TMT Diversity, Acquisition Decision-Making and Environmental Uncertainty

37

By

Ulienke Bruinsma S2241986

Supervisor: Prof. Dr. C.L.M. Hermes Co-Assessor: Mr. Dr. H.A. Ritsema

13-06-2016

Submitted for the degrees of

MSc International Financial Management MSc Business and Economics

MSc International Business & Management

Faculty of Economics and Business Department of Business Studies University of Groningen Uppsala University

TMT Diversity, Acquisition Decision-Making and

Environmental Uncertainty

2 Abstract

This study investigates the influence of TMT diversity on the likelihood and performance of cross-border acquisitions, including the moderating effect of

environmental uncertainty. Based on a sample of 599 acquisitions by 89 Dutch firms completed between 2004-2014, evidence shows that gender and nationality diversity increase the likelihood of a cross-border acquisition, though when environmental uncertainty is high this significance disappears and only age diversity is shown to be significant. Some evidence is found for a positive relation between age diversity and acquisition performance. However, when environmental uncertainty arises, diversity on the TMT shows to have a negative effect on performance.

3

T

1.INTRODUCTION ... 4

2.LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT ... 7

2.1. Cross-border acquisitions ... 7

2.1.1. Cross-border acquisitions in developing countries ... 7

2.2. Top Management Team (TMT) ... 8

2.2.1. TMT diversity ... 9

2.3. Hypothesis development ...11

2.3.1. Likelihood of a cross-border acquisition ... 11

2.3.2. Performance of a cross-border acquisition ... 13

2.3.3. Moderating effect of environmental uncertainty ... 15

3.DATA AND METHODOLOGY ... 17

3.1. Sample and data selection ...17

3.2. Variable description ...18 3.2.1. Dependent variables ... 18 3.2.2. Independent variables ... 20 3.2.3. Moderator variable ... 20 3.2.4. Control variables ... 21 3.3. Model description ...24 4.EMPIRICAL RESULTS ... 26 4.1. Descriptive statistics ...26 4.2. Regression results ...27

4.2.1. Likelihood of a cross-border acquisition ... 27

4.2.2. Performance of a cross-border acquisition ... 30

5.CONCLUSION ... 38

4

1.

I

This highly globalizing world, the strong economic growth in most regions of the world and the maturation of a number emerging markets result in competitive pressures on firms. In order to face these pressures many firm recognize the importance of going global in order to maintain a competitive edge. An increasingly important strategy for international expansion of firms are cross-border mergers and acquisitions, henceforth M&As. Even though the global M&A trend has been increasing till 2009, there has also been a high failure rate for firms conducting these strategies. At least 50 percent of the M&As failed to achieve its goals (Weber et al., 2014). Therefore, it could be expected that firms would avoid M&A activities and would search for other strategies to achieve market share and profitability goals. This seemed to be the case for European MNEs for a period of 5 years, as their divestments averaged 68% of their gross purchases between 2009 and 2012, rising to 87% in 2013-2014 (UNCTAD, 2015). Reasons for this are the poor economic situation in Europe in the aftermath of the global crisis, as well as the heightened uncertainty resulting from the repeated shocks associated with the Eurozone debt crisis. However, this divestment trend reversed in 2015 when economic circumstances improved and financial conditions stabilized. From then on, the combination of decreasing divestments and a steady level of gross purchases of companies resulted in net acquisitions of assets of US$179 billion in the first half of 2015. This is a major increase compared to the US$46 billion net divestment in the first half of 2014 (UNCTAD, 2015). The amount of cross-border M&As are expected to continue growing, although at a slower pace. Apparently firms keep finding M&As an attractive strategy to internationalize, even with the high degree of risk it involves. Even more interesting are the decisions of firms to conduct M&A in developing countries, as environmental uncertainty is high for these M&As, due to greater institutional distance. What drives the decision of firms to conduct an acquisition activity in developing countries and how does it affect its performance? The answer could be found at the top of the companies.

5 argument why some firms keep on using M&As to internationalize, whereas other firms do not. Research has shown that the TMT’s role and composition can be a driving force of strategic innovation (Barkema & Shvyrkov, 2007). Mainly diversity in TMTs results in different values and cognitive bases, which induces debates and therefore leads to strategic innovation. The upper echelon approach, serving as theoretical framework in this thesis, argues that organizational outcomes are reflections of the values and cognitive basis of the top of an organization (Hambrick & Mason, 1984). Previous research based on this theory indeed found that diversity within TMT influences the choice of entry mode (Nielsen & Nielsen, 2011) and how TMT diversity influences the pace and success of acquisition. However, results reported by these previous studies are inconsistent, potentially due to the failure of authors to specific the type of diversity they measured (Harrisson & Klein, 2007). This paper will take this into account by looking at diversity as variety specifically, which refers to the differences in knowledge and experience among unit members (Harrison & Klein, 2007). Next to that, previous research on acquisitions lacks evidence on the role of TMT diversity in acquisition decisions with regard to developing countries. Gaining evidence on this is important, as decision-making might be different for these countries. Mainly due to higher environmental uncertainty resulting from greater institutional distances. Therefore, the following overarching research question will be examined in this paper:

RQ: How does diversity in the TMT of a firm in a developed country influence firm decisions with regard to acquisition decisions in developing country, compared to similar decisions in developed countries?

6 developed country. More specific, environmental uncertainty might increase the influence of TMT demographic characteristics on organizational actions (Carpenter & Frederickson, 2001) and therefore strengthen the positive relation between TMT diversity and the likelihood and performance of cross-border acquisitions.

A sample of 599 acquisitions conducted by 89 Dutch firms within the time period 2004-2014 is used to test the theoretical predictions. A binary logistic regression for the likelihood of a cross-border acquisition shows that gender and nationality diversity increase the odds of a cross-border acquisitions. However, this significance disappears when environmental uncertainty is high, i.e. a target in a developing country. In contrast, age diversity appears to be positively related to the odds of a cross-border acquisition under high uncertainty circumstances. The performance of the cross-border acquisitions is determined by and event study using the cumulative abnormal return (CAR) on the stock market as a result of the acquisition announcement. An OLS regression shows that age diversity positively influences the performance of the cross-border acquisition, though this effect is negative when environmental uncertainty is high. Also nationality diversity is shown to be negatively related to acquisition performance under high environmental uncertainty. Overall these findings show weak positive evidence for TMT diversity on the likelihood and performance of cross-border acquisition. The direction of the effect of TMT diversity on acquisition performance becomes negative when environmental uncertainty is high.

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2.

L

2.1. Cross-border acquisitions

An acquisition is defined as the transfer of ownership, which takes place when the bid of the acquiring company for the target is accepted or when the acquirer actively purchases shares of the target company to obtain the majority of ownership (Ross et al., 2011). A cross-border acquisition fulfils the above-described characteristics. However, it also includes a target firm from a country that is different from that of the bidder company. Cross-border acquisitions have seen a great increase in frequency and value over the last 20 years (Gaffney, Karst & Clampit, 2016). The aim of firms conducting a cross-border acquisition is to strengthen their market position, expand their businesses, seek useful resources such as complementary intangible assets or realize efficiency gains by restructuring their business on a global basis. Yet, a cross-border acquisition is more complex compared to a domestic one, including differences in political and economic environments, quality of accounting and information disclosures, cultural and corporate governance norms and bilateral trade relationships between countries.

2.1.1. Cross-border acquisitions in developing countries

Complexity of the cross-border acquisitions increases when it has been conducted in a developing country. The reason for this is the increase in institutional distance between developed and developing countries – referring to the extent of dissimilarity between host and home institutions - which can be decomposed in three parts, i.e. regulative, normative and cognitive distances (Kostova, 1997). Regulative distance relates to the distance in formal institutions, e.g. law, whereas normative and cognitive distances relate to the distance in informal institutions, e.g. norms, values and culture.

The greater institutional distance between developed and developing countries results in both opportunities and challenges for the acquiring firm.

8 their labour intensive activities to developing countries. On the other hand, a challenge of an acquisition in developing countries is the necessity to learn about the new environment. Greater institutional distance increases the liability of foreignness (LoF). Zaheer (1995) introduced LoF as “the costs of doing business abroad that result in a competitive disadvantage relative to domestic firms”. There are different types of LoF: (i) costs due to spatial distance, (ii) costs due to unfamiliarity with the local environment, (iii) costs resulting from the host country environment (e.g. customers in host country only buying products from host country companies due to economic nationalism or lack of legitimacy of foreign firms), (iv) cost from the home country environment (e.g. due to export restrictions). In order to decrease the costs arising due to LoF and reap the benefits of an acquisition in a developing country, more resources, especially knowledge, experience and social connections, are necessary. A source of these resources could be the key decision makers in a firm. This will be explained in more detail in the section below.

2.2. Top Management Team (TMT)

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2.2.1. TMT diversity

Especially the diversity within a TMT is believed to influence strategic decision-making. A number of studies explore the relationship between TMT diversity and strategic decision-making, but inconsistency exists with respect to the outcomes these studies report. On the one hand there are studies showing the benefiting effect of diversity within the TMT on decision-making. Heterogeneous teams are found to be more innovative (Wiersema & Bantel, 1992), have greater problem-solving skills, and employ multiple perspectives. These benefits increase the number and variety of alternatives debated upon in a TMT when considering a certain strategy. On the other hand, there are also studies finding that diversity can hinder decision-making. The different cognitive schemes can lead to gaps between teammates’ interpretations on the situation, therefore triggering conflict (Cronin & Weingart, 2007). This can, as a result, lead to less effective decision-making processes (e.g. Hambrick, Cho & Chen, 1996), less communication among team members, decreasing constructive debate, hampering strategic innovation, and therefore, lowering the positive outcome of an organization (O’Reilly, Snyder & Boothe, 1993; Barkema & Shvyrkov, 2007).

Harrisson & Klein (2007) argue that this inconsistency exists due to the failure of authors to specify the type of diversity they measured. Scholars frequently and casually use the term diversity and such synonyms as heterogeneity, dissimilarity, and dispersion. Yet the precise meaning of diversity they have in mind is not clear. In their article Harrison and Klein seek to give a more specific understanding of diversity. This paper will build on their concept of diversity, which they define as “the distribution of differences among the members of a unit with respect to a common attribute, such as tenure or ethnicity”. Based on this definition they distinguish three different types of within-unit diversity: separation, variety and disparity. An explanation of these different types of diversity is given below.

10 implies that the members are equally split into two teams with totally different values, beliefs and opinions. The effect of diversity as separation on strategic decision-making is negative as it reduces cohesiveness, increases interpersonal conflict and results in more distrust. Secondly, diversity as variety refers to the differences in knowledge and experience among unit members. Minimum variety occurs when all members belong to the same category, so all have similar knowledge and experience. Here, adding an extra member of the same category to the team will not result in an information gain. On the other hand, maximum variety is the richest possible distribution of information, occurring when each member within the team comes from a different category (e.g. all different educational backgrounds). So a varied team possesses unique or distinctive information, resulting in greater creativity and innovation, which in turn positively influences strategic decision-making. Finally, diversity as disparity represents vertical distances, e.g. in rank or status. Minimum disparity occurs when all the members occupy the same position. On the other hand, maximum disparity implies that one unit member outranks all others in power, wealth, contacts, or other resources. There is a negative influence of diversity as disparity on strategic-decision making, as disparity results in more within-unit competition, reduced input from members and even a potential withdrawal.

11 knowledge and experiences of the managers. More specific, the different knowledge and experiences of managers drives their personal values, beliefs and attitudes with regard to acquisition decisions, e.g. due to personal experiences they might change their attitudes towards risky investments. Implying that diversity as variety is the most interesting and proper diversity type to consider with regard to the likelihood and performance of acquisitions. The expected direction of this relationship is explained below.

2.3. Hypothesis development

2.3.1. Likelihood of a cross-border acquisition

A strategic-decision making process is often seen in terms of formulation and implementation. The formulation process is considered for this first hypothesis on the likelihood of a cross-border acquisition. The strategy formulation process involves the generation and evaluation of alternatives, as well as the decision on the final acquisition, which are expected to be positively influenced by TMT heterogeneity.

Strategy formulation requires an analysis of the external threats and opportunities and the internal strengths and weaknesses. This analysis will eventually result in alternatives that are assessed and debated upon before making a decision. In this case, a homogenous TMT is less likely to come up with novel alternatives (Wiersema & Bantel, 1992), resulting in the team members being less likely to criticize each other’s ideas. They may therefore overlook important details, succumb to inertia, and suffer from groupthink causing them to reinforce rather than break familiar investment patterns (Finkelstein & Hambrick, 1996). Alternatively, a more heterogeneous team is expected to come up with a greater number and more varied alternatives, due to the greater knowledge and experience available within the TMT. The increased cognitive base will result in implementing multiple perspectives, innovative ideas and greater creativity (Bantel & Jackson, 1989). Next to that, a more heterogeneous team can rely on their heterogeneous backgrounds to gather information from different internal and external contracts (Jackson, 1992).

12 greater variety of resources, knowledge and expertise within a heterogeneous team will make them more likely to see a greater number of potential targets. Also, especially an increase in nationality diversity will increase their understanding of the international business environment. As a result it makes the TMT better able to see international investment and growth opportunities, thus increasing the likelihood of acquiring cross-border targets.

13 To summarize, a positive relationship between TMT diversity and the likelihood of a cross-border acquisition can be expected. Based on the discussion above, the following hypothesis will be tested:

H1: A more heterogeneous TMT will positively influence the likelihood of a cross-border acquisition compared to a more homogeneous TMT

2.3.2. Performance of a cross-border acquisition

This study will additionally investigate the performance of a cross-border acquisitions completed by a diverse TMTs compared to one undertaken by a more homogenous team. It is difficult to isolate the effect of an acquisition on long-term performance, as the reason for undertaking an acquisition can differ per firm. For instance, an acquisition might be undertaken to give access to intangible resources, broaden the geographic scope, enhance position in the market or create (financial) synergies. Using a single performance measure such as for instance profitability will mean that every acquisition with low profitability will be considered as unsuccessful. However, even with a low profitability an acquisition might be successful, as the main goal of undertaking the acquisition might be different from making profits. In order to take this into account, this study will focus on short-term shareholder wealth creation of the acquiring firm. The announcement of an acquisition brings new information to the market, which updates the expectation of investors. Following the efficient market hypothesis, this new information should immediately be reflected in the share price, which causes an abnormal return around the announcement of an acquisition. The abnormal return, also known as the announcement return, can be estimated by the difference between the actual return and the expected (benchmark) return (Martynova & Renneboog, 2008).

14 al., 2000; Naldi & Nordqvist, 2008). However, building on this theory it can also be argued that diversity within a TMT is expected to have a positive effect on resource availability. The increase in knowledge and experience will provide them with more diverse links to the external environment. This might prove of great importance when conducting complex strategic activities such as a cross-border acquisition. The more and various links to the external environment, next to the different knowledge, experiences and perspectives, will make a heterogeneous team better able to tackle challenges that arise as a result of international business activities. In particular nationality diversity within a TMT benefits the performance of a cross-border acquisition. Nationality diversity will more likely increase the links to foreign countries, which results in an increase of resources, social network and interconnections within the TMT. This can be used to improve the relationship with stakeholders and therefore enhancing the firms’ external legitimacy and reducing the firms’ uncertainty (Luo, 2005). Next to that, also age and gender diversity result in different perspectives on the TMT due to different knowledge and experiences. The older members on a team most likely have a lot of experience, whereas the young members maybe lack experience. Yet, these young members can look afresh at certain situations, without being biased by earlier experiences. This way, old and young members challenge each other, increase creativity and are consequently more likely to increase performance of a complex cross-border acquisition. With regard to gender diversification, currently there are relatively few women sitting on Dutch TMTs (Dutch Daily News, 2014). However, adding women to the team, and thus increasing diversity, can be beneficial when a firm operates in a complex environment (Francoeur, Labelle & Sinclair-Desgagné, 2007), which arises due to a cross-border acquisition. One reason for this is the increase in knowledge, perspective, creativity and judgment when adding women to the team. Also investors are expected to recognize the benefit and importance of the additional resources due to nationality, age and gender diversity. Therefore, it is expected that abnormal returns around the announcement date of a cross-border acquisition are higher when a heterogeneous TMT is in charge compared to when a homogeneous TMT is.

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H2: A more heterogeneous TMT will positively influence the performance of a cross-border acquisition compared to a more homogeneous TMT

2.3.3. Moderating effect of environmental uncertainty

A moderator variable is a third variable that affects the strength of the relationship between a dependent and independent variable. In this study, TMT diversity is the independent variable and the likelihood and performance of a cross-border acquisition are the dependent variables. The relationship between this independent and dependent variables is expected to be stronger when environmental uncertainty is high, which is the case when the target is situated in a developing country. This will be explained in greater detail below.

16 due to complexity in developing countries. It will build on Carpenter and Frederickson (2001), who state that “the more uncertain the decision making situation, the more likely it is that TMT demographic characteristics will be manifest in organizational actions and outcomes”. The reason for this is that an increase in complexity implies that there is even more room for the managers’ own perceptions, as less is known on the situation, allowing for more own interpretation. How this will affect both hypothesized relationship will be explained below.

It is hypothesized that TMT diversity positively affects the likelihood of a cross-border acquisition. This relationship is expected to strengthen when environmental uncertainty is higher. This argument is built on the increasingly importance of diversity within a TMT when complexity of the environment increases. The complexity of the environment increases debate even further and therefore even more alternatives are considered (Canella et al., 2008). Next to that, they will also be better able to assess these alternatives and see opportunities. As mentioned before, the costs of liability of foreignness are higher in a developing country. More knowledge, experience and social connections are necessary to decrease these costs and reap the benefits of an acquisition in a developing country. A heterogeneous provides this increase in resources. Therefore, also with regard to acquisition performance, there is more to gain with a heterogeneous TMT in target in a developing country as compared to a target in a developed country.

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H3a: Environmental uncertainty has a significant moderating impact on the positive relationship between TMT diversity and the likelihood of a cross-border acquisition

H3b: Environmental uncertainty has a significant moderating impact on the positive relationship between TMT diversity and cross-border acquisition performance

3.

D

3.1. Sample and data selection

Data on acquisition deals are collected from the electronic database Zephyr, which is one of the most comprehensive databases on M&A deals (Bureau van Dijk, undated). The deals included in the sample all meet the following criteria:

(1) The target can either be public or private and domestic or cross-border, whereas the acquirer must be a Dutch stock-listed company. Only stock-listed acquirers are included as they have a higher rate of disclosure which provides more accurate and extensive data. Next to that, Dutch stock-listed companies are required to apply a two-tier board model, in order to take into account the rights of all stakeholders. This board model consists of a management board (‘Raad van Bestuur’) and a board of directors (‘Raad van Commissarissen’). The latter only consists of nonexecutive directives, whereas the management board only consists of executives. This, for instance, implies that the CEO is only part of the executive board, so there is no CEO duality, which assures the independence of the board of directors (Van Veen & Elbertsen, 2008). Independence of the board of directors is necessary as they are responsible for monitoring and advising the management board, during which they have to take into account the interests of all stakeholders. The TMT, on the other hand, is responsible for managing the company and realizing its goals. This puts the TMT in charge for making key strategic decisions such as acquisitions, whereas the board of directors is quite removed from these decision and often dependent on information from the TMT in this respect (Nadolska & Barkema, 2014). These characteristics of the Dutch two-tier board model make it most appropriate to consider the management board in this context.

18 (3) The deal is completed between 2004 and 2014, with a final stake which is greater than 50%.

(4) Deals conducted by financial firms (US SIC 6000-6999) are excluded from the sample as these firms are subject to a special regulatory environment (Fuller, Netter & Stegemoller, 2002).

(5) The acquirer’s ISIN code and acquisition announcement date are available in Zephyr, which is needed to connect the data of the different databases.

(6) TMT data must be available.

The final sample, when taking the above-mentioned criteria into account, consists of 89 firms conducting 559 deals in total. This total amount of deals consists of 443 cross-border deals and 116 domestic deals. Of these cross-border deals, 97 are conducted in developing countries (as defined by the UN).

Data on the composition of top management teams in the Netherlands is derived from the Top Management Team database. This database is setup under supervision of dr. van Veen, who is an associate professor at the University of Groningen. Data for the database is retrieved from different sources, e.g. annual reports and other databases such as Orbis. The database contains complete data on the TMTs of the 100 largest Dutch companies over several years. The 100 largest Dutch companies include companies listed on the AEX, AMX and AScX and a number of large non-listed companies. The latter are not used in this study, as only listed companies are considered.

Data on stock returns is collected from Thomson Datastream. Datastream is a database covering extensive information on all listed companies worldwide and it contains macro economic and financial data for most countries.

3.2. Variable description 3.2.1. Dependent variables

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3.2.1.1. Likelihood of a cross-border acquisition

The likelihood will be measured by using a binary value that takes the value one for a firm engaging in a cross-border acquisition and zero for a firm engaging in a domestic acquisition.

3.2.1.2. Performance of an acquisition

The performance of the cross-border acquisition will be determined by an event study using the cumulative abnormal return (CAR) on the stock market as a result of the acquisition announcement. Two well-known methods for calculating abnormal returns are available: the market-adjusted abnormal returns model and the market model (Brown & Warner, 1985; MacKinlay, 1997). The former model is an often-used model in M&A studies (e.g. Fuller et al., 2002; Faccio, McConnell & Stolin, 2006). By applying this market-adjusted model of Brown and Warner (1985), instead of the market model, the effect of prior acquisitions by the firm during the estimation period is excluded on the announcement return of the acquisition of examination. Meaning that acquisitions of bidders that acquire multiple targets can be included without causing endogeneity problems. Therefore, this study will also apply the market-adjusted abnormal returns model, which can be presented as:

ARi,t = ri,t – Rm,t (1)

Where ARi,t is the market-adjusted abnormal return at time t of stock i, ri,t is i’s stock

return at time t, and Rm,t is the return on the local market index at time t.

The event window will consist of 3 or 5 days surrounding the announcement, which is consistent with previous research (Fuller et al., 2002; Uysal, 2011). This is done to control for potential information leakage. This can occur when information is released to a small group of investors prior to the event, which results in a stock price reaction in advance (Bodie, Kane & Marcus, 2010). In order to examine the effect of announcement on the value of the firm during the complete event window, the CAR is calculated as following:

CARi(t1,t2) = ∑𝑡_𝑡=𝑡2 𝐴𝑅_𝑖,𝑡

1 (2)

Where (t1,t2) is (-2,+2) or (-1,+1) days surrounding the acquisition announcement, and

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3.2.2. Independent variables

TMT diversity, in particular diversity as variety, will be considered as independent variable. Variety measures the within-unit diversity of categorical data, and the Blau index is a well-known and often used measure for diversity as variety (Harrison & Klein, 2007; Miller & Triana, 2009). The following formula is applied when calculating the Blau index:

B = [1 − ∑(𝑝_𝑖)2_{] (3)}

Where p is the percentage of members in the ith group.

The top manager characteristics are collected and coded in order to be able to calculate the Blau index and to measure the within-unit diversity of each TMT, theses codes can be found in Appendix 1. The characteristics considered are: (1) age of the executives; (2) gender; (3) nationality. The higher the value of B, the greater the diversity on the variable measured. The minimum of the index is always equal to zero, whereas the maximum depends on the number of categories of a particular variable. The theoretical maximum can be calculated with the following formula (I – 1)/I, where I refers to the number of categories of the variable (Biemann & Kearney, 2009). This means that the maximum of the index is higher when more qualitatively different categories are included. The number of categories and theoretical maximum for each diversity variable can be found in table 1.

Table 1: Theoretical maximum Blau indices

This table shows the number of categories and theoretical maximum of the Blau index for the different diversity variables.

Diversity variable Categories (I) Theoretical maximum

Gender 2 0.5

Age 9 0.889

Nationality 34 0.971

Due to these differences in maximums, the “overall” diversity within a team cannot be calculated by simply adding or averaging the indices (Harrison & Klein, 2007). Therefore, they will be considered separately.

3.2.3. Moderator variable

21 takes the value 0 when it is a cross-border acquisition in a developed country and takes value 1 when the target is located in a developing country.

3.2.4. Control variables

Next to TMT diversity, there are other factors expected to influence the likelihood of a cross-border acquisition and its performance. In order to reduce omitted variable biases there are two different sets of control variables included. Previous literature usually includes a set of market and accounting variables related to the firm and the acquisition deal as control variables (Moeller, Schlingemann & Stulz 2004). Next to that, several concepts related to the TMT are expected to have influence on the hypothesized relationships. The most common and important control variables used in acquisition and TMT literature are included in this research and explained below.

Firstly, control variables for the model that investigates the relationship between TMT diversity and the likelihood of a cross-border acquisition are described. Firm characteristics that are controlled for include acquirer firm size, since larger firms are more likely to have the resources and expertise to enter foreign markets (Barkema & Shvyrkov, 2007). Firm size is captured by the market value of equity three months prior to the announcement. Additionally, a control variable for firm profitability is included, as more profitable firms are better able to overcome structural and financial difficulties inherent to a cross-border acquisition. Return on assets (ROA) has been used to control for this. The final firm characteristic controlled for is the degree of internationalization on the announcement date, as a firm with a higher degree of internationalization might be more likely acquire cross-border, due to its greater experience in foreign countries. It has been accounted for this by looking at the foreign assets as a percentage of total assets. A TMT characteristic that is controlled for is the TMT size, as larger teams can have more expertise, knowledge and resources, which can reduce the risks and costs of internationalization and therefore stimulate internationalization (Rivas, 2012). TMT size is captured by the number of members which are part of the TMT (Barkema & Shvyrkov, 2007).

24 in work-related attitudes. Appendix 3 gives an overview of the defined clusters. Announcement returns are expected to be lower for a culturally distant target as investors fear that conflicts arise between target and acquirer due to the cultural differences (Chatterjee, 1992).

3.3. Model description

The study consists of three steps. Firstly, the likelihood of an acquiring firm engaging in a cross-border acquisition will be tested (Hypothesis 1). This will be tested using a binary logistic model. The regression model is as follows:

DCROSSi = β0 + β1TDIVi-1 + β2ADIVi-1 + β3GDIVi-1 + β4NDIVi-1 + β5SIZEi + β6ROAi +

β7INTERNATi + β8TMT SIZEi + εi

The dummy variable DCROSSi refers to whether an acquisition was domestic or

cross-border. TDIVi-1, ADIVi-1, GDIVi-1 and NDIVi-1 respectively represent tenure

diversity, age diversity, gender diversity and nationality diversity. The diversity variables are measured one year prior to the announcement (i – 1). There are two reasons for this. Firstly, an acquisition is a complex strategic decision, which requires cognitive effort of the TMT to evaluate the target company in a deliberate manner in order to make a conscious decision on whether to engage in the deal (Nadolska & Barkema, 2014). Next to that, the influence of a new member in a TMT might not be immediately visible. SIZEi is the size of the acquirer and ROAi represents firm

profitability. INTERNATi refers to the degree of internationalization of the acquirer on

the announcement date. Finally, TMT SIZEI refers to the size of the acquirer TMT.

After looking at the likelihood of a cross-border acquisition, the performance of the cross-border acquisition will be tested (Hypothesis 2) by an ordinary least square (OLS) regression. The regression model is as follows:

CARi = β0 + β1TDIVi-1 + β2ADIVi-1 + β3GDIVi-1 + β4NDIVi-1 + β5SIZEi + β6TQi +

β7DLISTEDi +β8VALUEi + β9DINDUSTRYi + β10DCASHi + β11DSTOCKi + β12TMT

SIZEi + β13DCRISISi + εi

CARi refers to the cumulative abnormal returns around the announcement date. TQi

25 whether the target was listed, VALUEi refers to the relative deal value and

DINDUSTRYi is a dummy variable on whether the acquirer and target are in the same

industry. The dummy variables DCASHi and DSTOCKi indicate whether the

acquisition is paid by cash or stock only, the variable ‘a mixture of payment’ is omitted from the regression model to avoid the dummy variable trap. Finally, the dummy variable DCRISISi indicates whether an acquisition occurred during the crisis

years.

Finally, the moderating effect of a cross-border acquisition in a developing country (Hypothesis 3a and 3b) will be tested using an interaction effect in the above stated regression. This results in the following regression models:

DCROSSi = β0 + β1TDIVi-1 + β2ADIVi-1 + β3GDIVi-1 + β4NDIVi-1 + β5DCROSS DCi +

β6DCROSS DCi * TDIVi-1 + β7DCROSS DCi * ADIVi-1 + β8DCROSS DCi * GDIVi-1 +

β9DCROSS DCi * NDIVi-1 + β5CONTROLSi + εi

CARi = β0 + β1TDIVi-1 + β2ADIVi-1 + β3GDIVi-1 + β4NDIVi-1 + β5DCROSS DCi +

β6DCROSS DCi * TDIVi-1 + β7DCROSS DCi * ADIVi-1 + β8DCROSS DCi * GDIVi-1 +

β9DCROSS DCi * NDIVi-1 + β5CONTROLSi + εi

26 which represents the perceived performance of an acquisition of shareholders based on the data available in the market. More specifically, investors base their expectations on the current TMT and the investors’ expectations, as revealed in CARs around the announcement date, and are unlikely to influence TMT composition changes in the future.

4.

E

4.1. Descriptive statistics

Table 2 provides the descriptive statistics of the variables in terms of mean, median, minimum, maximum and standard deviation. All continuous variables are winsorized on the 1% and 99% level in the upper and lower quantile respectively to control for outliers. The statistics show that 74.3% of the acquisitions included in the sample have been cross-border and for 16% of the acquisitions a target in a developing country was acquired. Next to that, the acquisitions are overall positively perceived by the shareholders, as both event windows, [-1,+1] and [-2,+2] show positive CARs of 0.8% and 1.9% respectively.

As explained earlier, the diversity indexes are not comparable as the maximum depends on the number of categories per diversity index. However, the means of the diversity variables given in table 2 can be compared with their theoretical maximums as given in table 1. It shows that gender (0.031) and nationality (0.321) diversity within the TMTs is relatively low when compared to their theoretical maximums of 0.5 and 0.971 respectively. On the other hand, age is shown to be relatively diverse within the teams, as the mean is 0.572 compared to a theoretical maximum of 0.889. When looking at the included control variables, it can be found that 19.2% of the deals is paid by cash only, compared to 1.5% solely stock paid deals, this implies that 79.3% of the deals was paid by a mixture of cash and stock. The average number of members in TMTs is 4 and 30% of the deals were conducted in the crisis years (2007-2009).

27 multicollinearity problems in regression analysis. Table 3 shows that none of the coefficients is exceeds this threshold, therefore it can be stated that no multicollinearity problems exist.

Table 2: Descriptive statistics

This table contains descriptive statistics for the independent variables DCROSS, CAR [-1,+1] and CAR [-2,+2], the key explanatory variables GDIV, ADIV, NDIV and DCROSS DC and control variables. An explanation of the variables can be found in Appendix 4.

4.2. Regression results

This section presents the results from the regressions which are used to test the hypothesis. Firstly, the results for likelihood of a cross-border acquisition will be presented. After this the results on the performance of the cross-border acquisitions will be presented. Both regressions will be conducted with and without industry and cluster control variables.

4.2.1. Likelihood of a cross-border acquisition

Table 4 presents the results of the binary logistic regression to predict the likelihood of a cross-border acquisition. Model 1-3 show the likelihood of a cross-border acquisition compared to a domestic acquisition. Model 4-6 show the results of cross-border acquisition in a developing country as compared to a cross-cross-border acquisition in a developed country.

Variable N Mean Median Minimum Maximum Std. Dev.

28 Model 1 only includes the control variables, INTERNAT has not been included yet, as data for this variable is not available for each deal. INTERNAT will be included later in order to test robustness of the overall model. The model shows that an increase in TMT size significantly increases the odds of a cross-border acquisition. This finding is consistent with Barkema & Shvyrkov (2007). The control variables LNSIZE and ROA do not significantly influence the likelihood of a cross-border acquisition in this first model. However, acquirer size does show a positive significant effect on the likelihood of a cross-border acquisition when including the independent variables in model 2 and 3. The positive effect of firm size on the likelihood of a cross-border acquisition is consistent with Barkema & Shyrkov (2007). Also a higher degree of internationalization of the acquirer, INTERNAT, significantly increases the odds of a cross-border acquisition. This suggests that more experience in foreign countries, due to a greater degree of internationalization, indeed results in a greater likelihood of acquiring abroad. When looking at the independent variables, it shows in model 2 that gender diversity and nationality diversity have a significant positive effect on the odds of a cross-border acquisition. When including INTERNAT in model 3, the significant positive impact of gender diversity is robust, however, the effect of nationality diversity is no longer significant. With regard to age diversity, the models show no significant effect on the likelihood of a cross-border acquisition, even though the descriptive statistics showed that age diversity in the TMTs was quite high. A possible explanation for this is that age diversity does not provide the required knowledge and experiences necessary to recognize and evaluate cross-border opportunities. Another possible explanation is that young members in the team are reluctant to share their opinions and knowledge, overwhelmed by the knowledge and experience of their older fellows. Therefore, the scope of knowledge used is reduced to only the knowledge and experiences of the older members are used. Overall, the significance of gender and nationality provide partial evidence for hypothesis 1, which states that a more heterogeneous TMT will positively influence the likelihood of a cross-border acquisition compared to a more homogeneous TMT.

30 gender and nationality diversity, which is not true. Therefore, the latter explanation cannot be confirmed.

4.2.2. Performance of a cross-border acquisition

Table 5 presents the results of the ordinary least squared regressions to predict the performance of cross-border acquisitions. By conducting the Skewness/Kurtosis tests for normality, the null hypothesis that the sample is normally distributed must be rejected. However, non-normality should not be problematic as the sample size is large enough. Also, the null hypothesis that all error variances are equal has to be rejected. This implies heteroskedasticity, which results in biased standard errors, while OLS assumes that the standard errors are both independent and identically distributed. Robust standard errors will be used to relax these assumptions, as using robust standard errors results in more accurate p-values without changing the coefficient estimates. Two different event windows are considered when looking at the cumulative abnormal returns, [-1.+1] and [-2,+2], of which the results are shown in panel A (model A-D) and B (model E-H), respectively. Industry and cluster dummies are excluded in the odd numbered columns and included in the even numbered columns. Relative deal value, VALUE, is not available for all deals, but this control variable has been included in some of the models in order to test robustness.

31 acquirer are in the same industry is found to have a significant negative effect on the CARs in the second event window (panel B). This is inconsistent with previous literature, which states that announcement returns are higher when the target and acquirer are in the same industry.

The independent variables are included in model B and F. The CARs in the first event window are found to be significantly positive effected by age diversity. This finding is robust when including industry and dummy variables in column 4. Also in column 6, which controls for relative deal value, age diversity is still significantly positive related to CARs [-1,+1]. In column 5, where no dummy variables for industry and cluster are included, age diversity is no longer significant at the 10% level. However, the t-statistic shows that it would have been significant at the 15% level. The significant effect of age diversity disappears in the second event window. With regard to the other diversity variables, it can be found that only gender diversity has a significant positive effect in column 5. However, this effect is not robust in the other models and neither in the second event window. Nationality diversity appears to have an insignificant effect on the CARs for both event windows.

Based on this it can be concluded that only weak evidence is found for hypothesis 2, which states that more heterogeneous TMTs will positively influence the performance of a cross-border acquisition compared to more homogeneous TMTs. Solely age diversity appears to have a positive effect for the CARs in the first event window, and even this effect is not robust in the second event window.

32 effect on the CARs in the second event window. Hypothesis 3b states that the relation between TMT diversity and performance is stronger in case of high environmental uncertainty. This hypothesis, however, has to be rejected. Accepting this hypothesis would imply that the effects found in models A-C and model E-G are significant and stronger in models D and H, but this is not the case. These negative findings for age and nationality slightly confirm the aforementioned explanation that diversity hampers decision-making due to the inability to act as a team and reach an agreement. Also, the significant positive effect of age diversity on the likelihood of a cross-border acquisition under high environmental uncertainty might indicate that age diverse teams are overconfident. As they are more likely to acquire a target in a developing country, though, the subsequent performance of this acquisition is negative.

The weak evidence for the effect of TMT diversity on acquisition performance could imply that cumulative abnormal returns is not the appropriate measurement for performance in this context. Investors might not take into account the diversity of the TMT when responding to an acquisition announcement. Another performance measure could have resulted in stronger evidence, either positive or negative.

34

Table 3: Correlation coefficients

This table contains the correlation coefficients between all major variables, namely the independent variables DCROSS, CAR [-1,+1] and CAR [-2,+2], the key explanatory variables GDIV, ADIV, NDIV and DCROSS DC and control variables. An explanation of the variables can be found in Appendix 4.

DCROSS CAR [-1,+1]

CAR

[-2,+2] GDIV ADIV NDIV

35

Table 4: Regression results for likelihood of a cross-border acquisition

This table shows the estimates of the logistic regression of the dependent variable and control variables on the likelihood of a cross-border acquisition. Model 1-3: Dependent variable equals 1 if the deal was cross-border. Model 4-5: Dependent variable equals 1 if the deal was cross-border in a developing country. The t-statistics are presented in parenthesis. The symbols ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively. An explanation of the variables can be found in Appendix 4.

Dependent variable DCROSS DCROSS DC

36

Table 5: Regression results for cross-border acquisition performance

This table shows the estimates of the OLS regressions of the dependent variable and control variables on the performance of cross-border acquisitions. Panel A presents the results for the first event window [-1+1], whereas panel B presents the results for the second event window [-2,+2]. The odd numbered columns do not include industry and cluster controls, whereas the even numbered columns include industry and cluster controls. The t-statistics, based on White heteroscedasticiy consistent standard errors, are presented in parentheses. The symbols ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively. An explanation of the variables can be found in Appendix 4.

Panel A CAR [1,+1] A B C D [1] [2] [3] [4] [5] [6] [7] [8] Constant 0.0331*** 0.0297** 0.0185* 0.0180 0.0214 0.0337 0.0158 0.0163 (3.65) (2.19) (1.80) (1.35) (1.05) (1.50) (0.74) (0.65) GDIV 0.0185 0.0192 0.0699** 0.0708 0.0467 0.0287 (1.26) (1.28) (2.09) (1.38) (0.87) (0.48) ADIV 0.0451*** 0.0427*** 0.0337 0.0469* 0.0381 0.0450 (3.81) (3.34) (1.45) (1.86) (1.44) (0.111) NDIV 0.0011 0.0041 0.0028 0.0115 0.0134 0.0226 (0.18) (0.55) (0.18) (0.67) (0.75) (1.12) DCROSS DV 0.1210*** 0.1096 (3.23) (1.57) DCROSS DV*GDIV 0.0357 0.1293 (0.50) (1.00) DCROSS DV*ADIV -0.1407*** -0.1038 (-2.66) (-1.14) DCROSS DV*NDIV -0.0769*** -0.0702* (-2.62) (-1.71) LNSIZE -0.0032*** -0.0029** -0.0035*** -0.0032*** -0.0041** -0.0048* -0.0041** -0.0042 (-3.30) (-2.52) (-3.22) (-2.67) (-2.01) (-1.87) (-1.99) (-1.63) TQ -0.0016 0.0042 -0.0189 -0.0104 0.0131 0.0113 0.0069 0.0111 (-0.13) (0.28) (-1.59) (-0.69) (0.61) (0.35) (0.32) (0.32) DLISTED -0.0031 -0.0047 -0.0026 -0.0035 -0.0078 -0.0012 -0.0083 -0.0029 (-0.51) (-0.75) (-0.45) (-0.60) (-1.04) (-0.14) (-1.08) (-0.33) DINDUSTRY -0.0028 -0.0028 -0.0029 -0.0035 0.0003 -0.0006 0.0019 -0.0024 (-0.84) (-0.77) (-0.87) (-0.96) (0.05) (-0.06) (0.25) (-0.02) DCASH 0.0028 0.0016 0.0009 0.0003 0.0031 0.0033 0.0016 0.0004 (0.72) (0.39) (0.24) (0.08) (0.47) (0.46) (0.22) (0.05) DSTOCK -0.0013 -0.0039 0.0042 -0.0005 -0.0100 -0.0162 -0.0129 -0.0214* (-0.20) (-0.53) (0.37) (-0.06) (-0.89) (-1.54) (-1.07) (-1.78) TMT SIZE -0.0003 -0.0003 -0.0016** -0.0017** -0.0029* -0.0034* -0.0029* -0.0033 (-0.58) (-0.52) (-2.50) (-2.38) (-1.83) (-1.88) (-1.67) (-1.66) DCRISIS 0.0011 0.0018 0.0006* 0.0014 -0.0049 -0.0049 -0.0068 -0.0077 (0.32) (0.50) (0.18) (0.39) (-0.67) (-0.65) (-0.93) (-1.09) LNVALUE 0.0027 0.0009 0.0032 0.0017 (1.18) (0.34) (1.40) (0.68) Dummy variables included

Industry No Yes No Yes No Yes No Yes

Cluster No Yes No Yes No Yes No Yes

Observations 443 443 443 443 112 112 112 112

37 Panel B CAR [-2,+2] E F G H [9] [10] [11] [12] [13] [14] [15] [16] Constant 0.0651*** 0.0908*** 0.0697** 0.0899** -0.0190 0.0191 -0.0168 0.0050 (3.76) (2.81) (2.40) (2.11) (-0.41) (0.43) (-0.38) (0.10) GDIV 0.0610 0.0708 -0.0565 -0.0504 -0.1521 -0.1731 (0.75) (0.81) (-0.55) (-0.35) (-0.83) (-0.81) ADIV 0.0170 0.0217 0.0042 0.0071 -0.0095 -0.0037 (0.56) (0.50) (0.11) (0.15) (-0.17) (-0.06) NDIV 0.0307 0.0177 -0.0412 -0.0548 -0.0327 -0.0530 (0.90) (0.48) (-1.26) (-1.09) (-0.91) (-0.98) DCROSS DV 0.0771 0.1134 (1.07) (1.28) DCROSS DV*GDIV 0.1638 0.4367 (1.03) (1.58) DCROSS DV*ADIV -0.0515 -0.1267 (-0.39) (-1.01) DCROSS DV*NDIV -0.0733 -0.0014 (-1.47) (-0.02) LNSIZE -0.0079*** -0.0110*** -0.0101** -0.0120** -0.0050 -0.0083 -0.0058 -0.0080 (-2.60) (-2.72) (-2.40) (-2.52) (-1.30) (-1.37) (-1.23) (-1.20) TQ 0.0414 0.0204 0.268 0.0097 0.0868 0.0922 0.0796 0.1127 (1.23) (0.49) (0.72) (0.19) (1.25) (1.23) (1.14) (1.26) DLISTED 0.0358 0.0353 0.0355 0.0362 0.0471 0.0503 0.0490 0.0474 (0.85) (0.79) (0.84) (0.80) (0.99) (0.95) (0.96) (0.89) DINDUSTRY -0.0212*** -0.0207** -0.0200*** -0.0203** -0.0224 -0.0226 -0.0250 -0.0251 (-2.86) (-2.31) (-2.76) (-2.30) (-1.39) (-1.00) (-1.19) (-1.03) DCASH 0.0040 0.0050 0.0060 0.0058 -0.0163 -0.0104 -0.0144 0.0081 (0.25) (0.28) (0.40) (0.36) (-0.84) (-0.70) (-0.82) (-0.51) DSTOCK -0.0439* -0.0456* -0.0423 -0.0455 -0.0760* -0.0690** -0.0783* -0.0732** (-1.78) (-1.79) (-1.40) (-1.51) (-1.87) (-2.10) (-1.90) (-2.08) TMT SIZE 0.0012 0.0008 -0.0006 -0.0007 0.0074 0.0084 0.0089 0.0099 (0.65) (0.40) (-0.25) (-0.27) (0.91) (0.81) (0.90) (0.85) DCRISIS 0.0125 0.0147 0.0147 0.0168 0.0105 0.0184 0.0080 0.0120 (0.96) (1.10) (1.05) (1.13) (0.56) (0.69) (0.42) (0.49) LNVALUE 0.0149 0.0143 0.0161 0.0159 (1.55) (1.24) (1.52) (1.28) Dummy variables included

Industry No Yes No Yes No Yes No Yes

Cluster No Yes No Yes No Yes No Yes

Observations 443 443 443 443 112 112 112 112

38

5.

C

The number of cross-border acquisitions increases, even though these acquisitions are more complex when compared to domestic deals and failure rates are high. Complexity increases even further when a target in a developing country is acquired. Previous research suggests that acquisitions decisions are driven by the TMT of a firm, especially TMT diversity has been found essential. Though, former researchers report inconsistent evidence and a possible explanation for this is the failure to specify the type of diversity measured. Next to that, often no distinction is made between cross-border acquisition in developed and cross-border acquisitions in developing countries. However, the difference in environmental uncertainty between these two types of countries might require different knowledge and information and different decision-making processes. This research fills this gap, by firstly looking at diversity as variety specifically and secondly making a distinction between cross-border acquisition decisions in developed and developing countries.

39 This study offers some important managerial implications. Firms are increasingly pressured to internationalize, resulting in an increase of cross-border acquisitions. These cross-border acquisitions go along with great managerial and operational complexity, especially when the target is situated in a developing country. This research implies that firms who are planning to acquire in developed countries should increase overall diversity on the TMT. However, firms planning to acquire in developing countries should recognize that diversity on the TMT is not beneficial, and performance will be greater with a homogeneous TMT.

40

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45

A

1:

C

B

This table how the diversity variables are classified in order to calcute the Blau index. Variable Classification Categories