intention corresponds to its main principle: “an ongoing sense of

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obligation or duty to serve others based on the intention to uphold the covenantal relationship” (Hernandez 2012, p. 174, emphasis added through italics). Then why do previous studies find direct effects of stewardship on behavior? Schepers et al. (2012) focus on employees’ extra-role behavior, rated by managers, and defined as “employees’ discretionary efforts that are not captured in formal job descriptions” (p. 6). When managers evaluate such broad behaviors of their employees, they tend to factor in not only their true performance, but also their attitude and overall stance towards work (Pettijohn et al. 2001). Managers may therefore implicitly reflect on an employee’s willingness to exert effort, e.g., share knowledge. We find that customer stewardship leads ETEs to form behavioral intentions to share knowledge. Thereafter they carefully consider which piece of information is truly worth sharing. This provides an interesting avenue for future research: is one type of knowledge more likely to be shared under similar levels of customer stewardship than another type? If so, are these insights shared directly, or is this again a deliberate process of intention preceding action? Our study also reveals that knowledge sharing intention does not translate into actual sharing when the ETE experiences a high level of formal coordination. Our explanation here is that constant referrals to contractual obligations communicates a manufacturer’s mistrust in or worries about the incompetence of the ETE (Jap and Ganesan 2000). Mistrust and incompetence perceptions are known barriers to translate intentions into behavior in creative and unstructured environments (Blauth, Mauer, and Brettel 2014; Kim, Im, and Slater 2013). However, literature also outlines an opposite role of formal coordination. Communications about expectations and obligations reduce uncertainty and provide direction in the type of know-how that is needed in the project and the way it can be interpreted (Hirst et al. 2011). Temporary workers hence perform better in uncertain environments when the interests of customers are clearly and frequently expressed (Moorman and Harland 2002). Our findings provide support for this dual role of formal coordination.

3.6.2 Effect of distributive fairnessSecond, we find a clear relationship between distributive fairness, knowledge sharing intention and behavior. This adds to earlier research on fairness theory, which postulates individuals will expend effort to maximize the mutual outcome of fair balance situations (Lai, Lui, and Tsang 2016). ETEs then recognize that shared knowhow will benefit not only the customer, but also their own company. This instills them with confidence that they will also personally benefit in future. For instance, supplier managers may reward ETEs that have participated in successful projects with bonuses or salary raises. Extant works have conceptualized both indirect (McLarty and Whitman 2015) and direct effects of fairness perceptions (Yi and Gong 2008) on employee behavior independently, but our integration of both perspectives in a moderated mediation model reveals more subtleties. Distributive fairness influences knowledge sharing behavior

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through intentions when the level of formal coordination is low, but there is a direct effect when formal coordination is high. In terms of the dual role discussed above, here formal coordination sets clear boundaries and expectations in an uncertain environment. The on-the-job communication of rules and obligations indicates that peers are well informed on the project’s characteristics and between-firm agreements. This reassures the ETE that perceptions on work relationships are shared by others in the innovation project and thus reinforces the effect of distributive fairness on knowledge sharing behavior.

3.6.3 Aligned rewards or interest?Finally, we add to the ongoing debate in literature whether aligned interests and rewards are overlapping or distinct phenomena (Le Breton-Miller, Miller, and Lester 2010; Martynov 2009). As we outlined above, the effectiveness of such alignments to stimulate ETE behavior differs under alternate levels of formal coordination. Under conditions of low formality, both the alignment of interests and alignment of rewards drive knowledge sharing behavior through intention. Under conditions of high formal coordination, only alignment of rewards (directly) drives knowledge sharing behavior. Interestingly, fairness perceptions did not facilitate ETE knowledge sharing behavior when there was a medium degree of formal coordination. Perhaps medium formal coordination is associated with occasional and unexpected referrals to contractual obligations that confuse the ETE rather than set clear work expectations. However, further work is needed to substantiate this thought. We conclude that the (total) effects of aligned interests and rewards on knowledge sharing behavior are additive under low formal coordination and exclusive under high formal coordination. The total effect of joint customer stewardship and fairness perceptions under conditions of very low formal coordination (10th percentile, joint total effect = .117 + .141) is .258 while under conditions of very high formal coordination (90th percentile) the total effect amounts to .248 which solely comes from distributive fairness. Although effects can still be observed when formal coordination is medium-low to medium-high, the sum of both effects is lower than under more extreme conditions of formal coordination. This complex interplay between aligned rewards and interests under different formal coordination conditions provides challenges for manufacturers that aim to manage the knowledge contributions of ETEs. The next section provides cues and advice for R&D (project) managers, and their employees, to improve ETE knowledge sharing in collaborative R&D.

3.6.4 Managerial ImplicationsWe suggest R&D managers to make a conscious choice with regard to the level of formal coordination in collaborative R&D projects. Managers should instruct their employees either to heavily rely on informal coordination mechanisms of trust and mutual understanding or to provide strict guidance to ETEs by frequently referring to contract obligations.

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However, it could be that there already is a strong and long-standing tradition of (in)formal coordination in innovation projects in place. Regardless of being managerially instructed or culturally determined, the degree of formal coordination in collaborative R&D projects is crucial to stimulate knowledge sharing behavior of ETEs. We outline two possible scenarios to facilitate such exchange of knowledge. First, when formal coordination is high, managers are encouraged to emphasize the benefits for the supplier of being involved in this project. They should convince the ETE that his/her knowledge contribution allows both parties to attain their interests and that future revenues are fairly distributed over the parties involved. Such actions to create a “we are in this together” atmosphere require a manager to understand the feelings and emotions of ETEs. Thus, manager traits such as being emotionally intelligent and empathic are important in relieving any ETE fairness concern. We further advise to avoid on-the-job fairness dialogues because ETEs may feel that manufacturers try to renegotiate the work conditions. Second, when formal coordination in R&D projects is low, managers have to do more. In addition to the actions outlined above to stimulate perceptions of distributive fairness, they have to make sure that ETEs experience a sense of stewardship for the manufacturer’s well-being. This can be done by nurturing three basic social needs of ETEs (Schepers et al. 2012): (1) provide ETEs a great deal of autonomy to work and share know-how in the R&D team. This makes these external experts more willing to take full responsibility. Providing high levels of autonomy is a logical companion to having a low degree of formal coordination in the project. (2) Secure ETEs’ relatedness to the R&D project and make them ‘feel at home’. This can be done by having extensive onboarding procedures and inviting ETEs to informal events that the company may organize on a regular basis. (3) Be appreciative of the unique competency that ETEs bring to the table. Employees that are valued for their knowledge feel more control over the outcomes of unstructured situations and are hence more willing to take responsibility in R&D projects.

3.6.5 Strengths, limitations & future researchAs with any study, our work is subject to some limitations that at the same time provide interesting cues for future research. First, we designed our research to maximize its generalizability by obtaining scores from a variety of project types across several companies. Although this ruled out single company bias, it also limited the possibility to collect data at the project- and inter-organizational level. Obtaining such data typically hinges on exclusivity contracts. Future research may specifically explore contextual effects in ETEs’ knowledge sharing processes. Second, not all shared knowhow holds the same value for the manufacturer. Hence, future research may account for the applicability, quality, or innovativeness of shared insights. Additionally, we focused on knowledge sharing but scholars could also

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consider whether the relationships uncovered are different for sharing different types of tacit or explicit knowledge (e.g., written documents, drawings, specific instructions). Finally, the concept of knowledge sharing takes the perspective of the ETE, whereas the concept of knowledge transfer considers both the sender and receiver of knowledge. Future research could investigate whether the mechanisms used in this study also explain knowledge transfer success. Perhaps manufacturer employees equally balance interests and rewards and this may help or hinder them to accept external insights from ETEs. This could provide further insight why certain R&D projects are more successful than others.

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4Mind the Gap: How Identifi cation Gaps

Infl uence External Team Members’ Innovative Work Behavior in

New Product Development Projects

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When manufacturers involve specialists from supplier firms in new product development projects, the contributions often are not sufficiently innovative. Previous literature identifies organizational identification as an important driver of external technology experts’ (ETEs) innovative work behavior (IWB), but ETEs may identify with their own organization (supplier), their client (manufacturer), or the R&D project, prompting potential identification gaps. This study introduces inter-organizational identification incongruence and intra-organizational identification congruence as two concepts to explain how the presence or absence of identification gaps influences ETEs’ IWB. A survey of 187 ETEs affirms that identification (in)congruence is associated with IWB and also can be managed using onboarding and goal socialization tactics. Together, the results extend contemporary discussions in organizational identification literature and provide valuable insights to new product project managers.

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4.1 Introduction

Developing new products and services with the assistance of external partners has become a common practice (Markham and Lee 2013). External technology experts (ETEs) are employees of a supplier firm who work jointly with a client (manufacturer) on its research and development (R&D) projects, with the promise of closing knowledge gaps, introducing out-of-the-box ideas, and infusing specialist competences. Leveraging their expertise represents a key asset for manufacturers, because it improves the novelty, cost efficiency, time-to-market, and success of the new product (Berchicci 2013). Manufacturers that rely solely on internal competences increasingly find themselves outperformed in R&D (Bstieler 2006). Despite this increased use of ETEs though, many manufacturing firms still face disappointing R&D outcomes (Ragatz, Handfield, and Scannell 1997; Wynstra and Pierick 2000), seemingly because ETEs fail to be innovative or proactive enough to benefit R&D project performance (De Visser et al. 2014). This failure might emerge because ETEs confront tensions between the interests of their employer (i.e., supplier firm) and their client (i.e., manufacturer). For example, before sharing expert knowledge, an ETE must consider whether the know-how is sensitive information that might enable the manufacturer to integrate backward in the supply chain. In this position, ETEs bear most of the risks of disclosing too much, to the potential detriment of their employer. If they limit their efforts to share information and generate or implement new ideas in R&D projects, the expected outcomes of involving ETEs in R&D may fail to materialize. A better understanding of how to manage ETEs’ contributions to manufacturers’ R&D projects thus is highly relevant. Prior research suggests that identification with an entity, such as the manufacturer or the R&D project, motivates innovative behavior (Blader and Tyler 2009). Identification implies a sense of membership with and favoritism toward a social group (Ellemers, De Gilder, and Haslam 2004). Identification thus can motivate people to act in the interest of the focal entity (Ashforth and Humphrey 1993; Lee, Carswell, and Allen 2000; Sluss and Ashforth 2007), but ETEs have a unique position, in that they are exposed to various identification options and could feel simultaneous identification with the supplier, the manufacturer, and the R&D project team. With this study, we investigate the forms of identification for ETEs involved in manufacturers’ R&D projects, with the prediction that the presence or absence of identification gaps (i.e., different levels of identification with different entities) strongly influence ETEs’ innovative work behaviors (IWB), more so than an absolute level of identification with specific entities (i.e., supplier, manufacturer, or R&D team). Strong, multiple identifications may necessitate behavioral trade-offs that limit exchanges of know-how. We rely on identification theory (Ashforth, Harrison, and Corley 2008; Ashforth and Mael 1989; Scott and Lane 2000) and organizational

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socialization theory (Bauer et al. 2007; Chen 2005; Cooper-Thomas and Anderson 2006), and we seek to expand identification and IWB literature in several directions. First, ETEs can identify simultaneously with different entities (Wieseke et al. 2012); we add to identification research by analyzing the effect of identification gaps across different entities rather than the individual effects of identifying with multiple entities. To do so, we propose the concepts of inter-organizational identification incongruence and intra-organizational identification congruence. Incongruence reflects differences in an ETE’s identification with the supplier, relative to that with the manufacturer. Dominant identification with the supplier’s interests may lead ETEs to consider them, rather than the manufacturer’s intended R&D outcome. Such trade-offs could affect their IWB. Congruence instead indicates agreement in ETEs’ identification with both the R&D team and the manufacturer, which should lead them to feel comforted and supported and stimulate their increased effort. In contrast, differences in identification might create cognitive arousal, which is needed to perform innovatively. Internal team members likely take such differences for granted, whereas they are salient for ETEs and should affect their behavior. Exploring these gaps and multiple identifications and their effects on ETEs’ IWB therefore is pertinent. Second, we add to previous research on IWB (Janssen, 2000; Scott & Bruce, 1994), which refers to the intentional creation, introduction, and application of new ideas in a work role, group, or organization in an effort to benefit a group or organization (De Jong and Den Hartog 2010). A wide variety of IWB antecedents have been identified empirically, including organizational support (Zhou and George 2001), autonomy (Prieto and Pérez-Santana 2014), team composition (Hülsheger, Anderson, and Salgado 2009), trust perceptions (Wu, Parker, and De Jong 2014), and identification (Becker et al. 1996; Farmer, Van Dyne, and Kamdar 2015). However, the role of identification (in)congruence remains uncertain. Identification generally functions as a motivator for such discretionary effort, but ETEs balance multiple competing identifications that could block their IWB during their boundary-spanning work on R&D projects. Third, identification is an ongoing process by which people define their group memberships (Cooper & Thatcher, 2010); we distinguish two strategies that can help ETEs identify with the manufacturer’s interests both before and during their interactions with the R&D team and manufacturer. Many organizations carefully select, train, and prepare ETEs before allowing them on an R&D project (Bauer et al., 2007; Cooper-Thomas & Anderson, 2006), but the effects of such practices on ETEs’ identification with the manufacturer and the project team are unclear. For example, goal socialization—which includes explaining goals, providing feedback about individual and team performance, and behavioral coaching by R&D project managers (Cooper-Thomas & Anderson, 2006)—may help ETEs identify with a single entity (Bauer et al. 2007; Hu and Liden 2011), but we do not know its effect in R&D contexts in which different identification foci “compete.”

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We outline the theoretical foundation of our conceptual research model next, then present our hypotheses. After outlining the empirical tests of our hypotheses, which rely on survey data gathered from 187 ETEs involved in R&D projects at 7 global, high-tech manufacturers, we present the results and some conclusions.

4.2 Theoretical Background

4.2.1 Social Identity, Self-Categorization, and Identification TheoryAccording to social identity theory, ETEs join an entity and recognize its “central, enduring and distinctive characteristics” (Scott and Lane 2000, p. 44), which shape its identity and reflect its goals, ways of working, values, and norms (Ashforth, Harrison, and Corley 2008). Identification occurs when ETEs perceive membership in an organization, department, project, or workgroup (Mael and Ashforth 1992). In the self-categorization process, ETEs judge an entity’s characteristics relative to their own personal values. If a match arises, ETEs feel psychologically connected to the entity’s fate, destiny, success, and failures, leading them to think and act in the group’s interests (Van Knippenberg and Van Schie 2000). In an ETE’s work environment, the group might be the supplier, manufacturer, or R&D project team (Becker et al. 1996).

4.2.2 Multiple Identities and Identification GapsMultiple identifications can coexist between and within organizations (Wieseke et al. 2012; Fombelle et al. 2011; Edwards and Peccei 2010). Employees may enter into multiple cognitive in-group memberships, reflected in their simultaneous identification with their workgroup (Ellemers, De Gilder, and Haslam 2004), their department (Fombelle et al. 2011), their organization (Van Dick et al. 2008), or even other organizations (Van Leeuwen, Van Knippenberg, and Ellemers 2003). Multiple identifications can result in identification gaps (Van Leeuwen, Van Knippenberg, and Ellemers 2003), defined as cognitive perceptions of incongruent or congruent identity demands for two or more identifications that a person seeks to merge cognitively (cf. Ashforth, Harrison, and Corley 2008). Between organizations an ETE might identify with the supplier and the manufacturer, as higher-level entities, and then need to decide which interests to serve. The R&D project teams are more proximal to ETEs and therefore constitute lower-level entities. If ETEs can merge the demands of multiple higher-level entities, these identifications can peacefully coexist (Ashforth, Harrison, and Corley 2008), but if higher-level identifications differ, strong in-group versus out-group feelings might arise and trigger more pronounced behavior in favor of the entity with the strongest identification (Richter et al. 2006; Van Dick et al. 2008). Then ETEs might be less likely to share their expertise and suggest innovative ideas for the manufacturer’s R&D project team if their higher-level identifications are incongruent and their identification with the supplier is stronger.

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Within organizations, higher-level identities are inclusive, abstract, distal, and strategic, whereas lower-level identities are exclusive, concrete, proximal, and operational (Kraus et al. 2015). Identification with an R&D team therefore should lead ETEs to commit to lower-level goals, but identification with the organization as a whole drives commitment to the manufacturer’s vision (Ashforth and Johnson 2001; Riketta and Van Dick 2005). In general, identification with lower-level entities influences individual behavior more strongly than identification with superordinate entities (Van Knippenberg and Van Schie 2000). However, ETEs also might account for organizational interests and “calibrate” their behavior in the R&D project team. That is, they weigh the long-term effects of their actions against short-term outcomes (Ashforth, Harrison, and Corley 2008; Van Dick et al. 2008), in which case they might behave less opportunistically and more innovatively when their identifications within the organization are congruent. In summary, we consider differences in the levels of identification between (supplier versus manufacturer) and within (manufacturer versus R&D project team) organizational boundaries.

4.2.3 Innovative Work Behavior As a crucial driver of R&D project performance (De Jong and Den Hartog 2007), IWB involves the intentional effort to influence work outcomes in a positive manner, by bringing about change and newness to processes, products, services, and customer solutions. Although routinely studied as a unitary construct, IWB comprises multiple dimensions, including idea generation, idea championing, and idea realization (De Jong and Den Hartog 2010; Janssen 2000; Scott and Bruce 1994). Idea generation, or coming up with novel approaches, products, and services related to an identified problem, requires the combination and organization of information that people gather through their work experiences (Amabile 1988; De Jong and Den Hartog 2010). It should not be confused with idea exploration, which entails a creative search for new insights; some scholars argue that idea exploration is a personality trait, not part of IWB (De Jong and Den Hartog 2010). Idea championing is the effort to find and build coalitions that support a generated idea by being persistent, expressing enthusiasm, and having confidence in the idea in seeking support from stakeholders (Howell, Shea, and Higgins 2005). Finally, idea realization means following through, learning, and modifying the idea to ensure it gets developed into a new product or service. Although describing the R&D process as discrete stages is easier, in practice, people iteratively combine varied R&D and IWB activities (De Jong and Den Hartog 2010; Scott and Bruce 1994). Consistent with the latest developments in IWB theory (De Jong and Den Hartog 2010), we thus regard IWB as a unified construct. Figure 4-1 outlines our conceptual model.

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Figure 4-1: Conceptual model

4.3 Hypotheses

4.3.1 Inter-Organizational Identifi cation IncongruenceWhen two organizations are similar in their key characteristics, employees may identify with both, one of them, or neither (George and Chattopadhyay 2005). When employees need to serve the interests of different organizational entities, they compare salient characteristics and likely prefer identifi cation with one entity over the other, resulting in identifi cation incongruence. Greater incongruence is associated with stronger in- versus out-group comparisons, which then become strong drivers of behavior (Brewer, Manzi, and Shaw 1993). When the difference between in- and out-group identifi cation is sharp and salient, people actively behave to benefi t the in-group while obstructing the out-group (Ashforth, Harrison, and Corley 2008). This phenomenon, refl ecting negative inter-group stereotyping, is relatively widespread. Wieseke et al. (2012) note that sales representatives’ team membership leads them to make invidious distinctions between their work team and distant headquarters. Sarala and Vaara (2010) also observe that employees involved in a merger see themselves more positively than the employees of the acquiring company. Identifi cation differences between entities also can explain behavior. George and Chattopadhyay (2005) state that a psychological contract motivates employees to work toward the interests of the strongest identifi cation entity. When they identify strongly with an initial or baseline entity, people also sense threats from other entities (Steele and Aronson 1995). According to Glynn, Kazanjian, and Drazin (2010), R&D team members who identify with their

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team defend its identity from perceived threats from other teams, to prevent being distracted from their R&D project work. Thus, perceived differences in identification may become cognitive hindrances or aids for ETEs who deploy their energy to benefit R&D projects for the manufacturer (Ashforth and Mael 1989). We therefore posit that ETEs act in favor of their strongest identification. They display more IWB toward the R&D project team if incongruence is characterized by a stronger identification with the manufacturer but less IWB if it is characterized by stronger identification with the supplier. Formally:

H1: Inter-organizational identification incongruence is associated with ETEs’ IWB, such that IWB increases when identification with the manufacturer is greater, relative to identification with the supplier.

4.3.2 Intra-Organizational Identification CongruenceEngaging in IWB inherently implies taking risks. For ETEs to behave innovatively, they need to feel as if they are members of both the manufacturer and the R&D team (De Jong and Den Hartog 2007). An ETE who does not perceive such membership has little motivation to take risks; not feeling like part of the manufacturer makes the ETE question the relevance of his or her IWB. In response, the ETE might start to stress the team’s short-term outcomes rather than the long-term goals of the manufacturer, which is detrimental to exploring new R&D solutions (Ashforth, Harrison, and Corley 2008; Van Dick et al. 2008). That is, ETEs just look to complete the R&D project, not come up with solutions that are likely to pay off in the future. When ETEs experience feelings of belonging to both the manufacturer and the R&D team, they do not suffer any distraction due to questions of belonging (Hornsey & Hogg, 2000; Richter et al., 2006). High identification with both the manufacturer and R&D project team maximizes ETEs’ motivation to deploy their cognitive resources. An implicit psychological contract then motivates them to expend efforts to realize dual goals and purposes (Fombelle et al. 2011), which is more likely to lead to the generation and implementation of innovative ideas and practices that benefit the R&D project team and manufacturer. Therefore, we expect that higher levels of intra-organizational identification congruence drive ETEs’ innovative behavior. H2: Intra-organizational identification congruence between the R&D project team and manufacturer is associated with ETEs’ IWB, such that IWB is greater when ETEs’ identification with both entities is high rather than low.

4.3.3 Managing Identification (In)Congruence Testing the hypothesized effect of identification gaps on ETEs’ IWB requires insights into how R&D project managers can influence (in)congruence. A thorough, complete understanding of the person’s role and relationship with a social entity is a key determinant of identification (Epitropaki 2013; Kreiner and Ashforth 2004). Because ETEs

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shift across boundaries and confront competing identifications, we focus on onboarding and goal socialization practices that help ETEs understand the salient characteristics and demands of their work context. Onboarding. The manufacturer can carefully select, train, and prepare ETEs for their tasks. Such onboarding activities conducted by the manufacturer, regarded as Human Resource Management (HRM) input controls by management control scholars (e.g., Ouchi 1979; Snell 1992), clarify organizational values and goals. Facilitating information flows can help them achieve uncertainty reduction and facilitate the generation of a sense of belonging. Such activities likely affects the (in)congruence of identification both between and within organizations (Caldwell, Chatman, and O’Reilly 1990; Pearce 1993). In information-seeking processes, people arrive at updated self-concepts by acting in organizations and their departments (Cooper-Thomas and Anderson 2006). With onboarding, ETEs obtain information about salient characteristics of the R&D project team and manufacturer (Chen 2005), which gives them a better understanding of their new work environment and others’ perspective (Bauer et al. 2007). Onboarding typically includes perspective-taking exercises (Chen 2005) and helps ETEs get to know team members on a personal level (Cooper-Thomas and Anderson 2006), which drives feelings of belonging (Epitropaki 2013). Such activities communicate “the way we do things around here” and help new employees transition more smoothly from a previous to the current work context (Cooper-Thomas and Anderson 2006). Onboarding also provides ETEs a consistent image of the manufacturer and its R&D team, because the flow of information is orchestrated by the manufacturer. In contrast, in on-the-job learning, situational differences in identification likely arise, because ETEs might struggle to understand the manufacturer’s organization outside their assigned R&D project team. Overall, we expect that onboarding enhances identification with the manufacturer rather than the supplier and facilitates identification congruence between the manufacturer and R&D project team.

H3a: ETE onboarding is associated with inter-organizational identification incongruence, such that identification with the manufacturer increases relative to identification with the supplier when onboarding is greater.

H3b: ETE onboarding is associated with intra-organizational identification congruence, such that identification with the R&D project team and manufacturer increase when onboarding is greater.

Goal socialization. Setting and explaining goals, providing feedback about individual and R&D project performance, and output coaching also can support ETEs’ identification processes (Rousseau 1998b). The goals and the aims of the supplier and manufacturer often do not align, and the characteristics and interests of the supplier are naturally more salient to the ETE, because the supplier provides a reference point that has been shaped over time (George and Chattopadhyay 2005). To facilitate identification with the manufacturer, ETEs need detailed, consistent accounts of the manufacturer’s

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R&D project goals. Goal socialization continually provides this information, which should enhance identification with the manufacturer relative to the supplier. It also fosters continued dialogues that help ETEs understand the hierarchy of goals. In turn, ETEs may be more likely to accept the goals as their own and generate feelings of relatedness to the manufacturer and its R&D project team (Van Dick et al. 2008). We hypothesize: H4a: ETE goal socialization is associated with inter-organizational identification

incongruence, such that identification with the manufacturer increases relative to identification with the supplier when goal socialization is greater.

H4b: ETE goal socialization is associated with intra-organizational identification congruence, such that identification with the R&D project team and manufacturer increase when goal socialization is greater.

4.4 Methodology

4.4.1 Research Setting and Data CollectionTo test our framework, we collected survey data from ETEs involved in the R&D projects of seven global manufacturers in high-tech industries. These manufacturers rely on a wide range of components from specialist suppliers for their R&D. The suppliers that participate in the R&D process help secure seamless integration and component solutions, which requires substantial information exchanges and creative problem solving. Therefore, ETEs’ IWB is crucial for the R&D projects to succeed. In cooperation with R&D managers from the seven manufacturers, we selected one or more R&D projects that required large resource investments and depended heavily on know-how and solutions from ETEs. We specifically selected R&D projects that concerned the development of a new high-tech product, excluding refinements within an existing product generation. These projects require larger investments in terms of time and resources (Hernandez-Espallardo, Molina-Castillo, and Rodriguez-Orejuela 2012) and are full of risk and uncertainties (Calantone, Chan, and Cui 2006). Furthermore, such projects require new external knowhow to be integrated during the project (Chai et al. 2012). We selected only those projects that were in an advanced phase of development (nearing first release) or that were recently completed (within the last 6 months). In cooperation with the manufacturers we contacted the management of the suppliers involved and asked for their cooperation. After the explicit agreement of the suppliers, we identified the ETEs involved based on human resource and R&D project records. We excluded temporary hires (e.g., from consulting firms) because they may lack experience and the ability to understand the conflicts of interest among the parties. The remaining 536 ETEs, who participated in 72 projects, were sent an email pre-announcement that they would soon receive an invitation to participate in a survey. The announcement indicated that both manufacturer and supplier management supported their participation. We also

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guaranteed confidentiality and anonymity. A next email contained the survey link and a specific reference to the name of the project on which the respondent should take in mind throughout answering the survey. In total 536 ETEs, who participated in 72 R&D projects, were identified and received an invitation to participate. On average, 2 ETEs per project responded to our survey, which limited the opportunity to identify multi-level structures in our data. The average team size in the sample was 18.4 members. To check the representativeness of our sample, we compared the mean values of age, gender and job tenure of our sample to those of relevant studies conducted in the R&D domain. The mean age reported varies from 30 (e.g., Chi, Huang, and Lin 2009) to 45 (e.g., Petroni 2000), with multiple studies reporting a mean of 39 years (Cordero, Ditomaso, and Farris 1996; Faraj and Sproull 2000; Zenger and Lawrence 1989). The mean in our sample (36.4 years), including standard deviation (8.8 years) corresponds to these samples. Similarly, a meta-analysis conducted by Ng and Feldman (2013) investigates the relationship between age and innovative behavior. Their sample comprises 6153 individuals with a mean age of 36. This again corresponds to the mean age of our sample. The relatively high percentage of men in our dataset (81%) corresponds to percentages found in other recent studies, e.g. Chi et al. (2009;78%), Garcia Martinez et al. (2017; 74,2%), Cordero et al. (1996; 75%), and Tortoriello and Krackhardt (2010; 90%). Finally, the mean job tenure of our sample (6.4 years) also corresponds to earlier work: e.g. Tortoriello and Krackhardt (2010; 5.2 years) and Zenger and Lawrence (1989; 5.7 years). Therefore, based on sample comparisons we concluded that our sample is representative for R&D projects.

4.4.2 Sample CharacteristicsWe received 236 surveys, for a 44.0% response rate. We had to discard 39 incomplete surveys. Some basic screening identified a further 10 surveys that had to be excluded; their response patterns indicated response bias (e.g., straight-lining), leaving 187 responses for the final analysis. Among these respondents, 81% were men (see Table 4-1). The mean age was 36.4 years (23% younger than 29 years, 27% between 30 and 35 years, and 14% older than 45 years). In terms of experience in an ETE function, the mean was 5.7 years (35% for about 2 years, 24% between 2 and 6 years, 32% between 6 and 10 years, and the remainder with more than 10 years of experience). The distributions of age, gender, and experience are representative for ETEs in high-tech R&D projects (Bstieler 2006; Jassawalla and Sashittal 1998).

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Table 4-1: Sample descriptives

Gender % Tenure in function %

Male 81 <2 35

Female 19 2 – 6 years 24

6 – 10 years 32

Age % >10 years 29

Less than 30 years 23

30 – 35 years 27 Tenure at manufacturer %

36 – 45 years 36 Less than 10 months 29.4

Over 45 years 14 10 months to 4 years 60.4

Over 4 years 10.2

We tested for non-response bias by comparing answers from early respondents with those from late respondents (Armstrong and Overton 1977). The t-tests did not reveal significant differences in the means or standard deviations of our focal constructs. Non-response therefore does not seem to pose a threat to the validity of our results. We also ran analyses of variance on our focal constructs to determine if their means differed across manufacturers or projects. We found no significant differences and thus conclude that manufacturer or project bias does not affect our results.

4.4.3 MeasuresWe used validated scales from existing research to operationalize the constructs of our conceptual model. The survey was pretested with 15 practitioners and academic researchers prior to the actual data collection; minor wording changes were made in response to their feedback. We measured IWB with six items from De Jong and Den Hartog (2010) and Janssen (2000), related to idea generation, idea championing, and idea realization. The responses, recorded on 5-point semantic differential scales, tapped the frequency with which respondents displayed varying behaviors during their involvement in the R&D project (1 = “Never,” 5 = “Continuously”). We assessed the identification of an ETE with the different entities (supplier, manufacturer, R&D project team) using five items from Mael and Asforth (1992). This is a regularly used scale by identification researchers (e.g., Wieseke et al. 2012; Fombelle et al. 2011; Riketta and Van Dick 2005). Answers were provided on a 7-point Likert scale, anchored by 1 = “Strongly disagree” and 7 = “Strongly agree.” These five items appeared in different sections of the survey, in accordance with the distinct referents. For example, all questions related to supplier identification were merged with other questions related to supplier characteristics.

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To measure efforts to onboard ETEs before they joined the manufacturer’s R&D project team, we used four out of a seven-item scale (1 = “Strongly disagree,” 7 = “Strongly agree”) from Snell (1992). This scale, also called “HRM input control” is routinely used in strategic human resources literature (Yu and To 2011), and it captures the selection and training of ETEs before allowing them to join a department or project team. We specifically selected only those items for the survey that related to pre-training and selection of ETEs and left out items relation to skill development. We further adopted four items from Jaworski and MacInnis (1989) to capture the extent to which ETEs received goal information and feedback in relation to their task and role. These items regularly appear in studies that investigate the effects of goal setting and feedback mechanisms, also referred to as “output control”, on employee behavior (e.g., Evans et al. 2007). We adapted construct naming to better align with what role SE output control plays in relation to identification: highlighting which goals are important for the manufacturer and allowing a further dialogue and deeper understanding of them. Finally, we included control variables to account for alternative explanations in identification and IWB, such as relational orientations and belongingness (Cooper and Thatcher 2010). We controlled for the effects of gender, age, and tenure with the supplier and manufacturer.

4.5 Data Analysis and Results

4.5.1 Measurement ModelWe conducted a confirmatory factor analysis in AMOS 22 to examine the psychometric properties of our measures and the fit of the measurement model. As Table 4-2 shows, the recommended thresholds for reliability and convergent validity were met for all measures (Bagozzi and Yi 1988). The factor loadings and average variance extracted (AVE) were above .50, and the composite reliabilities were greater than .70. The results in Table 4-3 confirm discriminant validity for all constructs (Fornell and Larcker 1981); the AVE consistently exceeded the squared correlation (i.e., shared variance) with any other construct.

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Table 4-2: Summary of measurement scales

VARIABLE FL CR AVE

Onboarding (Snell 1992) .846 .583

I received substantial training from the customer before becoming part of this project. .907

An established staffing procedure was used by the customer to select me for this project. .766

A thorough selection process was used by tha customer firm to select my company for this project.

.676

My company trained/prepared me specifically for working on this customer project. .682

Goal socialization (Jaworski and MacInnis 1989) .893 .677

Specific performance goals are established for my task by the R&D team/R&D team leader. .851

The extent to which I attain my performance goals is critically evaluated by the R&D team/ R&D team leader.

.846

If I do not meet my performance goals, the R&D team/team leader requires me to explain why. .764

Feedback concerning the extent to which I achieve assigned goals is provided to me on a regular basis by the R&D team/ R&D team leader.

.827

Workgroup identification (Mael and Ashforth 1992) .884 .608

When someone criticizes this team, it feels like a personal insult. .774

This team’s successes are my successes. .806

When someone praises this team, it feels like a personal compliment. .837

If a story in the media criticized this team, I would feel embarrassed. .835

When I talk about this team, I usually say “we” rather than “they.” .629

Supplier identification (Mael and Ashforth 1992) .876 .589

When someone criticizes my company, it feels like a personal insult. .769

My company’s successes are my successes. .817

When someone praises my company, it feels like a personal compliment. .858

If a story in the media criticized my company, I would feel embarrassed. .739

When I talk about my company, I usually say “we” rather than “they.” .634

Manufacturer identification (Mael and Ashforth 1992) .903 .652

When someone criticizes this customer company, it feels like a personal insult. .821

The successes of this customer company are my successes. .749

When someone praises this customer company, it feels like a personal compliment. .873

If a story in the media criticized this customer company, I would feel embarrassed. .842

When I talk about this customer company, I usually say “we” rather than “they.” .744

Innovative work behavior (De Jong and Den Hartog 2007) .920 .658

I searched out new working methods, techniques or instruments. .833

I generated original solutions to problems. .743

I encouraged key members to be enthusiastic about innovative ideas. .820

I attempted to convince people to support an innovative idea. .861

I systematically introduced innovative ideas into work practices. .828

I contributed to the implementation of new ideas. .775

Notes: FL = factor loading, CR = Cronbach’s alpha, AVE = average variance extracted.

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Table 4-3: Discriminant and convergent validity of the constructs

CONSTRUCT 1 2 3 4 5 6

1. Onboarding .764

2. Goal socialization .520** .823

3. Supplier identification .487** .321** .767

4. Manufacturer identification .563** .416** .610** .807

5. R&D team identification .431** .347** .499** .631** .780

6. Innovative work behavior .283** .234** .195** .292** .262** .811

Mean 4.258 4.860 5.575 5.033 5.851 3.814

Standard deviation 1.462 1.351 1.226 1.499 .974 .643

Notes: The square root of average variance extracted is on the diagonal. * Correlation is significant at the .05 level (two-tailed). ** Correlation is significant at the .01 level (two-tailed).

We also tested for potential common method bias. First, an unrotated factor analysis in SPSS showed that the first factor explained only 27.4% of the variance. Second, we tested the measurement model and modeled a common latent factor to load on all manifest variables. The single factor revealed a common variance of 16.8%, further relieving concerns (MacKenzie and Podsakoff 2012; Podsakoff et al. 2003). Third, we checked the variance inflation factors (VIF) and concluded that multicollinearity was not a concern, because the highest VIF was 1.830.

4.5.2 Analytical Procedure and Results We tested our hypotheses with a two-step approach. To start, we assessed the effects of (in)congruence in identification on ETEs’ IWB using polynomial regression. Precautions are required when modeling predictors as difference scores (Edwards 1994; Edwards and Parry 1993; Foreman and Whetten 2002), to avoid confounding the effects of each difference score component on the outcome. A remedy to these drawbacks is polynomial regression with response surface analysis (Shanock et al. 2010). Therefore, we first mean-centered the aggregated values of R&D team identification, manufacturer identification, and supplier identification to test the effects of our difference score constructs on IWB. The relationship among the components X (supplier identification), Y (manufacturer identification), and outcome Z (IWB) can be written as

Z = b0 + b1X + b2Y + b3X2 + b4XY + b5Y

2 + e. (1)

With identification congruence, X and Y take similar values, so substituting Y = X produces

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Z = b0 + (b1 + b2)X + (b3 + b4 + b5)X2 + e. (2)

The slope of the effect of congruent identification is denoted by (b1 + b2) and its curvature by (b3 + b4 + b5). The slopes and curvatures then can be tested for significance. With identification incongruence, X and Y instead take dissimilar values, such that Y = –X. Substituting in Equation 1, the equation for the line of incongruence is Z = b0 + (b1 – b2)X + (b3 – b4 + b5)X

2 + e. (3)

The slope of the effect of incongruent identifications is denoted by (b1 – b2) and the curvature by (b3 – b4 + b5). Again, the slope and curvature can be tested for significance. Table 4-4 contains the outcomes of the polynomial regression to examine the effects of identification incongruence between the manufacturer and supplier and identification congruence between the manufacturer and R&D project team.

We have predicted that inter-organizational identification incongruence enhances the IWB of ETEs when their manufacturer identification increases relative to their supplier identification. Table 4-4 indicates that the association between incongruence and ETEs’ IWB is significant, negative, and linear (β = b1 – b2 = -.283, p < .01). Because supplier identification serves as the X variable and manufacturer identification is the Y variable in our polynomial regression, the negative coefficient supports the direction of our hypothesis. The surface plot in Figure 4-2 provides further support for H1. Only the slope of the incongruence line is significant; the non-linear association cannot be confirmed, and neither can a congruence association.

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Table 4-4 : Results of polynomial regression to examine the effects of identification (in-)congruence

Z: Innovative work behavior (dependent variable)

Independent variables: X = Supplier identification X = R&D team identification

Y = Manufacturer identification Y = Manufacturer identification

Unstdcoeff.

Std.Error

t-value

p Hyp.Unstd coeff.

Std.Error

t-value

p Hyp.

POLYNOMIAL REGRESSION:

X (b1) -.093 .071 -1.309 .192 .053 .090 .588 .558

Y (b2) .189 .077 2.468 .015 .147 .081 1.800 .074

X2 (b3) -.057 .032 -1.749 .082 .055 .067 .824 .411

XY (b4) .012 .036 .332 .740 -.022 .050 -.434 .664

Y2 (b5) .027 .022 1.220 .224 .014 .021 .641 .522

SURFACE TESTS:

Congruence line slope (a1 = b1 + b2)

.096 .114 .846 .399 .200 .069 2.889 .004 H2

Congruence line curvature (a2 = b3 + b4 + b5)

-.018 .028 -.647 .518 .047 .037 1.271 .205

Incongruence line slope (a3 = b1 – b2)

-.283 .095 -2.986 .003 H1 -.094 .157 -.594 .553

Incongruence line curvature (a4 = b3 – b4 + b5)

-.042 .069 -.602 .548 .091 .116 .785 .433

CONTROLS:

Tenure with the supplier -.012 .008 -1.515 .131 -.012 .008 -1.494 .137

Tenure with the manufacturer .001 .010 .087 .930 .001 .010 .059 .953

Age -.001 .006 -.152 .879 .001 .006 .113 .910

Gender (1 = male) .227 .121 1.881 .062 .234 .122 1.919 .057

Identification with the R&D team .070 .067 1.049 .296 n/a n/a n/a n/a

Identification with the Supplier n/a n/a n/a n/a -.007 .050 -.142 .888

R2 .148 .131

R2-adjusted .099 .082

F 2.657** 3.052***

F change 4.325*** 4.671***

Notes: N = 187. * p < .05. **p < .01. ***p < .001.

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Fi gure 4-2: Response surface for supplier and manufacturer identifi cation on IWB

Notes: The solid line represents the congruence line for identifi cation (Y = X); the dashed line represents the incongruence line for identifi cation (i.e., inter-organizational identifi cation incongruence; Y = –X).

In H2 we predict a positive association between intra-organizational identifi cation congruence and an ETE’s IWB, such that IWB is higher when both identifi cations are high rather than low. We fi nd empirical support for this hypothesis (β = b1 + b2 = .200, p < .01), as well as visual confi rmation from the surface plot in Figure 4-3. That is, IWB is higher when identifi cation with the manufacturer and R&D project are both high, rather than both low.

F igure 4-3: Response surface for manufacturer and R&D team identifi cation on IWB

Notes: The solid line represents the congruence line for identifi cation (i.e., intra-organizational identifi cation congruence; Y = X); the dashed line represents the incongruence line for identifi cation (Y = –X).

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As a second step in our analyses, we also investigate the effects of onboarding and goal socialization using linear regression (Table 4-5). Having confirmed the hypothesized incongruence (X = -Y) and congruence (X = Y) effects, we can compute aggregate scores for both constructs. For the inter-organizational identification incongruence measure, we subtracted each individual item in the manufacturer identification scale from the corresponding supplier identification item. This transformation produced five new items that capture inter-organizational identification incongruence. We used a similar procedure for our congruence measure but added the corresponding items (manufacturer identification + R&D project team identification). The two new constructs then provided the dependent variables for analyzing the antecedents of identification (in)congruence.

Table 4-5 : Results of hypothesis testing

DEPENDENT VARIABLES

Inter-organizational identification incongruence a

Intra-organizational identification congruence b

Unstd.Coeff.

Std.Error

t-value

p Hyp.Unstd.Coeff.

Std.Error

t-value

p Hyp.

PREDICTORS:

Intercept 2.053 .623 3.294 .001 8.045 .949 8.475 .000

Onboarding -.155 .073 -2.115 .036 H3a .412 .112 5.583 .000 H3b

Goal socialization -.125 .075 -1.673 .096 H4a .297 .114 2.613 .010 H4b

CONTROLS:

Tenure with the supplier -.017 .014 -1.224 .223 .043 .021 2.042 .043

Tenure at manufacturer -.023 .007 -3.420 .001 .001 .010 .054 .957

Age .012 .011 1.031 .304 -.047 .017 -2.708 .007

Gender (1 = male) -.146 .230 -.635 .526 .248 .350 .708 .480

R2 .125 .377

R2-adjusted .096 .356

F 3.661** 15.889***

F Change 4.300** 33.882***

Notes: N = 187. * p < .05. **p < .01. ***p < .001.

With H3a we argue that onboarding is associated with inter-organizational identification incongruence; in support of our prediction, identification with the manufacturer increases relative to identification with the supplier when onboarding is higher, according to the significant negative association of onboarding practices with inter-organizational identification incongruence (β = -.155, p < .05). With H3b we argue that onboarding is associated with intra-organizational identification congruence; again, we confirm this hypothesis, because identifications with the R&D project team and manufacturer increase

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when onboarding is higher, according to the significant, positive coefficient (β =.412, p < .001). Finally, we predict that goal socialization affects ETEs’ perceptions of (in)congruence. We find a significant positive association of goal socialization with intra-organizational identification congruence (β = .297, p < .01), in support of H4b. However, we do not find any negative association of goal socialization with inter-organizational identification incongruence (β = -.125, n.s.), so H4a cannot be supported. The control variables indicate that tenure with the manufacturer strongly reduces inter-organizational identification incongruence, which is not surprising; more experience with the manufacturer’s processes likely eases identification. Tenure with the supplier positively relates to intra-organizational identification congruence. Perhaps more experienced ETEs can better understand the differences between an innovation project and the larger, inert manufacturer organization in which it is embedded. Rather than assuming that the goals of the two entities vary, they likely recognize, based on their experience, that R&D projects require an environment that breaks free from conventions. In contrast, congruence between identification with the manufacturer and the R&D team is lower for older ETEs. Because older ages generally correlate with lower acceptance of new and unusual ideas, the differences between the inert organization and the innovative project may seem more salient to older ETEs.

4.6 Discussion & Conclusions

4.6.1 Theoretical ImplicationsThis study has sought to determine whether identification gaps influence the IWB of ETEs participating in manufacturers’ R&D projects. We provide important insights into the forces that drive IWB in work contexts that involve multiple identification foci. Accordingly, this study advances extant theory in four main ways. First, we add to recent discussions in identification theory. Identification results from ingroup versus outgroup comparisons, such that members identify with one entity over the other (Ashforth and Johnson 2001). Yet multiple identifications also may converge (Ashforth et al., 2008). Driven by these theoretical arguments and our empirical observation that even competing identities may coexist in an individual, we explicitly consider whether identifications with two entities are congruent or incongruent. We find that inter-organizational (i.e., supplier and manufacturer) and intra-organizational (i.e., R&D team and manufacturer) identification comparisons influence the IWB of ETEs in manufacturers’ R&D projects. Their IWB is highest when identification with the manufacturer is high and identification with the supplier is low. Yet ETEs’ IWB is motivated by a situation in which identification with the manufacturer and R&D team

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are both high. Inter-organizational identification incongruence and intra-organizational congruence thus both influence ETEs’ IWB. Second, for identification theory, we provide a new methodological perspective. Previous work predominantly investigated simple, direct effects of identification with each entity (Riketta and Van Dick 2005; Van Dick et al. 2008). Ignoring (in)congruence effects may have left some subtle effects undiscovered. To substantiate our point, as a post hoc test we regressed IWB on supplier, manufacturer, and R&D team identification without accounting for any effects of their (in)congruence. Only manufacturer identification related significantly to ETEs’ IWB (β = .102, p < .05). This finding conflicts with results that suggest identification with the lowest organizational level (i.e., team) is the strongest predictor of behavior (Riketta and Van Dick 2005), and it leaves many of the subtleties revealed in Figure 4-2 and 4-3 unexposed. Third, in advancing innovation literature, we inform discussions about whether conflicting goals within organizations are constructive or destructive to innovation behaviors. For example, the incompatibility of job demands resulting from divergent company versus customer needs (i.e., role conflict) may trigger simplified cognitive strategies, such as narrowing perceptual attention, that limits innovative work capabilities (Gilboa et al. 2008). However, role conflict also may arise in a situation in which standard responses do not satisfy all interests. If employees then feel activated, they may adapt or modify elements of the work context to find a solution. A similar discussion considers whether identification (in)congruence drives IWB. One line of reasoning states that the absence of identification tensions provides a motivational work environment, without distractions due to belongingness questions (Hornsey and Hogg 2000; Richter et al. 2006). Another line asserts that identification with only a lower-order entity (i.e., R&D team) makes ETEs think and act, free from the restrictions the higher-order entity (i.e., manufacturer or supplier) may impose (Ashforth, Harrison, and Corley 2008). Our findings support the former line of reasoning: IWB is triggered when ETEs perceive the nested entities in harmony. Perhaps it reflects their natural search to understand how project goals relate to those of the manufacturer, which helps the ETEs identify with both. Fourth, we add to theory on how to manage employee organizational identification. Previous studies concentrate on managing a single identity (Brewer, Manzi, and Shaw 1993; Van Dick et al. 2008); we focus on drivers of identification (in)congruence across different entities. Pre-project onboarding mechanisms to prepare ETEs for their work in the manufacturer’s R&D project team appear more strongly associated with identification (in)congruence than is on-the-job control, in the form of goal socialization. Such ongoing controls may be needed to take the pulse of project progress, team processes, and achievement of intended outcomes (Snell 1992). However, goal socialization may come too late in the work process to help ETEs shift from identification with the supplier to identification with the manufacturer. Feelings of belongingness may begin with the first contact and then become sticky (Sluss and Ashforth 2007). Goal socialization also

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can help ETEs build congruent identifications between the R&D team and manufacturer organization, by enabling a dialogue about how the project fits with superordinate aims. Onboarding is a pre-entry activity and therefore helps prepare the ETE to switch identities better. In turn, it helps balance out the identification gaps within an organization. These findings are in line with observations that onboarding has a positive effect on the alignment of individual employees with work goals in specific work contexts (Ouchi 1979; Yu and To 2011).

4.7 Managerial Implications

Our findings suggest that R&D project managers struggling to manage R&D projects involving ETEs must recognize that they need to make not just physical but mental transitions across organizations. That is, they need to move from serving suppliers’ interests and obligations to serving the manufacturer with innovative suggestions and the implementation of new solutions to R&D challenges. Although it is difficult to reduce ETEs’ identification with their own (supplier) company, it may be necessary to stimulate and maximize IWB. When ETEs have strong identification with both the supplier and manufacturer, their behavior tends to balance the interests of both organizations, limiting their IWB and contributions to the R&D project team. We advise R&D project managers to start managing ETEs’ identification processes before the actual start of the innovation project. During recruitment, selecting ETEs that express enthusiasm and eagerness to support the manufacturers’ aspirations will make it easier to create a team that identifies with the manufacturers’ interests. In contrast, ETEs with an extremely strong identification with their own organization, such that they exaggerate or take undue pride in the suppliers’ achievements, may have a hard time recalibrating. Beyond the cognitive state of mind, managers should consider easily observable, demographic characteristics. Selecting relatively young ETEs with longer tenures with their supplier organization may enhance intra-organizational identification congruence: They have the desirable combination of a thorough understanding of innovation contexts and greater acceptance of outside-the-box ideas. Managers might keep track of and measure ETEs’ level of identification with the manufacturer and R&D project during project execution. Project managers might be trained to ask inconspicuous questions during progress meetings to test an ETE’s attachment to the manufacturer or R&D project (e.g., “Do you feel we are on the right track with this program?”). Alternatively, the project manager could ask internal team members, employed by the manufacturer, to monitor and share their observations about whether ETEs refer to the supplier or the manufacturer as “us.” Such insights add to and may be even more valuable than traditional job satisfaction measures that do not capture cognitive engagement. Furthermore, when applying goal socialization as a means

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to manage ETE performance, managers are encouraged to have a true dialogue with ETEs such that the ETE fully understand goals, how they relate to overarching goals and can relate their own personal values to them. Finally, to unlock the IWB of ETEs, R&D project managers should inform them about the reasons for initiating the R&D project in which they are involved, as well as the project’s aims, beyond the short-term deliverables. In particular, they should detail the rationale for embedding the R&D project in the larger organization. Alternatively, project managers could stimulate understanding of organizational goal hierarchies by setting up self-managed teams that make interpersonal coaching and feedback on contributions to higher-level organizational goals into a common practice. Such practices should work to align intra-organizational identification.

4.8 Limitations and Further Research

We used a key informant approach in our data collection, which provided the best design, given the restrictions imposed by the research context. Asking the R&D project manager to score ETEs’ innovative contributions during the project would have added robustness, but it was impossible due to privacy concerns and the need for explicit consent. Furthermore, we asked the respondents to reflect on their level of identification at a specific time, but identifications are dynamic (Ashforth, Harrison, and Corley 2008). Identification builds when people are connected to a group for a longer time and may dissipate when interactions halt. We used covariates to control for time dimensions (tenure with both organizations); additional research also might study how identification gaps evolve over time, relate to project duration, and affect IWB differently. Finally, we specifically focused on a supplier–manufacturer context, in which ETEs are likely to experience dual identification. Identification trade-offs obviously might arise in many other contexts, such as joint ventures, mergers, and outsourced customer services. The specifics of each research setting suggest the need for care before generalizing these findings outside the R&D context. Additional research could expand our study to other domains.

5Discussion and conclusions

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DISCUSSION AND CONCLUSIONS

This final chapter summarizes and discusses the main conclusions and insights derived from Chapters 2, 3 and 4. The various findings are integrated and discussed against existing theoretical backgrounds from which the main theoretical implications are derived. The chapter further presents three key practical implications and highlights overall research limitations and possible future research avenues.

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5.1 Synopsis

Supplier-manufacturer collaborations have become crucial for the innovation of products and services of high-tech manufacturers. Supplier experts (SEs) interface between supplier and manufacturer and thus personify the supplier’s knowledge contributions to high-tech manufacturers’ innovation programs. With the aim to develop the best possible customer offerings, manufacturers want SEs to share their most valuable knowhow and spur innovation when interfacing with their employees. While manufacturers expect SEs to take the interests of the manufacturer’s innovation project to heart, many innovation managers still apply a short-term engagement perspective (i.e., a focus on getting the project done) and treat SEs as “the external person”. The human factor at play can, however, not be underestimated: manufacturers’ need external experts with vested interest in the manufacturer’s success to benefit from their expertise. This research follows and augments contemporary research to obtain a deeper understanding of how contractual, relational and psychological governance influence SEs’ motivations to share expertise in supplier-manufacturer collaborations (cf. Huang, Hsieh, and He 2014; Lai, Lui, and Tsang 2016). Such understanding is important since the SEs face competing interests when they have to decide on sharing sensitive information with the customer (i.e., the manufacturer’s employees). First, the SE needs to respect and protect the supplier’s commercial interests. At the same time, as an SE operates at the supplier-manufacturer interface, the manufacturer expects that the SE will align his/her behavior with the manufacturer’s business interests. Operating in an environment with conflicting interests, SEs are expected to experience social complexities and blurred organizational boundaries. This dilemma has been the motivation for this research project that intends to answer the following key question: What contractual, relational and psychological mechanisms motivate or discourage supplier experts to share their expertise and innovative insights with a manufacturer? Chapter 1 explained the overall research approach that was used to address the research question and objectives underlying this dissertation. Chapter 2 discussed the first study. Here, we adopted an inter-organizational perspective drawing on agency theory (Eisenhardt 1989) to test the effects of contractual and relational governance mechanisms on knowledge sharing behaviors across organizational boundaries. Also we differentiated between the effects of these mechanisms on explorative and exploitative knowledge sharing. Studies 2 and 3, reported in chapters 3 and 4, focused on the context where knowledge sharing and innovative work behavior is most crucial, i.e., collaborative R&D projects. Study 2 examined the behavioral decision process of external technology experts. More specifically, we drew on reasoned-action theory (Ajzen and Fishbein 1977), principles of stewardship (Davis, Schoorman, and Donaldson 1997) and fairness theory (Fehr and Schmidt 1999) to explore how to explain ETE’s knowledge sharing behavior. Chapter 4 drew on social identification theory (Mael and Ashforth

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1992) to examine antecedents and consequences of identification with the manufacturer relative to identification with the supplier. We also studied dual identification with the manufacturer and its R&D team. The (in)congruency of identification with the different entities related to SEs’ innovative work behavior in collaborative R&D constellations. To summarize, this research has resulted in a deeper understanding on what drives SE knowledge sharing behavior and how contractual, relational and psychological governance motivate SEs’ to share knowledge. The remainder of this chapter summarizes the insights obtained from each study and discusses the theoretical and practical implications. Overall limitations of the research approach are explained and suggestions for future research are given.

5.2 Main conclusions and insights from the chapters

5.2.1 Conclusions and insights from study 1The objectives of the first study where to: (1) provide clarity if and to what extent contractual and relational governance mechanisms stimulate FSE knowledge sharing, and; (2) determine if and to what extent contractual and relational governance mechanisms explain the different types of knowledge being shared. Using survey data from 70 relationship managers from a large multinational firm and applying partial least squares path modeling, the study confirms that both contractual and relational governance relate to SE exploitative and exploratory knowledge sharing. More specifically, the study concluded that the use of incentives is discouraged when exploratory knowledge exchange is important. Incentives seem to limit the solutions space necessary for radical innovation. However, we did not find any negative effects of incentives on exploitative knowledge sharing. Clearly specifying expectations in formal contracts between supplier and manufacturer is an antecedent to both types of knowledge sharing. Relationship quality was found to have a similar pattern of effects, while relationship manager experience positively influenced exploratory knowledge sharing only. Lastly the study reveals that relational governance factors (i.e., relationship quality and relationship manager experience) are stronger drivers of knowledge sharing than are contractual governance factors (i.e., contractual incentives and specification level). Contractual governance can even be counterproductive; suppliers are more willing to share exploratory knowledge in relationships where the quality of the relationship is characterized by cooperation, responsiveness, empathy and trust.

5.2.2 Conclusions & insights from study 2The second study aimed at understanding the cognitive processes underlying a SEs’ choice to either share or not share knowledge with employees of the manufacturer. The objectives of this study were to: (1) determine whether perceptions of distributive fairness and customer stewardship are important drivers of ETEs’ intentions to share knowledge;

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(2) whether knowledge sharing intentions leads to knowledge sharing behaviors; (3) whether formal coordination influences the relationship between intention and behavior, and; (4) investigate the interplay between formal coordination, distributive fairness and customer stewardship. The study bases its findings on data from a self-reported survey design, covering 187 ETE’s who participated in R&D projects at seven global high-tech manufacturers. Regression-based floodlight analysis of direct and indirect effects in a full moderated mediation model shows that SE knowledge sharing behavior is the result of the ETE’s intention to engage in knowledge sharing behavior. This intention is affected by the ETE’s perception of customer stewardship and distributive fairness. The results of this study further confirm the complex effects of contractual and relational governance. The more manufacturer employees refer ETE’s to the formal terms in the contract, the less the ETE’s customer stewardship perceptions translate into knowledge sharing behavior. In contrast under high levels of formal coordination, a perception that future rewards will be fairly distributed enhances knowledge sharing behavior. This effect also occurs for low levels, but not for medium levels, of formal coordination.

5.2.3 Conclusions & insights from study 3Study 3 focused on exploring the effects of a SE’s social identification on knowledge sharing. More particularly, the objectives of this research were to: (1) test whether a stronger identification with the manufacturer relative to identification with the supplier (i.e., identification incongruence) is associated with higher levels of ETEs’ innovative work behavior; (2) whether identification with the manufacturer and the R&D team (i.e., intra-organizational identification congruence) influences ETEs’ innovative work behavior; (3) whether identification (in)congruence can is influenced by goal socialization, and: (4) whether identification (in)congruence is influenced by onboarding practices. The study tests the hypotheses using survey data from 187 ETEs participating in collaborative R&D projects. A two-step analytical approach, involving polynomial and linear regression, confirms that ETEs compare both the suppliers’ and the manufacturers’ identity and identify with both to a certain extent. Our research showed that a stronger identification with the manufacturer than with the supplier positively associates with innovative work behavior. A strong dual identification with the manufacturer and its R&D team similarly drives ETEs’ innovative work behavior. This research thus highlights the complexity in balancing competing identities and interests. It also shows how psychological governance (here: identification) can cognitively hinder or increase an individual’s energy and motivation to behave innovatively in support of a manufacturer’s R&D project. Results further confirm that both onboarding and goal socialization are effective mechanisms to ‘manage’ identification (in)congruence. The better ETEs are prepared for their role in the R&D team (onboarding), the more strongly they psychologically attach themselves with the manufacturer relative to the

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supplier. Moreover, onboarding and goal socialization align and strengthen identifi cation with the manufacturer and the R&D team.

5.3 Overall conclusions The main research question of this project was to uncover whether contractual, relational and psychological governance mechanisms affect SEs to share their expertise and innovative insights with a manufacturer. The next section groups the results of the three distinct studies into these governance domains and refl ects on fi ndings comparing it to conclusions from existing literature (see fi gure 5-1).

Figure 5-1: Dissertation outcomes depicted across the three governance domains

5.3.1 Contractual governanceContracts designed to guide collaborative activities and curb opportunistic behavior are often at the basis of inter-organizational collaborations (Eisenhardt 1989). However, agency theorists have outlined the limitations of contracts in these environments (Bergen, Dutta, and Walker 1992; Eisenhardt 1989; Logan 2000). Specifi cally, contracts seem insuffi cient to promote discretionary behavior. This research fi nds that contracts

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can still play an important role in value creation in supplier-manufacturer collaborations, but some important conditions need to be met. The first study (Chapter 2) reveals that contractual specifications need to provide SEs with a clear frame of reference to apply their knowledge. The reduced ambiguity in the work environment stimulates SEs to share knowledge to enhance and/or create new market offerings (Alavi and Leidner 2001; Easterby-Smith, Lyles, and Tsang 2008). Without clearly guiding specifications, the SEs need to put in more effort to find out what expertise is valuable for the manufacturer. These cognitive resources can consequently not be spent on sharing expertise. Findings of this research suggest that clear frames of reference enable SEs to provide new perspectives and insights, allowing manufacturers to incrementally or radically innovate their products and services (study 1, Chapter 2). Whereas contract specifications seem to promote knowledge sharing, contractual incentives limit SEs’ exploratory knowledge sharing for radical innovations. It could be that contractual incentives limit the solution space for SEs by placing emphasis on solutions that will lead to realizing the specified rewards. Hence, although outcome-based incentives are commonly suggested for to govern behavior in inter-firm collaboration (Eisenhardt 1989), they may not be effective when realizing radical innovation is the ultimate goal. They may put too much focus on obtaining short-term gains rather than optimizing the mutual longer-term perspective (Osterloh and Frey 2000). Study 2 (Chapter 3) reveals another dark side of contractual governance: R&D managers and employees ‘controlling’ SEs by referring to contractual agreements may obstruct SEs’ knowledge sharing process. The process is a complex interplay between manufacturers’ coordination efforts and individual motivations during project work. Customer stewardship and distributive fairness beliefs motivate knowledge sharing, but the level of formal coordination determines the strength and direction of these relationships. This may be because relationships require certain levels of trust, whereas formal coordination can communicate mistrust and/or incompetence (Jap and Ganesan 2000). Interestingly, the study finds that for formal coordination to facilitate stewardship or fairness perceptions to translate into knowledge sharing, the level of coordination should be either low or high, not medium. High levels of formal coordination reduces ETEs’ worries about exploitation of their shared knowledge, while low levels of coordination stimulate SEs to consciously build knowledge sharing intentions. This research suggests that medium levels of coordination are associated with occasional and unexpected referrals to contractual obligations, which seems to confuse rather than motivate SEs. Further research is needed to substantiate this idea though.

5.3.2 Relational governanceThe studies jointly provide evidence that in particular relational governance mechanisms influence SEs’ knowledge sharing intentions and behavior. This conclusion aligns with social capital theory (Coleman 1988), which suggests that social relations surrounding

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individuals affect discretionary behaviors. This dissertation supports this theory especially in the context of knowledge work across organizational boundaries and with a multitude of stakeholders. Study 1 (Chapter 2) explains the role of relationship quality on knowledge sharing behavior: interactions that are characterized as cooperative, responsive, based on empathy, and trust foster knowledge sharing. Literature also acknowledges the opposite: a lack of relationship quality, and especially trust, triggers counterproductive strategic behavior such as knowledge hoarding (Holten et al. 2016; Jiang et al. 2013). Study 2 (Chapter 3) shows the role of customer stewardship, which enhances knowledge sharing intention. Scholars have used stewardship theory to better understand human behavior and interactions in longer-term relationships (Hernandez 2012; Le Breton-Miller, Miller, and Lester 2010). This research shows that the principles underlying stewardship theory seem to be effective also for shorter-term, uncertain and volatile relationships. Customer stewardship builds SE knowledge sharing intentions as the SE perceives a sense of obligation to serve the manufacturer, aiming to further build and retain the relationship. The same holds when SEs perceive that rewards from the collaboration are fairly distributed. SEs evaluate investments into the relationship, both by manufacturer and supplier, against possible outcomes. When the investment-return ratio is perceived as fair, SEs are open to share their knowhow. This indicates that SEs are well aware of relationship specific investments and interests on inter-organizational level, which aligns with recent research in innovation literature (Lai, Lui, and Tsang 2016).

5.3.3 Psychological governanceSocial identification theory (Mael and Ashforth 1992) provides a perspective on how individuals, through identification, are motivated to behave discretionary in support of organizations and coworkers (Farmer, Van Dyne, and Kamdar 2015). Identification with a social entity secures a deep personal attachment of individuals which makes them adapt to their work environment and go beyond role prescriptions (Van Dick et al. 2008). This study shows that identification can align SE behavior with the innovation goals and objectives of the manufacturer and its R&D team. Previous work predominantly investigated identification with single entities, taking an in- or out-group perspective (cf. Riketta and Van Dick 2005; Van Dick et al. 2008). However, in collaborative, inter-organizational settings where multiple identities exist, these identities may compete. This observation, to date, has hardly been investigated. Study 3 (Chapter 4) addresses this gap in literature, confirming that manufacturer and supplier identities indeed compete just as they co-exist. The study provides evidence that SEs can identify with both supplier and manufacturer interests at the same time. Identification gaps explain differences in SE innovative work behaviors observed. When identification with the manufacturer is relatively stronger compared to the supplier, SEs behave innovatively in support of the manufacturers’ R&D aims. Furthermore, when

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identification with both manufacturer and R&D team is high, SEs are motivated to explore and share ideas and champion them to secure implementation. This championing may be because SEs feel personally attached to the aims of the manufacturer and R&D team. Earlier work substantiates this thought, finding that the absence of identification conflicts provides a motivational work environment without distractions (Hornsey and Hogg 2000). Although study 3 predominantly focused on psychological governance in the form of identification, the study also investigated the motivational role of contractual and relational governance mechanisms. This was done to uncover whether identification (in)congruencies can be managed. Findings indicate that pre-project onboarding as a means to get SEs familiar with organizational history, achievement, culture and view of the future is important. This may be because it generates pride at the side of the SE to be involved with this particular manufacturer, leading to a stronger identification with the manufacturer relative to the supplier. When SEs are on board and have assumed their work, then it is for SEs to stay informed and receive feedback on their work progress vis-à-vis project aims. Specifically goal socialization was found to assist SEs’ identification with the manufacturer compared to the supplier. This also strengthened congruent identifications between R&D team and manufacturer. Contractual and relational governance mechanisms thus affect a SE’s psychological state, which guides work behavior with regard knowledge sharing.

5.4 Practical implications

The findings presented in this dissertation have several implications for companies and their managers (both manufacturers and suppliers) which depend on external SEs to share their expertise to improve existing or create entirely new products or services. Overall, the findings show that next to contractual governance, also relational and psychological governance are important for SEs’ knowledge sharing and innovative work behavior. Managers are encouraged to: (1) build contracts such that they guide expertise sharing, (2) build SEs’ sense of ownership to sustaining and growing the relationship, and (3) make SEs identify strongly with their organization, the project team and its aims. These three recommendations are detailed in the next sections. First, when managers should realize that contracts affect interactions in which manufacturers are looking for external parties to share their expertise. Spending efforts to clearly specify the terms of engagement is worthwhile. Ill specified contracts may leave the other party guessing, or raise conflicts that need resolving first. Also, unclear specifications may lead to irrelevant expertise being shared and thus a lower efficiency and quality of knowledge influx. Contracts thus should focus on defining what added value should be provided by the supplier. Next, managers are encouraged to complement

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their contracts with incentive schemes that reward and highlight the importance of expertise sharing rather than focusing on the distribution of short term gains. When SEs recognize that shared knowhow will benefit both manufacturer, supplier and R&D team, the confidence that all parties will benefit in the future may make SEs realize the opportunities for personal growth and development. Future rewards on realizing specified project commitments and on personal efforts beyond the letter of the contract should leave the SE with the impression that the cooperation is fair and balanced. Perceptions of imbalance in future payoffs are expected to distract SEs from freely engaging with manufacturer employees. Finally, managers should understand that the contract is primarily a means for attaining a joint goal by formally defining the relationship. Relationships often have a life beyond the contract term though; having commitment to realizing shared future goals is key to successful manufacturer-supplier collaborations. Second, managers are encouraged to consciously build SEs commitment to the relationship. This can be done by having tenured managers guide day-to-day interactions since their experience nurtures high-quality exchange relationships by sharply demarcating expertise gaps in the organization and detecting potential collaboration pitfalls, such as opportunistic behavior, in an early stage. They may have strategies to deal with misappropriation concerns or perceptions of hoarding expertise in an effective manner. Furthermore, managers should keep an eye out for issues in how employees cooperate, respond (i.e. timeliness, effectivity), take an empathic stance towards one another and trust the other party to stay true to the contract, be competent, and have the goodwill to serve the partnership. A final advice to manufacturers is to secure that suppliers send in those SEs that care for the manufacturers’ vision and aims. SEs that are eager to contribute and see themselves as the new steward of the manufacturer will work hard to secure innovation project success. Finally, specific investments in making SEs feel part of the team will ultimately be rewarded with expertise sharing and innovative behavior. Selection and training activities of the manufacturer to prepare SEs for their role in collaborative R&D projects are important. Disclosing historical successes, compelling visions of the future, recent noticeable customer wins and unique competences compared to competition may foster identification with the manufacturer. Manufacturers and their employees should further spend considerable efforts to explain the goals, background and relevance of the R&D project to SEs. Managers are encouraged to onboard SEs as if they were new employees joining the organization for an indefinite period. The key here is that the less SEs feel different, the more they become attached. Consistency in communication from the manufacturer’s managers and R&D staff towards SEs is important to foster a unified identity perspective. Next, placing emphasis on how goals increase the potential to achieve mutual new successes, will further aid a SE’s personal attachment and commitment. When fully attached, SEs will do whatever needed to enable, and be part of, the manufacturers’ success.

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5.5 Limitations & avenues for future research

As with any research, limitations are inevitable, yet, they provide avenues for future researchers interested in the domain of supplier-manufacturer collaborative innovation. Beyond the limitations and suggestions for future research presented in the previous chapters, this section cuts across all studies and describes three main limitations of this research project. These limitations are briefly discussed and suggestions for future research are proposed. A first limitation is that the three studies investigate SE discretionary behaviors in two distinct areas of a manufacturers’ value chain (i.e., the customer-facing side, or the supply side). One could argue that these areas are distinct, and that knowledge sharing and innovative work behavior have a different meaning and vary in context and content. However, the main aim of this research was to better understand SEs’ knowledge sharing and innovative work behavior in the wider context of supplier-manufacturer settings. More particularly, the goal was to uncover how this behavior is influenced by contractual, relational, and psychological governance mechanisms. Therefore, although contextual differences exist among the three studies, these difference do not prevent making generalizations across the findings. Still, future research could test the models presented in the various studies in other areas of a manufacturer’s value chain. A second limitation is that the dependent variables differ across the three studies. The first study differentiates among two types of knowledge sharing behavior: exploitative and explorative knowledge sharing. The second study does not differentiate between these two types of knowledge sharing behaviors. The third study discusses innovative work behavior, which comprises idea generation, idea exploration (or testing), idea championing, and idea implementation. As such one could argue that the third study is conceptually different. However, innovative work behavior also (partially) includes knowledge sharing because this behavior is strongly related to idea generation, championing and implementation (De Jong and Den Hartog 2010). Therefore, although conceptual differences exist, the three studies do complement each other. Future research could nevertheless study the full set of dependent variables in a single study. A final limitation is that our data was obtained through self-reported surveys. This approach provides an opportunity for common method bias. Longitudinal data and from different sources would be less prone to such errors. Well-accepted and validated methods to alleviate common method bias concerns were applied in each study (cf. MacKenzie and Podsakoff 2012) and did not reveal major concerns. Nevertheless future researchers are encouraged to use longitudinal designs and multiple sources to validate and enhance the findings reported in this research.

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Summary

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Summary

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Summary

Supplier-manufacturer collaborations have become crucial for the innovation of products and services of high-tech manufacturers. Supplier experts (SEs) form the interface between supplier and manufacturer, and personify the supplier’s knowledge contributions to high-tech manufacturers’ innovation programs. Manufacturers want SEs to share their most valuable knowledge and spur innovation when interfacing with their employees. However, they struggle to motivate SEs to share their expertise. One reason is that SEs operate in an environment where interests (i.e., goals, division of labor, and risk sharing) are often misaligned or conflicting. To enhance SEs’ knowledge sharing behavior, manufacturers can employ a variety of governance mechanisms: contractual, relational and psychological governance. Contractual governance comprises all mutually agreed upon, written and unwritten management-initiated activities to influence behaviors in support of the agreed objectives. Such governance is typically designed at the inter-organizational level before the operational supplier-manufacturer interactions start. Relational governance structures supplier-manufacturer interactions through self-control based on trust and social norms. Relational governance operates on a variety of levels such as the inter-organizational, department, and individual level. Psychological governance operates at the individual-level and is based on an individual’s deep personal commitment to honoring the obligations of the exchange relationship. Two specific SE behaviors are relevant in the context of joint value creation in supplier-manufacturer collaborations: knowledge sharing behavior and innovative work behavior. SEs’ knowledge sharing behavior reflects the sharing of expertise such that it can be appropriated by the manufacturer and its employees to create innovations. SEs’ innovative work behavior is a broader construct that describes the intentional efforts of SEs to come to new outcomes beneficial for the manufacturer by generating, championing and implementing ideas. Both behaviors enable the manufacturer to refine existing or create new products and/or services using the expertise held by SEs. Motivated by the observation that SEs typically operate in an environment of social complexities and blurred organizational boundaries that complicate sharing expertise, this dissertation intends to answer the following key research question:

What contractual, relational and psychological mechanisms motivate or discourage supplier experts to share their expertise and behave innovatively in favor of a manufacturer?

Three distinct studies address this question. Jointly, the studies cover both the front end and the back end of a manufacturer’s value chain. Study 1 focuses on the motivational or inhibiting role of contractual and relational governance on SEs’ knowledge sharing behavior in the back end of the value chain. Study 2 investigates the cognitive

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Summary

process underlying SEs’ knowledge sharing intention and behavior at the front end of the value chain. Specific attention is given to exploring how relational governance interacts with contractual governance in stimulating knowledge sharing behavior. Study 3 also focuses on the front end of the value chain and discusses how psychological governance motivates or inhibits innovative work behavior. The study also further investigates the relationships between contractual, relational, and psychological governance.

Insights from study 1Manufacturers often outsource customer-facing services to specialist suppliers (i.e., call centers, repair services, etcetera). SEs of these outsourced service providers directly interact with customers of manufacturer and receive their feedback and insights. Feeding these insights back to the manufacturer may enable the improvement of existing products and services, and coming up with completely new value propositions. Accordingly, this study distinguishes between exploitative and exploratory knowledge sharing, builds a conceptual model including contractual and relational governance, and empirically tests the model based on survey data from 70 relationship managers who manage outsourced service partners. The survey results show that contractual governance can both promote and inhibit knowledge sharing. Specifically, the use of incentives limits exploratory knowledge sharing. In contrast, contract specificity enhances both exploitative and exploratory knowledge sharing. Relationship quality also positively relates to both types of knowledge sharing, while relationship manager experience positively influences exploratory knowledge sharing only. Finally, the study reveals that relational governance (i.e., relationship quality and relationship manager experience) relates more strongly to knowledge sharing behavior than contractual governance (i.e., contractual incentives and specification level).

Insight from study 2The influx of external expertise is critical in collaborative R&D settings where SEs should integrate supplier technology into the manufacturers’ product/services. Study 2 uses a motivation-intention-behavior framework to examine how customer stewardship and distributive fairness (i.e., relational governance mechanisms) motivate SEs to share knowledge. Furthermore, the study explores whether efforts by the manufacturer to formalize the interaction with SEs (i.e., contractual governance) influences the motivation-intention-behavior relationships. The conceptual framework is empirically tested with survey data collected from 187 SEs who participated in R&D projects at seven global high-tech manufacturers. The results confirm the complex effects of contractual and relational governance. The more formal the interaction between manufacturer employees and SEs, the less SEs’ customer stewardship perceptions translate into knowledge sharing behavior. In

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contrast, high levels of formal coordination makes distributive fairness a more powerful antecedent to knowledge sharing behavior. Interestingly, the positive relationship between distributive fairness and knowledge sharing behavior also holds under low levels of formal coordination, but not for medium levels of this moderating variable.Insights from study 3Study 3 examines the role of psychological attachment in influencing SEs’ innovative work behavior in collaborative R&D settings. Specifically, the study builds on the observation of identification scholars who posit that individuals can identify (i.e., feel a sense of “oneness”) with multiple ‘social entities’, such as departments and organizations, at the same time. So far, however, it remains unclear how SEs behave in case of differential levels of identification with the supplier, the manufacturer, and the R&D team. The study develops hypotheses on the presence or absence of “gaps”, or (in)congruences, between different SEs’ identifications. These hypotheses are empirically tested using survey data, as in study 2, from 187 SEs participating in collaborative R&D projects. A two-step analytical approach, involving polynomial and linear regression, confirms that SEs’ stronger identification with the manufacturer compared to the supplier positively associates with innovative work behavior. A strong dual identification with the manufacturer and its R&D team also drives SEs’ innovative work behavior. The results thus show that psychological governance in the form of identification affects an individual’s motivation to behave innovatively in support of a manufacturer’s R&D project. The results further confirm that both onboarding and goal socialization are effective mechanisms to ‘manage’ identification (in)congruence. The better SEs are prepared for their role in the R&D team (i.e., because of onboarding practices), the more strongly they identify with the manufacturer relative to the supplier. Next to onboarding, explaining goals and providing feedback about individual and team performance (i.e., goal socialization) strengthens identification with the manufacturer and the R&D team.

Overall conclusionsOverall, the research concludes that all three governance types influence SEs to display expertise-sharing behaviors that may ultimately help manufacturers innovate their products. As a practical implication, among others, this dissertation encourages managers to critically evaluate contractual governance structures, such as creating clear contract specifications. This provides SEs with a clear frame of reference for knowledge sharing. In addition, managers are advised to design incentives in such a way that knowledge sharing is rewarded. The results also urge managers to align their formal coordination structures in R&D collaborations with SEs’ perceptions of their work environment. Furthermore, high levels of relationship quality and high-quality exchange relationships between employees and both organizations stimulates SEs to contribute their knowledge. Finally, managers should make SEs feel at home because the resultant psychological attachment increases

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Summary

their efforts to contribute to the manufacturer’s success. Onboarding and coaching SEs, as if they were new employees, helps to build personal attachment and commitment. In terms of limitations, it should be noted that the results of the studies should be compared with care since study 1 focuses on the back-end of a manufacturer’s value chain, while studies 2 and 3 focus on the front-end. Also the focal behaviors differ per study, and the data to test the conceptual frameworks developed in each of the studies, was collected mainly through self-reported surveys.

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About the author

150

About the author

151

About the author

Jelle de Vries was born on April 19th, 1978 in Mierlo, the Netherlands. After completing his bachelor degree in Industrial Engineering in 2003 at the Fontys University of Applied Sciences in the Netherlands, he followed the master in Innovation Management at Eindhoven University of Technology, also in the Netherlands. In 2011, he graduated within the Innovation, Technology Entrepreneurship & Marketing group of the department of Industrial Engineering and Innovation Sciences on the master thesis topic of knowledge sharing in buyer-seller relationships. In January 2012 he started a PhD project at the same university. The results of this work are presented in this dissertation. His first journal article on knowledge sharing in inter-organizational relationships was published in 2014 in Industrial Marketing Management. Since 2003 Jelle is employed at Royal Philips, working in a variety of Supply Chain Management and Procurement roles. In 2012, he joined the companies’ procurement department and held a variety of roles, primarily operating in the Netherlands. Currently he is Commodity Cluster Leader, managing a globally distributed team of 14 strategic buyers for all computing, software, display and user interfacing components embedded in Philips’ products. He also acts as the Lead Procurement Business Partner for Philips’ HealthCare Informatics and Population Health Management business. Additionally, he supports Philips’ Enterprise Platforms business unit to secure the creation and management of key supplier contracts and relationships. Jelle was nominated as a High Potential within Philips in 2009, following which he obtained specialized training in Philips’ high-potential development center. Two years later, he was selected to participate in the second stage of this program to further develop individual and leadership skills. In 2013, he was awarded the Philips Procurement Innovation Excellence award for his work on a groundbreaking product concept involving the seamless integration and projection of Healthcare data on Google glass devices.

Release External Expertise in Collaborative Innovation

Jelle de Vries

Eindhoven University of Technology

Department of Industrial Engineering & Innovation Sciences

Supplier-manufacturer collaborations have become crucial for the innovation of products and services

of high-tech manufacturers. Supplier experts (SEs) interface between supplier and manufacturer

and thus personify the supplier’s knowledge contributions to high-tech manufacturers’ innovation

programs. With the aim to develop the best possible customer offerings, manufacturers want SEs to

share their most valuable knowhow and spur innovation when interfacing with their employees. While

manufacturers expect SEs to take the interests of the manufacturer’s innovation project to heart,

many innovation managers still apply a short-term engagement perspective (i.e., a focus on getting

the project done) and treat SEs as “the external person”. The human factor at play can, however, not

be underestimated: manufacturers’ need external experts with vested interest in the manufacturer’s

success to benefit from their expertise. This dissertation shows how contractual, relational and

psychological governance mechanisms influence external experts to share their expertise in support of

manufacturers’ innovation aims.

Keywords: Knowledge sharing, innovative work behavior, R&D collaborations, inter-organizational

innovation, supplier expertise, relational governance, contractual governance, psychological

governance

Release External Expertise in Collaborative InnovationJelle de Vries

Release External Expertise in Collaborative Innovation

I N V I T A T I O N

to the public defense of my dissertation

The public defense will take place on Thursday December 14th 201716.00 hrs, at the ‘Senaatszaal’ of the Auditorium at Eindhoven University of Technology. Address: De Groene Loper 1, Eindhoven

After the public defense there will be a reception.

ParanimfenFemke Huijbregts-de VriesAukje Janssen-de Vries

Jelle de VriesTorenweg 145731 CJ Mierlojelle.de.vries@philips.com