The effects of strategic alliances on strategic flexibility during business model innovation

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The Effects of Strategic Alliances on

Strategic Flexibility during Business

Model Innovation

Kevin Berveling

K.A. Berveling

6153194

June 29th, 2015

Msc. in Businness Administration – Strategy Track

University of Amsterdam

Faculty of Economics and Business

Supervisor: Dhr. Dr. S. Kortman

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Abstract

This study draws on resource-based and transaction costs theories to develop a model on the effects of strategic alliances on strategic flexibility during business model innovation. While traditional resource-based and transaction costs theories in the context of strategic alliances, often neglect the role of relationship specific characteristics. This study recognizes the importance of trust between the organizations in strategic alliances. The study uses responses from 61 organizations involved in a strategic alliance to empirically test the model. The model supports that access to resources provided by the strategic alliance, and inter-organizational trust between organizations in the alliance improve strategic flexibility outcomes. Trust has a greater ability to improve strategic flexibility during high levels of business model innovation.

Key words: business models; innovation; negotiation costs; resource-based view; strategic

alliances; strategic flexibility; transaction costs economics; trust.

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1. Introduction

The globalization of markets, rapid technological change, shortening of product life cycles, and increasing aggressiveness of competitors force organizations to create strategic flexibility (Duncan, 1976; Volberda, 1996). Strategic flexibility is widely defined as the ability of the organization to respond continuously to unanticipated changes, and unexpected consequences of predictable changes (Aaker and Mascarenhas, 1984; Evans, 1991; Sanchez, 1995). Scarce empirical research on measuring the effects of strategic flexibility, show a positive effect of strategic flexibility on performance (Grewal and Tansuhaj, 2001; Nadkarni and Narayanan, 2007) Hence, as building strategic flexibility capabilities improve organizational performance, it is important to examine the antecedents to strategic flexibility. The existing literature generally cites resource flexibility (Combe et al., 2012; Grewal and Tansuhaj, 2001; Sanchez, 1995), management cognition (Combe and Greenley, 2004; Nadkarni and Narayanan, 2007), and flexible structures (Ocasio, 1997; Sanchez and Mahoney, 1996) as antecedents to strategic flexibility.

Gaps nevertheless exist in understanding how organizations attain strategic flexibility (Combe et al., 2012). It is argued that increasing the number of strategic alliances generally enhance organizational strategic flexibility (Das and Teng, 2000; Young-Ybarra and Wiersema, 1999) as they improve the organization’s ability to develop new technologies, products, and markets (Volberda, 1996). However, Bock et al. (2012) found that greater alliance dependencies during business model innovation decrease organizational strategic flexibility. When organizations are innovating their business model to explore new opportunities, the organization is challenged with unfamiliar and unpredictable contingencies (Amit and Zott, 2010). Possibly, these uncertain elements of business model innovation increase the negotiation costs between the organizations in the strategic alliance (Artz and

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Brush, 2000; Harrigan and Newman, 1990). Thereby outweighing the expected benefits of increased access to new technologies, products, and markets. Accordingly, as Bock et al. (2012) point out, there appears to be a need to examine how the characteristics of strategic alliances affect strategic flexibility outcomes during business model innovation.

This study aims to address to the shortcomings in the existing literature by developing a theoretical framework, and empirically test how partnership characteristics affect strategic flexibility outcomes during business model innovation. The model integrates, theories of resource acquisition and transactions cost economics, as well as the presence of relational trust. To empirically test the model, a sample of 61 Dutch organizations involved in strategic alliances, is analysed. This study is the first to examine and empirically test the role of trust in strategic alliances on strategic flexibility during business model innovation.

The thesis is structured as follows. In the subsequent section mainly cited antecedents of strategic flexibility in the literature are identified. I find that strategic alliances affect organizational strategic flexibility, but the relationship is more complicated that generally assumed, especially in the context of business model innovation. In section three, the data collection method and analytical techniques are discussed. The results are presented subsequently. I find that resource access and trust in strategic alliances are positively related to strategic flexibility outcomes. Business model innovation positively moderates the relationship between trust and strategic flexibility. The findings are discussed in section five. Finally, the limitations of this study are discussed and opportunities for future research are presented.

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2. Literature review

This section starts with reviewing the literature on strategic flexibility. Next, the literature on strategic alliances is examined, to discusses how strategic alliances may affect strategic flexibility outcomes. Finally, the literature on business model innovation is reviewed to build hypotheses about possible moderating effects of business model innovation on the relation between strategic alliances and strategic flexibility.

2.1 Strategic flexibility

The term strategic flexibility has been generally used by strategy researchers to denote the organizational ability to respond advantageously to various demands from dynamic competitive environments (Aaker and Mascarenhas, 1984; Sanchez, 1995; Young-Ybarra and Wiersema, 1999). Definitions of strategic flexibility, however, vary from one context to another (Evans, 1991). Sanchez (1995) defines strategic flexibility, for example, in the context of product competition, as determined by the inherent flexibilities of the resources available to the organization, and the organizational abilities to allocate those resources to new courses of action. Similarly, Harrigan (1980) studies strategies for overcoming exit barriers in one industry, and entering another industry, wherein strategic flexibility is the ability to redeploy resources to this new industry. In contrast, in the context of environmental changes strategic flexibility involves a rather reactive capability (Grewal and Tansuhaj, 2001). In this context, strategic flexibility is defined as the ability to adapt to environmental changes (Aaker and Mascarenhas, 1984).

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flexibility is less valuable (Nadkarni and Narayanan, 2007). When organizations do not need to respond reactively to environmental changes, the cost of strategic flexibility to an organization may even offset the benefits (Grewal and Tansuhaj, 2001). In the contrary, empirical research show a positive relationship between strategic flexibility and performance when organizations are operating in dynamic industries (Nadkarni and Narayanan, 2007), or have to respond to environmental shocks such as economic crises (Grewal and Tansuhaj, 2001). Thus, in highly volatile markets strategic flexibility is a valuable organizational capability to increase performance.

Antecedents to strategic flexibility are mentioned in the literature, but little consensus exists regarding the conceptualizations (Combe et al. 2012). From a resource-based perspective, strategic flexibility depends on the available flexible resources to the organization, and the coordination flexibility to apply those resources to alternative courses of action (Sanchez, 1995). From this perspective organizations enhance strategic flexibility by building slack and liquid resources, and effectively allocating managerial attention to exploit the resources (Grewal and Tansuhaj, 2001; Hitt et al., 1998). Slack resources are those that are “in excess of the minimum necessary to produce a given level of organizational output” (Nohria and Gulati, 1996:1246). More slack resources allow organizations to generate more strategic options to respond to competitor strategies (George, 2005). Greater liquidity of resources allow them to be transformed to alternative forms at minimal cost (Jones and Ostroy, 1984).

Other researchers emphasize on the importance of management cognition for developing slack and liquid resources that allow the generation of strategic options (Combe and Greenley, 2004). Nadkarni and Narayanan (2007), for example, refer to management cognition in the form of strategic schemas. Strategic schemas are the lenses that strategic decision-makers use to interpret data and translate into actions (Huff, 1982). The complexity

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of strategic schemas promotes strategic flexibility as it accommodates a diverse set of alternative strategy solutions in strategic decision-making (Nadkarni and Narayanan, 2007). Likewise, cognitive structures may negatively impact strategic flexibility by limiting decision-makers thinking and blinding them from innovative options (Sharfman and Dean Jr, 1997). Thus, according to these researchers, decision-makers vary in the ability to generate strategic flexibility.

Finally, the literature cites antecedents to strategic flexibility related the organization’s formal structure (Bock et al., 2012). Modularity in product and organization design, for example, reduces the need for managerial coordination and makes loosely coupled organization structures possible. Loosely coupled organization structures reduce the cost and complexity of adaptive coordination, and improve strategic flexibility capabilities to respond to changes in the environment (Sanchez and Mahoney, 1996). Similarly, simplifying organization structures enable managers to focus on recognizing opportunities from changing environments (Ocasio, 1997). In contrast, reconfiguration of existing activities does not improve managerial focus, and has a negative effect on strategic flexibility (Bock et al., 2012; Nadkarni and Narayanan, 2007). Structural mechanisms such as alliances also affect the strategic flexibility of organizations (Young-Ybarra and Wiersema, 1999). Strategic alliances may support flexibility as they provide access to resources of other organizations, but may also burden the organization with complex coordination costs.

2.2 Strategic alliances

A strategic alliance is a “flexible organizational mode” that allow organizations to combine their strengths in order to increase mutual performance (Mody, 1993:512). Motivations for creating strategic alliances have mainly emerged along two perspectives.

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Firstly, researchers that adopt a resource-based perspective argue that strategic alliances provide organizations access to valuable resources (Das and Teng, 2000; Harrigan, 1988; Varadarajan and Cunningham, 1995). Secondly, researchers adopting a transaction cost economics rationale, suggest that alliances allow to minimize transaction costs between the organizations involved (Dyer, 2002; Gulati, 1995; Judge and Dooley, 2006). These traditional approaches, however, are rather static, in which the importance of relationships is ignored (Nooteboom, 1996). Accordingly, other researchers therefore argue that the social network in which the organization is embedded motivates the creation of strategic alliances (Goerzen, 2007; Ring and Van de Ven, 1994; Saxton, 1997). In this perspective, trust is often argued as the “magic ingredient” for alliance relationships (Young-Ybarra and Wiersema, 1999:439).

2.2.1 Access to resources via alliances

The resource-based view (RBV) argues that organizations that possess valuable resources have relatively superior performance (Barney, 1991; Peteraf, 1993; Wernerfelt, 1984). Valuable resources are resources that are rare, imperfectly imitable and non-substitutable (Barney, 1991). These resources are imperfectly mobile (Peteraf, 1993), and cannot be effectively acquired in the market. Instead organizations exists to create, recombine, and transfer certain types of resources (Kogut and Zander, 1992). Valuable resources are then often inseparable from other resources in organizations (Chi, 1994). Hence, strategic alliances are used as organizational modes to access valuable resources that are that are otherwise unavailable to the organization (Eisenhardt and Schoonhoven, 1996). As a result, strategic alliances improve the strategic position of organizations as they allow to access other organizations’ resources that are valuable for realizing competitive advantages (Das and Teng, 2001, 2000; Ireland et al., 2002; Varadarajan and Cunningham, 1995). For

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example, the resources can be used develop an organization’s own resources by combining them with resources of the other organization (Das and Teng, 2000; Varadarajan and Cunningham, 1995).

In addition to access to existing resources, organizations often engage in strategic alliances with the intent to learn from their alliance partners by experimenting with new technologies and products (Hamel, 1991; Lei et al., 1996; Mody, 1993). For example, international strategic alliances allow the organization to learn from the alliance partner how to compete in foreign markets (Barkema et al., 1997; Parkhe, 1993). Likewise, strategic alliances provide the organization the financial needs, knowledge and experience to develop of new technologies and skills (Hamel et al., 1989; Teece, 1992). Hence, not only pooling the existing resources of organizations but also the potential to learn from other organizations motivates creating strategic alliances.

In short, from a resource-based perspective the rationale for entering into a strategic alliance is it to exchange, combine, and develop valuable resources with other organizations when these resources cannot be efficiently obtained through market exchanges. Such resources, provide the slack (George, 2005), and improve the organization’s ability the develop new knowledge and skills to change the core competences of the organization (Hitt et al., 1998), create new resources (Ireland et al., 2002) and technologies (Teece, 1992), and to compete in new markets (Barkema et al., 1997). Therefore, I expect that, access to resources of other organizations involved in the strategic alliance will enhance the organization’s strategic flexibility.

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2.2.2 Negotiation costs in the alliance

In the transaction cost economic (TCE) perspective, all economic activities are considered as transactions. The costs of these transactions are influenced by the frequency of the transaction, the uncertainty held in those transactions, and the asset specificity of the transactions (Williamson, 1979). Although TCE argues choosing the governance mode that minimizes the sum of production and transactions costs, its application has emphasized the importance of the costs of the transaction rather than the production costs (Leiblein, 2003). Accordingly, alliances are preferred “when the transaction costs associated with an exchange are intermediate and not high enough to justify vertical integration” (Gulati, 1995:87). Thus, from a transactions cost rationale, strategic alliances are formed the minimize transaction costs (Jarillo, 1988).

While transaction costs are generally influenced by multiple aspects (Williamson, 1979), in an exchange relationship these costs are primarily determined the costs of negotiating (Artz and Brush, 2000). Other researchers even argue that the costs of negotiating are equivalent to transaction costs between two parties (Demsetz, 1988). Negotiating costs are those costs that arise necessary for an exchange. These costs depend on the amount of time needed for the negotiations (Anderson and Narus, 1990). That is, negotiation costs are higher, when the negotiations between two organizations within a strategic alliance require more time. Thus, as negotiations cost increase, the ability of the organizations to quickly adapt to changing conditions declines (Artz and Brush, 2000). Therefore, I expect that the more costly the negotiations between the organizations involved in the strategic alliance are, the less are the strategic flexibility capabilities of the organization.

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2.2.3 Trust between the organizations in the alliance

The resource-based and transaction costs rationales, however, have been criticized for not considering adequately the social context within which the strategic alliances are embedded (Nooteboom, 1996; Saxton, 1997). According to those researchers, it is not the access resources or the ability to minimize transaction costs per se, but the characteristics of the relationship between organizations, that motivates creating strategic alliances. In other words, rather than viewing each alliance as a separate transaction, researchers taking this approach, focus on the importance of the relationship between the organizations involved. Accordingly, several researchers argue the most importance of trust as element of a valuable relationship (e.g. Ariño and Ring, 2000; Das and Teng, 2001; Zaheer et al., 1998; Zaheer and Venkatraman, 1995).!

Trust, however, is difficult concept to study as it has been conceptualized in many different contexts and at different levels (Langfield-Smith and Smith, 2003). The study of trust has its roots in psychology, wherein the focus is on trust within individuals. On an inter-personal level, trust can be defined as having positive attitudes towards the other in a risky situation (Gambetta, 1988). Although the individuals working at an organization may change over time, their roles and routines likely remain the same. Trust then may reside within these roles and routines of the organization (Ring and Van de Ven, 1994). Extending Gambetta's (1988) definition of trust to the inter-organizational level, it refers to the extent to which one organization has a positive expectation regarding the other organization’s motives and reliability in relationships involving risk (Das and Teng, 1998; Hutt et al., 2000; Judge and Dooley, 2006).

Sako (1998) identifies three types of trust relevant in inter-organizational relationships: contractual trust, competence trust and goodwill trust. Contractual trust reflects

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the extent to which is expected that the other organization will strive to fulfil its obligations.

Competence trust is the perception that the other organization has the capability to perform according to the specified agreement or contract. In contrast, goodwill trust is the expectation the other organization has the intention to perform in accordance with those agreements (Nooteboom, 1996). In other words, inter-organizational trust is determined by the reliability, predictability, and fairness perceptions regarding the other organization (Zaheer et al. 1998). That is, inter-organizational trust increases the expectation that both organizations will (1) fulfil their obligations, (2) behave in a predictable manner, and (3) will not act opportunistically.

Trust may improve strategic flexibility in various ways. Firstly, trust may directly affect the strategic flexibility capabilities of an organization. That is, building trust in itself is a source of competitive advantage (Barney and Hansen, 1994).Once a strong form of trust in alliance is build and established, organizations experience performance outcomes that exceed what the organization would achieve had they acted solely in their own best interest (Anderson and Narus, 1990; Kanter, 1994; Mjoen and Tallman, 1997). Organization in high-trust relationships, for example, might not require a quality inspections for supplied goods, thus decreasing monitoring costs (Sako, 1998). Consequently, it is argued that trust between organizations in a strategic alliance is positively related to the ability to adjust advantageously to changes in the environment (Doz, 1996; Young-Ybarra and Wiersema, 1999). Therefore I expect that inter-organizational trust between the organizations in the strategic alliance is positively related to strategic flexibility outcomes.

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2.2.4 Trust as increasing access to resources

Trust may also have an indirect effect on strategic flexibility through greater access to valuable resources. Organizations are more willing to make asset specific investments under conditions of high trust in the relationship, as there is less expectation that the other party will fail to perform on its obligations (Sako, 1998). Research found evidence that greater relation-specific assets are positively related to alliance performance (Dyer, 1996; Dyer and Singh, 1998). In addition, trust promotes organizational learning (Sako, 1998). That is, organizations in high-trust relations are likely to have greater opportunities to learn from the other organizations in the alliance (Dodgson, 1993). Organizations in high-trust relations, for example, are more likely to exploit opportunities to the mutual benefit of both organizations, which would otherwise not have been exploited had transactions depended solely on contracts.

Thus, as trust promotes asset specific investments (Sako, 1998), and improves the potential for organizations to learn from their alliance partners to create new products (Ireland et al., 2002) and technologies (Teece, 1992), and exploit new markets (Barkema et al., 1997), the value of the pool of resources available in the strategic alliance will be greater. From hypothesis 1, I expect that resources access is positively related to strategic flexibility. Therefore, I expect that trust has an indirect positive effect on strategic flexibility through access to more valuable resources.

Hypothesis 3b: Resource access positively mediate the relation between trust and strategic flexibility

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2.2.5 Trust as reducing costs of negotiation

Finally, trust may have an indirect effect on strategic flexibility through a decrease in negotiation costs (Sako, 1998; Zaheer et al., 1998). Trust has emerged as an important way of reducing opportunism. When trust is present, there is the expectation that the partner will be cooperative and consider the mutual benefits of both organizations. As trust counteracts the fear of opportunistic behaviour, people may not choose to rely upon detailed contracts to ensure predictability. There is less need for slow and costly detailed contracts (Nooteboom, 1996). As a result, agreements are reached more quickly and easily as organizations are more willingly to arrive at a “meeting of the minds” (Zaheer et al., 1998:144). Additionally, trust reduces the abuse of unequal bargaining power (Langfield-Smith and Smith, 2003). Hence, negotiations between the organizations are less costly as a result of higher trust (Zaheer et al., 1998). As negotiation costs decrease, the ability of the organizations to quickly adapt to changing conditions increases (Artz and Brush, 2000). Thus, I expect that trust also indirectly improve strategic flexibility through a decrease of negotiation costs.

Hypothesis 3c: Negotiation costs positively mediate the relation between trust and strategic flexibility

2.3 Business model innovation

As prior research has shown, strategic alliance outcomes are less clear in the context of business model innovation (Bock et al., 2012). Thus, the hypothesized relations between the characteristics in strategic alliances and strategic flexibility, however, may be affected by the degree in which the organization is innovating its business model. Developing

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hypothesizes for possible moderating effects of business model innovation, however, requires having a solid understanding of what a business model is, as it turns out there is no consistent definition in the literature (Schneider and Spieth, 2013). This sub-section therefore starts with reviewing what a business model is.

2.3.1 Business models

Since the mid-1990s the business model has received growing attention from both academics and practitioners (George and Bock, 2011; Zott et al., 2011). Despite this increasing interest, there is no consensus on what a business model actually is (Lambert and Davidson, 2013; Morris et al., 2005; Zott et al., 2011). Most attempts to define to business model fail to clearly distinguish the concept from business strategy (Magretta, 2002; Osterwalder et al., 2005; Schneider and Spieth, 2013). The business model has accordingly been defined as the “statement of how a firm will make money and sustain its profit stream over time” (Stewart and Zhao 2000:290), “stories that explain how enterprises work” (Magretta 2002:97), “heuristic logic that connects technical potential with the realization of economic value” (Chesbrough and Rosenbloom 2002:529), and the “logic, data and other evidence that demonstrates how a business creates and delivers value to customers” Teece 2010:173). Hence, the different definitions leave room for multiple interpretations as they only partially overlap (Schneider and Spieth, 2013). Researchers tend to use definitions that suit the focus of their studies best (Zott et al., 2011). As a result, it is difficult to reconcile the findings with each other and build upon prior work.

Osterwalder et al. (2004, 2005) identify the most frequently mentioned elements of business models in the literature to assist a conceptualization of the business model. These most cited elements are “value proposition, target customer, distribution channel,

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relationship, value configuration, capability, partnership, cost structure and revenue model” (Osterwalder et al., 2004:43). The concept thus, provides rather a holistic perspective that allows managers to take an integrated view on their organization’s activities, then a focus on a single function such as the product market strategy (Amit and Zott, 2010; Schneider and Spieth, 2013). In other words, the business model entails more that just explaining how the organization works and how it makes money.

2.3.2 Business model innovation

Researchers have generally recognized the "capacity for reinventing your business model" (Hamel and Välikangas, 2003:53) as a means to achieve superior performance for organizations exposed to high environmental volatility (Baden-Fuller and Morgan, 2010; Chesbrough, 2007; Chesbrough and Rosenbloom, 2002; Lindgardt et al., 2009; Teece, 2010). In response to this challenge, interest in business model innovation has significantly increased in recent years (George and Bock, 2011; Zott et al., 2011). However, due to lack of a consistent definition of the business model concept itself (Lambert and Davidson, 2013; Morris et al., 2005; Zott et al., 2011), the relatively young field of research on business model innovation cannot build on an well-defined theoretical foundation (Schneider and Spieth, 2013). Furthermore, the existing literature lacks systematic and large-scale studies on the business model innovation concepts (Bock et al., 2010).

At a rather abstract level, business model innovation has been defined as the "process of designing a new, or modifying" the business model (Amit and Zott, 2010:2). According to Lindgardt et al. (2009) it is required to change at least two elements of business model to be a innovation. Bucherer et al. (2012) argue that during business model innovation, the organization aims to carefully change the core business logic. Business model innovations are

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key means to commercialize new ideas and technologies (Chesbrough and Rosenbloom, 2002). Moreover, some researchers even argue that as soon as products and processes are stable, business model innovations are the only the remaining option for organizations to innovate (Boutellier et al., 2010). In other words, when product or process innovations can be imitated at minimal costs, business model innovations offer a unique potential source of competitive advantage.

2.2.3 Moderating effects of business model innovation

As business model innovation involves commercializing new technologies, products and markets (Chesbrough and Rosenbloom, 2002), one would expect that access to resources through the strategic alliance partner (Das and Teng, 2000; Varadarajan and Cunningham, 1995) is more valuable during business model innovation. From hypothesis 1, increased access to resources is expected to improve the organization’s ability to develop new technologies, products and markets (Barkema et al., 1997; Ireland et al., 2002; Teece, 1992) that are required for business model innovation (Bucherer et al., 2012; Leih et al., 2014). Therefore, I expect that increased access to new knowledge, skills, and capabilities required for change, provided by the alliance partner, is of more value to the organization during high levels of business model innovation. In other words, the higher degree of business model innovation gives to the organization more opportunities to advantageously use the increased access to resources to change and enhance strategic flexibility.

Hypothesis 4a: The degree of business model innovation positively moderates the relationship between resource access and strategic flexibility.

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When organizations are innovating their business model to explore new opportunities, the organization is challenged with unfamiliar and unpredictable circumstances (Amit and Zott, 2010). As uncertainty increases, the organizations in the alliance may have different perceptions about the future (Artz and Brush, 2000; Jones and Ostroy, 1984). In addition, organizations that are innovating their business models likely have more conflicting future goals. Consequently, the organizations probably desire different contract terms. Thus, when organizations innovate their business model, the desire to negotiate about the contract terms increases (Nooteboom, 1996). As the negotiations require more time they and become more costly, the ability quickly adapt to changing conditions declines (Artz and Brush, 2000; Zaheer et al., 1998). Therefore, I expect that:

Hypothesis 4b: The degree of business model innovation negatively moderates the relationship between negotiation costs and strategic flexibility.

Sosna et al. (2010:385) argue that business model innovation can be seen as an initial experiment followed by continuously improving the business model based on a “trial-and-error learning approach involving all echelons of the organization”. Thus, there are more opportunities for organizational learning during high levels of business model innovation. As trust accelerates organizational learning abilities (Sako, 1998), I expect that trust has a greater ability improve strategic flexibility capabilities, as a result of more opportunities to learn, during high levels of business model innovation.

Hypothesis 4c: The degree of business model innovation positively moderates the relationship between trust and strategic flexibility.

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2.4 Research model

To summarize, this study hypothesize that resource access is positively (H1) and negotiations costs are negatively (H2) related to the strategic flexibility of an organization. Trust is expected to both directly (H3a) and indirectly, via increased access to resources (H3b) and decreased negotiation costs (H3c). Finally, the degree of business model innovation is expected to moderate the relationships between respectively resource access (H4a), negotiation costs (H4b), trust (H4c) and strategic flexibility. Figure 1 graphically illustrates the conceptual framework develop from the literature.

Figure 1: Conceptual framework

Business model innovation Resource access Negotiation costs Trust Strategic flexibility

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3. Data and method

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To test the hypotheses of this study, a survey methodology was used. This section starts with a description and discussion of the sample and data collection method. Next, the measures used for testing the hypotheses and their reliabilities are examined. Finally, this section outlines the statistical analyses that were performed to obtain the results for this study. !

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3.1 Sample and data collection

Using a survey methodology for testing the hypotheses is consistent with similar studies on performance and flexibility in inter-organizational relationships (e.g. Ariño, 2003; Artz and Brush, 2000; Bock et al., 2012; Young-Ybarra and Wiersema, 1999). The survey was designed in collaboration with three other students studying organizational innovation and flexibility. As a result, the survey consisted of more measurement constructs than are being used in this study. Combining the constructs and sending it as one survey contributed to the sample size. However, the greater length of the survey may also have resulted in more incomplete recordings. The survey was created using online survey software. Web-based surveys have the advantages of being time and costs efficient (Wright, 2005), allowing to focus on other parts. For similar reasons, invitations to the online survey were sent electronically to our private and business networks.

Surveys were sent to 700 people working at Dutch organizations. The survey was translated from the original English version to Dutch using the back-translation technique to ensure that the original meaning was preserved. Data were collected in April 2015. 239 responses were received (response rate was 34%). We pretested the instrument to examine

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testing resulted in setting a lower limit to complete the survey of 10 minutes in order to consider the response reliable. After eliminating responses that were completed in less then 10 minutes, and those with incomplete information, the final sample size was 112. In this final sample 61 (54%) organizations were involved in a strategic alliance.

In answering questions about strategic alliances, respondents were asked to select the most important collaboration. This collaboration could be any kind agreement with another organization in the pursuit of a common goal. It should be noted that this is a broader definition of strategic alliances than most researchers on the topic use (e.g. Ariño and Ring, 2000; Young-Ybarra and Wiersema, 1999). Ariño and Ring (2000:2), for example, define a strategic alliance as a “formal agreement between two or more business organizations to pursue a set of private and common interests through the sharing of resources in contexts involving uncertainty over outcomes”. The broader definition, however, was used in order to get a respectable sample size.

The informants in the full sample were employed at level of middle management (27%), line management (10%), employee (39%), or entrepreneur (21%). The average organizational tenure was 11.1 years (SD = 9.6). 35% of the respondents had worked for less than 5 years at the organization. As a result, this study recognizes that informants may had insufficient knowledge about the organization, for answering questions about organizational innovation and flexibility,as a limitation. Kortmann et al. (2014) suggest relying on top-level executives, as informants as they are possess the required knowledge. Likewise, researchers such as Kumar et al. (1993) and Young-Ybarra and Wiersema (1999) suggest that a single reliable informant is preferred over multiple respondents with varying knowledge about the phenomenon. However, due to limited time and resources, this study does not add informant competency as a constraint. The reliability of the respondents as a result of insufficient knowledge about the phenomena was recognized as a limitation.

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The full sample presented a broad diversity in organization size, ranging from small (1 to 5 employees) to large (more than 5,000 employees), but oversampled organizations organization in the financial (42%) and industrial sector (26%). No significant differences between the full sample and the subsample with strategic alliances were found. Appendix A compares descriptive statistics on the organizations in the samples, as well as the respondents’ employment level, and average organizational tenure.

3.2 Measures

Established measurement items in peer-reviewed literature were adopted to increase the validity of the measures. The measure for business model innovation was an exception. Data were screened for incorrectly entered data, missing values, outliers and normality, prior to computing scale means. Kolmogorov-Smirnov and Sharpiro-Wilk tests for normality indicate the data were non-normal. The descriptive statistics for skewness and kurtosis of the individual items were examined to determine the degree to which the data was non-normal. In general, both skewness and kurtosis were close to zero, indicating there were only slight deviations from normality. Exceptions were two items measuring resource access. In both cases, the items were substantively negatively skewed. Accordingly, these items were transformed to normalize their distributions.

Item-to-total correlations were examined to determine if the individual items were meaningfully correlated with the overall factors. Using the cut-off suggested by Saunders et al. (2011) only those items with loading above .30 were included in the final scale. The content validity of each multi-item measure was also considered in deciding whether to drop a particular item. Internal-consistency reliability estimates were assessed through Cronbach’s Alpha. Saunders et al. (2011) suggest that alpha coefficients above .70 constitute an

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acceptable level of reliability. The sample survey items and the estimates for Cronbach’s alphas of the measures are shown in Appendix B.

Strategic flexibility. Six questionnaire items developed by Zhou and Wu (2010) were used to measure strategic flexibility. The items were based on Sanchez’s (1995) study on the flexible allocation and coordination of resources in response to changing environments. Respondents were asked to rate statements how the organization responds to changes in the environment, on 7-point Likert scales ranging from 1, ‘strongly disagree’ to 7, ‘strongly agree’. The corrected item-to-total correlations were all above the 0.30 cut-off. Consequently, the unweighted individual item scores were summed to create the dependent variable. Cronbach’s Alpha for this scale was .88.

Resource access. A list of strategic goals for the strategic alliance (Ariño, 2003) was adapted to create the measure for resource access via strategic alliances. Respondents were asked to rate, on seven-point Likert-type scale, to what degree the partnership enables their organization to gain access to new markets, technologies and skills. The item-to-total correlation of one individual item was below the .30 cut-off. Possibly respondents had misinterpret the item, to what degree the partnership gained access to markets in the same industry. Dropping this item, while keeping the items to what degree to partnership gained access to markets in another industry, technologies and skills, resulted in an estimated reliability above the suggested lower limit. Cronbach’s Alpha was .78.

Negotiation costs. Five items developed by Artz and Brush, (2000) were used to measure negotiation costs. Respondents were asked to rate statements about the amount of preparation time for the negotiations, the amount of time spent in actual negotiation, the number of bargaining sessions, and the amount of conflict in the relationship. In the original study a 5-point Likert scale was used. However, to remain consistent the scale was adapted to a 7-point Likert scale, ranging from 1, ‘strongly disagree’ to 7, ‘strongly agree’. One item

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was dropped due to an item-to-total correlation below the recommended .30 cut-off. As the item was a reverse-coded item, this indicates there was possibly acquiescence bias in the data. The other four items were summed to create the measure for negotiation costs. Cronbach’s Alpha was .72.

Trust. To measure trust, four items developed by Young-Ybarra and Wiersema (1999) were used. Respondents were asked to rate, on 7-point Likert scale, statements reflecting their organization’s trust in the other organization in the strategic alliance. These items represented the three elements of inter-organizational trust identified by Zaheer et al. (1998) (i.e. reliability, predictability, and fairness). One item was poorly correlated with the overall factor. Again this was a reverse-coded item.The three remaining items were merged to create the measure for trust. Cronbach’s Alpha was .83.

Business model innovation. Six items developed by a former student at the University of Amsterdam were used to measure the degree business model innovation. These items were based on the research of the Boston Consultant Group (Lindgardt et al., 2009). Respondents were asked to rate statements, on 7-point Likert scales, about whether the organization’s target segment, product or service offering, value chain, costs structure, revenue model and way of deploying and developing people had significantly changed in the last 5 years. The individual item scores were summed to create one measure of business model innovation. Cronbach’s Alpha was .83.

Organization size. Organization size may affect strategic flexibility and innovation efforts (Aspara et al., 2010; Damanpour, 1992; Mabert et al., 2003). For example, small organizations have more output flexibilities, as a result of their more flexible cost structures, than their larger competitors (Fiegenbaum and Karnani, 1991). On the contrary, larger organization size is positively related with organizational innovation (Pierce and Delbecq, 1977) as size increases differentiation and the availability of resources (George, 2005).

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Therefore, I control for organization size in the research model. Organization size was defined by the number of employees working in the organization.

Industry. Different industries may present organizations with different external pressures for innovation. To control for the effect of external pressures industry was added as a control variable. Respondents were asked to select the industry affiliation of their organization based on GICS codes. The industrial and financial sectors represented more than two-third of the organizations in the sample. Therefore, I include industrials and financials as dummy variables.

Innovation rate. Changes in external environment may force organizations may be

forced organizations to innovate and develop strategic flexibilities (Voelpel et al., 2005). To control for exogenous drivers, a control variable measuring the overall innovation rate of the industry was included. For this measure respondents were asked to assess the rate of innovation of new operating processes and products or services in the industry in which their organization is operating.

3.3 Analysis

Statistical analyses were performed using the statistical software program SPSS. To test the hypothesized direct effects of resource access, negotiation costs, and trust, on strategic flexibility, hierarchical regression analyses were performed. In the first step, only the control variables were included in the model. If the model was statistically different from zero, R2 indicates what percentage of the variance strategic flexibility was explained by the control variables. In the second step resource access, negotiation costs, and trust, were added. Consequently, if the model was statistically different from zero, the change in R2 showes what percentage of the variance strategic flexibility was explained by resource access,

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negotiation costs, and trust, after controlling for organization size, industry and innovation rate.

To test the mediating effects of resource access and negotiation costs on the relation between trust and strategic flexibility, the macro PROCESS for SPSS was used (Hayes, 2012). PROCESS includes a model to analyze the mediating effect of a variable (M) on the relationship between one independent variable (X) and one dependent variable (Y). The model shows the total effect of the independent variable on dependent variable, separated in a direct effect and indirect effect through the mediator. The significance of the indirect effect was firstly tested used the causal step method, as proposed by Baron and Kenny (1986). Using this approachthere were significant mediation effects if the criteria in all the following steps were met: (1) X significantly contributed to the variance in Y, (2) X significantly contributed to the variance in M, (3) M significantly contributed to the variance in Y when controlling for X, and (4) the effect of X on Y decreased substantially when M was entered simultaneously with X as a predictor of Y.

Alternatively, bootstrapping procedures were used to test the significance of the indirect effects. This method has the advantage that it does not require the assumption of normality of the sampling distribution (Hayes, 2012). Bootstrapping is a method that resamples the data set to estimate the indirect effect. By repeating the resampling of the data sets thousands of times, empirical approximations of the sampling distributions of the direct effects were developed and used to create confidence intervals for the indirect effects (Preacher and Hayes, 2008). If the confidence interval excluded zero, the indirect effects were statistically different from zero.

PROCESS, however, did not include a model for testing the mediation effects of one variable on the relationship between two independent variables and one dependent variable. Consequently, the mediation effects of resource access and negotiation costs on the

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relationship between trust and strategic flexibility were analyzed in two separate models. In each model one of the variables was entered as the independent variable, while the other variable was entered as a control variable. In this way, the mediation effect of one independent variable was tested while controlling for effects of the other variable.

PROCESS was also used to test the moderation effects of business model innovation, on the relationship between resource access, negotiation costs, and trust, and the dependent variable strategic flexibility. The moderation model in PROCESS shows the coefficient for the products of the independent variables and the moderator, together with its test of significance (Hayes, 2012). If this coefficient was statistically different from zero, there was a significant moderating effect. In addition, PROCESS also shows the proportion of the total variance in strategic flexibility uniquely attributable to the interaction and its test of significance. PROCESS did not include a model for testing the moderation effect of one variable on the relationship between three independent variables and one dependent variable. Therefore the moderation effects were analyzed in three separate models while controlling for effects of the other variables.

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4. Results

The results of the statistical analyses are presented in this section. However, first, a correlation analysis is examined to test for the regression assumptions of the models. Thereafter results of the hierarchical regression, which shown the direct effects of resource access, negotiation costs, and trust in alliance relationship, on the strategic flexibility of the organization, are presented. Following, the results of the mediation effects resource access and negotiation costs on the relationship between trust and strategic flexibility are presented. Finally, results of the hypothesized moderating effects of business model innovation are reviewed.

4.1 Correlation analysis

Table 1 shows the means, standard deviations, correlations, and estimated reliabilities of the measures used in this model. All the measures were close to normally distributed. The maximum variance inflation factor between any two measures in the model was 2.17. This statistic is well below the guideline of 10 which Neter. et al. (1990) suggest as indicative of a multi-collinearity problem. Thus, the correlations show no particular strong associations among the measures that would indicate multi-collinearity. Resource access was significantly positively correlated with strategic flexibility (r = .39, p < .01). This is conforming the hypothesized direction. Trust was also, conforming the hypothesized direction, significantly positively correlated with strategic flexibility (r = .38, p < .01). Finally, business model innovation (r = .18, p < .05) and the external rate of innovation (r = .19, p < .05) were significantly correlated with strategic flexibility. Negotiation costs were not significantly

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V ari abl es M SD 1 2 3 4 5 6 7 8 9 rga ni za tion s iz e 3.54 1.59 -tri al s .26 .44 -.26** -ina nc ia ls .42 .50 .64** -.50** -tion ra te 4.82 1.07 .14 -.05 .16 -tra te gi c fl exi bi lit y 4.26 1.01 .00 .19* -.12 .25* (.88) sourc e a cc es s 4.63 1.18 .08 .1 1 .1 1 .18 .39** (.78) egot ia tion c os ts 3.75 1.12 .14 .06 .06 .24* -.10 .02 (.72) T rus t 5.00 1.06 .02 .08 -.10 .09 .38** .27* -.12 (.83) ine ss m ode l i nnova tion 4.50 1.21 .17* .07 .05 .30** .18* .17 .23* .00 (.83) e: N = 61. Re lia bi lit ie s a re re port ed a long t he di agona l. la tion i s s igni fi ca nt a t t he .05 l eve l (one -t ai le d). la tion i s s igni fi ca nt a t t he .01 l eve l (one -t ai le d). ab le 1 : D es cri pt ive s ta tis tic s a nd c orre la tions

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4.2 Direct effects

Hierarchical regression was performed to examine the abilities of resource access, negotiation costs, and trust in strategic alliances to predict the degree of organizational strategic flexibility. The statistical results are presented in Table 2. In the first step of the hierarchical multiple regressions only the control variables organization size, industry affiliation and the rate of innovation were entered as predictors for strategic flexibility. This model was statistically significant F (4, 57) = 2.83, p < .05 and explained 17% of the variance in strategic flexibility. After entry of the variables for resource access, negotiation costs, and trust, in step 2 the total variance explained by the model as a whole was 34% F (7, 54) = 3.02; p < .01. Thus, the introduction of resource access, negotiation costs, and trust, in strategic alliances explained additionally 17% of the variance in strategic flexibility, after controlling for organization size, industry affiliation and the rate of innovation (ΔR2 = 17, F (3, 54) = 4.71, p < .01).

Table 2: Hierarchical Regression Model of Strategic Flexibility

R R2 _ΔR2 _B _SE _β Step 1 .41 .17* Organization size .08 .10 .12 Industrials .52 .32 .24 Financials -.27 .36 -.13 Innovation rate .30 .12 .32* Step 2 .59 .34** .17** Organization size .03 .09 .05 Industrials .54 .30 .25 Financials .00 .34 .00 Innovation rate .26 .11 .28* Resource access .20 .10 .24* Negotiation costs -.14 .11 -.16 Trust .24 .11 .25*

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From the final model of the hierarchical regression, presented as step 2 in Table 2, a number of significant direct relationships were found. First, resource access via alliances were positively related to strategic flexibility (β = .24, p < .05). Thus, hypothesis 1 was supported. Second, inter-organizational trust was positively related to strategic flexibility (β = .25, p < .05). Hypothesis 3a was supported. Finally, the control variable innovation rate was positively related to strategic flexibility (β = .28, p < .05). No significant relation was found between negotiation costs and strategic flexibility. Hypothesis 2 was not supported.

!

4.3 Mediation effects of resource access and negotiation costs

First,a mediation analysis was performed to examine the mediation effect of resource access on the relationship between trust and strategic flexibility. Figure 2 shows the path diagram illustrating this meditational relationship. As shown in Table 3, the total effect of trust on strategic flexibility was statistically different from zero (b = .30, p < .05). This can be separated into the direct effect (b = .24, p < .05), and the indirect effect through increased resource access (.27)(.21) = .06. To test for significance of this indirect effect, initially the causal step strategy, as proposed by Baron and Kenny (1986), was used. In step 1, trust contributed significantly to the variability in strategic flexibility (b = .24, p < .05). In step 2, trust contributed significantly to the variability in resource access (b = .27, p < .05). In step 3, resource access significantly contributed to the variance in strategic flexibility when controlling for trust (b = .21, p < .05). Finally, the effect of trust on strategic flexibility decreased from .29 to .24 when resource access was entered simultaneously with trust as a predictor of strategic flexibility. Thus, from the causal step strategy, resource access was a significant mediator for the relationship.

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In addition, the significance of the indirect effect was tested using bootstrapping procedures. Unstandardized indirect effects were computed for each of 5,000 bootstrapped samples. The bootstrapped unstandardized indirect effect was .06, and the 95% bias-corrected bootstrap confidence interval ranged from .01 to .22. As this confidence interval excluded zero, the indirect effect was statistically different from zero. Thus, in both methods the indirect effect was significant. Resource access had a significant mediation effect on the relation between trust and strategic flexibility. Hypothesis 3b was supported.

Second, a mediation analysis was performed to examine the mediation effect of negotiation costs on the relationship between trust and strategic flexibility. From Table 3, the total effect of trust on strategic flexibility, in this model, was statistically different from zero (b = .26, p < .05). The indirect effect of trust on strategic flexibility through negotiation costs was, however, not significant. From the second step in the causal step strategy,trust did not significantly account for the variability in negotiation costs (b = -.14, p > .20).Additionally, the 95% bias-corrected bootstrap confidence interval ranged from -.01 to .11. As this confidence interval excluded zero, the indirect effect was not statistically different from zero. Thus, both methods did not show significant indirect effect. Negotiation costs had no

† p < .10; * p < .05; ** p < .01; *** p < .001. Trust

Resource access

Strategic flexibility Figure 2: Standardized regression coefficients for the relationship between trust and

strategic flexibility as mediated by negotiation costs. The standardized regression coefficient between trust and strategic flexibility, controlling for resource access, is in parentheses.

.24* (.06*)

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significant mediation effect on the relation between trust and strategic flexibility. Hypothesis 3c was not supported.

! !

!

4.4 Moderating effects of business model innovation

The statistical results are summarized in Table 4. No support was found for a positive moderation effect of business model innovation on the relationship between resource access and strategic flexibility (b = .06, p > .20) and (∆R2 = .01, F(1, 51) = .50, p > .20). Hypothesis 4a was not supported. Similarly no significant moderating effect of business model innovation was found on the relationship between negotiation costs and strategic flexibility (b = .04, p > .20) and (∆R2 = .00, F (1, 51) = .22, p > .20). Hypothesis 4b was not supported. In contrast, there was a positive and significant (b = .18, p < .05) moderating effect of business model innovation on the relationship between trust and strategic flexibility. By adding business model innovation as a moderator between trust and strategic flexibility to the model, it explained a significant increase in the variance in strategic flexibility ∆R2_{= .04,} F(1, 51) = 3.48, p < .05.

Table 3: Mediation Effects of Resource Access and Negotiation Costs

Variables Model 1a _{Model 2}b

Total effect Trust on Strategic Flexibility .30* (.11) .26* (.11)

Direct effect Trust on Resource Access .27* (.13)

Direct effect Resource Access on Strategic Flexibility .21* (.10)

Direct effect Trust on Negotiation Costs -.14 (.13)

Direct effect Negotiation Costs on Strategic Flexibility -.14 (.11)

R2 _.34** _.34**

Note: N = 61. a_{Negotiation costs, organization size, financials, industrials, and innovation}

rate were included as control variables. b_{Resource access, organization size, financials,}

industrials, and innovation rate were included as control variables

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Figure 3 visualizes the interaction effect of business model innovation. The lines showing the mean, one standard deviation above the mean, and one standard deviation below the mean of the degree of business model innovation, represent respectively, moderate, relatively high, and relatively low degree of business model innovation in an organization. Among those organizations that had a relatively low degree of business model innovation (-1 SD), the level of inter-organizational trust within the strategic alliance had no effect on strategic flexibility outcomes (with conditional effect of .02, p > .20). But those organization that had a moderate (mean) and a relatively high (+1 SD) degree of business model innovation, those that had high levels of inter-organizational trust received higher levels of strategic flexibility (with conditional effects of .24, p < .05 and .46, p <.01, respectively). Thus, business model innovation was a significant moderator of the relationship between trust and strategic flexibility. Hypothesis 4c was supported.

Figure 3: Simple slopes of trust predicting strategic flexibility for 1 SD below the mean, the mean, and 1 SD above the mean of the degree businness model innovation.

3.5 3.8 4.1 4.4 4.7 5.0 !1# !0.5# 0# 0.5# 1# S tr ate gi c F le xi b ii ty

Standard Deviations of Trust

- 1SD on BMI Mean of BMI +1SD on BMI

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Table 4: Moderation Effects of Business Model Innovation

Variables Model 1 Model 2 Model 3

Constant 2.35 (1.91) 1.90 (1.83) 2.37** (.77) Main effects Resource access -.05 (.39) Negotiation costs -.31 (.39) Trust .24* (.11)

Business model innovation -.29

(.39) -.19 (.37) -.05 (.10) Interaction effects

Resource access × Business model innovation .06 (.08)

Negotiation costs × Business model innovation .04

(.09)

Trust × Business model innovation .18*

(.10) Control variables Resource access .22* (.11) .18† (.10) Negotiation costs -.14 (.11) -.17 (.11) Trust .23* (.12) .24* (.12) Size .03 (.09) .03 (.09) .04 (.09) Industrials .54† (.31) .52 (.31) .66* (.30) Financials .01 (.34) .00 (.34) .12 (.34) Innovation rate .28* (.12) .26* (.12) .27* (.11) R2 _.35** _.39** _.39**

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5. Discussion

This study integrated both resource-based and transaction costs arguments, as well as the presence of behavioural aspects in the form of trust, into a theoretical framework. Subsequently this framework was used to empirically test the effects of strategic alliances on strategic flexibility during business model innovation. The study confirms that resources access is related to strategic flexibility, but no significant relationship was found between negotiations costs and strategic flexibility. Trust is found to both directly, and indirectly improve strategic flexibility through enhanced access to valuable resources. Finally, the degree of business model innovation positively moderates the relationship between trust and strategic flexibility. The results are discussed in this section.

The results indicate that resource access provided by the partner organization in the strategic alliance is positively related to the strategic flexibility of an organization. This provides evidence that strategic alliances enable organizations to access valuable resources that are otherwise unavailable. These resources are not only the physical resources available, but can also be intangible such as experience and technological know-how. Resources of the organizations involved in the strategic alliance can be combined to develop new technologies, products, and markets. This will improve the organizational abilities to respond to changes in the environment and thus enhance strategic flexibility. This result is in support of the research-based rationale to create strategic alliances (Das and Teng, 2000; Eisenhardt and Schoonhoven, 1996) and those researchers who argued that strategic flexibility depends on the availability of resources (e.g. Grewal and Tansuhaj, 2001; Hitt et al., 1998; Sanchez, 1995).

Inter-organizational trust in the strategic alliance is positively related to the strategic flexibility of an organization. Trust directly improves strategic flexibility, as trust in itself is a

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source of competitive advantage. This finding is consistent with those of Doz (1996) who found that, as inter-organizational trust in alliances is build over time, organization have greater adaptabilities to compete in a dynamic environment, and Young-Ybarra and Wiersema (1999) who found that, inter-organizational trust will have a positive impact on the ability to adjust to changing environmental demands through modification or termination of the alliance.

Inter-organizational trust is also indirectly positively related to strategic flexibility through resource access. Trust promotes building relation-specific resources (Sako, 1998), which are more valuable to the organizations (Dyer, 1996). In addition, trust also accelerate organizational abilities to learn from their alliance partner to develop new resources (Sako, 1998). These will make available pool of resources in the alliance more valuable. It is likely that access to a more valuable pool of resources fosters the creation of superior adaptive capabilities.

In contrast to resource access and trust, this study expected to find a negative relation between negotiation costs and strategic flexibility. The results, however, did not indicate significant negative effect of negotiation costs on the strategic flexibility capabilities of an organization. One possible explanation is that although the negotiation costs in a strategic alliance of a manufacturer and its supplier is an effective measure for the total transaction costs between the two parties (Artz and Brush, 2000), it may be not for the strategic alliances in the sample of this study. That is, in contrast to a strategic alliance of manufacturer and its supplier, the strategic alliances in this sample referred to any kind of agreement with another organizations in the pursuit of a common goal. As a consequence, the transaction costs within the strategic alliances in this sample possibly included more than just the costs of negotiating. For example, the costs resulted from monitoring the partner performance relative to the contract and dealing with the violations of contractual commitments (Gulati, 1995).

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The results of the moderating variable analysis indicate that there was a positive moderating effect of the degree of business model innovation on the relationship between trust and strategic flexibility. Trust between organizations in a strategic alliance has greater abilities to improve strategic flexibility during high levels of business model innovation. Organizational abilities to learn how to adapt to environmental changes are greater in high-trust relationships (Sako, 1998). As business models can be seen as experiments (Sosna et al., 2010), it is likely that there are more opportunities to learn and build strategic flexibility capabilities during high levels of business model innovation.

The moderating effects of the degree of business model innovation on the relationships between resource access and strategic flexibility, and, negotiation costs and strategic flexibility, were not significant. Likely, negotiation costs is not an effective measure for transaction costs that the organizations in this sample encounter with their partners. A possible explanation for the insignificant moderating effect of the degree of business model innovation on the relationship between resource access and strategic flexibility is the following. While access to resources through the strategic alliance partner might be more valuable during high levels of business model innovation, the organization may be unable to exploit these existing resources to new opportunities. Alternatively, high levels of business model innovation may require managerial attention that would otherwise be used for generating strategic options to improve strategic flexibility (Bock et al., 2012; Sanchez, 1995).

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6. Conclusions

The findings address to Bock's et al. (2012) call for further research directed at understanding how strategic alliance characteristics affect strategic flexibility outcomes during business model innovation. The study recognizes the importance of trust while integrating both resource-based and transaction costs rationales for creating strategic alliances, into a theoretical framework to measure strategic flexibility during business model innovation. Findings indicate that access to resources and inter-organizational trust in strategic alliances both improve strategic flexibility capabilities of the organization. Trust has a greater ability to improve strategic flexibility during high levels of business model innovation.

This is the first study to examine the role of trust between organizations in strategic alliances on strategic flexibility outcomes during business model innovation. Accordingly, the study also responds to the call (Schneider and Spieth, 2013) for more research on the effects business model innovation.

6.1 Limitations

While the findings of this study provide valuable insights, this study is not without limitations. First, there are several limitations in the nature of the sample that should be considered in the interpretation of the results. The sample size was relatively small. To test the individual predictors Green (1991) suggests that 100 + the number of predictors is the minimum acceptable size. The effective sample size was probably even smaller as the survey was not restricted to a single respondent of an organization. That is, multiple respondents within the same organization could have taken the survey while this study threats the inputs

The effects of strategic alliances on strategic flexibility during business model innovation