Eindhoven University of Technology MASTER The dynamics of trust and control in innovation ecosystems Cobben, D.Y.P.

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Eindhoven University of Technology

MASTER

The dynamics of trust and control in innovation ecosystems

Cobben, D.Y.P.

Award date:

2018

Link to publication

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The Dynamics of Trust and Control in Innovation Ecosystems

D.Y.P. Cobben 0822031

Supervisors

Dr. ir. B. W alrave, Industrial Engineering, TU/e Dr. C. Castaldi, Innovation Sciences, TU/e Dr. M. Cloodt, Industrial Engineering, TU/e

Company supervisor

Dr. N. Roijakkers, Management, Science and Technology, OU

In partial fulfilment of the requirements of the degrees of Master of Science in Innovation Management

And

Master of Science in Innovation Sciences

September 2018

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TU/e, School of Industrial Engineering and Innovation Sciences Subject code: 0EM045, 1GZM45

Subject heading: innovation ecosystems, governance mechanisms, focal actor, alliances, output control, social control, behavioural control, competence trust, goodwill trust, partner alignment, alliance management capabilities.

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Abstract

Within innovation ecosystems, a focal actor uses governance mechanisms to mitigate the risks of opportunistic behaviour. In innovation ecosystem literature, the importance of governance mechanisms and focal actors has been mentioned briefly. In alliance literature, governance mechanisms have been extensively described. As ecosystems are a subset of alliances, we used constructs from alliance literature to understand how governance mechanisms are used by the focal actor within innovation ecosystems. Scholars have also mentioned the importance of partner alignment for successful innovations. To mitigate the risk of opportunistic behavior and to also align partners, this research has focused on understanding how the focal actor uses governance mechanisms to influence partner alignment. Based on an exploratory multiple case study research, this research found a list of partner stimulators, like for example leadership, communication and expectation management, that influence the effectiveness of governance mechanisms used by focal actors in aligning partners. When partner alignment stimulators are present, the effectiveness of governance mechanisms is enhanced and partners feel more aligned. We also developed a model that explains the relation between partner alignment stimulators, governance mechanisms and partner alignment. In addition to identifying partner alignment stimulators, we found that constructs regarding governance mechanisms from alliance literature can be applied to an innovation ecosystem setting when partner alignment are considered next to them. We contribute to the literature on innovation ecosystems by explicitly considering partner alignment stimulators that influence the relation between governance mechanisms and partner alignment. In particular, we found that trust-based governance mechanisms function as partner alignment stimulators for control-based governance mechanisms. Future research could focus on understanding whether the effect of partner alignment stimulators is an example of a moderating or mediating effect.

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Foreword

It was during one of my classes that I realized that everything in this world is connected and that we simply need to collaborate to create a more sustainable world. This moment was my inspiration to pursue a double degree. I realized that I not only needed knowledge regarding transitions and sustainability (Innovation Sciences), but also about management, strategic implications and governance mechanisms (Innovation management). Indeed, my double degree gave me the basis that I need to enable my future ambitions. After spending six years at the Eindhoven University of Technology, I realize that we have to think out of the box to collaboratively design and implement the solutions that our world needs. As a result of all those years, this work represents my thesis for the degrees of Master of Science in Innovation Management and Master of Science in Innovation Sciences.

First, I would like to thank my two supervisors Carolina Castaldi and Bob Walrave. Without you, I would not be able to complete this work. Carolina, thank you for your inspiration, your constructive and valuable feedback, your quick responses, and endless support. Bob, thank you for the constructive and valuable feedback, the way you inspired me to rethink and reconsider my work to enhance its quality, and the feedback on my academic skills. Though double degree programs can impose some (bureaucratic) challenges, you both immediately welcomed me as one of your students. Thank you for believing in me throughout the process.

Moreover I would like to thank my ‘’company’’ supervisor. Nadine, thank you for providing me with the opportunity to conduct my master thesis at the Open University in Heerlen in such an inspiring and open environment. In the past months you made me feel welcome at the OU, supported me with your insights and years of experience and showed me that open innovation is such an interesting research field. Thank you for believing in me and supporting me every day. I would also like to thank all the people I was able to work with at the Open University and the way they took me in as I was one of them from the start. I felt absolutely welcome!

I also want to mention the support I have received from my friends. I would like to specifically thank Isabel, Huyen, Wendy and Diederik for their mental support. Every time I was hesitating, stressing or just needed someone to complain to, they were there for me to provide their support. I would also like to thank Frank. Thank you for the way you supported me, the many hours you have spent on reading my work, the honest and valuable feedback you gave and the endless discussions that we had regarding our theses and golden retrievers. Your feedback definitely brought my thesis to the next level!

The ones I absolutely should not forget are my family and my beloved boyfriend Koen. First, I would like to thank and honour my beloved grandfather. Though he is no longer living among us, he was the one who inspired me to start studying, the one who always believed in me and the one that supported me every day. Thank you my dear grandfather for everything. My dear family, thank you for the support you gave me, the way you believed in me, the understanding that my thesis had to be my number one priority and the way you provided distraction when I needed it. Koen, thank you for the way you supported me, the way you believed in me, every time you cooked, and you were there to simply listen to me. Without you, this process would have been far more difficult.

With this work, it is time to finish my time at the Eindhoven University of Technology. I would like to thank all the people I met at the TU/e and all the knowledge and experience that they gave me. You have made me realize who I am, what my ambitions are and what I need to be happy. It is now time to end this journey and continue to the next one. I am eager to apply all my experiences and knowledge in the following journey and implement all this knowledge to contribute to a more sustainable world where people are eager to collaborate! Open University, here I come!

Dieudonnee Cobben, Sittard, 2018

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1. Introduction

Innovation ecosystems have the potential to produce innovations for sustained industrial competitiveness by offering an innovative environment that allows multidisciplinary collaboration (Adner, 2006; Wang, 2009). These ecosystems are collaborative agreements in which a wide variety of partners combine their individual technologies and/or services into a valuable innovation. Typically, a focal entity governs this arrangement (Gobble, 2014) and facilitates joint value capture and creation via partner alignment (Adner, 2006; Adner and Kapoor, 2010; Adner, 2017).

Value capture and creation within ecosystems represents a complicated process as it can be difficult to create a sense of collective value (Lopes-Berzosa and Gawer, 2014) and/or to ensure that actors behave in congruence with the interest of the ecosystem instead of behaving opportunistically - that is acting out of self-interest (Boudreau, 2010). When partners in an innovation ecosystem perceive a risk of opportunistic behaviour, they will do everything to protect themselves (Das and Teng, 1998; Das and Teng, 2001). To mitigate the risk of opportunistic behaviour, governance mechanisms, that is trust and control based governance mechanisms, can be used (Gulati, 1995; De Man and Roijakkers, 2008). Trust- based governance mechanisms aim to reduce opportunistic behaviour by creating internally motivated actors by the presence of trust (De Man and Roijakkers, 2008; Gulati, 1995) whereas control-based governance mechanisms do so by means of formal rules and procedures (De Man and Roijakkers, 2008).

Within innovation ecosystem literature, the importance of both trust (Bercovitz, Jap and Nickerson, 2006; Autio and Thomas, 2014) and control-based governance mechanisms (Adner and Kapoor, 2010) has been mentioned as a driving factor of successful collaborations. Scholars also described the influence of the focal firm on successful collaborations via the use of governance mechanisms (Adner, 2017; Jacobides, Cennamo and Gawer, 2018). Scholars call for more research on creating a better understanding on the use of governance mechanisms within innovation ecosystems (e.g., Maes and Roijakkers, 2017; De Man and Roijakkers, 2008; Adner, 2017). So far, initial research regarding innovation ecosystems has been mainly based on interviews with focal actors (Vanhaverbeke, 2017);

the opinions of the partners within ecosystems were often not considered. As a result, there are not many insights yet on the internal dynamics within innovation ecosystems. To the best of our knowledge no empirical research has been done yet concerning the use of governance mechanisms by focal actors neither on the alignment of partners within innovation ecosystems (Meier, Lütkewitte and Mellewigt, 2016; Adner, 2017).

Though no empirical research has been done yet within innovation ecosystems, trust and control-based governance mechanisms have been extensively researched in the alliance literature (e.g., Gulati, 1995;

Hagedoorn, Roijakkers and Kranenburg, 2008; De Man and Roijakkers, 2008). In the alliance literature, several types of both trust and control-based governance mechanisms have been identified (Das and Teng, 1998; Das and Teng, 2001). Also the use of alliance management capabilities that influences the use of governance structures has been extensively described in alliance literature (Schilke and Goerzen, 2010; Schreiner, Kale and Corsten, 2009). Innovation ecosystems are seen as a specific subset of alliances and networks (Gulati, Puranam and Tushman, 2012). They have specific properties that distinguishes them, for instance their focus (Adner, 2017). Given their specificities, it remains unclear whether we can directly use concepts from the alliance literature within an innovation ecosystem context. Research is required to understand how governance mechanisms are exactly used within innovation ecosystems.

As many unknowns remain in the use of governance mechanisms, many questions still remain. For instance, how are governance mechanisms used by focal actors within an innovation ecosystem context?

And how effective are these governance mechanisms in aligning partners? Are focal actors able to align the partners in the ecosystem? As such, a need exists to further investigate the use of governance mechanism by focal actors in the context of innovation ecosystems. Therefore this thesis tackles the following research question:

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How do focal actors influence, by means of governance mechanisms, partner alignment within the innovation ecosystem?

By means of an exploratory multiple case study, we have researched how focal actors use governance mechanisms within innovation ecosystems to influence partner alignment. The research was conducted through theoretical sampling. During this process, theory was generated, collected and analysed at the same time to develop new theory. This study has an inductive character meaning that existing literature regarding governance mechanisms, alliances and innovation ecosystems was used to understand the use of different governance mechanisms by focal actors and its effectiveness in aligning partners. This was done using four specific cases in the fields of blockchain, artificial intelligence, climate change and sustainability.

Understanding the influence of trust and control used by the focal actor on the dynamics within innovation ecosystems augments existing ecosystem literature and could contribute to sustained industrial competitiveness of organizations. Also, understanding the effect of governance mechanisms on the partners of an innovation ecosystem could contribute to the understanding on what factors that are present in innovation ecosystems (e.g., leadership, communication, discussions, transparency, etc.) to stimulate the effectiveness of governance mechanisms. By building upon the extant literature on alliances, this research provides an initial attempt to connect the fields of alliance and ecosystem literature to understand how different types of governance mechanisms are used by focal actors within innovation ecosystems and how this influences partner alignment. Whereas most existing research is still in the concept definition phase, this research is one of the first that provides insights in the dynamics of governance mechanisms in innovation ecosystems through empirical research.

The following chapter starts with an explanation of the innovation ecosystem and alliance concepts.

Next, trust and control as governance mechanisms are characterised. After the theoretical aspects are explained, the method used to answer the research question is described. Next, the results of the study and a model are discussed. This research concludes with a discussion on how trust and control are used by focal actors in innovation ecosystems to influence partner alignment.

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2. Theoretical framework

Firms have typically opted for internal innovation development. However, to respond quickly to complex changes in technology and customer demands, firms have started to collaborate with all kind of partners in the last two decades. Both innovation ecosystems and alliances are examples of collaborations between firms. Gulati et al. (2012) have stated that ecosystems partly overlap with alliances, forming a specific subset of alliances. Though in essence they both aim at collaboration, innovation ecosystems and alliances both have their own specificities. First, alliances focus on the firm- level, whereas innovation ecosystems focus on activities. Second, in alliances the real purpose of the collaboration is often not revealed and value propositions are often not known, whereas in innovation ecosystems value propositions are leading (e.g., in attracting the right partners). Third, alliances are generally not focused on developing knowledge and inventions, whereas ecosystems do focus on these two activities (Adner, 2017). Last and contrarily to alliances, ecosystems do not always contain formal alliances or binding forms (Jacobides et al., 2018). Innovation ecosystems are thus a stand-alone concept, requiring structure and resolved coordination challenges (Jacobides et al., 2018).

In the field of innovation ecosystems, still many research opportunities remain. With regard to governance mechanisms, their importance has been mentioned, but to the best of our knowledge no empirical research has been done yet. Current research has not focused yet on understanding how governance mechanisms are exactly used in an innovation ecosystem context (e.g., Adner and Kapoor , 2010; Maes and Roijakkers, 2017; De Man and Roijakkers, 2008). In alliance literature, the use of governance mechanisms has been extensively researched (e.g., Das and Teng, 1998; Das and Teng, 2001; De Man, 2006). As a result of existing research, different forms of trust and control-based governance mechanisms and different risk types were identified in alliance literature. As trust and control-based governance mechanisms have not been researched yet in an innovation ecosystem setting and innovation ecosystems are seen as a subset of alliances, in this thesis definitions out of alliance literature regarding governance mechanisms are applied in an innovation ecosystem context. This is done to understand how focal actors use governance mechanisms in an innovation ecosystem context and how it influences the alignment of partners.

2.1. Innovation ecosystems

The innovation ecosystem is a unique collaboration in which several actors combine their individual offerings into a specific innovation solution (Autio and Thomas, 2014). More specifically, these ecosystems involve relations that are not decomposable into a combination of multiple bilateral relations. A wide variety of actors contribute material resources and human capital and collaboratively form the institutional environment required for successful innovation (Jackson, 2015). To materialize a value proposition, an alignment structure for the multilateral set of partners is required (Adner, 2017).

Innovation ecosystems can deliver high-tech solutions that firms are less likely to deliver on their own (Autio and Thomas, 2014; Adner and Kapoor, 2010). A striking example of an innovation ecosystem is the PRoF Project in Belgium. More information about the PRoF case can be found in box 1.

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10 Box 1 PRoF case

Inspired by a study that showed the potential impact of open innovation, Jan van Hecke, CEO of a furniture producer in the health care sector, decided to introduce open innovation in the health care sector. At first, Van Hecke organized a meeting for all types of actors working in the health care sector to inspire and innovate together. At this first meeting only 10 out of 50 invitees showed up.

Still this was the starting point for a long-lasting innovation ecosystem. It was at this first meeting that it was decided that PRoF would be the platform to integrate knowledge from all kind of partners to develop the health care innovations of the future.

PRoF consists of a large and a small consortium; the small consortium consists of industry parties whereas the large consortium consists of experts, care institutions, and hospitals and their personnel and patients. The small group pays a member fee, the large group gives feedback and generates ideas.

Ideas are generated in special brainstorm sessions along a number of key words. After ideas are generated, every partner from the small consortium produces a specific module of the total concept.

A 9:1 rule is used in the ecosystem; for every small member, nine large non-commercial partners are required. In that way, the non-commercial character is guaranteed. This structure also enables the integration and implementation of customer feedback.

Over the course of years many more projects have followed like the Personalized Residence of the Future and the Patient Recovery Room of the Future. Also a yearly price, a PRoF Chair, Theme Days, and social innovation group have been added to the ecosystem. Van Hecke is still the facilitator of the ecosystem. Currently PRoF consists of 438 partners, the showroom attracts 4000 visitors a year, and exists for 9 years (Cobben and Roijakkers, 2018).

Innovation ecosystems are typically governed by a focal entity. This entity can be either a technology platform, a set of social or economic conditions or a company (Gobble, 2014). The focal entity develops the strategy of the ecosystem, institutional system and governs the technological architecture (Adner and Kapoor, 2010; Autio and Thomas, 2014). Often, a focal actor first develops a value proposition and then tries to find partners that are capable of contributing to the proposition. Gobble (2014) mentions that being a focal entity is more than just a cooperation decision; it is a business strategy. Focal entities often use their position to control the technological architecture or value-creating brand to enhance their own performance (Autio and Thomas, 2014). The ability of the focal entity to manage the network depends on its ability to influence and control the ecosystem, the match between the focal entity’s goal and the ecosystem’s goal and the structure of the ecosystem (Möller, Rajala and Svahn, 2005).

The success of an innovation developed by an innovation ecosystem depends on many different actors (Adner, 2006; Wang, 2009) as a variety of modules developed by different actors are combined into a (technological) solution (Mercan and Göktas, 2011; Wang, 2009). Members have their own internal challenges - either upstream (suppliers) or downstream (complementors) - in the development process of their specific part that have to be resolved before an innovation can be delivered to customers (Adner and Kapoor, 2010). Only when the focal entity, upstream component partners and downstream complementary partners cooperate, challenges can be resolved (Song, 2016). In order to collaborate and to solve the (internal) challenges, mutual agreement among partners is required. Only when a focal actor is able to recognize that interests of all partners have to be in line and that every partner has different perceptions of uncertainties and different strategies to handle these uncertainties, partners have an incentive to contribute to the ecosystem (Leavy, 2012; Gomes, Salerno, Phaal and Probert, 2018). The extent to which mutual agreement exist, expressed in terms of positions and flows, is also known as the level of alignment between partners. Mainly at the start of innovation ecosystems partners are still searching for compatible incentives and motives. When ecosystem development is more mature, the level of alignment often increases (Adner, 2017). Partner alignment can be measured as the extent to

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which the focal actor and partners within the innovation ecosystem understand each other’s interests and uncertainties (Adner, 2017; Leavy, 2012). Thus, the focal actor is responsible for partner alignment (Adner, 2017). An overview of the different characteristics of innovation ecosystems can be found in table 1.

Table 1 Ecosystem types and their characteristics

Characteristic/

Ecosystem type

Innovation ecosystem

Purpose Combination of technologies/modules into coherent customer-facing solution

Type of actors Focal entity, upstream component partners and downstream complementary partners

Governed by Focal entity Responsibility

governor

Strategy development, governance of technological architecture, partner alignment, attracting required partners for value proposition development Example Prof Project (Belgium)

2.2. Alliances

Alliances are defined by Gulati (1995) as agreements between two or more firms regarding the pooling of resources to explore and exploit market opportunities. Alliances are used to retrieve resources and competences that are not internally available for firms. The transaction costs for starting and managing an alliance are lower than the costs to get acquainted with new knowledge from scratch. Alliances are used by firms to obtain competitive advantages, acquire new technologies, enter new markets, share risk with others, establish economies of scale or access complementary resources and technologies (Gulati, 1995). It can be a difficult process for firms to fully obtain the benefits from alliances. Firms are mostly self-interested (opportunistic) and try to retrieve as much knowledge as possible for themselves (Leroi- Werelds, Pop and Roijakkers, n.d.). In general, collaborating companies put none to little effort in developing a common strategy; alliances rely upon the individual strategies of the involved companies (Roijakkers and Hagedoorn, 2006).

To manage the success of alliances and to prevent opportunistic behaviour, firms use alliance management capabilities. These capabilities comprise the capability of a firm to handle or manage an alliance (Anand and Khanna, 2000). Alliance management capabilities are important as industries often develop in unexpected ways, making it a serious challenge for firms to manage its dynamics (Van de Ven and Polley, 1992; Madhok and Tallman, 1998). To successfully manage alliances, six alliance management capabilities are crucial: coordination, communication, bonding (Schreiner et al., 2009), interorganizational learning, sensing and transformation (Schilke and Goerzen, 2010; Gomes et al., 2018). Capabilities are developed by either having experience (Siminon, 1997; Hoang and Rothaermel, 2005) or taking deliberate actions to develop structural mechanisms (Kale, Dyer and Singh, 2002). When alliance management capabilities are implemented successfully, alliance performance increases. Also, alliance management capabilities mediate the effect between alliance structures(e.g., governance structures) and alliance performance (Schilke and Goerzen, 2010). In table 2, the main characteristics of each capability can be found.

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Table 2 Alliance management capabilities

Alliance management capability Characteristics

Coordination  Stimulate implementation of actions

(Thompson, 1967);

 Need of adaptable procedures, rules and policies (Schreiner et al., 2009).

Communication  Information sharing and communication

(Mohr and Spekman, 1994; Larson, 1992);

 Shared understanding of obligations, engagement rules, mental models (Klimoski and Mohammed, 1994);

 Effective conflict management (MacNeil, 1981).

Bonding  Developed personal relationships (Luo,

2006; Yli-Renko, Autio, Sapienza, 2001);

 Norms of trust (Gulati, 1995);

 Psychological attachment(Schreiner et al., 2009);

 Mutual expectations of cooperation, trust and knowledge sharing(Schreiner et al., 2009);

 Conflict resolution(Schreiner et al., 2009).

Interorganizational learning  Knowledge transfer across

organizational boundaries (Dyer and Nobeoka, 2000);

 Routines (Martin and Salomon, 2003);

 Generating new knowledge (Schilke and Goerzen, 2010);

 Building new thinking (Schilke and Goerzen, 2010).

Sensing  High alertness to environment (Zaheer

and Zaheer, 1997);

 Identification of opportunities (Park, Chen and Gallagher, 2002).

Transformation  Creating a perfect fit (Schilke and

Goerzen, 2010);

 Flexibility (Schilke and Goerzen, 2010);

 Beyond routines (Schilke and Goerzen, 2010).

2.3. Governance mechanisms

In alliances, different reasons are found that result in a lack of trust; uncertainties in the environment, the incomplete nature of contracts, bounded rationality, and the behaviour of partners (opportunism) (Williamson, 1975). To prevent opportunistic behaviour (e.g., evasion and violation of agreements, refusal to adapt and forced renegotiation (Wathne and Heide, 2000)), firms use governance mechanisms (Gulati, 1995). Two types of governance mechanisms exist; control and trust-based governance mechanisms.

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13 2.3.1. Control-based governance

Control-based governance mechanisms aim at reducing the chance that a partner behaves in an opportunistic manner and protects the company via the use of the use of formal rules and procedures (De Man and Roijakkers, 2008; Das and Teng, 2001). The use of control-based governance mechanisms makes it more expensive for organizations to implement activities that only benefit their own goals. The consequences of opportunistic behaviour are determined in contracts (i.e. fines) (Parkhe, 1993). Control also specifies the labour division between firms; it functions as a guideline for the integration of activities, decision-making and how to handle in case of disagreements (Faems, Janssens, Madhok and Looy, 2008). There are different instruments to exercise control (Poppo and Zenger, 2002; Das and Teng, 2001; De Man, 2006).

Control can be classified into external measure-based control and internal value-based control. The former focuses on the establishment of formal rules, procedures, and policies to monitor and reward desirable outcome whereas the latter focuses on the establishment of organizational norms, values, culture, and the internationalization of goals to encourage desirable behaviour and outcome. The ideal form of control depends on the extent to which the output is measured and the knowledge that managers have regarding the transformation process (Das and Teng, 2001).

External measure-based control can be categorized into behavioural control and output control.

Behavioural control focuses on influencing the behaviour of partner firms via, for example, reporting devices, written notice of departure from agreements, accounting examinations, cost and quality controls, arbitration clauses and lawsuit provisions (Das and Teng, 1998). Output control focuses on defining the preferred output of an alliance via setting alliance goals, establishment of incentive systems and reward structures, and formal monitoring procedures (Dekker, 2004). Internal value-based control is also known as social- or clan control. Social control does not define behaviour or output; it rather focuses on socialization via the development of organizational consensus. It aims at developing organizational norms, values, culture and the internalization of goals to encourage desirable behaviour and outcomes (Das and Teng, 1998; Das and Teng, 2001). An overview of the different types of trust and control can be found in table 3.

Table 3 Control types

Control Types Examples

External measure-based Behavioural control  Reporting devices

 Written notice of departure from agreements

 Accounting examinations

 Cost and quality controls

 Arbitration clauses

 Lawsuit provisions Output control  Setting alliance goals

 Incentive systems

 Reward structures

 Formal monitoring procedures Internal value-based Social control  Common culture

 Socialization processes

 Common values

 Decision-making consensus

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To summarize, control-based governance mechanisms focus on value creation by sharing both costs and risks, creating market power, and the optimisation of cooperation processes. Value capture aims at sharing the cake equally among all partners (De Man and Roijakkers, 2008). Strategy, structure, and systems are the main focus, whereas informal aspects do not receive much attention (De Man, 2006).

Partners will never become an entity as they have their own agendas, which are more important than the collective goals (Sundaramurthy and Lewis, 2003).

2.3.2. Trust-based governance

In contrast to control, trust is presented in literature as a more flexible governance form; it is based on positive expectations of partners. The more firms cooperate, the more trust increases and the less control is used (Gulati, 1995). When trust-based governance mechanisms are used, every partner is allowed to join, leadership is decentralized, and cultural differences are valued leading to more long-term relations (De Man, 2006). The aim is to reach complementary goals with internally motivated actors that trust one another (De Man and Roijakkers, 2008; Gulati, 1995). In case of trust, firms worry less about opportunistic behaviour and, as such, reduce control-specific actions (Das and Teng, 2001). In unpredictable environments, trust is a valuable mechanism as contracts are not able to fully capture the market dynamics (De Man and Roijakkers, 2008).

An initial level of mutual trust is required to start arrangements (Hagedoorn et al., 2008). Repeated ties (e.g., common history of collaborations) increase the level of trust (Hagedoorn et al., 2008; Gulati, 1995;

Roijakkers, 2003). As partners get to know each other and trust increases, they are often more willing to share information. There are different instruments for trust (Grandori, 1997; Schrader, 1991; Martinez and Jarillo, 1989; Saxton, 1997; Thomas and Ely, 1996; Chua, Chrisman , Steier and Rau, 2012; Thomas and Ely, 1996; Chatenier, Verstegen, Biemans, Mulder and Omta, 2010; Roijakkers and Hagedoorn, 2006, 2014; Kale, Singh and Perlmutter, 2000).

Two different types of trust are identified; goodwill- and competence trust. Goodwill trust is based on the expectation that a partner intends to fulfil their role and is influenced by previous experience with a partner. In general, the goodwill is based on attitudes of specific personnel, also described as trust guardians (Child, 2001). This type of trust is based on personal trust between persons and is related to relational risk; goodwill trust reduces the perceived relational risk (Lui and Ngo, 2004; Das and Teng, 1998; Das and Teng, 2001). Competence trust is based on the expectation that a partner has the ability to fulfil their roles and is based on resources and reputation of a partner (Das and Teng, 1998; Das and Teng, 2001). This type of trust based on the resources and reputation of a partner and is related to performance risk; competence trust reduces the perceived performance risk (Das and Teng, 1998; Das and Teng, 2001). An overview of the different types of trust can be found in table 4.

Table 4 Trust types

Trust Examples

Goodwill trust  Attitudes of specific personnel

 Trust guardians

 Previous experience

 Common history

Competence trust  Resources

 Reputation

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Thus, trust-based governance mechanisms create value by learning and working together. Trust-based mechanisms aim at the commitment of all partners, good internal relations and mutual adjustments to eventually create trust (De Man and Roijakkers, 2008). Value capture aims at enlarging the size of the cake (De Man and Roijakkers, 2008). An overview of the differences between trust and control-based governance mechanisms can be found in table 5.

Table 5 Governance mechanisms and their characteristics

Characteristic/governance mechanisms

Control-based Trust-based

Aim Creating market power and

optimizing cooperation processes

Intrinsic motivation

Value capture Sharing cake equally among all partners

Enlarging the size of the cake Value creation Sharing costs and risks Learning and innovating

together

Main focus Strategy, structure, and systems Commitment, good internal relations and mutual adjustments

Entity or not? No Yes

Types Behavioural, output and social

control

Goodwill and competence trust

Relational risk High Low

Performance risk Depending on market characteristics

Depending on market characteristics

Examples  Governance structures;

 Formal rules;

 Shares;

 Exclusivity in contracts;

 Central leadership;

 Business plans;

 Service level agreements;

 Business clauses;

 Performance indices and expectations;

 Specified partner roles;

 Several informal mechanisms;

 Managerial agreements.

 Business trips;

 Letters of intent;

 Business meetings;

 Management transfer;

 Shared decision- making;

 Competency development;

 HRM procedures;

 Partner evaluation and classification schemes;

 Steering committees;

 Team establishment;

 Task forces and committees;

 Organizational culture;

 Specifically educated staff;

 Collaborative conflict resolution;

 Collaborative conflict resolution.

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16 2.3.3. Substitutes or complements?

Trust and control are different concepts, but they are often implemented in combination (De Man and Roijakkers, 2008; Hagedoorn et al., 2008). Whether trust and control are complementary or substitutes, depends on the level of relational and performance risk. Relational risk is the risk that partners behave opportunistically, whereas performance risk is the risk that factors like market uncertainty, competition, and governmental regulation result in an alliance not performing as expected (Das and Teng, 2001). De Man and Roijakkers (2008) state that different combinations of relational and performance risk result in varying usages of control and trust-based governance mechanisms, as found in table 6. The number of previous contacts between firms and the market risk thus influences the role of trust and control. Note that when trust and control act as complements, initial trust is essential. However, contracts are used to prevent possible opportunistic behaviour at the start. Over time, the role of control mechanisms decreases and the role of trust mechanisms increases (Hagedoorn et al., 2008).

Table 6 Relations risks and governance mechanisms

Performance risk/

relational risk

High Low

High Trust and control as

complements

Only trust

Low Only control Trust and control as substitutes

In this section, different concepts were explained to understand the insights that existing literature provides. We found that ecosystem literature is still in the concept phase when it comes to understanding governance mechanisms; authors have tried to grasp important aspects regarding governance, but seem to show little understanding of how different types of governance mechanisms are used within innovation ecosystems. Also findings regarding innovation ecosystem literature were mainly based on research on focal actors, ignoring the importance of partners within innovation ecosystems. In alliance literature governance mechanisms have been extensively described, also in relation to different types of alliances. In alliance literature also the use of management capabilities was described, that according to literature, mediate the relation between governance structures and alliance performance. As innovation ecosystems has its own specificities compared to alliances, this thesis will investigate whether it is meaningful to apply governance mechanisms as described in alliance literature directly to innovation ecosystems and whether a construct comparable to alliance management capabilities is present in innovation ecosystems. Still, as innovation ecosystems are a subset of alliances, it could be interesting to use the general definitions of types of governance mechanisms from the alliance literature and investigate how governance mechanisms are used by focal actors within an innovation ecosystem setting. Second, when it is understood how the focal actors use governance mechanisms within innovation ecosystems, it also can be understood how focal actors influence partner alignment within the innovation ecosystem by means of these mechanisms.

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3. Method

3.1. Methodology

This research was conducted by means of theoretical sampling, including four cases (Eisenhardt and Graebner, 2007). During this process, theory was generated by collecting and analysing data, at the same time. The initial data generation was based on the overarching research topics; governance mechanisms, alliance management capabilities and innovation ecosystems as briefly explained in the theoretical framework (Eisenhardt and Graebner, 2007). The four cases were selected based on their suitability to elaborate upon the relationships between the constructs of this research. To enable research on the dynamics of the use of governance mechanisms in an innovation ecosystem setting, a process study was used. More specifically, this research aimed at understanding how focal actors of innovation ecosystems influence the dynamics within the innovation ecosystem by means of governance mechanisms.

This study has an inductive character meaning that existing literature regarding governance mechanisms, alliances and innovation ecosystems was used to understand the use of different governance mechanisms by focal actors and its effectiveness in aligning partners. This was done using four specific cases in the fields of blockchain, artificial intelligence, climate change and sustainability. An exploratory multiple case study approach was chosen to enable initial research on the use of concepts from alliance literature within innovation ecosystem research and to enable the identification of emerging themes (Edmonson and McManus, 2007). Qualitative research design is appropriate, considering the limited amount of research on the use of governance mechanisms in innovation ecosystems, suggesting a need for explorative research (Adner, 2017). Additionally, the impact of governance mechanisms on the partners within innovation ecosystems has not been considered yet, suggesting a second need for explorative research. Third, it is not understood yet whether a moderating or mediating variable, comparable to alliance management capabilities, exists that influence the relation between governance structures and innovation ecosystem performance. Last, the complexity and dynamic character of governance mechanisms in innovation ecosystems suggests a need for empirical research to create a first understanding.

3.2. Case selection

All four cases reflect innovation ecosystems that focus on providing solutions to complex challenges.

Also, all four cases display a combination of both control and trust-based governance mechanisms being used. Relatively young ecosystems were chosen, as starting ecosystems are the most appropriate for displaying the dynamics of governance mechanisms. More specifically, at the start, partners still have to align to find compatible incentives and motives. Over time, activities, actors, positions, and links become stable and the impact that (new) governance mechanisms have, is likely to decrease (Autio and Thomas, 2013; Adner, 2017). Notably, the cases differ in for example their industrial background, number of actors, actor sizes, actor types, used technology, institutional environment and location - aiding generalizability of the results in case of comparable results (Eisenhardt and Graebner, 2007).

The researched ecosystems were labelled as innovation ecosystems as they all fulfil a number of conditions of innovation ecosystems. First, each ecosystem is managed by a focal entity that orchestrates the ecosystem (Autio and Thomas, 2014; Adner and Kapoor, 2010; Adner, 2017). Also, the participants of the ecosystem deliver the input for innovation; suppliers, research and educational organizations and customers are integrated into the innovation process (Adner and Kapoor, 2010). Third, in all the ecosystems the positions and activity flows between partners are still under construction (partner alignment structures not ready yet) (Adner, 2017). Last, the relationships in the four ecosystems are not decomposable to bilateral interactions (Adner, 2017).

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3.3. Data collection and data sources

We collected data by means of semi-structured interviews and a variety of (online) documentation. The first step of the data collection was to understand how focal actors use governance mechanisms in innovation ecosystems. The second step was to understand the impact of governance mechanisms implemented on partners within the ecosystem. The third step was to understand whether moderating or mediating variables are present that influence the relation between governance structures and innovation ecosystem performance. Looking beyond the focal actor makes data collection more complex, but enhances insights as single-respondent bias, in a network context is prevented (Eisenhardt, 1989).

Semi-structured interviews (22 in total) with key persons (see Appendix A) (i.e., representatives of organizations) were the most important information source. The interviews were held with both focal actors and partners from the ecosystems. When interviewees mentioned the use of a governance mechanisms or when signs of ineffectiveness were found, follow-up questions were asked to discover how and why they were implemented and what its impact was. The questions for the interviews (see Appendix B) were based on the descriptions of governance mechanisms in alliance literature and general ecosystem characteristics out of ecosystem literature. Interviews were continued until convergence was achieved. Interviews were conducted by the same researcher, for consistency. On average, the interviews took 30 to 60 minutes and were done via telephone or face-to-face. The interviewees were found via a chain-referral sampling approach; in all four cases, the focal actor (known via personal network) from the specific case provided us with contact details of other potential interviewees. For each case, the number of interviewees, their job titles and the type of organization they work for are given.

Additionally, (online) documentation and company documents were used as secondary data sources.

These data sources were used to describe the context of the cases and as complementary sources to the interview results. The combination of different sources, i.e. triangulation, was used to overcome possible biases and problems that could arise by the use of only one data source. As such, the validity of the results increases (Yin, 2013).

3.4. Data analysis

For the analysis, summaries of the interviews and (online) documentation were maintained. Results from the four different cases were constantly compared and merged into a coherent story regarding governance mechanisms. In the alliance literature (e.g., De Man and Roijakkers, 2008), governance mechanisms have been extensively described. In ecosystem literature, only its importance was briefly mentioned. To understand the use of governance mechanisms in an innovation ecosystem setting, first data was analysed to have a first grasp of how both trust and control were used in innovation ecosystems.

Then first-order themes were constructed by categorising the data (interview results and documentation) into characteristics that could belong to different types of trust and control. This was done by using the language of the representatives to understand the specific characteristics of governance mechanisms that focal actors used in innovation ecosystems. The first-order themes aimed at understanding the characteristics of trust and control within innovation ecosystems. Then second-order themes were constructed in which main constructs from alliance literature (e.g., output, social and behavioural control and competence and goodwill trust) were coupled to the different categories of trust and control that were identified within innovation ecosystems. The second-order themes were used to relate constructs from alliance literature to categories of trust and control in an innovation ecosystem context. An overview of the coding can be found in table 7.

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Table 7 Coding and evidence

Evidence by source First-order themes Second-order

themes

 Interviews  Output not known/constantly changing;

 System too dynamic for output definition.

Output control

 Interviews

 Secondary data  Common culture development;

 Business meetings;

 Joint activities;

 Joint goal development;

 Coaches;

 Communication;

 Education;

 Community creation.

Social control

 Interviews  Role definition;

 Governance structures;

 Responsibility definition;

 Conflict management;

 Structural agreements.

Behavioural control

 Interviews

 Secondary data  Letter of intent;

 Gentleman agreements;

 Moral responsibility;

 Open communication;

 Previous interactions;

 Staff attitude.

Goodwill trust

 Interviews  Required resources and knowledge;

 Reputation;

 Attractive partners;

 Capability to fulfil appointments.

Competence trust

In this research, we defined partner alignment as the extent to which the impact that the focal actor would like to reach by implementing governance mechanisms is in line with how the partners within innovation ecosystems experience the impact of governance mechanisms. In this research, we decided that partner alignment was found, when the impact of the focal actor was in line with the impact as experienced by the partners.

In table 7, the characteristics of different types of trust and control within innovation ecosystems are found. In alliance literature, six alliance management capabilities were defined. These capabilities mediate the relation between governance structures and alliance performance. Scholars did not mention how to define performance neither how to measure it. As innovation ecosystems are a subset of alliances, we presumed that in innovation ecosystems, a comparable concept as alliance management capabilities would be found. As partner alignment is crucial for success, we defined that innovation ecosystem performance is high when partner alignment is high. Indeed, a comparable construct was found, that also influences the relation between the governance structure and innovation ecosystem performance.

The governance structure in innovation ecosystems is a combination of several trust and control-based

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governance mechanisms. A variable, that we called the partner alignment stimulator, was found that influences the relation between governance mechanisms and partner alignment. When these stimulators were present in case of a specific governance mechanism, partner alignment was higher. An overview of these relevant partner alignment stimulators can be found in table 8. Our insights in how governance mechanisms are implemented emerged inductively, based on our coding and existing literature (Yin, 2013).

Table 8 Partner alignment stimulators

Governance type Relevant partner alignment stimulator

Output control -

Social control  Leadership;

 Expectation management;

 Trust.

Behavioural control  Open discussion;

 Communication;

 Commitment;

 Trust;

 Leadership.

Goodwill trust  Leadership;

 Internal organizational support;

 Communication;

 Character of organization;

 Continuation.

Competence trust  Innovation department;

 Resource allocation;

 Reputation;

 Foundation;

 Fear of competition;

 Representatives;

 External factors.

3.5. Case descriptions

3.5.1. Sustainability

In 1972, visionary and initiator Sietze Leeflang founded a community, the Kleine Aarde, in the municipality of Boxtel that aimed at developing small-scale techniques to disburden the environment.

The Kleine Aarde became a centre that focused on projects in the field of living, energy generation, food production, waste processing and savings on raw materials. Leeflang created a visitor centre, green house, a farm and a large yard at the Kleine Aarde. In January 2011 the Kleine Aarde was closed as a result of financial shortages. In the period 2011-2014, a knowledge centre tried to renovate and exploit the Kleine Aarde, but after one year already the project was almost bankrupt. In 2015, the municipality of Boxtel did a second try to find public or private partners that could continue the renovations and exploitation of the Kleine Aarde and as a result a diverse group of actors, consisting of educational and governmental organizations and companies under the entity “Collectief de Kleine Aarde”, developed a proposal for reallocation that was eventually chosen.

Collectief de Kleine Aarde aims at creating a sustainable, self-providing community and focuses at four different programs: the built environment, bio-based techniques / food, energy transition and social

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transition. The location itself fulfils educational purposes, inspiration, testing facilities (living labs) and reintegration of people that are distanced from the labor market. To do so, it aims to have a visiting centre, apartment complex, food park, ecological play fields, picnic spots and catering location in the nearby future.

The Collectief was first based on a partnership agreement, but a number of organizations like for example the schools are not allowed to take any risks from the partnership, resulting in the founding of a legal entity that was allowed to handle cash flows. A number of partners were not allowed to take part in the foundation as a result of existing laws. The foundation aims at the realization of the goals as recorded in the ambition document “de Kleine Aarde naar een frisse start” (feb16), the implementation of risk management of the visitor centre and field labs and cash flow management. On the grounds of the Kleine Aarde also a foundation has been started for a food garden (Voedseltuin Boxtel) in 2016 and an organization that aims at building a network for partners in the region that are interested in sustainability and citizen participation. In January 2018 a project leader has started, funded by one of the partners from the collaboration. His aim is to organize and lead the daily operations at the location of the Kleine Aarde in Boxtel.

3.5.2. Techruption

The remaining cases are located on the Brightland Smart Services Campus in Heerlen, the Netherlands.

As the three cases have the same underlying structures, first this structure is briefly explained. The campus aims at connecting large firms, research institutions, start-ups and students to innovate together.

But, the creation of a campus was not enough for the creation of innovations. To stimulate innovation processes, a development program has been started to facilitate the connections between people and eventually stimulate innovation. This development program, called Techruption, aims at connecting different organizations, flourishing knowledge within an ecosystem to accelerate innovation processes and building an innovative community. Organizations have to pay a fee to become a member of Techruption. In some cases, customized agreements are developed for organizations to become member of Techruption. The development program has three different focus areas; Blockchain, Artificial intelligence and Climate Change. Techruption uses five stages for the development of a new innovation;

ideation, exploration, experimentation, pilot and scale. The current version of the Techruption program has been focusing on the first three stages. After each stage, a decision is taken whether the innovation process will continue or whether its development will stop, also called stage gate decisions. To continue to the next stage, commitment is required from at least two partners from the Techruption program (exclusively knowledge institutions). The innovation process is facilitated by coaches. Though the following three focus areas are part of the same development program, each case is substantially different in for example their maturity, leader, type and amounts of partners, innovation focus, main technology and organizational aspects. Therefore each focus area is treated as a separate case study.

Each different focus area fulfils the characteristics of innovation ecosystems. In the following subchapters the three different focus areas are briefly explained.

3.5.3. Blockchain

Blockchain is hot; firms are increasingly researching its potential impacts and (future) applications.

Inspired by the trend, blockchain became one of the three focus areas of the Techruption program. The Blockchain track aims at using advanced, internet-based technology as a potential source of trust, prosperity, equality and security for society and business. The blockchain track is led by an independent Dutch research organisation. Blockchain is one of their research focus areas; the organisation has a lab to test the value of blockchain for different sectors and firms. The research organisation is responsible for aligning its activities to the general program and developing use cases. Next to an independent Dutch research organisation, also a banking house, pension funds, consultancy firms and technology firms are part of the ecosystem. Start-ups and students are not integrated yet in the program.

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Not only Blockchain, but also artificial intelligence is an upcoming technological research field. Its potential applications are under research by a wide variety of organisations. The Artificial Intelligence project aims at bringing together business, research and education to develop tools to support humans in various situations and provide services for improving business processes. The Artificial Intelligence case is led by the BISS institute. This institute integrates and connects education, business and research and is a collaboration of different research institutions and universities. Its unique network and knowledge position connects a number of interesting partners that could contribute to innovations in the field of artificial intelligence. Not only an institution, but also consultancy firms, pension funds, educational organizations and research institutions take part in the project. The program mainly consists of large firms. Start-ups and students are not integrated yet in the program.

3.5.5. Climate Change

Whereas the Block Chain and Artificial Intelligence tracks were born out of technological trends, the Climate Change track was inspired by the increasing call for sustainability and the lack of concrete action to reach the goals agreed upon in the Paris Climate Summit. Next to technological knowledge, it aims at showing the members of the Climate Change community which impact they have as a human to inspire them to handle climate change differently inside their company as a professional. In that way, they are stimulated to develop new business models and concepts to, if possible, use new technologies as accelerator. The Climate Change track is led by a Pension Service Provider. This company has a very innovative focus; they have developed their own specific innovation methods to stimulate the development of innovations that could fight climate change. The company not only integrates their innovative methods in the project, but is also responsible for aligning its activities to the general program, stimulating ecosystem development and developing use cases.

The Climate Change track is executed for the Brightlands Smart Service Campus by an external non- profit organization, the foundation Smart Climate Opportunities (SCO). This organisation is the working platform for the climate change track. It brings together industry, innovation, capital and science. It connects academics, scientists, governments, to eventually make the Netherlands an authority on technology for contribute to climate change. The foundation not only aims at stimulating innovation at the ecosystem level, but also provides education (the Smart Up Track and the Online Climate Course).SCO tries to connect the Techruption community to a broader network of Dutch company’s like other banking houses, energy organisations, consultancy organisations, insurance companies, governmental organisations, technology firms and universities. Start-ups and students are not integrated yet in the program.

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4. Findings

The case analysis focused on how focal actors use governance mechanisms in innovation ecosystems.

Not only its use was better understood, but also how partners within the innovation ecosystem experienced the implementation of governance mechanisms (partner alignment). The explanation captures the relationship between 1) the focal actor and the governance mechanisms, 2) the focal actors and partner alignment, and 3) governance mechanisms and partner alignment within innovation ecosystems. We now explain how three types of control and two types of trust are used by focal actors within innovation ecosystems. We also explain for every governance type how its use by the focal actor has influenced the alignment of partners. An overview of the representative quotes can be found in table 9.

4.1. Output control: is it even possible?

Output control can only be defined when the eventual output of an ecosystem is known (Das and Teng, 1998). Partners in all four innovation ecosystems agreed that the output was constantly changing. We observed that, until now, in three innovation ecosystems solutions were found in surprising fields with unique combinations of partners that could never be imagined upfront. In one innovation ecosystem partners developed a common goal, that implied a future output. However, the focal actor soon realized that this output could not be realized within the current setting and that output control was not possible, as illustrated by the following quote:

‘’The desired output as described in the vision document are difficult to be realized with the current available human capital and money.’’ (Senior manager governmental organization)

In three innovation ecosystems the goal of the ecosystem was to create disruptive innovation. These innovations can disrupt existing markets, but upfront it is not known in what fashion and whether they will disrupt. One focal actor added that the specific nature of the program the ecosystem belonged to did not permit to develop a clear idea of the output upfront; the program was not working in a solution- driven manner.

As the output was not clear yet and still changing, it was difficult to define how to control it. The ecosystems were not in a stable position yet and still very dynamic as for example partners were still being added, common goals were still being developed and social consensus was not reached yet. It can be even questioned whether focal actors are ever able to use output control in innovation ecosystems as a result of their dynamic character. As output control was not used, also focal actors did not influence partner alignment via output control. To summarize, in all four innovation ecosystem output was not able to be defined neither controlled.

4.2. Social control: the power of consensus and socialization

We observed that social control was widely used by focal actors within innovation ecosystems. Focal actors used social control already at the start of the ecosystem to create social consensus, a feeling of understanding and cohesion. Social control was mainly dominant at the start, as partners did not know each other yet, resulting in a lack of cohesion and consensus. We observed that the implementation of social control was a time-consuming process for focal actors, as it can be difficult to realize social consensus and a feeling of cohesion; focal actors constantly experimented on how social control could be used. Focal actors used social control as long as they felt that social consensus was not fully reached.

At the time of this research, social control was still used in all four innovation ecosystems.

A feeling of consensus was created via the creation and development of a common culture, creation of a community, business meetings, joint activities and joint goal development. In three innovation ecosystems, also coaches were used to create feelings of commitment and the right mind set and to educate partners to use the same new product development initiation processes. In one innovation ecosystem, the focal actor explicitly used social control to create commitment via a common point of departure, as illustrated by the following quote:

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‘’Common point of departure on common problem creates commitment.’’ (Senior manager business) Focal actors also used social control to create an innovative community where people no longer represented companies, but themselves.

Despite the efforts, in two innovation ecosystems the use of social control did not align partners; several partners felt that no common culture, feelings of cohesion or consensus was found, though the focal actors of these ecosystems felt that these aspects were present. The focal actors shared the opinion that a common culture and goal were found, that partners became more close and that knowledge gaps decreased. Several partners felt that they were not included and that no common culture and goal existed, mainly as a result of limited face-to-face meetings, as illustrated by the following quotes:

‘’No common culture; the members of the management team have seen each other one time this year.’’(Director business)

’’The common goal has been developed by the Techruption program and the people who initially started it; eventually it is a derivative of the campus goals.‘’ (Senior manager business) The last quote also shows that partners felt that the common goal was designed by a select group of partners. Within one innovation ecosystem, we observed that partners experienced the vision document as written behind a desk, without further considering the practical side of the story. Thus a mismatch in expectations was found between the focal actors and the partners of two innovation ecosystems; they both experienced the impact of social control differently. It is not clear whether the focal actors ever checked whether the partners experienced a feeling of cohesion and inclusion. The partners within these two ecosystems did not feel aligned, though we observed that the partners felt a bit closer at the time of this research than at the start of the ecosystem.

In two innovation ecosystems, focal actors succeed in aligning partners by the use of social control, as illustrated by the following quotes:

“The philosophy always had been clear and communicated well.” (Senior manager foundation)

“Partners that are collaborating for a while are quicker and more flexible in the innovation process.”

(Senior manager banking)

In these innovation ecosystems, partners experienced a common culture and understood the role of the coaches and the way communication was used to cultivate the common culture. One focal actor emphasized that in his ecosystem, the implementation of social was a time-consuming process, which required many iterations. Social control in this innovation ecosystem was successfully implemented as a result of extensive managerial efforts and constant learning loops. In the second innovation ecosystem, partners experienced social control as being used positively as a result of trust between the partners.

Still, partners noted that the common culture could be further improved upon. Overall, the focal actors succeeded in socializing and reaching consensus resulting in partner alignment.

The four innovation ecosystem show that the implementation of social control is a challenging process within innovation ecosystems. To the best of our knowledge, two focal actors did not check whether social consensus was reached. In two out of four innovation ecosystems social control did not have the impact the focal actors hoped for; partners were not aligned. In one innovation ecosystem, this was created by a lack of leadership; the focal actor did use social control, but most partners did not accept the focal actor as a leader. In the other innovation ecosystem, a lack of trust was found resulting in a lack of a common culture. In the other two innovation ecosystems, social control was able to align partners. The focal actors spent quite some effort on the creation of commitment. Also, within these ecosystems, partners accepted the focal actor as their leader and an initial feeling of trust existed. Still, the use of social control could be more efficient when the focal actors would check its effectivity in practice. Expectation gaps were found between the focal actors and the partners within the innovation

Eindhoven University of Technology MASTER The dynamics of trust and control in innovation ecosystems Cobben, D.Y.P.

The Dynamics of Trust and Control in Innovation Ecosystems

Abstract

Foreword

Table of contents

1. Introduction

2. Theoretical framework

2.1. Innovation ecosystems

2.2. Alliances

2.3. Governance mechanisms

3. Method

3.1. Methodology

3.2. Case selection

3.3. Data collection and data sources

3.4. Data analysis

3.5. Case descriptions

4. Findings

4.1. Output control: is it even possible?

4.2. Social control: the power of consensus and socialization