The formation of multi-partner alliances: an empirical analysis of the impact of competition and formal governance on partner diversity

MSC BA Strategic Innovation Management Thesis

The formation of multi-partner alliances: an empirical analysis of

the impact of competition and formal governance on partner

diversity

Faculty of Economics and Business Word count: 13.215

June 2016

2

1. INTRODUCTION ... 4

2. THEORETICAL BACKGROUND ... 6

2.1 Multi-partner alliances and their importance ... 6

2.2 Partner diversity in multi-partner alliances ... 7

2.3 Competition and multi-partner alliances ... 8

2.4 Hypotheses ... 9 3. METHODOLOGY ... 15 3.1 - Data collection ... 15 3.1.2 - Sample ... 16 3.2 - MEASURES ... 17 3.2.1 Dependent variables ... 17 3.2.2 Independent Variables ... 19 3.2.3 Control Variables ... 20 3.2.4 Study ... 21 4. RESULTS ... 22

4.1 Overall picture of diversity in the multi-partner alliances ... 23

4.1.2 Descriptive statistics and correlations ... 23

4.2 Regression results ... 24

4.3 Robustness Checks ... 27

5. DISCUSSION ... 29

5.1 Limitations and future research directions ... 32

5.2 Conclusion ... 33

6. REFERENCES ... 34

3 ABSTRACT

This thesis provides new insights in the phenoma of multi-partner alliance formation process. By focusing on the impact of the degree of competition and formal governance structure on partner diversity new understandings are provided regarding the diversity in multi-partner alliances. 94 firms engaged in 28 multi-partner alliances have been analyzed in order to determine how a formal governance structure and degree of competition influences partner diversity. Partner diversity is identified as industry diversity and national diversity. The findings show that a high degree of competition increases industry diversity due to possibilities of gaining resources. Furthermore, the results show that interaction between formal governance and high degree of competition significantly increases the industry diversity. For national diversity, no significant relation is found for the degree of competition. Moreover, the findings show that a more non-formal governance structure increases national diversity. Therefore managers face a trade-off between partner diversity and formal governance structure. I argue that managers should consider their environment carefully while forming multi-partner alliances and should be aware of the benefits and downsides of formal governance.

4

1. INTRODUCTION

Over the past two decades the number of multi-partner alliances formed has increased dramatically (Das and Teng, 2002). Reasons for this phenomena are shorter product life cycles, higher complexity of technologies and globalized competition (Lavie, 2006; Li, 2013). A multi-partner alliance is a collective voluntary organizational association that interactively engages its multiple members in multilateral value chain activities such as collaborative research, development, sourcing, production, or marketing of technologies, products or services (Lavie, 2006). Due to developments of the external environment, multi-partner alliances are for many companies not only an option but also a necessity to survive because multi-partner alliances can be a source of competitive advantage (Dyer and Singh, 1998). Multi-partner alliances provide the opportunity of value maximization through resource pooling, utilizing valuable resources and obtaining external knowledge (Das and Teng, 2000). Whereas the simplest alliance form is dyadic, multi-partner alliances are more complex due to multiple mutual relations. However, compared to dyadic alliances, multi-partner alliances provide more opportunities to access resources from multiple firms in order to create value, share risks and costs (Heidl et al., 2014; Lazzarini, 2007). In practice, multi-partner alliances can be found in many industries such as pharmaceutical industry (Rooijakkers and Hagedoorn, 2006), airline industry (Lazzini, 2007;) and car industry (Medcof, 1997). As Eisenhardt and Schoonhoven (1996) already pointed out, alliances are formed in dynamic environments. The fact that multi-partner alliances mostly occur in these industries indicate that environmental dynamics, and especially the degree of competition are of major influence on why and how multi-partner alliances are formed.

5 collaboration (Jiang et al, 2010). Partner diversity provides the possibility to acquire important knowledge that is crucial for survival in highly competitive environments (Boring, 2015). Drawing on Schumpeter’s (1935) view that innovation involves combination and recombination of existing and new resources available to the firm, increasing partner diversity will provide an increased possibility of external knowledge elements available to the focal firm (Lahiri and Narayanan, 2013) which could lead to innovation. The construct partner diversity will be divided in industry diversity and national diversity (Jiang et al., 2010). Furthermore, taking the transaction cost theory (Williamson, 1989) into consideration, this theory argues that alliances are a risky business because partner firms are considered to have individual incentives which are very likely to lead to opportunism. Especially while forming a multi-partner alliance in an environment with a high degree of competition, incentives to behave opportunistic are evident. Besides external factors such as the degree of competition, a formal governance structure can also influence the degree of partner diversity because a formal governance structure enhances the ability to manage a multi-partner alliance (Das and Teng, 2008). A more formal governance structure such as a joint venture can limit opportunism due to formal contracts and a mutual hostage situation (Kogut, 1988), and therefore increase the ability of a firm to manage partner diversity. Based on this reasoning I argue that an environment with a high degree of competition will increase the partner diversity (e.g. industry and national diversity). Secondly, I argue that a formal governance structure provides means to manage different partners and therefore also increases the partner diversity. Based on arguments from the resource based view (Barney, 1991) and the transaction cost theory (Williamson, 1989) this paper will examine how a formal governance structure and the degree of environmental competitiveness influences the partner diversity during the formation process of multi-partner alliances. The following main question is specified: How does a formal governance structure and the degree of competition influences partner diversity in multi-partner alliances?

6 evident by the environment and a formal governance structure provides means to manage multiple different partners. These findings enables managers to conscious make decisions about diverse partners and ways to manage these partners.

The remainder of this paper is organized as follows: In the next section, literature is discussed regarding competitiveness, alliance partner diversity and governance structure. Building on this literature, hypotheses are drawn. Then, the sample, data collection and measurement of the variables is discussed. Following, the results are presented, along with further analysis. In the final section results will be discussed. Furthermore, limitations and future research directions are given.

2. THEORETICAL BACKGROUND

In this section the existing literature is reviewed. In section 2.1 the existence of multi-partner alliances phenomena is discussed as well as its relevance. Furthermore, in section 2.2 the partner diversity construct is discussed. Following, in section 2.3 competitiveness and its impact on the formation of multi-partner alliances are discussed. In section 2.4 the logic behind the hypotheses is explained and hypotheses are formed which will be tested in this thesis.

2.1 Multi-partner alliances and their importance

7 important reason to cooperate with multiple partners (Henderson & Cockburn, 1996). Even though multi-partner alliances provide many advantages, downsides exist as well. Research has shown that multi-partner R&D alliances are more unstable than dyadic alliances because of their complexity, increased potential for free riding and opportunistic behavior (Heidl et al., 2014). The harm of non-cooperation in a dyadic alliance is restricted to both partners, whereas the effects of non-cooperation in multi-partner alliances are much bigger due to interdependencies of the partners (Li et al., 2012).

2.2 Partner diversity in multi-partner alliances

Partner diversity in alliances was first researched by Parkhe (1991). Diversity was divided in two constructs namely Type 1 and Type 2 (Parkhe 1991). Type 1 diversity includes the familiar interfirm differences that alliances were specifically created for to exploit. It deals with the reciprocal strengths and complementary resources. Type 2 diversity refers to differences in partner characteristics that often negatively affect the longevity and effective functioning of an alliance. This thesis focusses on Type 2 differences, whereas they provide the most opportunity to learn and create a competitive advantage for focal firms in the alliance. Different partner characteristics, or type 2 differences, may be overcome by iterative cycles of learning that strengthen the alliance partnership. Diverse partners in an alliance can enhance the innovative output of the alliance compared to low partner diversity (Sampson, 2007). In this thesis type 2 partner diversity is conceptualized following the work of Jiang et al. (2010) by dividing the construct partner diversity into two sub constructs namely (1) industry diversity which takes different industrial backgrounds into consideration and (2) national diversity, which considers different national backgrounds and cultures.

8 outcome and contributes in bearing the risk. Researchers have mainly focused on partner diversity in multi-partner alliances and innovation performance (Beers and Zand, 2013). Shown in their results is that geographical and functional diversity increase performance in radical and incremental innovation. Geographical diversity, or national diversity as conceptualized in this thesis, increases the sales of a new innovation because different national partners provide access to different end-markets (Beers and Zand, 2013).

Partner diversity can also cause tensions within the alliance (Parkhe, 1991). Differences in backgrounds are a prerequisite of conflict and coordination problems (Hennart and Zeng, 2002). Especially multi-partner alliances are subject to these problems due to the larger number of partners in one alliance. This increases the complexity and relationship patterns of multi-partner alliance. (Li, 2012). Furthermore, Li (2012) makes a distinction between net-based and chain-based multi-partner alliances. Net-based multi-partner alliances act in a relationship with the group as a whole whereas chain based multi-partner alliances are organized in a way that partners operate in a chain of univocal reciprocations to each other as individual units (Li, 2012). In net-based multi-partner alliances generalized exchanges and reciprocity patterns are present which make it harder to detect non-cooperation and opportunistic behavior (Li, 2012). Partners who coalesce as an in-group will identify themselves more with this sub-group than with the larger group, this could cause schisms between subgroups of partners. These subgroups could occur on the basis of nationality or similar industrial background, because these similarities imply that it is more likely that they have had similar experiences in their environment and therefore closely identify with each other (Lau and Murninghan, 1998). Diversity in partner characteristics are at the basis of the formation of fault lines within a multi-partner alliance. Sub-groups based on backgrounds in multi-partner alliances can cause instability of the alliance, and may lead to problems with managing multiple diverse partners.

2.3 Competition and multi-partner alliances

9 relationship with these important parties. Such an exclusive relationship provides firms with a competitive advantage in comparison with rival firms. In a dynamic and competitive environment firms should develop repeatedly more radical innovations as the product life cycle in these markets is much shorter (Bettis and Hitt, 2007). Gains of a radical innovation will quickly be spread towards rivals and advantages diminish fast over time. In order to achieve more radical innovations to keep up the pace with the market, firms can find a solution in multi-partner alliances. Wuyts (2014) found that in a technologically turbulent environment, diversity of an alliance stimulates a firms innovative outcomes. This argument implies that the degree of competition would stimulate diversity in alliances. Legitimacy can play an important role in signaling to external parties the worthiness of the firm and therefore increase the probability of engagement in a multi-partner alliance.

When the degree of competition further increases for firms in the marketplace, cooperation versus competition is the most salient in selecting alliance partners. Many studies (Ritala, 2012 Cygler, 2010, Gudmundsson and Lechner, 2006) suggest that alliances are most desired when they balance cooperation and competition which is also known as coopetition. The rationale above implies that the most desired alliances are among partners that are more or less equivalent in terms of size, profits and reputation in their own industry. When firms do so, complementary resources and know-how are more easily accessible (Kraatz, 1998). Alliances provide firms with access to external knowledge that provides the opportunity to adapt to competitive environments (Kraatz, 1998).

2.4 Hypotheses

The degree of competition in a market creates a strategic need which is met by the formation of multi-partner alliances. According to Eisenhardt and Schoonhoven (1996), dynamic and competitive markets urge firms to form alliances to meet their needs for technological resources. Two important theories in the alliance literature are the Resource Based View (Barney, 1991) and the Transaction Cost Theory (Williamson 1987). By using these theoretical backgrounds the following reasoning for hypotheses has taken shape.

Competition and partner diversity of multi-partner alliances

10 research has shown that more diverse partners will lead to more innovation compared to homogeneous partners (Sampson, 2007; Beers and Zand, 2007). Partner diversity consists out of two constructs drawn from Jiang et al. (2010): industry diversity which focusses on industrial backgrounds and national diversity, that considers national backgrounds.

Industry Diversity

Industry diversity refers to the level of which industrial backgrounds differ between the focal firm and those of the rest of the partners within the multi-partner alliance (Beckman and Haunschild, 2002). Previous research has shown that partners with heterogeneous industrial backgrounds can bring valuable inputs into the alliance, while partners from the same industry often are competitors with overlapping knowledge (Parkhe, 1991; Jiang et al., 2010). Partners from the same industry are more likely to have conflicts of interest and are more likely to provoke learning races (Doz and Hamel, 1998), increasing monitoring and safeguarding costs. Especially in environments where competitiveness plays an important role, the generation of new innovative products is key. Even though gains of radical innovations are spread quickly in the market, radical innovations are still essential for firm survival because they provide a temporary monopoly position on the innovation (Utterback and Suarez, 1991). Multi-partner alliances with partners from more diverse but related industries will enhance the possibility of an innovative output (Sampson, 2007) because radical innovations are an outcome of the integration of different technologies. Different technologies are possessed by firms originating from different industries, and the combination of firms from different industries, and thus technologies, increase the probability of radical innovation (Wuyts, Dutta and Stremersch, 2004).

11 Hypothesis 1a: The degree of competition is related to industry diversity in multi-partner alliance formation in a matter that a high degree of competition will increase the industry diversity of partners of the multi-partner alliance

Besides the degree of competition, a formal governance structure could influence the industry diversity of partners in a multi-partner alliance. The transaction cost theory (Williamson, 1987) argues that an equity based governance structure will enhance monitoring and control which in return will decrease the concerns of opportunism and therefore makes it possible to collaborate with more diverse partners. A mutual hostage situation, as proposed by a formal governance structure, provides a situation where industry diversity can be controlled for. Also, formal governance structure enhances the coordination of different knowledge bases possessed by alliance partners (Das and Teng, 2008). This integration is needed by partners with diverse industrial backgrounds due to the possession of different technologies and practices. Without coordination of a formal governance structure, the diverse backgrounds could be a liability instead of a benefit. In line with this reasoning the following hypothesis is drawn:

Hypothesis 1b: A formal governance structure positively enhances industry diversity

National diversity

12 Besides this international risk, partners from very different countries could face coordination and coordination costs because they have different approaches to problem solving and conflict resolution (Parkhe, 1991). However, in environments with a high degree of competition the necessity of having different national partners is higher because the high pace of the technology enhances the diffusion need by firms (Bettis and Hitt, 1995) this can be achieved by launching technologies in multiple markets.

In short, multi-partner alliances in an environment with a high degree of competition are more likely to consist out of partners with different nationalities because of different resources and easier market access. In line with this reasoning the following hypothesis is drawn:

Hypothesis 2a: The degree of competition is related to national diversity in multi-partner alliance formation in a matter that a high degree of competition will increase the national diversity of the partners of the multi-partner alliance

Successful alliances have two characteristics in terms of governance: limiting opportunistic behavior and coordination the resources of different partners (Hoetker and Mellewigt, 2009). In the formation process it is important to consider these success factors in order to achieve desired outcomes. As hypothesized earlier, the degree of competition provides motives for firms to enhance national diversity in multi-partner alliances due to several advantages of different partners. However, different national partners also bring hazards to the alliance. Cultural differences and differences in regulation practices could enhance the tension in a multi-partner alliance. In order to manage these tensions and potential schisms based on nationality a formal governance structure provides a solution. Williamson (1987) argues that a formal governance structure provides opportunities to enhance monitoring and prevent schisms due to formal commitments of different partners. Often, a formal governance structure is embodied as a joint venture (Osborn and Baughn, 1990) where all partners provide employees to this separate entity. This close collaboration of employees of different partners in a separate entity provide opportunities for better integration.

Short, a formal governance structure will enhance national diversity because it provides means to limit opportunistic behavior and enhance integration. In line with this reasoning the following hypothesis is drawn:

13 Interaction effect of competition and formal governance

The degree of competition provides firms with incentives to enhance partner diversity (e.g. industry and national diversity) in multi-partner alliances due to a number of advantages such as acquiring unique resources, sharing costs and risk and access to multiple end markets. (Das and Teng, 2008; Beers and Zand, 2013). The governance structure provides firms the ability to manage different partners via a formal commitment (Das and Teng, 2008; Lee and Cavusgil, 2006). The degree of competition and governance structure can jointly impact the decision of a firm to engage in a diverse multi-partner alliance. An equity based, or formal based governance structure, may provide means for managing conflicts. If a dispute among partners arises, the terms of a formal contract provides what is lawful and what is not (Ring and van de Ven, 1992) and therefore enhance the ability to manage different partners. Besides that a formal governance structure provides solid terms when partners are in conflict, Das and Teng (2008) argue that partners with resource heterogeneity require more alliance resource integration efforts. An equity based governance structure is an important mechanism to achieve resource integration. For example, if three firms have different human resources and organizational structures due to different national and industrial backgrounds, integration is important in order to successfully collaborate (Hoetker and Mellewigt, 2009). An equity based structure such as a joint venture brings the different parties together in one separate entity. Often, is this separate entity in one physical setting (Mjoen and Tallman, 1997) which provides means for better resource integration due to face-to-face communication which is found to be more effective for transferring knowledge among partners (Athanassiou and Nigh, 2000). Firms face a trade-off between the benefits of a multi-partner alliance with diverse partners and the risks involved with managing these different backgrounds. A formal governance structure provides firms with the ability to manage these diverse partners by limiting opportunistic behavior via a mutual hostage situation. In line with this reasoning. the following hypotheses are drawn regarding the formal governance structure and partner diversity:

14 Hypothesis 4: The interaction between the degree of competition and formal governance structure increases the national diversity of the partners in a multi-partner alliance

Figure 1 shows the proposed relations between the independent variables degree of competition and formal governance structure and partner diversity. Partner diversity is divided in industry and national diversity as explained earlier.

Degree of competition Partner diversity Industry diversity National diversity Formal Governance structure Firm Size Firm Age Alliance experience Alliance size Alliance Scope Crisis Year Aa

15 3. METHODOLOGY

In this section clearity is given regarding the data collection and sample. Also the dependent, independent and control variables are discussed. Furthermore the measures used are explained.

3.1 - Data collection

Based on Li ‘s (2013) research about multi-partner alliances, the following approach is used to create a sample. Firstly the categories of industries that will be investigated are determined. The categories that are identified are adopted from the work of Li (2013), Anand and Khanna (2000) and Cloodt, Hagedoorn and Van Kranenburg (2006). The industries are classified as following: high-tech manufacturing (SIC codes: 357, 365, 366, 367, 381, 382, 384, and 386) and high-technology services which include communications services (SIC codes: 481, 482, and 489) and software and computer-related services (SIC code: 737). The work of Annand and Khanna (2000) adds the Drugs industry (SIC 283), Chemicals (SIC 28, excluding SIC 283) and Cars (SIC 371). Cloodt, Hagedoorn and Van Kranenburg (2006) also identified the aircraft industry (SIC 372) as high-tech and will also be included in the sample. These industries provide a solid context for this research because in these industries survival depends on the ability to innovate and bring these innovations to the market in a quick manner (Børing, 2015) In order to ensure data availability, the focus in this thesis is on multi-partner alliances formed in OECD countries. In order to extract industry information the following criteria are used in ORBIS: (1) Active companies during the whole period, (2) located in a country that is member of the OECD (3) availability of financial data and number of employees and (4) expenditures on R&D during the period researched (5) a time frame between 2007 and 2011.

Secondly, information about multi-partner R&D alliances formed between 2007 and 2011 is retrieved from the SDC (Securities Data Corporation) database. These years are chosen to ensure data availability in the ORBIS and SDC database. Firms that were involved in mergers during the study period were excluded. Firms involved in mergers may influence the decision to the change of governance structure (Bierly and Coombs, 2004). Also alliances with ambigous or incomplete information are excluded from the sample.

16 each firm competing in an industry, and then summing the outcomes. The HHI number can range from close to zero to 10,000. The HHI is expressed as: HHI = s1^2 + s2^2 + s3^2 + ... + sn^2 (where sn is the market share of the firm). The more an industry has characteristics of a monopoly, the higher the concentration rate is, implying a lower degree of competition in the market. The HHI index is a well-accepted indicator to measure the degree of competition in markets and industries (Rhoades, 1993, Bamberger, Carlton and Neumann, 2001). Rhoades (1993) states that the HHI index is commonly used by the US government (e.g. FED and Justice Department) to determine the degree of competition in a market. The US government has declared that markets be classified into three different categories based on the HHI values. HHI below 1000 implies a competitive market with no dominant firms, in this thesis this group is identified as an environment with a high degree of competition. HHI between 1000-1800 implies a moderately concentrated market. Lastly, a HHI above 1000-1800 implies a concentrated market with one or more dominant firms in the market. The last two groups are identified in this thesis as low degree of competition. The HHI index is calculated for 16 industries mentioned earlier. In the following table the HHI index is shown for each industry. Years are shown, where T=1 is 2007 and T=5 is year 2011. Classification of markets based on Rhoades (1993) in total 8 markets are identified with a high degree of competition, 4 with a moderate degree of competition and 4 as concentrated markets. Average is calculated by using following formula A = S/N. An overview of the HHI index can be found in table 1 in the appendix.

3.1.2 - Sample

17

Table 2 - Overview of the sample

3.2 - MEASURES

The construct partner diversity is measured through the degree of industry diversity, national diversity and organizational diversity. By taking a focal firm perspective the variables are determined for each individual firm engaged in a multi-partner alliance in the sample.

3.2.1 Dependent variables Industry Diversity

Industry diversity is measured by using the firms SIC-codes. The SIC-code consists out of four numbers from which the industry and specific business groups can be identified. All databases used in creating the sample have SIC codes. Comparing the digits shows how different the alliance partners are within the multi-partner alliance. Using Jiang et al. (2010) categorization, five categories are drawn. The first category is ‘0’where no SIC digits are shared, ‘1’where 1 SIC digit is shared, ‘2’where 2 SIC digits are shared, ‘3’ where 3 SIC digits are shared and ‘4’ where all SIC digits are shared. Category ‘4’ shows firms that relatively close to each other in the industry.

Because of the categorical character of the variable it is possible to determine the diversity measured by the Blau index of diversity. Jiang et al (2010) uses the Blau index of Variability to calculate the diversity. The formula is as following:

SIC Code Number of firms

involved

Number of alliances

Classification

SIC 283 / Drugs 18 6 High degree of competition

SIC 371 / Motor vehicles and equipment 27 7 High degree of competition SIC 382 / Laboratory apparatus and

equipment

3 1 High degree of competition.

SIC 357 / Computer and office equipment

12 3 Low degree of competition

SIC 481 / Telephone communications 24 8 Low degree of competition

SIC 372 / Aircrafts and parts 10 3 Low degree of competition

18 Where p represents the proportion belonging to a group and i the number of different categories. The range of the diversity goes from 0 to 1, where a 0 implies a perfect homogeneous group and 1 a perfect heterogeneous group.

National Diversity

SDC-database provides information about the nationality of alliance partners. Looking for national diversity within the alliance, with ‘0’ for an alliance with no foreign partners, ‘1’ for an alliance with one foreign firm, ‘2’ for an alliance with two firms from foreign countries and so on. National diversity is a subjective norm and therefore it is hard to argue that a firm is relatively more diverse than the other, therefore different countries are used to measure national diversity (Jiang et al., 2010).

Altered Blau Method

Although the Blau index of diversity is a common accepted measure for diversity in groups there are some inconsistencies of the index in alliance literature. Initially the index is used to determine the diversity of a group. For example: a group with two women and one male would imply a score of 1- ((2/3)^2 + (1/3)^2) = 0.44. If I look at the nature of the variable in the example it is nominal and it is counted to use in the formula (two women, one male). The variables used for industry diversity are different of nature because the numbers represent to which extent different partners are similar or dissimilar.

When the theory of Blau Diversity index is applied to the data used in this thesis it shows some inconsistencies. The maximum of the index is calculated by (N-1/N) where N stands for the number of categories. This implies that for industrial diversity the maximum is 0.8. When using Blau, the categories need to be recoded from 0 – 4 to 1 – 5 (where 0 becomes 1, 1 becomes 2 and so on) because a zero used in the Blau formula would give faulty outcomes. One of the major inconsistencies with the formula is that it can exceed the maximum of 0.8. For a firm in an alliance with two other partners with whom there are no similarities the maximum would be 1 – ((2/10)^2) = 0.96 which is far higher than 0.8 mentioned before. So the use of these categories would imply that the maximum of the Blau will be exceeded, showing a statistical error.

19 squaring makes the differences of diversity proportional bigger. Therefore I propose the following formula:

Altered Blau: 1 – (∑ pᵢ / ∑ maxᵢ)

Clarified by the following example: Focal firm A has two scores compared to two other firms in the alliance namely a 3 compared to the first partner and a 4 compared to the second partner. According to the Blau index this would imply 1-((9/10)^2) = 0.19 whereas my approach without squaring and recoding would imply 1-(7/8) = 0.125. The 0.125 shows a more reliable scale of diversity of the alliance because a 3 and a 4 would imply an almost homogenous group (closer to 0). Also with the altered approach it is not possible to exceed the maximum, because the score always stays between 0 and 1 (by using the example: min: 1-(8/8) = 0 and max: 1-(0/8) = 1) Diversity of the number is altered by the number of alliance partners and different categories given following Jiang et al. (2010). In my thesis I will use both approaches to determine differences in the calculations. More examples of the calculations can be found in the appendix.

For national diversity it is also possible to use the altered Blau index. For example, an alliance with three partners with 2 from Germany and 1 from Spain. Following the initial coding of Jiang et al. (2010) this would imply a 2 for the Spanish partner and a 1 for the German partner. Using the altered Blau it would imply a score for the Spanish partner of 1 (completely heterogeneous) and for both German partners 0.5. This can alter when there are more partners from more different nationalities.

3.2.2 Independent Variables

Competition - The degree of competition is the independent variable in this study. As

proposed, it is assumed that this variable will influence the dependent variables. The degree of competition is measured by using the HHI (Rhoades, 1993) mentioned before. When a firm is located in an industry with a high degree of competition it will receive a ‘1’. When a firm is not active in an environment with a high degree of competition it will receive a ‘0’.

20 categories where ‘1’ represents an equity joint venture and ‘0’ represents a contractual agreement (Oxley and Sampson, 2004).

3.2.3 Control Variables

Next to the variables proposed earlier, other variables may also impact the formation of a multi-partner alliance. Therefore multiple control variables are included in order to examine this effect. In table 2 an overview is given regarding the control variables.

Alliance experience - Prior experience among partners in an alliance may aid cooperative processes (Anand and Khanna, 2000). This experience may impact the choice of diverse partners because previous experience enhances the possibility of previously created trust among partners. Therefore a dummy variable is included, where a ‘1’ implies that there have been previous alliances among partners in a period of 6 years prior the current alliance, and a ‘0’ when this is not the case (Sampson and Oxley, 2004).

Firm Size and Firm Age - Firm size reflects whether or not the partners are equal or different in terms of size. Firm age reflects how long a firm is already operating. Both characteristics are important because older and lager firms have, in general, more resources and a more established network to retrieve more benefits from their alliances (Kotha, Rindova and Rothaermel, 2001). Size is measured in this thesis by the number of employees. This data is derived from the SDC database and ORBIS. The age of a venture is determined from the founding date listed in the SDC's Global New Issues database and ORBIS and is shown as the number of years it is running (Li, 2013). The control variables age and size are both standardized in order to prevent outliers in the models. Standardizing equalizes the range of the data which ensures that variables contribute equally to the analysis (Cooper and Nakanishi, 1983).

Alliance Size - The size of an alliance can influence the diversity within a multi-partner alliance. The bigger the size of an alliance the possibility of more diverse partner increases (Cui and O’Connor, 2012) The size of an alliance is measured by using the SDC database and ranges from 3 till 6 partners in a multi-partner alliance.

21 Scope – The scope of an alliance can be an important prerequisite of how the multi-partner alliance is formed. When the alliance is formed with the idea for pure R&D or manufacturing activities the composition of a multi-partner alliance can be affected, because different partners are desired. Therefore the control variable scope is included by using a dummy variable. An ‘1’ implies a narrow scoped alliance a ‘0’ implies otherwise.

3.2.4 Study

In this thesis a five year period is examined in order to determine the impact of the degree of competitiveness on partner diversity and the interaction effect of formal governanc. To check the hypothesis formed this thesis consists out of several steps. First descriptive statistics are given of the sample including correlation between the different variables. A Pearsons correlation matrix is not applicable on this data due to the nature of the data used in this thesis, therefore a Spearman’s correlations are calculated (Steiger, 1980). Spearman’s correlations consider ordinal data and therefore better applicable on this thesis.

Next, the analysis is done by using a linear regression for industry diversity. For national diversity a Poisson regression is used because the variable is a count variable. The Poisson regression is a common used test in order to achieve predictive results with count

Variables Measures Source

Industry diversity Categories of Jiang et al. (2010) and the Blau index of Diversity

SDC

National diversity ‘0’ for an alliance with no foreign partners, ‘1’ for an alliance with one foreign firm, ‘2’ for an alliance with two firms from foreign countries and so on.

SDC

Competition Based on HHI index, firms in an industry with high degree of competition receive a ‘1’ otherwise a ‘0’

ORBIS

Formal governance structure A dummy variable, where ‘1’ represents a formal governance structure and a ‘0’ otherwise

SDC

Alliance experience A dummy variable where a ‘1’ implies that there have been previous alliances among partners in a period of 6 years prior the current alliance, and a ‘0’ when this is not the case

SDC

Firm age Number of years operational (Seen from 2016) ORBIS Firm size Number of employees currently employed at the firm.

Cross checked across both databases

ORBIS & SDC

Alliance size Number of partners in a multi-partner alliance SDC Crisis year A dummy variable whereas an alliance is created in 2007

a ‘1’ is given and a ‘0’ after 2007.

SDC

Alliance scope Dummy variable where a ‘1’ represents a narrow scoped alliance and a ‘0’ otherwise

SDC

22 variables (Cameron and Pravin, 2013). For each hypothesis an analysis is conducted. Because competition is coded as a dummy variable the outcomes of a linear regression need to interpreted differently (Agresti and Kateri, 2011). By using the following formula the linear regression used by industry is interpreted:

γ = Α + β x

Where Y is equal to the dependent variable diversity and x equal to the independent variable competition. A is substituted by the constant coefficient and β will be substituted by the coefficient associated with the independent variable competition, in this manner differences are shown between groups. The probability that competition increases diversity is interpreted in this way. The minimum confidence level of 90% is used to conclude if there are any implications for the partner diversity. Due to the small sample size a lower confidence level is accepted although a higher confidence level has a better predictive implications. It is argued by Fisher (1956) that even though a p-value is lower than the 95% confidence interval it is not completely random relation and has the intention to be predictive.

The interaction effect is tested by computing a new variable. By multiplying the degree of competition variable with the governance variable the interaction variable is created. The last model in all tests will include this variable to test for hypothesis 3 and 4. After the regressions two robustness checks are performed to ensure the outcomes of the hypotheses. First, an independent sample T-test is conducted to test the differences between the groups with the independent variables. Secondly, the altered Blau method is used to ensure outcomes of the regressions and independent T-test.

4. RESULTS

23 4.1 Overall picture of diversity in the multi-partner alliances

By providing an overview of how the different diversity dimensions are distributed in the sample, clarity is given regarding the data. First the dimension industry diversity is examined of all the alliances and partners. The data shows that 2 alliances are completely homogenous in terms of industry diversity. Furthermore, the data shows that industry diversity has an overall mean of 0.72 implying that the firms are relatively diverse. For the group with a high degree of competition the mean of industry diversity is 0.782 which is relatively higher compared to the mean of the other group. The mean of this group is 0.668. The closer the number is to 1 the more industry diversity there is in this group. National diversity has a maximum of 4 and a minimum of 0. In total 2 firms have a ‘4’ in terms of national diversity, showing that these firms score high in terms of national diversity. The governance structure shows a mean of 0.61 in the whole sample. 57 firms were engaged in 18 alliances that had a formal governance structure. 37 firms were engaged in 13 alliances with a non-formal governance structure.

4.1.2 Descriptive statistics and correlations

In table 3 the Spearman’s correlation matrix is shown. Correlations imply that two variables are present at the same time and that an increase of one variable is paired with an increase or decrease with another variable. Correlations do not imply causation by definition (Van Aken & Van der Bij, 2012). When analyzing table 3, I conclude that industry diversity is correlated with the independent variable competition at a 5% significance level. Furthermore, industry diversity and national diversity are negatively correlated at a 5% significance level. Also, Industry diversity and previous alliance experience are negatively correlated at a 1% significance level. A logical explanation for this phenomena could be that firms with diverse industrial backgrounds do not have previous relations of collaboration. Secondly, industry diversity is negatively correlated with firm size at a 1% significance level. Reasons for this finding can be that smaller firms are more specialized in terms of industry. A unexpected negative correlation is found between governance structure and national diversity at a 1% significance level. This finding implies that when national diversity is higher, formal governance structure is lower. Moreover, national diversity is positively significant with Scope of the alliance at a 5% significance level. Showing that if an alliance is narrow scoped, the national diversity tends to be higher.

24 firms size, implying that larger firms favor a more formal governance structure. Following, a formal governance structure is negatively correlated with scope at a 5% significance level. This correlation is in line with the findings of Sampson (2004), she found that pure scoped alliances are better arranged by a non-formal governance structure due to unintended knowledge spill overs.

Furthermore, firm age and firm size are positively correlated, a logical explanation of this is that older firms tend to be bigger (Park et al., 2002). Previous alliance experience is correlated with firm size, this seems plausible because bigger firms tend to have had more alliances with the same partners, and therefore more experience (Alvarez and Barney, 2001). The variable alliance scope is negatively correlated with governance structure at a 5% level. To examine for multicollinearity the Variance Inflation Factors (VIF) are calculated for each of the regressions. The maximum VIF within the models was 3.792 which is well below the 10 point rule proposed by Hair et al. (1995).

4.2 Regression results

The main subject of this thesis is to examine whether an environment with a high degree of competition enhances the need of more diverse partners. Furthermore, it examines if a formal governance structure is needed to manage the relations among different partners. Thirdly, the interaction effect of the degree of competition and formal governance structure is examined. In order to analyze the effect regressions are used. Model one in all regressions shows the control variables, model two adds the independent variable competition. The third model includes besides competition also the variable governance structure. Lastly, the fourth model examines the interaction effect of competition and governance.

26 1 – Regression coefficients are shown

2 – * p<0.1 **p<0.05 ***p<0.001 significance levels

Finally, the R square of Model 2 is 0.237 and for Model 4 0.358 implying that 23.7% and 35.8% of the variance is explained by the variables. This is relatively low however, it implies that other variables are of importance when forming an alliance in a more competitive

environment.

Table 5 provides results for the relationship between the degree of competition, formal governance structure and national diversity. A Poisson analysis is used to test this relationship because the dependent variable is a count variable. Model two shows that the degree of competition solely does not have any significant influence on the dependent variable (Exp. β = 1.333 p < 0.183), concluding that hypothesis 2a is rejected. The third model provides results for the relationship between formal governance structure and national diversity. This result is negatively significant showing that for a formal governance structure there is 0.612 (99% CI 1.157 to 2.941) more chance to have lower national diversity showing opposite results as proposed in hypothesis 2b. The fourth model includes the interaction effect, showing an insignificant relationship and therefore hypothesis 4 is not supported. The

27 Goodness of fit is close to 1 implying that the model there little to no over or under dispersion of the model. This result shows that the variance is not statistically different in another model implying a good model fit.

4.3 Robustness Checks

In order to check for structural validity two additional tests are performed. First an independent T-test is performed to check whether there are indeed differences among the groups as proposed in the hypotheses. Secondly, the altered Blau method is used to ensure the outcomes by using the measures used.

Independent T-test

For all dependent variables a T-test has been conducted. The sample was split based on the degree of competition and formal governance. First, the group with a high degree of competition (N= 48) was associated with industry diversity M = 0.782 (S.D. = 0.269). Compared to the group with a lower degree of competition (N = 46), industry diversity had a

Table 5 – Poisson regression results National Diversity

1 – Regression coefficients are shown

28 numerically lower mean M = 0.668 (S.D. = 0.285). To test whether both groups are associated with significantly different levels of industry diversity an independent sample t-test was conducted. To check for homogeneity of variances, a Levene’s F-test was conducted F(92) = 1.55 p = 0.216. The independent samples T-test was associated with a significant effect t(91) = -1.99 p = 0.05. Thus the low degree of competition group has statistical significantly lower industry diversity. This is in line with hypothesis 1a.

Secondly, the same approach was used for formal governance structure. The first group with a non-formal governance structure (N = 37) was associated with industry diversity M = 0.764 (S.D. = 0.301). Compared to the group with formal governance structure (N = 57) M = 0.702 (S.D. = 0.268), this group has a lower mean in terms of industry diversity. To test whether both groups are connected with different levels of industry diversity an independent t-test was performed. The Levene’s F-test was conducted to check for homogeneity of the variances F(92) = 0.043 p = 0.837. The T-test was not significant t(92) = 1.020 p = 0.311. These results reject hypothesis 1b, and are in line with the first findings.

The same approach was used for national diversity. The mean associated with the high degree of competition and national diversity is M = 0.88 (S.D. = 0.937) for the low degree of competition this is M = 1.09 (S.D = 0.985) the homogeneity of variances did not show a significant effect t(91) = 0.01 p = 0.974. The t-test was associated with an insignificant effect t (92) = 1.068 p = 0.288. Therefore no differences were found between the groups based on the degree of competition. This test is in line with earlier findings and rejecting hypothesis 2a.

The formal governance group (N = 57) has a mean of M = 0.74 (S.D. = 0.936). The group with a non-formal governance structure (N = 37) has a mean of M = 1.35 (S.D. = 0.889). The independent T-test showed significant differences t(92) = 3.172 p = 0.002. This is in line with the results, showing that the group with a non-formal governance structure has more diversity and therefore rejecting hypothesis 2b.

29 group was tested with national diversity. The group associated with high degree of competition and formal governance structure (N = 28) had a mean M = 0.71 (S.D. 0.937). The other group had a M = 1.09 (S.D. = 0.956). The independent T-test did not show any significant results, this is in line with earlier findings and rejects hypothesis 4.

Altered Blau Method

By testing the same hypothesis with the altered Blau method, validity and reliability of the results of the results are ensured. Table 6 which can be found in the appendix, shows the outcomes with the use of altered Blau method for industry diversity. The outcomes are consistent with the first regression. However, the significance level for the first hypothesis is (β = 0.138 p < 0.05) which indicates that the relationship is stronger by using the altered Blau method. In terms of the interaction effect the altered Blau provides the same results (β = 0.500 p < 0.01). When running the altered Blau for National diversity, the results show for the degree of competition and national diversity no significant result. The governance structure is significantly negatively influencing national diversity (β = -0.304 p < 0.001). In terms of the interaction effect between degree of competition and governance a significant result was found (β = 0.377 p < 0.05). This is in line with earlier results. However, the altered Blau method does show an interaction effect (β = 0.377 p < 0.05) implying that both formal governance and the degree of competition enhance national diversity. This is divergent compared to earlier results of both the Poisson and Independent t-test. Also the Spearman correlation imply that this finding is inconsistent. The results of the altered Blau index for national diversity can be found in table 7 in the Appendix.

5. DISCUSSION

30 competition influences partner diversity in multi-partner alliances? Formation is discussed as the diversity of a multi-partner alliance from the focal firm perspective. The results show that industry diversity is higher in an environment with a high degree of competition. Reasons for this phenoma are that different industrial backgrounds provide opportunities to innovate and internalize valuable external knowledge. This is in line with previous research research (Sampson, 2007; Beers and Zand, 2010) who found that partner diversity increases innovation output which is needed for survival in competitive environments.

The results show that a formal governance structure does not influence the industry diversity on its own. This is inconsistent with earlier findings (Sampson, 2007; Das and Teng, 2008) who found that an equity based governance structure contributes to the coordination and integration of different partners and contributes to innovative output. However, it could be argued that a formal governance structure increases the possibility of unintended knowledge spill-overs

However, the results provide support for hypothesis 3 showing that if the degree of competition is high and there is a formal governance structure in the multi-partner alliance the industry diversity will be higher. This is in line with the theoretical propositions stated in the literature review. A highly competitive environment demands a multi-partner alliance to be more diverse on industry level because different industrial backgrounds can give excess to new external knowledge (Sampson, 2007). The downsides of partner diversity in a multi-partner alliance, such as different practices, can be minimalized by a formal governance mode due to better integration and mutual hostage situation such as is the case at a joint venture.

31 altered Blau regression show some inconsistencies because the test is supporting this relation. Reasons for this finding could be found in methodological issues and the nature of this particular sample because the index shows a score between 0 and 1 of how diverse the alliance is for a particular partner. This score is relatively higher when there are more partners engaged in the alliance.

This study contributes to the existing literature by showing that an environmental factor such as the degree of competition influences the formation process of a multi-partner alliance in a matter that it enhances the need for industry diversity. The results of this study show that a formal governance mode provides firms with the ability to manage the relations within the multi-partner alliance when industry diversity is high. As stated in the literature review, diversity demands integration and safeguards in order to be able to manage multiple different partners.

Control Variables

In the models several control variables are used. At the sub construct industry diversity alliance experience is negatively influencing the relationship. An explanation for this finding could be that alliance experience for this study is mainly between companies that are situated in the same industry which negatively influences industry diversity. Other control variables such as firm size, firm age and crisis year do not consistently influence the dependent variables.

Managerial implications

32 the multi-partner alliance (Beers and Zand, 2014) which is associated with firm survival by Børing (2015).

5.1 Limitations and future research directions

Several limitations need to be discussed which offer insights for future research directions. Firstly, the sample consisting out of 94 active firms engaged in 28 alliances across 6 industries provides a relative small sample of firms engaged in multi-partner alliances. Also the fact that all these firms are engaged in countries that are a member of the OECD, this study does not show how this phenomena occurs in other important economies such as China and India. The generalizability of this thesis could be improved by including more countries and economies, because other industries could show different outcomes. In this thesis it was not possible to include these countries and economies due to limited resources, time and data. Future research should include more industries and countries in order to extend the sample and provide results that are more generalizable. Including fast growing countries such as China and India could provide more insights in how multi-partner alliances are formed in these rapid growing economies.

Secondly, the measure of competition is derived from the Herfindahl Hirschmann index (Rhoades, 1989) to identify different industries and their level of competitiveness. Another complementary measure that could have been used to measure competition is the reverse ratio of concentration by the eight firm theme (Li, 2013). Other features of the environment such as dynamism and uncertainty could provide more solid insights regarding the impact of the environment on partner diversity. A suggestion for future research is to create a more solid measurement of environmental competition by including multiple factors such as uncertainty and dynamism. This thesis excluded these factors due to limited time and resources available.

Thirdly, the databases SDC and ORBIS have its limitations. Although providing detailed information, ORBIS misses a lot of financial data of firms. Therefore it is not possible to investigate all firms in all industries due to data availability. The SDC database has this issue as well, providing an incomplete view (Schilling, 2009). Furthermore, the SDC database is biased towards the English language and therefore does not provide a complete overview of all firms and alliances (Schilling, 2009). Future research could cope with this limitation by cross-checking the data with other databases such as MERIT-CATI database.

33 diversity of patents that a firm possess, the IT infrastructure or differences in absorptive capacity and learning skills also impact the diversity of a partner (Das and Teng, 2008). Future research should focus on these other characteristics of partner diversity and test how these characteristics behave in environments with different degrees of competition and governance structures.

Fifthly, the formal governance structure is only measured by checking whether or not the alliance is a joint venture or not. This measure does not take into consideration a minority or majority equity share. This could affect the ability to manage different partners due to power dynamics (Das and Teng, 1998). To include different types of governance structures, a better picture could have been made to determine the ability to manage diversity. Also could this provide more clearity regarding the outcomes of formal governance structure and national diversity. Future research should include a more complete measure of governance structure.

Lastly, the Blau index of diversity used in this paper has its limits. Originating from the studies of Human Resource Management (Blau, 1977) the index measures the diversity of a group. The index is adopted by Jiang et al. (2010) putting it in a alliance portfolio perspective. An altered Blau formula is proposed which attempts to cope with these limits however, the altered Blau index show some inconsistencies with the findings. The method should be tested further on different samples.

5.2 Conclusion

The main purpose of this thesis is to examine how a formal governance structure and the degree of competition influence partner diversity in a multi-partner alliance. By testing a sample of 94 firms engaged in 28 alliances across 6 industries located in OECD countries the following results are found The research suggests that a high degree of competition increases the need for industry diversity among partner. For national diversity there is no significant difference in alliances in different competitive environments. Finally, the selection of a more structural governance method enhances the industry diversity in a more competitive environment due to opportunities to manage different alliance partners. Given the popularity of multi-partner alliances in these industries managers should keep in mind that while forming a multi-partner alliance these characteristics can help to enhance industry diversity.

Acknowledgements

34

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