The Influence of Cross-National Distance on the Governance Mode Choice of International Alliances: The Moderating Role of Repeated Ties

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The Influence of Cross-National Distance on the

Governance Mode Choice of International Alliances:

The Moderating Role of Repeated Ties

T.L. Luijben

Supervisors

University of Groningen - Dr. M.J. Klasing Newcastle University - Dr. S. Reissner

ABSTRACT

Cross-national distance describes the differences between countries. Due to globalisation these differences bring issues that firms need to consider. In this study, I will test the relationship between four distance measures, namely, institutional, cultural, economic and geographical distance with the governance mode choices of international alliances. This effect does not operate in a vacuum, therefore I also analyse the effect of a moderator to test how repeated ties impacts the relationship. I used a sample of 1345 international alliances from 46 different countries from 2000-2019. The results show that alliances with high institutional distance lead to more hierarchical governance modes and geographical distance leads to less hierarchy. Furthermore, the results on repeated ties show that institutional and cultural distance lead to less hierarchy and economic distance to more hierarchy. My research advances the understanding of distance in relation to governance mode choices of international alliances and provides more depth by including repeated ties as a measure of trust.

Keywords: cross-national distance; governance mode; international alliances; repeated ties;

transaction costs; institutions; trust

Master’s Dissertation: Advanced International Business Management & Marketing

Submission Date: 7 December 2020 Word Count: 11.151

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ACKNOWLEDGEMENT

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The critical role of national context for firms is a central topic in international business and management (Westney, 1993; Kostova et al., 2020). Every country is unique, which creates differences. Countries can differ from each other in terms of economic development, institutional quality, language, demography, culture, geography and more. These differences are called “distance” and can be defined as the extent of contextual similarity or dissimilarity between any two countries (Kostova, 1996). The attention distance has received is reflected by Zaheer et al. (2012, p. 19) who stated: “essentially, international management is management of distance”. Early research on the subject was mainly concerned with psychic (Johanson & Vahlne, 1977) and cultural (Kogut & Singh, 1988) distance. As distance increases, it become more difficult to communicate effectively with firms in the host country, because the perceived knowledge and understanding of the other decreases. The flow of information gets impeded due to differences in education levels, culture, language, culture and political systems (Dow & Karunaratna, 2006; Hakanson & Ambos, 2010). More recently, scholars have developed more comprehensive representations of distance by including economic, geographic and institutional aspects (Ghemawat, 2001; Berry et al., 2010). Cross-national differences and their impact on firm specific outcomes have enjoyed much attention from researchers (Hutzschenreuter et al., 2016; López-Duarte et al., 2019). Scholars have researched the effects of cross-national distance on entry mode choice (Brouthers et al., 2008), performance (Evans & Mavondo, 2002; Gaur & Lu, 2007), ownership structure (Xu et al., 2004), choice of host market location (Xu & Shenkar, 2002) and other decisions and outcomes (for a review see Werner, 2002; or Jin-Hyun & Robert, 2010). In general, distance makes it more challenging to operate in a host market as it introduces complexity and friction (Vermeulen & Barkema, 2002; Shenkar et al., 2008), which makes it more difficult for firms to succeed abroad.

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5 Alliances can be governed by an array of structures or modes. The structural preferences has been an important focus of research (Gulati, 1995; Teng & Das, 2008; Das & Teng, 2000). Governance modes can be distinguished by the degree of hierarchy of the partnership, in terms of control, equity involvement and integration (Pisano, 1989). At one end of the spectrum are equity alliances, characterised by higher levels of hierarchy, and on the other end, non-equity alliances have lower levels of hierarchy. Work drawing on transaction cost economics (TCE) has argued that firms should choose the governance mode that minimises costs related to governing and monitoring transactions (Williamson, 1975, 1985). Choosing the most suitable mode makes sure firms can share and protect knowledge in an alliance (Pisano, 1989; Oxley, 1997). The theoretical reasoning behind alliance structures has been explained with various other perspectives. For example, organisational learning (Hamel, 1991), resource dependency (Pfeffer & Nowak, 1976), network theory (Rowley et al., 2000), resource-based view (Das & Teng, 2000) and institutional theory (DiMaggio & Powell, 1983). The theoretical foundation of this study will be TCE and institutional theory.

The relationships between governance mode choices, the host country environment (Roy & Oliver, 2009; Globerman & Nielsen, 2007; Oxley, 1999), and distance (van Kranenburg et al., 2014; Abdi & Aulakh, 2012; Niesten & Jolink, 2018; Steensma et al., 2000) have been studied extensively. Although these studies have related several dimensions of distance to governance, they have never provided a full conceptualisation. Mainly because the field has not agreed upon a common framework of distance (Berry et al., 2010). Therefore, every researcher makes their own choices, which dimensions of distance to include in their study and which not.

Two firms that have formed repeated alliances are more likely to trust each other than firms that form an alliance for the first time (Gulati, 1995). Therefore, the issues that arise from distance can be counteracted by existing cooperative relationships and lead to less hierarchical governance mode choices. I have not found any research that combines research on repeated ties with cross-national distance. Therefore, I will fill this gap by proposing four hypotheses to test my main relationships and four hypotheses to test the moderator effects. The hypotheses are based on my research questions. Hence, my research will be centred around the following two questions:

What are the effects of cross-national “distance” on the governance mode choice of international alliances?

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The purpose of this study is to test the effects of the four measures of distance, namely, institutional, geographical, economic and cultural distance on governance mode choice. It is important to make distinction between distance measures, because they can impact the governance mode choices of alliances in different ways. Consequently, this will lead to a better understanding of the overall differences in the context of cross-national distance. It provides a more comprehensive conclusion about the relationship. The second purpose is related to the repeated ties. I will test how repeated ties moderates the main relationship in an international context. This will give a better understanding about the relationship effects of alliances and if repeated ties can reduce the problems that distance brings.

The hypotheses will be tested with alliances from three high-tech industries, because the majority of alliances are formed in technology intensive industries. The sample consists of 1345 international alliances from 46 different countries in a 20 year period. The 20 year period gives a good timeframe to test how many repeated ties have been formed between the same firms, consistent with prior research (Gulati, 1995).

My results show that institutional and geographical distance are the most dominant distance measures. Suggesting that firms primarily take these effects into account when they form alliances in a foreign country. The results also suggest that repeated ties generally lowers the hierarchy of alliances. The contributions of my research are two-fold. It confirms the importance of institutional and geographical context and opens the discussion of moderation effects on the relationship between distance and governance mode choice.

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2. LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT

The following section will present the literature review and hypotheses development. The research is grounded in Transaction Cost Economics and Institutional Theory. These theories show the rationale behind the key concepts in the research. First, transaction cost theory and the formation of alliances are explained. Second, cross-national distance and institutional theory. Third, the hypotheses are formed. And lastly, the conceptual framework is presented.

2.1. Governance Modes and Transaction Costs

TCE argues that firms should choose an organisational structure that optimises their economic efficiency by minimising costs of exchange (Williamson, 1981). In a situation where transaction costs would not exist, TCE suggests that all activities between firms would be executed as exchanges in the market. Firms would then operate on their own, in an arm’s length buyer-seller relationship. However, transaction costs are a reality, and market failures happen due to high transaction costs, which leads to the emergence of hierarchical entities (Williamson, 1985). Firms have to consider coordination costs of monitoring, controlling and managing transactions. And when these costs become too high, firms look for alternatives to market exchanges. Alliances are formed as a result of this market failure, when transaction costs associated with an exchange are intermediate: not low enough to justify markets and not high enough to justify full hierarchy or vertical integration (Bradach & Eccles, 1989; Williamson, 1985). Transaction cost are then optimal for collaboration. Films cannot do everything by themselves. Therefore, alliances are a hybrid form, lying between markets and hierarchies (Williamson, 1975). Firms reduce transaction costs by internalising control and monitoring functions that would otherwise be dictated by the market.

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because there is a greater alignment incentive for both parties, since it involves a two-way agreement (Oxley, 1997). Unilateral alliances can include forms such as: licencing agreements, supply agreements, manufacturing agreements and others. Bilateral alliances, in contrast, can include: research and development agreements, cross-licencing agreements and technology sharing agreements. If an alliance is governed by a non-equity structure, TCE suggests that the type of contract will change to be closer to either side of the spectrum of market and hierarchy, depending on the weight of the transactions costs (Pisano et al., 1988). The higher the transaction costs, the higher the hierarchy of the contract.

Equity alliances are formed when one of the partners takes a minority equity position in the other partner or when the partners choose to form a new joint venture (Pisano, 1989). Equity alliances are supervised by hierarchical instruments to ensure day-to-day functioning of the partnership (Reuer & Ariño, 2007). They are more hierarchical than non-equity alliances, as non-equity alliances are governed solely by the contract mechanisms. Important characteristics of equity alliances are collaboration and centralised control (Teng & Das, 2008). Which implies higher levels of hierarchy between the partners.

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9 transfer of tacit knowledge (i.e. skills, information flows, ideas, norms and other organisation specific characteristics that people in an organisation possess, which cannot be easily transferred to another organisation without human interaction). Even when firms have formed an equity alliance, it can be complex to transfer this knowledge.

Non-equity alliances offer other advantages over equity alliances. These agreements are easier to negotiate and partners can invest less time and resources in them. Furthermore, they offer more flexibility and easier dissolution (Johnson et al., 1996). However, partners are vulnerable to each other’s opportunistic behaviour, and partners can have trouble with motivating the other to make enough alliance-specific investments (Joskow, 1985). When firms are only connected through contracts it can be difficult to transfer tacit knowledge that is needed to develop new technologies (Hennart, 1988). Furthermore, partners should be aware of potential leakage of valuable intellectual property to the other party (Teece, 1986). Therefore, both equity and non-equity alliances have their benefits and drawbacks. There are reasons to choose for either partnership depending on the situation, uncertainty and goals of the collaboration.

2.2. Cross-National Distance and Institutional Theory

Firms that move abroad operate in unfamiliar business environments. Hence, they face disadvantages in the host country relative to the domestic firms (Hymer, 1976), this is called liability of foreignness (Zaheer, 1995) and includes coordination costs, transaction costs, labour costs, start-up costs, legal costs and other costs rooted in the unfamiliarity with the host environment (Salomon & Martin, 2008). Liability of foreignness increases with distance (Eden & Miller, 2004; Kostova & Zaheer, 1999), which makes it more difficult for foreign firms to establish legitimacy in the host country (Kostova & Zaheer, 1999). The larger the distance between countries, the more challenging it will be for firms to operate in the foreign country, which puts them at a disadvantage compared to local firms.

Costs related to cultural and economic differences between the home and host country have been shown to negatively affect the performance and survivability of foreign subsidiaries in the host environment (Zaheer & Mosakowski, 1997). Firms can form alliances with local firms to reduce the liability of foreignness and limit their risk by choosing to operate in countries that have similar institutional environments (Henisz & Delios, 2001; Holburn & Zelner, 2010) or via different entry mode types (Delios & Henisz, 2000; Martin & Salomon, 2003).

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with this approach its one-dimensionality, as it only concerns culture. Related to cultural distance is psychic distance (i.e. perceived differences between two countries in terms of language, education level, religion, political system and so forth), which argues that distance restricts the flow of information from one country to the other (Johanson & Vahlne, 1977; Dow & Karunaratna, 2006).

In recent decades efforts have been made to enrich the cross-national distance dimensions by forming more complete frameworks that include the broader spectrum of distance (Berry et al., 2010; Ghemawat, 2001). Ghemawat (2001) proposed the CAGE framework, which includes cultural, administrative (institutional), geographical and economic distance. The framework can be used to assess the impact of distance on firm outcomes. Because of cross-national differences, firm need to adapt, and they adopt practices that are customary in the foreign environment. This affects the way firms do business and influences the governance mode choices of international alliances.

Cross-national distance is rooted in institutional theory and three schools of thought have emerged from this: organisational institutionalism, institutional economics and comparative institutionalism (Kostova et al., 2020). These theories explain how countries differ from each other and how firms are influenced by these differences.

Organisational institutionalism describes how the actions of organisations are driven by social justification, since these actions will be judged by shareholders, customers, governments and society in general (DiMaggio & Powell, 1983). Therefore, organisations seek approval and legitimacy for their decisions from those parties. In order to gain legitimacy and increase their survivability in the market (Meyer & Rowan, 1977), firms adopt practices and structures that emerge from values, norms, beliefs and rules prevailing in the institutional environment (DiMaggio & Powell, 1983). When alliances conform to institutional pressures it can improve partnering firms’ reputation, images and resources, which can lead to a better market position (Dacin et al., 2007). Originally, institutional distance between two countries was defined as the difference between regulatory (enforcement of rules, legal systems and laws), cognitive (morals, ethics and other structures that are taken for granted in a society) and normative institutions (norms, values, cultures and beliefs).

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11 in institutional economics is mainly concerned with the home and host countries’ institutional quality. Greater institutional distance will lead to higher costs and risk, because of a lack of understanding about the foreign institutions.

Comparative institutionalism considers the wider institutional environment of two countries in terms of their socio-economic characteristics (e.g. Hall & Soskice, 2001). The well-received work from Berry et al. (2010) evolved from this theory. They have attempted to form a conceptualisation of cross-national distance by proposing a set of multidimensional measures. Following institutional theorising from Jackson & Deeg (2008). The proposed measures include, economic, financial, political, administrative, cultural, demographic, knowledge and global connectedness and geographical distance (Berry et al., 2010). They ground their dimensions on institutional theories of national business systems, national systems of governance, and innovation systems. First, national business systems are "particular arrangements of hierarchy-market relations becoming institutionalized and relatively successful in particular contexts" (Whitley, 1992, p. 10.). Countries are different from each other in terms of the characteristics of their business systems, with originate in demographical cultural, geographic, economic, demographic and political practices (Whitley, 1992). Second, national governance systems refer to the "set of incentives, safeguards, and dispute-resolution processes used to order the activities of various corporate stakeholders" (Kester, 1996, p.109). These systems originate from administrative and political institutions (Henisz, 2000). And third, national innovation systems refer to the configuration of institutions that support the development of technology and innovation (Nelson & Rosenberg, 1993). Countries differ in their abilities to produce knowledge and the extent to which they can leverage that knowledge by connecting to other countries (Furman et al., 2002).

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2.3. Distance Factors

In the next section the hypotheses are formulated. The four distance hypotheses are based on the work from Ghemawat (2001) and Berry et al. (2010), as the distance measures are taken from their conceptualisations. I will hypothesise how the various distance measure impact the governance mode choices of international alliances. I will also include four sub-hypotheses to test my hypotheses on the moderation effect of repeated ties on the main relationship.

2.3.1. Institutional Distance

Institutional distance can be described as the extent of dissimilarity in institutional environments between two countries (Kostova, 1996, 1999). The institutional environment is a key determinant of the location and governance choice of alliances (Kostova & Roth, 2002). In situations of low distance, it is likely that transaction costs are also low. This would imply that less hierarchical governance modes are needed to effectively govern an alliance. However, as distance increased, complications arise. The need for more adaption and responsiveness between the partner firms increases with institutional distance (Delios & Beamish, 1999). It becomes harder to do business in institutional distant countries, because firms are not familiar with the external environment. Doing business in a foreign country requires communication with competitors, suppliers, customers and the government. Differences in institutions will increase uncertainty and the costs of interactions. Furthermore, unfamiliarity with the foreign institutions is likely to increase the risk of a firm to misunderstand actions from external institutions (Dow & Karunaratna, 2006). Practices developed in the home country might need to be enriched with local knowledge when institutional distance is extensive (Brouthers et al., 2008). This takes time and requires resources. Therefore, it is beneficial for firms to form more hierarchical alliance modes with distant partners to prevent or reduce the risks that firms experience. In addition, foreign partners more often lack trust and understanding compared to domestic partners (Gulati, 1995), and perceive increased amounts of uncertainty as institutional distance rises (Chan et al., 2008; Anderson & Gatignon, 1986). As trust is lower in international alliances, it required more efforts to control for opportunistic behaviour, which results in higher transaction costs. Equity alliances can serve as a control mechanism for this lack of trust. The incentives of equity makes sure that neither firm will act opportunistically.

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13 operate in countries with weak institutions, the ones with valuable technologies (patents) are more likely to use more hierarchical governance modes (Gulati & Singh, 1998; Contractor et al., 2011). Weak institutions are characterised by corruption and low institutional stability. Opting for more hierarchical modes makes sure that technologies are better protected against the uncertain environment they operate in. In situations where institutional distance is high, firm experience uncertainty about the foreign environment as they are not familiar with the rules and regulations. It might be that their patents are not protected in the same way as their home country. These risks can be reduced by implementing more control and oversight to the alliance. Peng (2003) suggests that firms that depend more on relational alliances, such as equity alliances, can overcome uncertainties in emerging economies with weak regulatory institutions.

In summary, large institutional distance leads to higher costs, uncertainty and risk, because firms lack the understanding of the external environment. With a more hierarchical governance mode, the partners can monitor and control opportunistic behaviour more effectively and overcome a lack of trust. Hence, in situations of high institutional distance, firms prefer a more hierarchical governance mode. Leading to the first hypothesis:

Hypothesis 1: The greater the institutional distance between alliance partners, the likelier a

more hierarchical alliance governance mode is used

2.3.2. Cultural Distance

National culture consists of attributes that determine how people interact with one another, companies and institutions. Distance between countries is created by differences in social norms, language, religion and race (Ghemawat, 2001). Berry et al., (2010) describes cultural distance as differences in attitudes towards authority, trust, individuality, and importance of family and work.

Contradictory results have been found as to whether cultural distance leads to more or less hierarchy in alliances. These opposing arguments make it more difficult to propose a clear-cut hypothesis, as there is no agreed upon conclusion made by prior research.

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difficult to align goals and incentives. This reduces the trust in the other partner, as they do not know how the other is going to react in certain situations. The partners can prevent risk of conflicting goals and large upfront costs, by using less hierarchical governance modes. Choi & Contractor (2016) also found strong support for their hypothesis that large power distance leads to less hierarchical governance modes. Lower up-front investment means that the partnership can be terminated at a lower cost and is, therefore, more reversible. Large cultural distance makes it more difficult to understand the other partner. Therefore, it can be risky to engage in a hierarchical mode with high upfront costs, as both partners can have very different incentives and future goals. Hence, it would be better to keep initial costs low and use a less hierarchical alliance form.

The other steam of research argues that culture distance leads to uncertainty and a lack of trust, which increases transaction costs. To control for these issues, firms form more hierarchical governance modes. In general, partners with different cultural backgrounds experience more complications compared to domestic partnerships. These different cultural aspects make it more difficult to operate in unfamiliar environments, leading to uncertainty and complexity (Brouthers & Brouthers, 2001; Richards & Yang, 2007; Erramilli & Rao, 1993). Alliance partners encounter greater miscommunication and conflict; and lower trust and coordination (Das, 2006). Cultural distance can increase conflict and collaboration problems (Mowery et al., 1996). Furthermore, distance also makes it more difficult to transfer competencies internationally (Kostova & Zaheer, 1999). Moreover, the risk of opportunism also rises with cultural distance (Das, 2006). And if only less reliable information is available, alliance partners might not fully trust each other. By choosing an appropriate governance mode, firms can reduce some of the difficulties described (Anderson & Gatignon, 1986). To reduce opportunistic behaviour, alliances can choose for greater monitoring and control, which can be translated to more hierarchical alliances (Gulati, 1995; Hennart & Larimo, 1998). Accordingly:

Hypothesis 2: The greater the cultural distance between alliance partners, the likelier a more

hierarchical alliance governance mode is used.

2.3.3. Economic Distance

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15 (2007) explain that the economic distance between countries often relates to income and technological capabilities. The income levels of individuals is considered to be the most important economic attribute that creates distance between countries (Ghemawat, 2001). Therefore, GDP per capita will be a main driver for my research and hypothesis development. In general, developed industrial countries engage in more international activity relative to their economic size than developing ones (Ghemawat, 2001). Firms can manage their operation better in countries with little economic distance from their home country (Johnson & Tellis, 2008). When firms enter countries with large economic differences, they need to adjust to the new market conditions, thereby reducing the chance of success (Dunning, 1998). The new market condition increases uncertainty for firms. This leads to higher transaction costs, and higher transaction costs lead to more hierarchy in the alliance. Firms develop competencies and resources that relate to their specific market (Madhok, 1997). These capabilities are better translated to markets that are similar in economic development, because they possess the same characteristics. Furthermore, the economic development of a country influences the communication and interaction norms of firms (Dow & Karunaratna, 2006), which may imply additional costs and uncertainty when operating in countries with high economic distance. This makes it more difficult to effectively collaborate with economically dispersed partners. To combat uncertainty and difficulties in communication, it can be more beneficial to form more hierarchical partnerships. This will help against a lack of trust and make it easier to monitor the alliance against opportunistic behaviour. Moreover, knowledge can be transferred more effectively and efficiently in more hierarchical governance modes. These arguments lead to the hypothesis that firms with large economic distance prefer more hierarchical governance modes, as it counteracts the uncertainty and potential lack of trust in a relationship. Hence:

Hypothesis 3: The greater the economic distance between alliance partners, the likelier a more

hierarchical alliance governance mode is used.

2.3.4. Geographic Distance

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geographic distance increases, so do transportation and communication costs. Although, technology agreements do not always involve tangible flows, geographic distance still limits the effectiveness of knowledge transfer and information flows (Ghemawat, 2001; Hansen & Løvås, 2004). This means that increased geographical distance will lead to slower technology transfer, resulting in less transfers overall (Hansen & Løvås, 2004). Searching for relevant competences will be more costly and time consuming when geographic distance is high (Sorenson & Stuart, 2001). Furthermore, long transmission channels between R&D units and different time zones also limits the effective transfer of knowledge (Ambos & Ambos, 2009), while close proximity facilitates face-to-face and other forms of direct contact between partners, which improves the cooperative environment and fosters knowledge transfer and innovation (Antonelli, 2000; Ganesan et al., 2005). An increased risk of opportunism and asymmetric information can occur due to greater distance, which leads to the need for more monitoring and control (Bönte, 2008). These arguments suggest that geographic distance makes it more difficult to effectively collaborate, resulting in increased control and monitoring cost (Carr et al., 2001; Degryse & Ongena, 2005; Malhotra et al., 2009), thus increasing transaction costs. Although this is not the case in all industries, firms still have to keep these restrictions into mind. Overall, it becomes more difficult to build and strengthen long-term relationships (Boschma, 2005). To reduce the risk of opportunistic behaviour and transaction costs, firm will prefer more hierarchical governance modes. My hypotheses will therefore be:

Hypothesis 4: The greater the geographical distance between alliance partners, the likelier a

more hierarchical alliance governance mode is used.

2.3.5. The Moderating Role of Repeated Ties

Alliances that collaborate more than once, form repeated ties. So, the second time (and every time after that) firm enter into some form of alliance it is classified as a repeated tie. The basic premise of repeated ties among firms is the creation of trust (for a review see Valdés-Llaneza & García-Canal, 2015). Prior research has found contradictory results on how trust influences the choice of governance mode.

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17 to form agreements (Ariño et al., 2014). This makes it less costly to write extensive agreements. which would mean that firms would rely less on trust, and more on the agreement that they have written down. However, these arguments are mostly based on contractual agreements and do not consider equity.

The other steam of research argues that repeated ties and trust lead to less hierarchy in alliances. Repeated ties can limit the perception of opportunistic behaviour and as a result lower the employment of safeguards in subsequent alliances (Parkhe, 1993). As firms become more familiar with each other, they generally behave less opportunistically (Heide, 1994), which can influence the governance mode decisions of alliances in future interactions. By forming repeated partnerships, firms will learn from each other and are better able to predict each other’s behavioural patterns (Dyer & Chu, 2000). In turn, this reduces uncertainty as knowledge about the other partner increases (Casciaro, 2003). Repeated relationships also make sure that firms have a better understanding of each other’s capabilities, skills (Heide & Miner, 1992), technological know-how and management processes (Vanhaverbeke et al., 2002). As a result, the partners do not have to participate in relationship-building processes that are critical for first time partners (Inkpen, 2000). Firm that have collaborated before create relational capabilities (i.e. a firm’s a firm's willingness and ability to partner) that may replace formal governance mechanisms (Dyer & Singh, 1998). Information asymmetry exists when firms interact for the first time, as they do not possess all necessary information to make the right decisions. This asymmetry can be reduces when firms form new collaborations (Reuer & Koza, 2000). As a result, both parties have more confidence that the other party will not behave opportunistically and act out of self-interest (Barney & Hansen, 1994). Over time, alliances develop interorganisational routines, and creates a better understanding of the other partner’s procedures and culture, which allows firms to reduce the complexity of hierarchical mechanisms for coordination and monitoring, resulting in lower costs (Stump & Heide, 1996; Zollo et al., 2002). Empirical research shows that trust originated from repeated ties leads to a reduction in the complexity of partnerships and turns alliances in more self-enforcing agreements (Das & Teng, 2000; Reuer & Ariño, 2007). Firms with repeated ties tend to prefer less hierarchical modes over equity alliances, because trust is used as a control mechanism (Gulati, 1995).

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Hypothesis 5a: Repeated ties moderates the relationship between institutional distance and

governance mode, such that less hierarchical modes are more likely to be chosen

Hypothesis 5b: Repeated ties moderates the relationship between cultural distance and

governance mode, such that less hierarchical modes are more likely to be chosen

Hypothesis 5c: Repeated ties moderates the relationship between economic distance and

governance mode, such that less hierarchical modes are more likely to be chosen

Hypothesis 5d: Repeated ties moderates the relationship between geographical distance and

governance mode, such that less hierarchical modes are more likely to be chosen.

2.4. Conceptual Framework

The aim of this dissertation is to study the effects of distance on the governance mode choices of international alliances. Furthermore, I try to answer the moderating role of repeated ties on the main relationship. The conceptual framework is presented in Figure 1.

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3. METHODOLOGY

The next section presents the methods, data and sample of the study. First, I explain how the dataset is chosen. Second, the dependent, independent and control variables are explained. And lastly, the statistical model is described.

3.1. Data and Sample

I will analyse the governance mode choices of alliances in high-technology intensive industry groups. My sample consists of alliances from year 2000 to 2019 to test for repeated ties between alliances. This timeframe is chosen, because it gives a good indication if firms have formed repeated ties with each other. Alliance data was collected from the Securities Data Company (SDC) Platinum database. This database is often used in alliance studies (e.g. Casciaro, 2003; Rahman & Korn, 2010). SDC collects its information from Securities and Exchange Commission (SEC) filings, news sources, trade publications and press releases. This extensive database provides information on alliance activities, industries and a wide variety of governance modes, including, joint ventures, R&D agreements, licensing agreements, marketing agreements, manufacturing agreements and supply agreements. The advantages of this database are the availability of a wide range of industries and its extensive and accurate searchability (Schilling, 2009). This is very useful in finding the alliances of interest. The amount of alliances, where firms share resources and assets, is mostly related to high-tech industries. A large majority of partnerships are made in a limited number of technology intensive industries (Hagedoorn, 2002; Hagedoorn et al., 2008). Hagedoorn et al. (2008) found that around 80% of alliances are from three SIC sectors namely chemicals and allied products; electrical and electronic equipment; and instruments and related products.

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included a subsample of my dataset, with data from 2014 to 2019, including 650 alliances. This subsample is created to test my moderator relationships.

3.2. Dependent Variable

In the sectors that I have used for my sample, non-equity alliances outnumber equity alliances considerably. This is evident from my sample as it includes 103 equity alliances and 1242 non-equity alliances. Therefore, it is critical to make a distinction between those non-non-equity alliances to get a better understanding of those differences. I have made the distinction in terms of hierarchy and level of interaction. Unilateral or “one-way” agreements are coded as “1”. Bilateral or “two-way” agreements are coded as “2” and the most hierarchical form, equity alliances, are coded as “3”. If an alliances includes multiple “one-way” contractual components such as licencing and manufacturing, they are coded as “2”

3.3. Independent Variables

Various sources have been used to measure the independent variables. For institutional distance the World Bank World Governance Indicators are used. Gross Domestic Product (GDP) per Capita from the World Bank will be used for Economic Distance. However, GDP per Capita from Taiwan is not available at the World Bank. Therefore, I collected data from the International Monetary Fund for Taiwan only. Hofstede’s cultural index is used for Cultural Distance and geographical distance data is collected from the CEPII GeoDist database. Lastly, firm age and firm size have been gathered from the Bureau van Dijk Orbis database.

3.3.1. Institutional Environment: World Governance Indicators

To measure the institutional environment of the countries, I use the World Bank’s Governance Indicators (Kaufmann et al., 2005). The advantages of this data source are its accessibility and extensive dataset (it includes over 200 countries). This make it one of the most comprehensive database for studying institutional characteristics used in a wide variety of studies (e.g. Hutzschenreuter et al., 2014; Lavie & Miller, 2008; Hakanson & Ambos, 2010).

The World Governance Indicators provides a country score from -2.5 (weak governance) to 2.5 (strong governance) for all indicators. The dataset includes six dimensions of governance.

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21 2. Political Stability and Absence of Violence, measuring the likelihood of violent threats to, or changes in, government, including terrorism;

3. Government Effectiveness, measuring the competence of the bureaucracy and the quality of public service delivery;

4. Regulatory Quality, measuring the incidence of market-unfriendly policies;

5. Rule of Law, measuring the quality of contract enforcement, the police, and the courts, as well as the likelihood of crime and violence;

6. Control of Corruption, measuring the exercise of public power for private gain, including both petty and grand corruption and state capture.

The overall goal is to measure the Quality of Governance in a country. Langbein & Knack (2010) found that the six indicators appear to measure the same broad concept, rather than being completely unique from each other. This might explain why countries that have a high score on one indicator, often also have high scores on the other indicators.

I adopted the formula for institutional distance previously used by Lavie & Miller (2008): ID = ∑ ( Iij− Iiu)

6 6 𝑖=1

Iij (Iiu) stands for the score of country j, the home country (or u, the host country) in year i. This variable has a right-skewed distribution, therefore I will measure institutional distance with its natural logarithm.

3.3.2. Cultural Dimensions: Hofstede

Cultural distance will be measured in terms of Hofstede’s cultural dimensions (Hofstede, 2011). These include:

1. Power Distance, related to the different solutions to the basic problem of human Inequality;

2. Individualism/Collectivism, related to the level of stress in a society in the face of an unknown future;

3. Masculinity/Femininity,related to the integration of individuals into primary groups; 4. Uncertainty Avoidancerelated to the division of emotional roles between women and men;

5. Long-Term Orientation, related to the choice of focus for people's efforts: the future or the present and past;

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The cultural differences between the home and host countries of the alliances will be calculated with the following distance formula (Kogut & Singh, 1988):

CD = ∑ {( Iij− Iiu ) Vi 2 } 6 6 i=1

Iij (Iiu) stands for the score of country j, the home country (or u, the host country) in year i and Vi stands for the variance of year i.

3.3.3. Economic Development: GDP per Capita

Various measures for economic distance have been used in previous research. These measures include inflation rate, imports and exports, Economic Competitiveness Indices from the World Economic Forum and Quality of Human Capital (Berry et al., 2010; Choi & Contractor, 2016; Fang et al., 2013). However, most commonly GDP per capita has been used to measure the difference in economic development between (Hakanson & Ambos, 2010; Malhotra et al., 2009; Tsang & Yip, 2007). Therefore, I will adopt the same measurement to capture this variable. Economic distance is measured by the calculating the absolute differences of GDP per capita per country pair. The variable is right-skewed and has a large variance, therefore I will measure economic distance by calculating its natural logarithm.

3.3.4. Geographic Distance: Great Circle Distance

Geographic distance is calculated with the “great circle distance” formula, often used in distance research (e.g. Berry et al., 2010; Hutzschenreuter et al., 2016). The formula is used to calculate the geographical distance between two points on a sphere, like the earth. The formula measures the distance between two countries using the coordinates, latitude and longitude. CEPII provides freely accessible data on geographical distance measured between the most populated city or the capital of two countries (Mayer & Zignago, 2011). The distance between the most populated cities is included in my research. Geographical distance is transformed to its natural logarithm to prevent the effect of its large variance.

3.4. Moderator Variable

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23 repeated ties on a subsample of my overall dataset from 2014 to 2019, which includes 650 alliances. However, data on repeated ties still takes into account data from earlier years. It could be that two firms have formed an alliance in 2008 and 2015. The information on this repeated tie is still included, but only the alliance of 2015 is included in the subsample.

3.5. Control Variables

In addition to the independent variables a number of control variables are proposed. I will control for firm size, because size can influence the bargaining power during negotiations (Sakakibara, 2010). Furthermore, larger companies might be less influenced by uncertainty, external environment fluctuations and partner opportunism (Aulakh et al., 2013). On the other hand, smaller firms might lack resources and/or experience (Reuer & Ariño, 2007). Firm age is also important to take into account, because older firms, due to experience, are likelier to have entered more alliances (Sorenson & Stuart, 2001). Size and age will be calculated as the absolute difference between alliance partners (same as country distance). Firm size is calculated as the absolute difference in revenue between the partners. Firm age is measured as the difference in incorporation date. Both size and age distance have right-skewed distributions and very large variances, therefore I use the natural logarithm of these variables. This makes the variables better interpretable and reduces the problems that skewness creates. The last control variable accounts for alliance industry. A dummy variable is created that captures whether both partners operate in the same 4-digit SIC industry “1”, or “0” if the partners have their primary operations in different industries (Oxley, 1997). Partners that operate in the same industry have more understanding about each other’s technological capabilities, (Chung et al., 2000) which reduces uncertainty. This suggests, that firm from the same industry might use difference governance modes than firms operating in different industries.

3.5. Statistical Model

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exploration agreements, funding agreements, manufacturing agreements, marketing agreements and technology transfer agreements. Bilateral alliances have higher hierarchy than unilateral alliances, but lower hierarchy than equity alliances. These include: cross-licensing agreements, R&D agreements and cross-technology transfer agreements. And equity alliances have the highest hierarchy.

4. RESULTS

Table 1 presents the descriptive statistics and correlation matrix for all variables. The highest correlations between the different measures of distance are below 0,5. Furthermore, I conducted a multicollinearity test and the results from this test show no problems. The VIF (Variance Inflation Factor) scores are all below 1,500 and the tolerance percentages all score above 0,700, indicating that my analysis has no problems with multicollinearity. Ordered logistics regression models are used to test the direct and moderation hypothesised effects. The models show how the independent and moderator variables affect the choice of governance mode in international alliances. The coefficients and standard errors are presented in Table 2 and 3. All models include an “a” and “b” version. Models “a” present the subsample results and Models “b” show the full sample results. Model 2b is used to test hypotheses 1-4, because this model is more reliable than Model 2a. Model 2b includes more alliances and a significant chi-square. Hypotheses 5a to 5d are mainly tested with Model 4a, a subsample, because this provides more meaningful results. The other models provide context and show the overall consistency of my results. The base model, Model 1, includes all control variables, firm size, firm age and same industry. Model 2 includes the main independent variables of hypotheses 1-4: institutional, economic, cultural and geographical distance. In Model 3, I added the Repeated Ties variable to see if any significant changes appear, but the model stays almost identical to Model 2. Model 4 represents the full model, where all independent and moderator effects are included. The Repeated Ties variable is included to moderate the effects of all distance measures on governance mode choice. As most repeated ties are formed later than 2010, I conducted the same test on a subsample of six years with data from 2014 to 2019, which should develop more meaningful results. This is presented in Model 4a.

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TABLE 2

Ordinal Logistics Regression Results Model 1 and 2

* p < 0.10; ** p < 0.05; *** p < 0.01. standard errors are reported in parenthesis.

TABLE 3

Ordinal Logistics Regression Results Model 3 and 4

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27 age differences between alliance partners increases the likelihood that more hierarchical governance forms are chosen.

Hypothesis 1 suggests that higher institutional distance leads to more hierarchical forms of governance. Model 2b shows that this variable is significant at the 1% level and has a positive effect on governance mode (β= 0.279, p<0.01). Throughout all models this variable is significant. Confirming my hypothesis that higher institutional differences increases the likelihood that alliances use more hierarchical forms of governance. Therefore, hypothesis 1 is supported.

Hypothesis 2 argues that higher levels of cultural distance lead a higher likelihood that partners chose more hierarchical governance modes. In Model 2b the coefficient for this variable can be found. The negative effects of the coefficient suggests that larger differences in culture between the alliance partner countries leads to lower hierarchical governance forms. The opposite of what I hypothesised (β= -0,024). However, the result is non-significant, so this conclusion cannot be made. Consequently, hypothesis 2 is not supported.

Hypothesis 3 is also tested in Model 2b. This hypothesis suggests that with greater economic differences, alliance partners are more likely to prefer higher levels of hierarchy. Nonetheless, my results show no indication that this is true, as this coefficient is not significant.

The last distance hypothesis, hypothesis 4, is related to geographical distance. The results of this effect are also shown in Model 2b. Results show a significance at the 10% level, but negative relation between geographical distance and governance mode choice, indicating that alliances with large distances are less likely to choose a more hierarchical governance mode (β= -0.095, p<0.10). This is contrary to what I hypothesised, therefore hypothesis 4 is rejected.

The results of the moderator effects are presented in Model 4a. Hypothesis 5, suggests that repeated ties will moderate the effects of the measures of distance, such that alliance partners are more likely to use less hierarchical modes of governance. Model 4a shows the results on the whole sample and shows clear differences with Model 4b, because most repeated ties are formed in later years. Therefore, it is more useful to look at the results of Model 4a.

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conclude that hypothesis 5b is supported. Hypothesis 5b predicts the moderating role of repeated ties on the relationship between cultural distance and governance mode. Model 4a indicates that the effect of repeated ties is negative and significant at the 5% level (β= -1,634, p<0.05). From these results we can conclude that hypothesis 5b is supported.

Hypothesis 5c concerns the moderating role of repeated ties on the relationship between economic distance and governance mode choice. Again, the results are significant at the 5% level, however, this time, the coefficient shows a positive effect (β= 1,467, p<0.05). This opposes the hypothesised effect. Thus, hypothesis 5c is rejected.

The last moderation hypothesis is about geographical distance. Model 4a shows a positive, but non-significant effect. Therefore, hypothesis 5d cannot be supported.

In summary, from the results, it becomes clear that institutional distance is the most dominant variable compared to the other independent variables. The results also show significant effects for geographical distance, but opposite of what I hypothesised. Furthermore, the repeated ties moderator shows strong results for institutional and cultural distance, consistent with my hypotheses. However, the moderating role of repeated ties shows the opposite results of my hypothesised outcomes for economic and geographical distance. The results will be further evaluated in my discussion.

4.1. Robustness Checks

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5. DISCUSSION

This study examined the effects of the different measures of cross-national distance on the governance mode choices of international alliances. Additionally, I set out to study the effects of repeated ties as a moderator. My contributions are two-fold. First, I contribute a deeper understanding of the different forms of distance on governance mode choices. Second, I provide a larger context by including the moderation effect of repeated ties.

The empirical results show support for multiple hypotheses. Without taking repeated ties in consideration, institutional distance is the most dominant, and probably most important measure of distance in the dataset. Geographical distance is also significant, but leads to opposite results from what I hypothesised. Repeated ties leads to less hierarchical governance modes in situations of high institutional and cultural distance. Below, I will discuss my results, present my limitations and show suggestions for future research.

The importance of institutional effects on governance mode choices has been addressed before in prior research (Oxley, 1999). Firms prefer more hierarchical alliances, because this protects them from the uncertainty they experience in the host environment and opportunistic behaviour from the foreign partner. My results are consistent with these previous results, indicating the importance of institutional distance on alliance decision making. However, my results underline the critical role of institutional distance in a wider distance perspective, thus giving more context to the relationship.

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impact on governance mode choices. It could be possible that certain dimensions of distance are less relevant and should therefore be excluded from the research. Maybe individualism or masculinity has no effect on governance mode choice, but long-term orientation does. Then it would be more relevant to separate the effects of cultural distance into different measures instead of combining them into one variable. This could provide more meaningful results. There are a couple of reasons why my results are inconsistent with previous research. My results are from recent data, and most studies have been conducted on data with an earlier timeframe (Li et al., 2010). The results might also differ, because of the studied industry, the chosen data sources or the size of the sample.

The results on geographical distance are significant, but negative. This is contrary to what was hypothesised and inconsistent with previous research (van Kranenburg et al., 2014). Geographic distance might lead to less hierarchical structures, because it becomes more difficult to form strong relationships when distance is high, especially in innovative environments (Boschma, 2005). It could be that high-tech industries prefer different governance modes than other industries depending on geographical distance. Another reason could be that technological advancements have made it less difficult to collaborate over large distances. It has become much easier to contact the partner firm and even have face-to-face communication over the phone or laptop. My sample consist of recent data, and this could mean that the issues of distance have been reduces considerably.

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5.1. Limitations

Readers should exercise caution when reading this dissertation for a couple of reasons. My dataset consists of three high-tech industries only. Therefore, the results might not be generalisable to non-high-tech industries. My dataset is dominated by non-equity alliances, and this has consequences for my results. This is not necessarily a limitation, but this has to be taken into account. Similar research with data from industries that are less dominated by technology could show distinct results. Especially in industries where equity alliances are more common. Although my sample includes firms from 46 different countries, they are primarily from western or industrial developed countries. My sample includes countries like Colombia, South-Africa, Thailand and Turkey, but they only appear a few times, and are therefore difficult to generalise. My sample is mainly dominated by developed ‘western’ countries. Furthermore, the data on repeated ties is relatively small and could be a potential source of bias as around 5% of my sample consists of repeated ties. The data might not be large enough to make sound conclusions. Furthermore, I did not take termination dates of alliances into account. So I know when alliances have started, but I do not know when they ended. It is important to keep this in might while interpreting the data on repeated ties.

The SDC database does not provide much information on the form of alliances, it was therefore only possible to divide the governance modes into three broad categories. Alliances are complex instruments and often more difficult to interpret then in terms of a simple term such as licencing agreement. It could be more meaningful to read the actual alliance agreement or contract and take conclusions from that information. This technique has been used by (Choi & Contractor, 2016), who divided the governance modes in four categories based on the degree of interaction and degree of complexity. They used a data source that provides more information on the alliances and could therefore, separate their alliances in more specific governance modes.

5.2. Future Research

There are several avenues for further research. First, a main limitation of my research is the small data of repeated ties. Therefore, a similar research could be conducted with a larger sample on repeated ties to give more reliable results. In my research, repeated ties is used as a proxy for trust, and this is tested with quantitative data. Future research can use interviews and qualitative data to get a deeper understand on the impact of trust on governance.

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of those differences on the governance mode choices of international alliances. Furthermore, it could be useful to test the relationship with a different moderator, such as R&D intensity or absorptive capacity (Zhang et al., 2007; Kim & Inkpen, 2005). Moderator effects could give a better understanding of how the main relationship of distance on governance mode choice is affected when additional variables are includes.

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6. CONCLUSION

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The Influence of Cross-National Distance on the Governance Mode Choice of International Alliances: The Moderating Role of Repeated Ties