Master Thesis: Does the national diversity of the Board of Directors influence internationalization? Country decision through M&A by Dutch listed firms

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Master Thesis: Does the national diversity of the

Board of Directors influence internationalization?

Country decision through M&A by Dutch listed firms

H.J.C.S. Roos

MSc. International Business & Management 13 - June - 2016

University of Groningen Faculty of Economics and Business

P.O. Box 800 9700 AV Groningen

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Abstract

Internationalization activity by companies is ever more prevelant today. Decision making by firms is, more often than not, based on the input of multiple individuals. In turn, these individuals are influenced by multiple characteristics such as age, tenure, gender, or experience. Existing research points towards a knowledge gap regarding the influence of a culturally diverse top management team on internationalization decision making, and in specific on country selection. In this thesis it is hypothesized that more nationalities within a board of directors diversifies the range of countries that the firm can interact with due to increased foreign knowledge. Results show a positive, but weak, relationship between board national diversity and the range of countries measured in the distance between weighted board of director’s score and target country score. When control variables are entered these findings are only partial significant. From the results it can be concluded that national diversity matters, even though the explanatory power is low. Measuring using the Psychic Distance Stimuli, which is more in line with the demographic variables measured, increased the strength of the model. When the scales used to operationalize the distance shifted to include more cultural aspects, the strength of the relationship rose indicating that nationality functions better as a proxy for culture rather than other country specific forms of knowledge.

Keywords

: Board of Directors; National Diversity; Internationalization; Top Management Team; Global Opportunity Index; Psychic Distance Stimuli; Upper Echelon Theory

1st supervisor: M.E. Inklaar, MSc

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Introduction

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Research into norms, values, and behavior sees an individual’s culture as an important factor influencing their decision making process (Ancona & Nadler, 1989; Pettigrew, 1992). Research by Reuber & Fischer (1997) investigates the influence of the managerial international experience, which is expected to be interchangeable with having a top management team with multicultural background. Therefore, a top management team consisting of a more culturally diversified group of individuals uses these multitude of differences as a basis for their decision making process. The strategic decision in the form of country selection is an activity that requires input from all members of the top management team. Because of that it is an excellent starting point for further research into the internationalization decision making processes within the top management team (Barkema & Shvyrkov, 2007).

Aforementioned literature indicates the need for further research into the influence of individuals on decision making by top management teams. Whereas age, tenure, and experience have been extensively researched, nationality is yet to be fully explored. While it is argued that having a certain nationality influences a person through behavior and values it also might bring institutional or economic insight. Currently it is unknown to what degree differences in nationality replace different kinds of knowledge needed for internationalization. The aim of this research is to investigate whether the nationality composition of top management teams influences international strategic decision making. In specific, the paper addresses whether there is a significant relationship between the national diversity of the top management team and the different countries these firms have entered. Therefore, the following research question is formulated: Does national diversity of board members act as substitute for other forms of knowledge needed when firms engage in international strategic expansion measured through the decision of country selection?

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This research is categorized as a deductive study, which entails that a hypothesis is formulated based on existing literature, followed by designing a research strategy in order to test this hypothesis (Wilson, 2013). The results will confirm or reject the validity of the hypothesis that is tested under certain circumstances. Logistic regression is used to test the former mentioned research question. This method is selected due to data containing mostly numerical data. Furthermore, the type of research question favors the use of this method where it is questioned if there is a relationship, and if so, to what extent. The data is collected from existing databases in combination with retrieving data from annual reports.

Firms of the Dutch AEX and AMX indices are included in the sample. The Netherlands is characterized by some features that make it a good choice to investigate. Firstly, the Netherlands is the fifth most prosperous country worldwide enabling businesses to prosper (United Nations Development Programme, 2015). Furthermore, the high entrepreneurial spirit, favorable business climate, and high amount of exports enable businesses to grow and expand (Holland Trade and Invest, 2016). Finally, data availability and accessibility enables the Netherlands to be researched. By selecting the Netherlands, it will impact the generalizability of the results. The majority of the firms included are of Dutch origin, resulting in compliance with Dutch law and code of governance. The used indices do represent mid to large firms and vary by industry. It is possible that firms originating from other countries, or active in certain industries, might not commonly use the public market in order to gain capital and lack representation by using Dutch or any indices.

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Literature

This chapter starts with providing insight on the influence of culture, and thus nationality, on individuals. Knowing that there are differences between individuals, the next section shows that individual opinions and knowledge do influence the decision making of a team. The last section of the literature indicates that internationalization requires strategic decision making that is in line with the tasks and responsibilities of the members of the board of directors. After this chapter it should be clear that members of the board of directors are influenced by their nationality and because of their advisory and monitoring tasks this will similarly impact the strategic decisions of the firm.

Influence of culture on individual behavior and decision making

Across the globe people tend to have different beliefs, and act according those beliefs. These differences are often categorized as cultural differences. Hofstede (1980) defines culture as homogeneous norms and values that are different between groups of humans and influence human behavior. A certain culture is maintained and transferred between the members of that group (Arnould & Thompson, 2005). These cultural values originate from multiple sources (e.g. society, regional, individual, familial), and are imposed on a human from birth providing them with restrictions and benefits thus influencing their behavior and decision making (Luna & Gupta, 2001). Differences in these aspects can vary between countries, regions, cities, and even organizations. Organizations must take account of these differences in order to be successful. To elaborate on the notion that culture does in fact influence behavior two models of comparing cultures are introduced Hofstede (1980) and GLOBE (House, Hanges, Javidan, Dorfman & Gupta, 2004). Followed by a more in depth look at Hofstede´s model.

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enter new firms. Whereas Hofstede started at IBM for its research the GLOBE model takes another approach with measuring both values and practices. Hofstede used existing secondary data to look for possible relationships regarding culture, whereas GLOBE acquired primary data specific for their research needs. The model takes into account more than 17,000 managers originating from 62 Countries and compares cultures based on nine different dimensions (Shi & Wang, 2011a, 2011b). To go more in depth into Hofstede’s models each dimension is briefly explained. Appendix G contains an extensive table providing more characteristics and sample countries that match a certain dimension. Hofstede started with four dimensions: (1) Power distance; (2) Individualism; (3) Masculinity; (4) Uncertainty Avoidance. Hofstede ended up adding a fifth and sixth dimension (5) Long Term Orientation and (6) Indulgence versus Restraint.

Firstly (1) Power Distance reflects the acceptability of less powerful members of a country towards the unequal distribution of power. A high power distance increases the distance between leader and subordinate resulting in a taller organization. Furthermore, people in countries with high power distance have an increased need in hierarchy and dependence needs.

(2) According to Hofstede (2015), the level of individualism in the individualism vs collectivism dimension depends on the degree of integration with groups. De Mooij (2004) states that, from a marketing perspective, cultural values such as prestige, status, symbolism, family, and groups are leading in countries with a low level of individualism, whereas high levels of individualism rather correspond with performance and design (Soares, Farhangmehr & Shoham, 2007).

(3) Hofstede (2015) says the following regarding masculinity: “The Masculinity side of this dimension represents a preference in society for achievement, heroism, assertiveness and material rewards for success. Society at large is more competitive. Its opposite, femininity, stands for a preference for cooperation, modesty, caring for the weak and quality of life...” Important characteristics are the gender separation of roles and working to live versus live to work. For employer’s, masculinity is visible in the attitude of its employees.

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for agreement. These people will often experience higher levels of stress and anxiety. On the opposite side people can be low in uncertainty avoidance. These individuals are more relaxed and willing to take more risks.

(5) People with a high Long Term Orientation (LTO) are said to have a rather long-term view of life. On the opposite side, people with a low LTO are rather short termed and live more in the moment. LTO influences customer behavior in that it reflects in their actions to save money for future needs and wanting to pay less for unnecessary products or services when that culture scores high on LTO (Ukessays, 2015; Hofstede, 2015). Appendix G elaborates further on the differences between high and low LTO.

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therefore can serve as proxy for culture. It is important to realize that even though nationality can serve as proxy in the general sense, cultures within a single country can vary.

There are multiple models in existence that provide insight into differences between countries. Models like Hofstede forces, Schwartz, and GLOBE focus on cultural aspects. Nevertheless, culture is only one way to characterize an environment. Other models try to capture national differences on a larger perspective. Three models will be highlighted to indicate other areas on which individuals can possess knowledge. These models are the CAGE model, Global Opportunity Index, and the Psychic Distance Stimuli.

Firstly, the CAGE model created by Ghemawat (2001) operationalizes the distances between countries in such a manner that a larger difference between countries results in a higher risk for failure and where it can be argued that having individuals from different nationalities increases diversity of knowledge and consequently reduces risk. Caligiuri, Lazarova and Zehetbauer (2004) clearly outline the multiple variables used in the CAGE model which empathizes on multiple forms of distance. Besides cultural it also includes administrative, geographic, and economic distance. The CAGE model is often used by researchers but also serves a practical duty to the degree that it is suitable for managerial positions.

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Table 1: Buildup of the Global Opportunity Index

Economic Fundamentals

Economic Fundamentals measures the extent to which a country's macroeconomic environment is conducive to foreign direct investment. A value of 10 indicates very strong economic fundamentals, while a value of 0 indicates relatively weak conditions.

• Macro performance

• Openness to trade and FDI

• Quality and structure of labor force

• Physical infrastructure

Ease of Doing Business

Ease of Doing Business measures explicit and implicit costs associated with business operations. A value of 10 indicates very low costs of doing business in a country, while a value of 0 indicates very high costs.

• Accounting and disclosure requirements

• Costs of terrorism and crime

• Tax burden

• Costs of starting a business

• Costs of enforcing contracts

• Costs of resolving insolvency

Quality of

Regulations/Regulatory Barriers to Investment

Quality of Regulations/Regulatory Barriers to Investment assesses the effectiveness of policymaking and enforcement in a country and similarly reflects the extent to which a country's laws and regulations prevent the free flow of trade and investment. A value of 10 indicates efficient enforcement of policies and minimal barriers to capital flows, while a value of 0 indicates the opposite.

• Extent and burden of regulation

• Corruption

• Transparency

• Extent of controls on capital

Rule of Law

Rule of Law reflects the extent to which a country's legal system protects investors and property rights to support and enhance business investment. A value of 10 indicates commitment to the rule of law, while a value of 0 indicates the opposite.

• Legal infrastructure

• Protection of property rights

• Protection of investor rights

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The last 3 models provide insight into forms of knowledge, besides culture, that are country specific. These models can be differentiated by the amount of culture that is included. Besides nationality as proxy for culture, it might also function as proxy for these other influences as well that impact decision making.

Individual executives/ and their influence on the Top

Management Team

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Initially, upper echelon research was mainly focused on the individual executive level (e.g. CEO). However, leadership of a complex organization is the shared activity of the top management team and is therefore a more appropriate level of reserach (Hambrick & Mason, 1984; Hambrick, 2007). The collective backgrounds, experiences, values and personalities of the top management team influences strategic decisions, especially in the case of foreign expansion when the entire management team is involved. (Barkema & Shvyrkov 2007). In this paper, the composition of the top management team is defined as “the collective characteristics of top team members, such as their values, cognitive bases, personalities, and experiences” (Finkelstein, Hambrick & Cannella, 2009). As a result, top management team’s heterogeneity or diversity encompasses the variations in these collective characteristics within the top management team.

In conclusion, individual executives constitute the top management teams and their individual backgrounds, experiences, values and personalities comprise the collective top management team composition and diversity. Therefore, top management teams are a more appropriate setting to study the implications of the upper echelon theory, especially in the case of internationalization and foreign expansion. As national culture influences individual executives, who in turn constitute the top management team, the relationship between national diverse top management teams and their strategic decisions is highly relevant. The next section will therefore explore the relationship between national diverse top management teams and internationalization.

Internationalization and strategic decision making

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Critics of these models claim that entering a country that is dissimilar is only viable if the firm is able to gain additional knowledge in the home-country prior to entering abroad. Cuervo-Cazurra (2011) suggest the following forms of knowledge are essential: ”Knowledge to manage complexity, developed by having multiple operations at home; knowledge to manage differences in competitive conditions, developed by operating in business-to-business industries, and knowledge to manage differences in institutional environments, developed by allying to a foreign firm at home.” The institutional knowledge suggested by Cuervo-Cazurra can be defined as the set of norms, rules and regulations that determine economic relationships in a society (North, 1990). Research by Blomstermo et al. (2004) indicated three factors that are required for success in internationalization, but does not differentiate the necessity of these factors in advance. The factors are:

● Internationalization knowledge; ● Foreign business knowledge; ● Foreign institutional knowledge.

In this thesis it is argued that especially foreign institutional knowledge can be substituted by having a multicultural team, which natively have this knowledge. In addition, the concept of liability of foreignness emphasizes the importance of foreign institutional knowledge. Doing business abroad brings additional risks, costs, and opportunities (Hymer, 1976). The liability of foreignness is the force that induces extra costs for foreign organizations due to differences in cultural, economic and political environment (Zaheer, 1995). Consequently, culturally diverse top management teams could overcome these costs and risks due to their natively acquired foreign institutional knowledge.

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Figure 1 Conceptual Model

expansion takes place within the cultural blocs the firm is active in (HQ and subsidiaries) or without the current cultural blocs, thus increasing the diversity and range of countries the firm operates. In addition to culture as institution, legislation of a country is another institution that can influence the selection of a specific country.

Both opposing views, which stresses either similarity or dissimilarity require some form of cultural knowledge (which could be considered to be a form of institutional knowledge) to successfully enter foreign countries. This study investigates whether the ability to manage differences in institutional environments could be substituted by having a cultural diverse top management team. In this thesis it is therefore hypothesized that foreign cultural and institutional knowledge gained through learning pre-emptively or through exploration, could reside within a top management team when it is built upon multiple individuals with different cultures. As a result, both increased cultural and institutional knowledge and a culturally diverse top management team increase the diversity of countries attractive to a firm for expansion.

These relationships are captured in the conceptual model in Figure 1 and result in the following hypothesis:

Hypothesis: A higher nationally diverse top management team will have a positive significant influence on the range of countries the firm internationalizes to.

Country Diversity Nationally Diverse Top

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Methodology

The appropriateness of the research design is the first subject discussed in this chapter. This is followed by the sample selection of M&A transactions. This forms the basis from which we determine the nationality diversity and distance. Dependent, independent, and control variables are third to be discussed. This includes diversity and how this is measured. This chapter concludes with the data processing and analysis which includes preliminary analysis like frequency and distribution.

Appropriateness of the Research Design

This research investigated if there is a significant relationship between the independent and dependent variables. If the main goal of this research had been to explore how this relationship is built up, a qualitative approach would have been an obvious choice. Since this is not the case, the quantitative approach is the preferred method. Furthermore, data availability and variable selection favored categorical or numerical data. In addition to testing the general relationship between a national diverse board of directors and internationalization through country selection, this research included control variables in the regression to enrich and deepen the findings.

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Chairperson of the Board, Chairperson, CEO, COO, President, Senior Vice-Presidents, and Executive Vice-Presidents (Tihanyi et al., 2000). Besides the existence, availability of data is important. For this paper the Netherlands was chosen as being the most favorable country to focus on due to both existence and availability of data.

Sample and Procedure

Merger & Acquisition (M&A) data from Zephyr provides the internationalization transactions used in order to measure the diversity. Zephyr is a database provided by Bureau van Dijk and contains comprehensive data which is updated on an hourly basis. It contains the needed M&A data, IPO’s, and private equity deals. This database is limited such that it only contains data starting in 2007. This limitation reduced the time scope from 10 to 8 years (1-1-2007 to 21-12-2014).

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Besides filtering out the sample firms in the M&A data, multiple other filters have been applied. A filter was added to remove M&A data that represented an increase in ownership. This was done as a decision to increase ownership does not reflect a decision for country selection. Furthermore, the date is filtered to ensure all entries were announced during the years 2007 to 2014. By using the announcement date rather than the completion date the goal is to reduce time lag. The assumption is made that at the moment of announcement the board of directors is in agreement with the decision and thus had the chance to provide assistance and governance. Often there is a time difference between announcement and execution. Within this timeframe board members could have joined or left which would influence the measurement of distance since the board of directors would not be a good reflection of the board at time of the country selection. In the end the data represented 35 AEX and AMX firms and 430 M&A entries on which further analysis has been done.

Variables

Dependent variable (DV)

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transactions but also states that increased board involvement increases the effectiveness of post-merger integration plans.

In order to find out if firms enter a wider range of countries the distance is measured between the board of directors and the target country of their corresponding M&A activity. This is operationalized by using a measuring scale in order to quantify, or score, each country. The target country is a simple conversion whereas the board is slightly more complicated. For the board of directors, a weighted score is created using the different nationalities of which the board of directors is comprised. To summarize, the absolute difference between the weighted score of the board of directors and the target country is used to indicate the distance between them. In this research this DV will be referred to as: “M&A Distance – B-T”. For extended analysis the dependent variable is replaced by two slightly different versions. First, instead of the difference between the board of directors and the target country this DV looks at the distance between the home county of the firm and the target country and is referred to as “M&A Distance – H-T”. For the last DV a different measuring scale is used. This is done in order to check the scale used to operationalize. Using this second scale it is possible to see whether the power of the relationship is different when the scale contains more cultural aspects. By doing this, conclusions can be drawn as to whether nationality is a better proxy for demographic factors rather than other institutional and business orientated forms of knowledge. This DV is based on the distance between home and country and will be referred to as: “M&A Distance – H-T-PDS”.

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comparison even less reliable. The Global Opportunity Index differentiates by not only investigating basic economic variables but also explores the regulatory and legal aspects. There are two general assumptions when using this index. The first assumption is that when a person has a certain nationality it is likely that this person has working experience in their country of origin and has obtained functional knowledge of doing business in that specific country. The second assumption is that countries with similar weighted scores are likely to be comparable for understanding the way the country is organized. For example, Belgium and the Netherlands might have similar scores which means, based on this assumption, a Belgian top management team member is able to understand and work with/in the Dutch environment. The use of this index has some limitations that potentially influence results. The Global Opportunity Index represents countries from a 2015 perspective. At the moments of the M&A transactions, used in this research, may have a slight variation resulting in the used data not fully representing a specific transaction in time. Even though the founders of this index confirm this variation, they do note that this change is limited to a relatively small convergence of developed and developing countries. In order to test the predictive power of nationality as proxy for national business and institutional-knowledge a similar model is used in order to make comparison possible. This second model called the Psychic Distance Stimuli was created by Douglas Dow (2006). Psychic Distance Stimuli is frequently used in the Uppsala school of research and is an operationalization of Psychic Distance.

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Independent variable (IV)

The IV is the national diversity of the board of directors. National diversity is the difference in nationality of the members on the board of directors. Where previous research (Caligiuri, Lazarova & Zehetbauer, 2004) only focuses on the ratio of domestic versus foreign members of the board of directors, this paper differentiates by investigating whether the influence of the nationality of each member is separately reflected on the decision of country selection. Using this method, it takes all different nationalities into account. For example, a firm with 10 board members with a ratio of 1 domestic and 9 foreign nationalities can contain from 2 to 10 different nationalities which are otherwise overlooked. The measurement of the diversity is done using the Blau-index, which is explained in the Diversity paragraph below the Control Variables section. The board of directors is used in the evaluation since they are responsible for, and represent, the larger strategic decisions taken by their firms. Often firms have a group of employees who can provide influence on these strategic decision but are not members of the board of directors. Nevertheless, these individuals are not included in this research since this information is not always provided by firms.

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Control variables

Following Tihanyi et al. (2000), Barkema and Shvyrkov (2007), and Nielsen and Nielsen (2013) this research included variables in the model to control for other top management team diversity factors and aspects influencing internationalization.

• Age Diversity; of the board of directors.

• Gender Diversity; which is listed as M for male and F for Female. • Function Diversity; Executive or Non-Executive.

• Board Size; the number of board members

Age as a demographic factor is often researched and found to influence strategic decision making performance (Hitt and Tyler, 1991). Wiersema & Bantel (1992) found more specifically a relationship between the average age of the top management and the frequency of organizational strategic changes. Further research finds that age influences knowledge and orientation (Datta and Rajagopalan, 1998). This influence originates from the necessity of older individuals to obtain a higher level of security and safety (Bantel and Jackson, 1989., Grimm and Smith, 1991).

Gender is a demographic factor that can be viewed from 2 basic perspectives: ethical and economical. Ethically, gender is a focus point of corporate governance from the last decade where it is desired to have a balanced representation of males and females in the board of directors to reflect a more equitable outcome. From an economical perspective gender diversity is claimed to have other benefits. Smith et al, (2006) states that a higher diversity may increase a firm’s competitive advantage due to the positive increase of the image of the firm. Carter et al. (2003) find a positive and significant relationship between Tobin’s Q and the proportion of women on the board. Opposite literature views gender diversity as a negative variable of board performance due to homogenous groups communicating more and having similar experience and opinions (Earley & Mosakowski, 2000) and heterogeneous groups encounter more emotional conflicts (Tajfel & Turner, 1979).

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(Raad van Commisarissen). In this thesis this separation is referred to as Function. Since executive members are assumed to be more active in the firm it is possible for these individuals to gather more internal knowledge which can result in increased power of their opinions.

Research by Haleblian and Finkelstein (1993) states that the size of the board of directors is more often considered to be a control variable since it can influence strategy and its decision making potential. These authors explain their statement by claiming that smaller groups experience less communication and coordination issues compared with larger groups. Group size is found to have a U-shaped relation in regards to advantages and disadvantages (Hambrick & D’aveni, 1992). Positive aspects of larger boards originate from having more knowledge and increased perceptions of issues and possibilities. In this thesis the size is the quantitative amount of board members.

Diversity

While it is clear that the Global Opportunity Index is used to operationalize differences between board composition and the target country this data still needs to be transformed to indicate the diversity of the variables. Many formulas and methods exist to calculate the variety of a group. For the measurement of the Function-, Gender-, and Nationality Diversity variables this paper uses the Blau´s index (Blau, 1977) for variety using the formula B = 1-, where p is the percentage of members in the ith group (Polzer, Milton & Swann, 2002). This index has been selected due to it being frequently used and is applicable to a multitude of data. The formula for the Blau´s index is shown below. This index, when using more than 2 different measurable varieties, range from 0 (Homogeneous) to 1 (heterogeneous). In the case of only 2 varieties (e.g. gender with male or female), the range varies from 0 to 0,5. If comparison is made between a variety of 2 diversities it is needed to normalize the results. The diversity in terms of age is calculated by dividing the standard deviation by the mean, a measure for diversification of interval level variables (Murray, 1989).

𝐵𝑙𝑎𝑢 𝑖𝑛𝑑𝑒𝑥 𝑓𝑜𝑟 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 = 1 − 𝑃₆7 8

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Data Processing and Analysis

In order to test the hypothesis, this research used the Pearson correlation to test for possible relationships and Ordinary Least Squares (OLS) regression analysis to analyze the variance of each model. This selection is based on the type of data. This method is likely to produce results that can be used to accept or reject the hypothesis. Before the main analysis first the descriptive statistics (Table 2), the Pearson correlation (Table 3), and the distribution have been determined (Figure 2).

The sample consists of 430 M&A events from the start of 2007 until the end of 2014. Of the boards of directors used in this analysis the size ranged from 6 to 23 members (Mean= 11.52, SD= 3.32). The variables Gender- and Function Diversity both had 2 options (e.g. Male/Female and Executive/Non-Executive). After calculation of the corresponding diversity and normalization of the results the scores should be interpreted as followed: A score of 1 equals a fair distribution between males/females and executive/non-executives. The lower the score of these diversities the larger the gap between these 2 options. The Function Diversity indicates a reasonable division of function where, as expected, there are more non-executives than executives (M= 0.83, SD= 0.2). Gender Diversity showed a wider gap between male and female (M= .36, SD= .28). Looking at the correlation matrix in Table 3 there are no signs of multicollinearity between the IV and control variables. The additional test for possible sum variables is done through the Cronbach’s Alpha analysis. The score of this analysis ranges from 0 to 1. A sum variable should be computed when the output is minimum 0,6. The Cronbach’s Alpha for these variables did not require any recoding of variables being only ,07.

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Figure 2 Distribution National Diversity

Table 2 Descriptive Statistics Frequencies

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Results

In order to analyze whether M&A Distance – B-T, is influenced by National Diversity a regression analysis is performed. National Diversity is regressed on the difference between board and target country based on the Global Opportunity Index scale. Model 1 contains only the control variables. In Model 2 National Diversity is added to see what influence the IV has given the control variables. The full output is listed in Appendix C. As shown in Table 4 Model 1 was significant, R=.206, Adjusted R2=.034, F(4,425)=4.726, p=.001. Of the control variables in Model 1 only Function Diversity is significant; B=.632, t(430)= -3.897, p < .05. When National Diversity is added in Model 2 the explanatory power of the model increased, R=.225, Adjusted R2=.040. Even though Model 2 is significant p< .001, only the variables Gender Diversity and Function Diversity met the criteria of P < .05. For this model the Durbin-Watson score was d = .777 and did not fulfill the criteria of 1.5 < p < 2.5 meaning that it can be assumed that there is a first order linear auto-correlation.

Table 4 Regression Analysis

DV Board - Target DV Home - Target DV Home - Target PDS Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Constant 1,159*** 1,033*** 1,335*** 1,136*** 4,308*** 3,66*** Age Diversity -0,002 0,002 -0,002 -0,019 0,02 -0,037 Board Size -0,004 -0,015 -0,25* -0,285* -1,084*** -1,2*** Gender Diversity -0,200 -0,223* -0,666*** -0,528** -2,653*** -2,202*** Function Diversity -0,632*** -0,545** -0,018 -0,012 0,005 0,026 Independent Variable National Diversity 0,297 0,468** 1,529*** R 0,206 0,225 0,192 0,028 0,325 0,362 Adusted R2 0,034 0,040 0,028 0,042 0,097 0,121 N 430 430 430 430 430 430 *p < ,05. **p < ,01. ***p < ,001.

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amount of the variance of M&A Distance – H-T; F(4,425)=4.049, p<.05, R = .192, Adjusted R2 = .028). Of the control variables in this model only Gender Diversity and Function Diversity are significant. The explanatory power of model 4 increased when National Diversity was included, R= .230, Adjusted R2= .042. In this model the control variables Board Size and Age Diversity are not significant. The control variables Gender Diversity, Function Diversity, as well as the IV do show signs of significance. Residual statistics show that the values for maximum and minimum are not within the range of -3.29 and -3.29 meaning that there are outliers present in the data. For the multicollinearity assumption the data was tested and no indicators are found (National Diversity, tolerance = .612, VIF = 1.633; Function Diversity, tolerance = .734, VIF = 1.362; Gender Diversity, tolerance = .956, VIF = 1.046). The data did not meet the assumption of independent errors (Durbin-Watson = .688) where it is expected to score between 1.5 and 2.5.

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Conclusion & Discussion

Conclusion

Since the activities of the board of directors keeps increasing, their influence and input will increase in a similar fashion. This research aimed to clarify whether national diversity within a board of directors influences the country selection occurring with mergers and acquisitions. Other demographic factors have been previously researched to a larger extent with mixed results. Nationality has been ignored in most cases or treated superficially, for instance by using ethnicity as proxy. Theory indicates that nationality and culture will influence an individual’s behavior and decision making. The upper echelon theory states that these different opinions and behaviors consequently influence the output of the top management team. This lead to the formulation of the following research question: “Does national diversity of board members influence the firm’s strategic internationalization decisions through country selections?”. This is answered through the results retrieved from the analysis. The research was done by generating a sample of Dutch firms listed on the AMX and AEX indices. These firm’s annual reports provided information for each individual board member containing: gender, age, function, and nationality. This data is combined per board and nationality was operationalized by using the Global Opportunity Index and the Psychic Distance Stimuli. Merger & Acquisition data was retrieved from the data base Zephyr which provides data from 2007 to 2014.

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and IV. This larger distance results from the Netherlands being rated relatively high on the Global Opportunity Index. When a board becomes more diverse the weighted score is likely to decrease because of the inclusion of individuals from lower scoring countries. Nevertheless, the explanatory power of both model 3 and 4 did only increase incrementally. These models indicate that when diversity occurs within large and medium sized Dutch firms, they are diversifying with nationalities that are scoring lower on the Global Opportunity Index. The majority of the Foreign M&A activity is oriented towards countries which are similarly scoring lower on the index.

For models 5 and 6 the dependent variable was again changed. National Diversity was regressed against the absolute difference between the home and target country but this time measured using the Psychic Distance Stimuli index. Results indicate a similarly significant but stronger relationship. These models conclude that the significant variables, which can be classified as demographic, are a better fit using the Psychic Distance Stimuli. The underlying argument is that this index is compiled by using relatively better, more, or stronger demographic measures whereas the Global Opportunity Index has a stronger focus on the environment. Based on the results of these models it can be concluded that National Diversity is not sufficient as predictor for business and institutional forms of knowledge that are country specific. Besides the significance of the results the relations (R-values) are low from which can be concluded that again there is only a weak relationship.

Discussion

Board composition is becoming increasingly important. This is mainly due to the change in responsibilities and the contribution the board of directors can provide by increasing their support activities relative to their governance tasks. Previous research rather focuses on demographic factors like age and gender and how this might influence performance. Nationality is often overlooked or included on a wide ethical scale. Especially since internationalization is happening more frequently and the failure rate is high, National Diversity might be able to replace certain kinds of information needed to be successful. This leads to the first of four contributions this paper tried to achieve.

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Secondly, these results could have provided firms with evidence that nationalities are a sufficient variable to keep in mind when internationalizing. Unfortunately, this research did not manage to fulfill these contributions due to the explanatory power of the findings. Thirdly, using the Global Opportunity index, which is strongly correlated with the Psychic Distance Stimuli, see Appendix H, it is found that nationality is a stronger predictor when comparing it to a more culture related measuring scale (Psychic Distance Stimuli instead of the more environment oriented Global Opportunity Index). Fourthly, the empirical contribution of this paper is the extension of upper echelons theory with the characteristic cultural and national diversity in relation to internationalization and country selection. This study could have provided practical implications - for example the structure and formation of the selection and recruitment policies regarding top management teams and the potential selection of countries for international diversification. Due to the very low explanatory power of the relationship this research must conclude that national diversity does, in this research setting, have no practical use. Theoretically national diversity can still provide valuable insight but not as proxy for non-country forms of knowledge. Further research on this is required.

Limitations and suggestions for future research

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that positively influences generalizability. Nevertheless, some industries potentially engage Merger & Acquisitions strategies differently due to the motives for these activities (Brouthers & Brouthers, 2000). This should be taken into account with future research. Secondly, multiple measuring scales exist to measure distance between countries. This can vary from culture, geographical, institutional and more. For this research a scale is preferred which presents insight from a balanced perspective rather than solely focusing on culture. Besides the existence of these scales this research also battles with their availability. Some of these, more frequently used, measuring scales are not within range and cannot be used. The Global Opportunity Index is selected due to providing insight on four different aspects and where it is theorized that a nationality with a similar score can be understood in a similar fashion. It is critical to keep in mind that this is not always true. The Global Opportunity Index does focus rather more on environmental aspects. For this reason, a second measuring scale is introduced. The Psychic Distance Stimuli is often used and accepted by the Uppsala school of research where it is commonly used to measure distances. This measure provides directly comparable results between countries and includes more culture oriented variables. This might be the reason for having a higher explanatory power in the analysis.

Thirdly are the motives of firms. Firms have multiple reasons to engage in (cross-border) Mergers & Acquisitions. From a value creating perspective firms can; diversify revenue streams, increase efficiency through economies of scale (Seth, 1990), and absorb competitors to increase buyers bargaining power (Calipha, Tarba & Brock, 2010). The differentiation that should be acknowledged is between absorption and operationalization. When a firm wants to absorb knowledge, less interaction is needed on a longer term basis. This might be considered the easier option of the two. This potentially neglects the influence of the target county as opposed to when the firm wants to have operations in the target country. In the latter mentioned, firms need to have more knowledge regarding customers and their behavior. This should not matter to the same extent with firms following a knowledge absorption strategy.

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Appendix

Appendix A: Global Opportunity Index

Rank Score Country Code Economic

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65 4.31 Bulgaria BGR 6.09 6.05 5.20 4.20 134 2.31 Burundi BDI 2.09 3.26 2.80 3.40 111 3.17 Cambodia KHM 3.32 4.21 4.00 4.30 126 2.64 Cameroon CMR 2.45 3.95 3.40 3.40 6 6.19 Canada CAN 6.14 8.00 8.00 8.80 20 5.69 Chile CHL 6.68 6.68 7.80 7.30 52 4.68 China CHN 6.32 5.79 6.30 5.00 59 4.39 Colombia COL 5.59 5.58 5.30 5.50

72 4.18 Costa Rica CRI 4.95 5.94 6.00 4.00

117 3.06 Côte d’Ivoire CIV 3.00 4.79 4.70 2.80

27 5.41 Cyprus CYP 6.14 6.42 7.00 7.50

43 4.82 Czech Republic CZE 7.00 6.52 5.60 5.00

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53 4.65 Georgia GEO 4.77 7.26 5.90 5.30 18 5.71 Germany DEU 6.18 7.78 7.60 7.00 76 4.13 Ghana GHA 2.82 5.52 6.00 6.30 74 4.17 Greece GRC 5.82 6.05 4.70 4.30 94 3.54 Guatemala GTM 3.82 4.79 6.10 3.00 135 2.30 Guinea GIN 1.86 4.05 3.30 2.30 97 3.46 Guyana GUY 3.00 4.21 5.70 4.40 132 2.40 Haiti HTI 3.36 3.42 3.80 1.40 113 3.16 Honduras HND 3.45 4.47 4.80 3.10 2 6.78 Hong Kong HKG 7.86 8.52 8.30 9.20 46 4.78 Hungary HUN 6.77 7.15 5.70 4.30 14 5.85 Iceland ISL 6.41 8.42 6.70 7.70 82 3.92 India IND 4.00 4.21 4.60 6.80 78 4.09 Indonesia IDN 4.82 4.31 6.30 5.00

118 3.03 Iran, Islamic Rep. IRN 3.14 5.31 3.30 3.40

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102 3.40 Kenya KEN 3.14 3.47 5.30 5.10

28 5.33 Republic of Korea KOR 6.50 7.94 6.00 6.20

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70 4.25 Morocco MAR 4.50 6.36 5.70 4.70 116 3.07 Mozambique MOZ 2.86 4.31 4.40 3.80 57 4.45 Namibia NAM 4.36 5.31 6.30 6.30 123 2.75 Nepal NPL 2.23 4.00 3.50 4.00 11 6.00 Netherlands NLD 7.04 7.84 8.40 6.70 4 6.25 New Zealand NZL 4.95 8.21 8.50 9.60 98 3.46 Nicaragua NIC 3.23 5.37 5.70 3.00 115 3.10 Nigeria NGA 2.59 4.21 4.10 4.60 7 6.12 Norway NOR 5.64 7.84 8.80 8.30 25 5.43 Oman OMN 5.54 7.73 7.50 6.40 96 3.50 Pakistan PAK 3.18 4.42 4.70 5.20 49 4.76 Panama PAN 6.00 6.21 7.10 4.50 93 3.63 Paraguay PRY 3.86 5.68 5.10 3.50 62 4.34 Peru PER 5.23 5.37 5.80 5.30 86 3.80 Philippines PHL 5.09 4.52 5.70 3.70 51 4.74 Poland POL 5.95 6.94 5.00 5.80 34 5.14 Portugal PRT 6.14 7.78 6.30 5.50 26 5.43 Qatar QAT 6.45 7.42 7.10 6.20 63 4.34 Romania ROU 5.45 6.47 4.80 5.00

80 4.01 Russian Federation RUS 6.27 6.47 4.30 3.00

56 4.47 Rwanda RWA 3.00 6.47 6.60 6.30

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107 3.30 Senegal SEN 2.68 4.52 5.30 4.00

92 3.65 Serbia SRB 4.68 5.89 3.40 4.30

83 3.85 Seychelles SYC 4.59 5.15 4.40 5.10

108 3.30 Sierra Leone SLE 2.23 4.79 4.40 5.10

1 6.96 Singapore SGP 7.64 8.78 9.20 9.20

75 4.17 Slovakia SVK 5.59 6.05 5.10 4.10

39 4.91 Slovenia SVN 6.50 7.26 5.40 5.40

35 5.14 South Africa ZAF 5.23 6.05 6.30 8.10

40 4.85 Spain ESP 6.54 6.10 6.10 5.50

66 4.29 Sri Lanka LKA 4.41 6.26 5.20 5.60

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8 6.11 United Kingdom GBR 6.54 7.52 7.60 8.90 19 5.71 United States of America USA 6.45 7.42 6.30 8.40 38 4.96 Uruguay URY 4.77 7.52 7.20 5.30 129 2.49 Venezuela VEN 4.14 4.00 3.10 1.20 85 3.83 Vietnam VNM 5.14 5.21 5.00 3.80

131 2.47 Yemen, Rep. YEM 2.18 4.68 3.30 2.20

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Appendix B: Sample Firms

Index Company name ISIN number

AEX ARCELORMITTAL S.A. LU0323134006

AEX UNILEVER NV NL0000009355

AEX KONINKLIJKE AHOLD NV NL0010672325

AMX AIR FRANCE - KLM FR0000031122

AEX KONINKLIJKE PHILIPS N.V. NL0000009538

AEX AEGON NV NL0000303709

AEX HEINEKEN NV NL0000009165

AEX RANDSTAD HOLDING NV NL0000379121

AEX ING GROEP NV NL0000303600

AEX AKZO NOBEL NV NL0000009132

AEX KONINKLIJKE DSM N.V. NL0000009827

AEX NN GROUP NV NL0010773842

AEX KONINKLIJKE KPN NV NL0000009082

AMX KONINKLIJKE BAM GROEP NV NL0000337319

AEX TNT EXPRESS N.V. NL0009739424

AEX ASML HOLDING N.V. NL0010273215

AEX SBM OFFSHORE N.V. NL0000360618

AMX POSTNL N.V. NL0009739416

AMX DELTA LLOYD NV NL0009294552

AEX WOLTERS KLUWER NV NL0000395903

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AMX GRANDVISION N.V NL0010937066

AMX FUGRO NV NL0000352565

AMX ARCADIS NV NL0006237562

AMX SLIGRO FOOD GROUP N.V. NL0000817179

AEX GEMALTO N.V. NL0000400653

AMX USG PEOPLE N.V. NL0000354488

AMX OCI N.V NL0010558797

AEX AALBERTS INDUSTRIES NV NL0000852564

AEX UNIBAIL - RODAMCO SE FR0000124711

AMX IMCD N.V. NL0010801007

AMX TKH GROUP N.V. NL0000852523

AEX KONINKLIJKE VOPAK N.V. NL0009432491

AMX TOMTOM NV NL0000387058

AMX CORBION N.V. NL0010583399

AMX ASM INTERNATIONAL NV NL0000334118

AMX BE SEMICONDUCTOR INDUSTRIES NV NL0000339760

AMX EUROCOMMERCIAL PROPERTIES N.V. NL0000288876

AMX WERELDHAVE NV NL0000289213

AMX NSI N.V. NL0000292324

AMX WAREHOUSES DE PAUW BE0003763779

AMX VASTNED N.V. NL0000288918

AMX GALAPAGOS N.V. BE0003818359

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AMX APERAM SA LU0569974404

AEX ABN AMRO GROUP N.V. NL0011540547

AMX FLOW TRADERS N.V. NL0011279492

AMX INTERTRUST N.V. NL0010937058

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Appendix C: M&A Distance – B-T

Descriptive Statistics Mean Std. Deviation N GOI_B_T ,5041 ,60763 430 BoardMembers 11,521 3,3217 430 G Diversity ,355258 ,2770966 430 F Diversity ,825590 ,1996131 430 Age Diversity 7,632594 1,8218283 430 National Diversity ,522351 ,2367686 430 Correlations GOI_B_T BoardMembers G Diversity F Diversity Age Diversity N Diversity Pearson Correlation GOI_B_T 1,000 ,018 -,055 -,184 ,066 ,114

BoardMembers ,018 1,000 ,041 -,215 ,033 ,568 G Diversity -,055 ,041 1,000 -,180 ,075 ,147 F Diversity -,184 -,215 -,180 1,000 -,381 -,334 Age Diversity ,066 ,033 ,075 -,381 1,000 ,010

N Diversity ,114 ,568 ,147 -,334 ,010 1,000

Sig. (1-tailed) GOI_B_T . ,355 ,126 ,000 ,085 ,009

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Variables Entered/Removeda

Model Variables Entered Variables Removed Method

1 Age Diversity, BoardMembers,

GDiversity, FDiversityb . Enter

2 NDiversityb _{. Enter}

a. Dependent Variable: GOI_B_T b. All requested variables entered.

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 6,745 4 1,686 4,726 ,001b Residual 151,647 425 ,357 Total 158,392 429 2 Regression 8,048 5 1,610 4,539 ,000c Residual 150,344 424 ,355 Total 158,392 429

a. Dependent Variable: GOI_B_T

b. Predictors: (Constant), AgeStdv, BoardMembers, GDiversity, FDiversity

c. Predictors: (Constant), AgeStdv, BoardMembers, GDiversity, FDiversity, NDiversity

Model Summaryc

Model R R Square Adjusted R Square

Std. Error of the

Estimate Durbin-Watson

1 ,206a ,043 ,034 ,59734

2 ,225b ,051 ,040 ,59547 ,777

a. Predictors: (Constant), AgeStdv, BoardMembers, GDiversity, FDiversity

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Excluded Variables

Model Beta In t Sig.

Partial Correlation Collinearity Statistics Tolerance VIF Minimum Tolerance 1 NDiversity ,116b 1,917 ,056 ,093 ,612 1,633 ,612

b. Predictors in the Model: (Constant), AgeStdv, BoardMembers, GDiversity, FDiversity

Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics

B Std. Error Beta Tolerance VIF

1 (Constant) 1,159 ,265 4,382 ,000 BoardMembers -,004 ,009 -,023 -,469 ,639 ,951 1,052 GDiversity -,200 ,106 -,091 -1,894 ,059 ,968 1,033 FDiversity -,632 ,162 -,208 -3,897 ,000 ,793 1,261 AgeStdv -,002 ,017 -,005 -,105 ,917 ,852 1,174 2 (Constant) 1,033 ,272 3,801 ,000 BoardMembers -,015 ,011 -,082 -1,431 ,153 ,674 1,484 GDiversity -,223 ,106 -,102 -2,102 ,036 ,956 1,046 FDiversity -,545 ,168 -,179 -3,239 ,001 ,734 1,362 AgeStdv ,002 ,017 ,007 ,139 ,890 ,838 1,193 NDiversity ,297 ,155 ,116 1,917 ,056 ,612 1,633

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Collinearity Diagnosticsa

Model Dimension Eigenvalue Condition Index

Variance Proportions

(Constant) BoardMembers GDiversity FDiversity AgeStdv NDiversity

1 1 4,516 1,000 ,00 ,00 ,01 ,00 ,00 2 ,331 3,695 ,00 ,01 ,92 ,01 ,00 3 ,082 7,415 ,00 ,31 ,02 ,29 ,05 4 ,063 8,483 ,00 ,44 ,01 ,02 ,42 5 ,009 22,703 1,00 ,24 ,04 ,67 ,52 2 1 5,382 1,000 ,00 ,00 ,01 ,00 ,00 ,00 2 ,333 4,019 ,00 ,01 ,93 ,01 ,00 ,00 3 ,166 5,688 ,00 ,02 ,01 ,06 ,01 ,37 4 ,069 8,816 ,00 ,00 ,02 ,20 ,41 ,04 5 ,041 11,480 ,00 ,91 ,01 ,03 ,03 ,53 6 ,008 25,423 ,99 ,06 ,03 ,70 ,54 ,06

Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value ,2571 ,8911 ,5041 ,13696 430

Residual -,86904 2,44563 ,00000 ,59199 430

Std. Predicted Value -1,804 2,825 ,000 1,000 430

Std. Residual -1,459 4,107 ,000 ,994 430

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Appendix D: M&A Distance – H-T

Descriptive Statistics Mean Std. Deviation N GOI_H_T ,5377 ,68013 430 BoardMembers 11,521 3,3217 430 GDiversity ,355258 ,2770966 430 FDiversity ,825590 ,1996131 430 AgeStdv 7,632594 1,8218283 430 NDiversity ,522351 ,2367686 430 Correlations

GOI_H_T BoardMembers GDiversity FDiversity AgeStdv NDiversity Pearson Correlation GOI_H_T 1,000 ,028 -,071 -,157 ,018 ,145 BoardMembers ,028 1,000 ,041 -,215 ,033 ,568

GDiversity -,071 ,041 1,000 -,180 ,075 ,147

FDiversity -,157 -,215 -,180 1,000 -,381 -,334

AgeStdv ,018 ,033 ,075 -,381 1,000 ,010

NDiversity ,145 ,568 ,147 -,334 ,010 1,000

Sig. (1-tailed) GOI_H_T . ,280 ,072 ,001 ,358 ,001

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Variables Entered/Removeda

Model Variables Entered Variables Removed Method

1 AgeStdv, BoardMembers,

GDiversity, FDiversityb . Enter

2 NDiversityb . Enter

a. Dependent Variable: GOI_H_T b. All requested variables entered.

Model Summaryc

Model R R Square Adjusted R Square

Std. Error of the

Estimate Durbin-Watson

1 ,192a _,037 _,028 _,67066

2 ,230b ,053 ,042 ,66576 ,688

a. Predictors: (Constant), AgeStdv, BoardMembers, GDiversity, FDiversity

b. Predictors: (Constant), AgeStdv, BoardMembers, GDiversity, FDiversity, NDiversity c. Dependent Variable: GOI_H_T

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 7,286 4 1,821 4,049 ,003b Residual 191,158 425 ,450 Total 198,444 429 2 Regression 10,513 5 2,103 4,744 ,000c Residual 187,931 424 ,443 Total 198,444 429

a. Dependent Variable: GOI_H_T

b. Predictors: (Constant), AgeStdv, BoardMembers, GDiversity, FDiversity

Master Thesis: Does the national diversity of the Board of Directors influence internationalization? Country decision through M&A by Dutch listed firms