The potential determinates of nationality diversity in Top Management Teams in the World’s Top 100 non-financial TNCs

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Master Thesis for International Business and Management

The potential determinates of nationality diversity in

Top Management Teams in the World’s Top 100

non-financial TNCs

By Huayin MA

S1665219

Supervisor: Dr. Kees van Veen University of Groningen Faculty of Economics and Business

October, 2009

Aquamarijnstraat 669 9743 PR Groningen

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Abstract

This paper tests the potential effects of firm size, DOI and country of origin which can influence the nationality diversity on Top Management Teams (TMTs) of world’s Top 100 non financial Transnational Corporations (TNCs). The study shows that the positive relations among total assets, total sales, Internationalization Index, Transnational Index and nationality diversity within these world’s top 100 non-financial TNCs. However, there is no positive relation among total employees, total affiliate, country of origin and TMTs nationality diversity of TNCs, which is inconsistent with former research.

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Introduction

Nowadays, globalization has played a vital role in the unprecedented increase of worldwide trade in the past 50 years. In economic terms, globalization refers to the growing economic integration of the world, such as trade, investment that increasingly cross the international borders. Moreover, the dramatic increasing size and international activities of the largest Transnational Companies (TNCs) also shows the continuing globalizing trend in the world economy (World Investment Report 2008). A Top Management Team (TMT) is the most important group for making decisions in the company (Ruigrok and Greve, 2007:9). It plays a very significant role in order to maximize shareholders’ wealth through exercising control over top management (Kose and Senbet, 1998). When TNC is growing, they need to be more active in many different markets in order to obtain more international knowledge. Tugendhat (1972) stated that nationality TMTs’ diversity can bring both local and international market knowledge to the firms. The management level will slowly change from the level of “ethnocentric” to “geocentric” (Perlmuter 1969). Therefore, many TNCs have to face the problem of sole nationality of their Top Management Team (TMT) when they are becoming more and more globalized.

Earlier findings

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diversity of TMTs has attracted much attention of researchers in recent years and has been viewed from different perspectives. The former researches mainly covered the following areas:

Firstly, Staples (2007) found that TMTs nationality diversity is positively related with Cross-border M&As. Thereby, the company has sustained cross-border acquisitions that will consequences in a more nationality diversity in their board (Staples, 2008). Secondly, Ruigrok and Greve (2008) hypothesized that more foreigner in the workplace will lead positive reactions in TMTs of TNCs. General speaking; this expectation implies that the higher degree of foreign employees in a TNC will lead to the higher expected level of non-national board members. Thirdly, Heijltjes et al. (2003) mentioned many determinants of nationality diversity in their study, including firm size, the amount of foreign employee, the degree of foreign sales, geographic or cultural variety, the diversification of ownership, an international orientation of the firm and industry’s types. Next, Van Veen and Marsman (2008) showed that higher nationality diversity lead to better company performance. Van Veen and Elbertsen (2008) investigated that the TMTs nationality diversity is positively related with the number of countries where a company is active. Moreover, Caligiuri et al. (2004) concluded that national diversity of TMT affects the company’s internationalization level. And it is positively related to firm’s degrees of internationalization factors such as the firm’s foreign sales ratio, foreign assets ratio, foreign subsidiaries ratio and foreign employee ratio. Finally, Van Veen & Elbersen, (2007) and Van Veen & Marsman, (2008) stated that different countries have different governance regimes which lead different structures for recruitment of new board members.

Value of the Research

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items to this study field. On the one hand, most studies before were focused on the TMTs of Multinational companies (MNCs). However, the level of nationality diversity in TMTs of Transnational companies (TNCs) was hardly mentioned before. Both Transnational and Multinational corporations are the corporations which are operated in more than one country or nation at the same time. However, the Multinational Corporation is engaged in international operations in more than one foreign country. And a Transnational Corporation is a Multinational corporation that operates worldwide1. In this study, I will use a new sample of managers which was collected in term of the company types. The database is consisted of 1726 managers from 100 TNCs, which are the world’s top 100 non-financial TNCs. All of them are biggest companies in the world. And they all have activities in both home and host markets. Moreover, the dramatic increasing size and international activities of the largest Transnational Companies (TNCs) also indicates the direction of continuing globalizing in the world economy (World Investment Report 2008).

On the other hand, the relationship between nationality diversity within TMTs and the degree of internationalization (DOI) of TNCs was often neglected before. Most studies chose one or two indicators such as percentages of foreign assets, foreign sales, foreign employees and foreign subsidiaries instead of DOI. Most of them obtained positive results between the nationality diversity and the degree of internationalization. However, it is not representative and is easy to cause misunderstandings and negligence in nationality diversity and internationalization analysis. If we only measure the degree of internationalization by one character among companies’ foreign assets, foreign sales, foreign employees and foreign affiliates, the result will be less reliability. For example, Ford Motor Company has 155000 foreign employees which are bigger than Toyota Motor Corporation (113967). But, Ford only has 162 foreign affiliates which is less than Toyota (169). If we measured the DOI by foreign employees, the Ford Motor Company has higher DOI than Toyota. However, if we

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measure the DOI by foreign affiliates, the Toyota has higher DOI than Ford. Therefore, I decide to combine these four indicators together as the DOI in my research. I will go into the details of internationalization degree variables and test them on my database. In this way, we intend to have a better understanding of the continuing globalization process in TMTs of TNCs. This will help us to reconsider the nationality distribution from a new point of view.

Scope of the Research and problem formulation

In my research, we attempt to extend this aspect by looking at world’s Top 100 non-financial Transnational Companies (TNCs). The reason to choose TNC is that the world is becoming more and more globalized. And as mentioned before, these TNCs really need to globalize their TMT to get matched with this economic integration and gain more international knowledge. Also, in the previous researches, there was no researcher that had studied about the nationality diversity of TMTs for TNCs in deep. Till 1995, the number of Transnational Corporations has reached 40,000 in the world, which holds ninety percent of all technology and product patents of worldwide. The 300 largest corporations have held one-quarter productive assets in the world2. Therefore, these companies are the most international large companies in the world.

In this research, we will focus on the firm characteristics and country characteristics with nationality diversity of TMTs. The firm characteristics included two parts: the first part is called firm size, which includes the total assets, total sales, total employee and total affiliate. The second part is called degree of internationalization, which includes foreign assets, foreign sales, foreign employee and foreign affiliate. Since many researchers have only taken one or two indicators above instead of the firm size and the degree of internationalization in their studies, I have decided to put all these indicators in my research and see how they are related with nationality diversity of

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TMTs. In addition, Van Veen and Marsman (2008) argued that TMT’s nationality diversity is affected by the firm size and the country of origin. Their studies showed that different governance regimes have different structural of recruitment. Therefore, the country characteristic is measure by the country of origin about TNCs. These all make it very interesting to see if there is any globalization in world’s Top 100 non-financial TNCs’ TMTs. Consequently, the main idea for my research is found out:

To what extent is TMT nationality diversity related to TNCs’ size, DOI

and country of origin, in the world’s Top 100 non-financial TNCs?

The sub-questions of my research are formulated as:

(1) Do the TNC’s total assets, sales, employee and affiliate (Firm Size) influence nationality diversity of TMTs?

(2) Do the TNC’s foreign asset, sale, employee and affiliate (DOI) affect nationality diversity of TMTs?

(3) Does the TNC’s country of origin impact nationality diversity of TMTs?

Through a careful examination of all these factors, I expect to generate a systematic explanation of nationality diversity of TMTs of World’s Top 100 non-financial TNCs.

Structure of the Research

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Literature Review

This section displays an overview of the relevant literature which has been focused on TMT nationality diversity. The definition of diversity and the relationship between TNC’s nationality diversity and firm & country characteristics will be shown as follows.

Mandl (2003) stated that companies should become more and more global, if they need to maximize their effectiveness. Alexander and Esser (1999) investigated a global corporation sample that reported several interesting following findings. First of all, from 1995 to 1998, the percentage of companies with non-national directors increased from 39 to 60 per cent. Then, the percentage of companies with three or more non-national directors increased from 11 to 23 per cent. Moreover, Heijltjes (2003) focused on companies in Sweden and The Netherlands. These researchers reported that for 1999, the percentage of non-national members is 28.6 percent for Swedish firms and 26.6 percent for Dutch firms. They also mentioned that foreign board members contained about 10-11 percent of all the board members across these companies. It was a quite recent phenomenon that comparing the only 8.8 percent of Dutch companies which have non-national board members in 1990, now the number is 26.6 percent which they observed for 1999. Previous studies in the past years tested whether this increased globalization of firms is represented in their board structure. Research on the world’s largest MNCs’ boards of directors indicates increased board nationality diversity (Staples, 2007). In other words, MNCs are increasingly

composed of individuals from different countries.

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positive and negative aspects.

On one hand, Milliken en Martins (1996) has concluded that more diverse people will lead to the following result: greater the risk of lower satisfaction, lower group

identification, lower communication and dysfunctional conflict. It will make some serious challenges and increased turnover. These negative points of diversity are occurring in cases such as ethnicity and nationality (De Abreu dos Reis et al 2007). On other hand, Milliken en Martins (1996) found the diversity can enhance the following aspects such as a group’s creativity, decision making and the ability to process information. It will provide great opportunity for group to deal with complex environment. These positive points of diversity are based on the foundations like educational and functional background (De Abreu dos Reis et al 2007).

TMT’s Nationality diversity:

The nationality diversity of TMTs means that the sum of different nationalities within one top management team. The nationality diversity could affect values, knowledge, behavior and language of person (Hambrick et al, 1988). Therefore, the TMTs’ nationality diversity could effect team functioning and performances. In recent years, the TMTs nationality diversity is increasing, but it’s not widespread. Staple (2007) has conducted one follow-up research which stated 75% of 80 largest MNCs have least one foreigner in its board. However, only half of 80 MNCs has more foreigners in their board.

The TMT’s nationality diversity will bring both good and bad things to companies. On one hand, it can bring both local and international market knowledge to firm (Tugendhat, 1972). Also, it could catch more foreign capital to firms (Heijltjes et al. 2003). On other hand, it might bring some conflict, language and communication problems to companies (Tugendhat, 1972; Watson, Kumar and Michaelsen, 1993).

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perspective”. They argue that the top manager’s strategic directions are strongly influence by their age, background, career experiences and socio-economic

background. In other words, the top managers with international experience will more like to engage in international activities (Athanassiou and Nigh, 2000; Carpenter and Fredrickson, 2001; Sambharya, 1996; Tihanyi et al, 2000).

Firm characteristic

In previous research, TMT nationality diversity has been related with number of different firm characteristics. On one hand, it is firm size, which included total asset, total sale, total affiliates and total employee. Past research which has found that company size is positively related to diversification and TMT size (e.g., Eisenhardt & Schoonhoven, 1990; Grinyer & Yasai-Ardekani, 1981; Wiersema & Bantel, 1992). Heijltjes et al. (2003) mention many determinants of nationality diversity in their study, which including firm size, the amount of foreign employee, the degree of foreign sales, geographic or cultural variety, the diversification of ownership, an international orientation of the firm and industry’s types.

Moreover, Heijltjes (2003) stated that larger firms might have more foreigners within their boards. They should have more internationally active (Tihanyi et al. 2000) which lead the need for international knowledge and experience, and in order to build up an international diverse board (Van Veen and Marsman, 2008).

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foreign investors. Therefore, the following hypothesis has been formulated:

Hypothesis 1: The more total assets which firm has, the higher the nationality diversity in the Top Management Team (TMTs) of the World’s Top 100 non-financial TNCs.

Moreover, Van Veen and Marsman (2008) have used number of employees and number of sales to measure company size in their research. Although they have concluded that nationality diversity has a lower positive relationship with total sales and a strong positive relationship with the number of employees in their research. There is still some positive relationship between total sales and nationality diversity of TMTs. Besides, Heijltjes et al (2003) stated that in order to enhance company’s

international orientation and their ability to attract foreign capital, they need more foreigners enter in their board. The sample companies for this research are

transnational companies in the world. The tot al sales would be the capital point which is representative for measure company size in this study. Therefore, the following hypothesis has been formulated:

Hypothesis 2: The more total sales which firm has, the higher the nationality diversity in the Top Management Team (TMTs) of the World’s Top 100 non-financial TNCs.

Hypothesis 3: The more total employees which firm has, the higher the nationality diversity in the Top Management Team (TMTs) of the World’s Top 100 non-financial TNCs.

Finally, firm’s international activities have an indirect effect with its size (Tihanyi et al., 2000). Larger companies have more widely activities in order to develop

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Hypothesis 4: The larger the number of total affiliates about TNCs, the higher the nationality diversity in the TMTs of the World’s Top 100 non-financial TNCs.

On other hand, it is degree of internationalization (DOI), which consisted foreign asset, foreign sale, foreign employee and foreign affiliate. Previous research states that TMT nationality diversity has positive correlation with a firm’s DOI.

First of all, the companies that have more nationality diversity in their TMTs would avoid some foreign risk and get more international opportunities and (Tihanyi et al, 2000). Secondly, in order to develop more international activities, the firm needs more local market and institutional knowledge. These needs might be accomplished by nationality diversity of TMTs. (Van Veen and Marsman, 2008). UNCTAD formulates a transnationality index (TNI), which calculating the foreign sales ratio, foreign assets ratio and foreign employees’ ratio, to measure the DOI of a company. Caligiuri et al. (2004) conclude that internationalization of firm is positively related with TMTs’ nationality diversity which including foreign sales ratio, foreign assets ratio, foreign subsidiaries ratio and foreign employees’ ratio. Thirdly, Gomes and Ramaswamy (1999) pointed out that the foreign subsidiaries should be incorporated into firm’s DOI. Sanders and Carpenter (1998) also argue that the foreign affiliates are positively related with TMTs nationality diversity in their research. Therefore, I have come to the follow hypothesis:

Hypothesis 5: The larger the Transnationality Index (TNI) of TNCs, the higher the nationality diversity in the TMTs of the World’s Top 100 non-financial TNCs.

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Country Characteristic

Previous studies on country characteristics and nationality diversity of TMTs are much more focusing on the issue that different countries lead to different governance regime (Whitley 1992, Hall en Soskice 2001, Morgan, Whitley and Moen 2005). First of all, Van Veen and Marsman (2008) and Maclean et al. (2005) concluded that different country might have different governance regime which determined their recruitment of board member. Maclean et al (2005) also concluded that different governance regime can be determined by different country. In addition, the governance regime could influence the recruitment process in company. It might affect the accessibility of boards for people with other nationalities (Khurana, 2002; Maclean, Harvey and Press, 2006). It might determine who will be recruited in the final decision of the board.

Moreover, some comparative studies show that different governance regimes have different structural for recruitment of new board members. It is determining opportunities for foreign managers in their career. For example, French and German’s companies are more likely to provide all kind of positions within their internal labor markets. They would like to develop their managerial career within one company. However, UK has opposite situation than French and German (Stewart et al. 1994, Locke 1996, Whittington & Mayer 2000, Lane 2003). The strong internal labor market might cause difficulty to enter the board of foreigners. It also would restrict the recruitment of new executives who outside internal pool of candidates.

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Data and Methods

The methods of research are displayed in this section. Firstly, we will discuss the sample size used in this research. After that, the variables used in the study will be operational. Finally, the ways on how to collect data and qualify the sample will be given.

This study will be conducted with a deductive research process. Actually, a deductive research method should develop a conceptual and theoretical structure before the empirical observation is applied in this research. According to Gill and Johnson (1991), the deductive research method includes a nomothetic methodology that

focuses on the testing hypotheses according to the previous of scientific research. This might be more useful to evaluate causal relationship and deductive process. Moreover, the sample size; data collection procedures, analysis and measurement are major concerns of survey researchers. I will show the detail as follow:

Sampling & data

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The reason for choosing these 100 Transnational Corporations (TNCs) is that it is quite suitable for testing the hypothesis in this study. First of all, these 100 TNCs are the biggest corporations in the world. They are operating in many different markets. As what was argued before, the TMTs of these firms might be globalized. They need to obtain more international knowledge for the companies’ developments. After that, the world’s 100 largest transnational corporations (TNCs) are located in 40 foreign countries on average. For example, the biggest company is Deutsche Post, which has subsidiaries in 103 countries. The popular locations for foreign subsidiaries are the United States, The United Kingdom, Netherlands and China. Then, the world’s top 100 TNCs play a vital role in the global economy. The first three TNCs held an $ 877 billion obtained in foreign assets, which account for almost 25% of the total foreign assets of the top 100 TNCs.

As mentioned above, these 100 TNCs have higher degree of internationalization in the world. They have held the biggest percentage of foreign assets, employees, sales and affiliates in the world. It is very suitable for the analysis of the hypothesis four and five which related to the firm’s degree of internationalization and nationality diversity of TMTs.

What is more, Maclean et al (2005) also concluded that different governance regime can be determined by different country. In these 100 TNCs, 87 of them set their headquarters in the locations among United States, EU and Japan. The highest numbers of TNCs are located in US, which account for 25 firms. The EU had 53 TNCs and Japan had 9 firms. The different about country of origin for these 100 TNCs will influence the recruitment of new TMT members and the opportunities for foreign managers in TMTs.

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country of origin. And also, it gives a strongly support to my research.

Being considered as the primary data source, the nationality of TMT about these 100 TNCs will be collected from their company site and the companies’ annual reports. If necessary, the information will be supplemented by available sources on the internet, such as ‘Google Finance’ (finance.google.com), ‘ZoomInfo’ (zoominfo.com) or ‘Top Management’ (www.topmanagement.net).

Furthermore, two selections should be made on the demarcation of the Top Management Team (TMTs) which will be studied within top 100 TNCs. On one hand, some companies had two countries of origin after they had merged with each other. In my research, I decide to treat the members from these two company’s countries of origin as national board members. Otherwise, many members of TMTs have two citizenships. A foreign board member was defined by previous study as an individual whose citizenship is different from the company’s legal domicile (Alexander and Esser, 1999). In my research, I would like to recognize the individuals who have dual citizenship as a national member. For example, an individual with both Dutch and UK citizenship will be recognized as national members for UK Company.

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Variables and Measurement

Independent variables are selected from those which were used in previous studies. In this way, factors that could influence the TMTs Nationality diversity of Top 100 TNCs can be controlled. And the models used in this paper can be compared to those in earlier studies. Three independent variables are selected based on previous literature. These three major independent variables used in this paper include the firm size, Degree of Internationalization (DOI) and the company’s country of origin. The Multiple regressions are used to in my research. The dependent variables and independent variables are shown as follow.

Dependent variable:

Nationality diversity

This variable is measured by the TMT members with foreign nationality divided by the total number of board members. The nationality of managers is defined as the nationality as reported by the company. If it is not available, different sources will be used, such as the country of birth. In my research, the person who has dual citizenship which belongs to company’s country of origin will be recognized as national member. For example, an individual with both Dutch and UK citizenship will be recognized as a national member for UK Company.

The independent Variables

Degree of Internationalization

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foreign subsidiaries ratio and foreign employees’ ratio. Thirdly, Gomes and Ramaswamy (1999) pointed out that the foreign subsidiaries should be incorporated into firm’s DOI. Sanders and Carpenter (1998) also argue that the foreign affiliates are positively related with TMTs nationality diversity in their research. As mentioned before, I will use all of them in my research to measure the DOI of company.

In my research, the Degree of Internationalization will be operationalized in two ways. They are Transnational Index and Internationalization Index. UNCTAD formulates a transnational index (TNI), which accounts the foreign sales ratio, foreign assets ratio and foreign employee’s ratio, in order to measure the DOI of a company. Furthermore, the Internationalization Index is accounting the foreign affiliate ration for TNCs.

Firm Size

Firm size is usually measured by total assets, total sales, total employees and total affiliates of the company. According to the viewpoints in literature review, larger firms might have more foreigners within their boards (Heijltjes et al, 2003). They should have more internationally active (Tihanyi et al. 2000) which lead the need for international knowledge and experience, and in order to build up an international diverse board (Van Veen and Marsman, 2008). In addition, Van Veen and Marsman (2008) stated that in order to make the large firms more visible, they should be more often listed on stock markets. This could catch more attentions from foreigners of all kind positions. Also, this might enhance the nationality diversity in their board due to higher nationality diversity in their workplace. As argued in literature review, many studies only choose one or two indicators instead firm size of companies. It is not representative to use only the firm size. I decide to take all of them in my research. Therefore, the firm size will be operational by total assets, total sales, total employees and total affiliates of company in this research.

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Descriptive Results

In order to test the propositions, the details on how to process the tests were executed and the results are described as follow. On one hand, some general descriptions are presented. On the other hand, the preliminaries descriptions and results of each hypothesis are shown in this chapter.

General information of sample companies

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Table 1 Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Total number of all board

members 100 7 32 17.23 5.694

Total number of foreigners in

the board 100 0 20 4.62 3.941 Assets-foreign in millions of $ 100 18603 442278 52439.44 53448.880 Assets-total in millions of $ 100 20132 705656 99015.58 107494.011 Sales-foreign in millions of $ 100 1283 252680 40581.21 40557.304 Sales-total in millions of $ 100 5636 365467 70992.17 71715.153 Employees-foreign in millions 100 3965 540000 84283.43 85327.912 Employees-total in millions 100 17000 1910000 151895.84 210535.712 Affiliates-foreign 100 4 932 240.54 195.642 Affiliates-total 100 28 1434 348.15 276.286 Percentage of foreigners in the board 100 .00 .91 .2719 .21588 Valid N (listwise) ₁₀₀

Gillies and Dickinson (1999) concluded that 36.3 percent of 80 largest MNCs had at least one foreign member in 1993. In a follow-up study in 2005, Staples (2007) collected the data on the same companies and stated that the number had increased to 75 percent. However, he concluded that although diversity had become more widespread, it was still not very extensive. In these 100 sample companies, 10 of them are not having foreigners in the board, accounting for 10% of the total sample companies. It means that 90% of total sample companies have at least one foreigner in their TMTs.

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countries. The details of Company country of origin will be shown in the following Table 2.

Table 2 Company country of origin

Frequency Percent Valid Percent

Cumulative Percent Switzerland 4 4.0 4.0 4.0 United States 22 22.0 22.0 26.0 Netherlands 5 5.0 5.0 31.0 Canada 2 2.0 2.0 33.0 France 16 16.0 16.0 49.0 United Kingdom 11 11.0 11.0 60.0 Germany 14 14.0 14.0 74.0 Australia 1 1.0 1.0 75.0 Japan 9 9.0 9.0 84.0 Mexico 1 1.0 1.0 85.0 Ireland 1 1.0 1.0 86.0 Spain 3 3.0 3.0 89.0 Italy 2 2.0 2.0 91.0 Sweden 2 2.0 2.0 93.0 South Korea 2 2.0 2.0 95.0 Finland 1 1.0 1.0 96.0 Norway 1 1.0 1.0 97.0 Malaysia 1 1.0 1.0 98.0 Singapore 1 1.0 1.0 99.0 China 1 1.0 1.0 100.0 Valid Total ₁₀₀ _100.0 _100.0

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American, accounting 20.3% of total sample companies. In addition, the second largest group in this sample is 16%, which are formed by 277 German. The third largest group was formed by France (14.4%). What is more, United Kingdom (9.3%) and Japan (8.7%) also have the famous origin country of these board members. In addition, only 452 managers are working abroad. Therefore, a total of 26.19% foreigners are found among the total group of Top Management Team of the 100 TNCs. The detail information will be shown as the following Table 3.

Table 3 Native country of executive: where does this person come from?

Frequency Percent Valid Percent

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Brazil 11 .6 .7 95.0 India 18 1.0 1.1 96.1 South Africa 21 1.2 1.3 97.4 Portugal 2 .1 .1 97.5 New Zealand 3 .2 .2 97.7 Austria 10 .6 .6 98.3 Luxembourg 7 .4 .4 98.7 Poland 7 .4 .4 99.2 Denmark 2 .1 .1 99.3 China 2 .1 .1 99.4 Russia 1 .1 .1 99.5 Egypt 1 .1 .1 99.5 Argentina 2 .1 .1 99.6 Taiwan 1 .1 .1 99.7 Turkey 3 .2 .2 99.9 Greece 1 .1 .1 99.9 Northern Ireland 1 .1 .1 100.0 Total ₁₆₆₈ _96.6 _100.0 Missing System ₅₈ _3.4 Total ₁₇₂₆ _100.0

Testing Hypotheses

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In order to control the correlations between the TNCs total-assets, total-sales,

total-employees and total-affiliates, the regression analysis will be conducted. First of all, I will run regression on these four variables separately. Table 4 shows more detail information about regression analysis.

Table 4 Regression model of firm size

Model R

Square

F Sig T Beta Sig

Ln_Assets-total in millions of $ .083 8.882 .004a 2.980 .288 .004 Ln_Sales-total in millions of $ .087 9.308 .003(a) 3.051 .295 .003 Ln_Employees-total .025 2.499 .117(a) -1.581 -.158 .117 Ln_Affiliates-total .000 .020 .888(a) -.142 -.014 .888

The result shows that correlation between total assets and nationality diversity is significant (r=0.288, p=0.004, n=100), the correlation between total sales and nationality diversity also significant (r=0.295, p=0.003, n=100). However, the total employees (r=0.158, p=0.117, n=100) and total affiliates (r=0.014, p=0.888, n=100) are not significant with nationality diversity. Therefore, hypothesis one and two will be accepted, although the relationship is not very strong. The hypothesis three and four will be rejected.

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for foreigners of all kind positions. This might enhance the nationality diversity in their board due to higher nationality diversity in their workplace. Moreover, larger firms should have more internationally active (Tihanyi et al. 2000) which lead the need for international knowledge and experience, and in order to build up an international diverse board (Van Veen and Marsman, 2008).

One possible reason to explain the lack of a significant correlation between TMT nationality diversity, total employee and total affiliates is that the larger firms don’t have more employees or affiliates. For example, although Toyota Motor Corporation is the third largest TNC, it only has 299394 employees in total. However, Volkswagen Group is the sixth largest TNC in sample companies, which has 324875 employees in total. Therefore, the larger TNCs do not have to employ more employees and do their business in a wide variety of countries.

Furthermore, different industries might be another possible reason which causes the situation above. So, it is suggested to treat industry as control variable in the research. Firstly, I would like to take all these four variables in one regression at one time (see table 5-7). After that, I will add industry as control variable into regression to see if different of industries will affect the TMTs nationality diversity (see table 8-10). Table 5-7 will give more detail information as follows:

Table 5 Model Summary

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Table 6 ANOVA b

Model Sum of Squares df Mean Square F Sig.

Regression .446 4 .112 2.542 .045a Residual _4.168 ₉₅ _.044 1 Total _4.614 ₉₉ Table 7 Coefficients a Unstandardized Coefficients Standardized Coefficients

Model _B _{Std. Error} _Beta t Sig.

(Constant) _1.123 _.340 _3.301 _.001 Ln_Assets-total in millions of $ -.041 .046 -.146 -.888 .377 Ln_Sales-total in millions of $ -.044 .042 -.189 -1.058 .293 Ln_Employees-total in millions .000 .031 .002 .014 .989 1 Ln_Affiliates-total .014 .030 .051 .468 .641

a. Dependent Variable: Percentage of foreigners in the board

The result shows that the independent variables-TNCs total-assets, total-sales, total-employees and total affiliates, which I choose to explain nationality diversity of TMTs, are quite convictive. The F-test is significant with 0.045 and R and R square are 0.311 and 0.097 separately. However, we can see that all the variables are not significant in the T-test. But the signs of the coefficients for these variables are

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Table 8 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .345(a) .119 .072 .20791 Table 9 ANOVA (b) Model Sum of

Squares df Mean Square F Sig.

Regressio

n .551 5 .110 2.548 .033(a)

Residual 4.063 94 .043

1

Total 4.614 99

Table 10 Coefficients (a)

Unstandardized Coefficients

Standardized Coefficients

Model B Std. Error Beta t Sig.

(Constant) .895 .368 2.430 .017 Ln_Assets-total in millions of $ -.026 .047 -.092 -.553 .582 Ln_Sales-total in millions of $ -.033 .042 -.139 -.771 .443 Ln_Employees-t otal in millions -.006 .031 -.026 -.198 .843 Ln_Affiliates-tot al .007 .030 .027 .244 .808 1 Industry in which the company operates - based on UNCTAD list .006 .004 .174 1.555 .123

The result shows that the control variable-industry which I choose to explain

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expectation too. They are assets-total （Beta:-0.092）, Sales total (Beta: -0.139), Employees-total (Beta: -0.026), Affiliates-total (Beta:0.27) and Industry (Beta: 0.174). In order to see the possible reasons for this situation, one correlation matrix will be show as Table 11.

Table 11 Correlations Matrix of dependent variable, firm size

1 .288** .295** .158 .014 . .004 .003 .117 .888 100 100 100 100 100 .288** 1 .805** .483** .177 .004 . .000 .000 .079 100 100 100 100 100 .295** .805** 1 .589** .213* .003 .000 . .000 .033 100 100 100 100 100 .158 .483** .589** 1 .438** .117 .000 .000 . .000 100 100 100 100 100 .014 .177 .213* .438** 1 .888 .079 .033 .000 . 100 100 100 100 100 Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Percentage of foreigners in the board Ln_Assets-total in millions of $ Ln_Sales-total in millions of $ Ln_Employees-total in millions Ln_Affiliates-total Percentage of foreigners in the board Ln_Assets-t otal in

millions of $ in millions of $Ln_Sales-total

Ln_Emplo yees-total

in millions Ln_Affiliates-total

Correlation is significant at the 0.01 level (2-tailed). **.

Correlation is significant at the 0.05 level (2-tailed). *.

The correlation analysis shows that the hypothesis one and two are supported.

Specifically, TMTs nationality diversity of world’s top 100 TNCs is positively related with total assets and total sales of companies.

The results for these variables in the regression above might be the consequence of multicollinearity between itself and other variables. For example, sales-total has significant correlations with Employees-total (Pearson correlation: 0.589, Sig.: 0.000) and Employees-total has significant correlations with affiliates-total (Pearson

correlation: 0.438, Sig.: 0.000).Also, Industry has significant correlations with assets-total (Pearson correlation: 0.438, Sig.: 0.000). See table 11.

Moreover, the fifth hypothesis assumes that the degree of international actives (DOI) is positively related to TMT nationality diversity. As it was argued before, DOI can be measured in the different ways. According to the Tihanyi et al (2000), the most

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of foreign sales/total sales, foreign assets/total assets, foreign employees/total employees and foreign subsidiaries/total subsidiary.

In this paper, the DOI will be operational in two ways: The Transnational index and ratio of Internationalization index. On one hand, the Internationalization index is calculated by the number of foreign affiliates divided by the total number of affiliates. On the other hand, the transnational index is calculated as the average of the three ratios: foreign assets to total assets, foreign sales to total sales and foreign

employment to total employment. The correlation analysis will be shown as follows.

Table 12 Correlation Matrix of dependent variable and DOI of TNCs

1 -.151 -.155 -.032 .043 .300** .212* .277** . .133 .124 .749 .673 .002 .034 .005 100 100 100 100 100 100 100 100 -.151 1 .687** .320** .188 -.084 -.090 -.421** .133 . .000 .001 .061 .406 .372 .000 100 100 100 100 100 100 100 100 -.155 .687** 1 .468** .238* -.077 -.075 -.398** .124 .000 . .000 .017 .448 .459 .000 100 100 100 100 100 100 100 100 -.032 .320** .468** 1 .531** .143 .088 .002 .749 .001 .000 . .000 .156 .385 .984 100 100 100 100 100 100 100 100 .043 .188 .238* .531** 1 .285** .351** .112 .673 .061 .017 .000 . .004 .000 .267 100 100 100 100 100 100 100 100 .300** -.084 -.077 .143 .285** 1 .199* .193 .002 .406 .448 .156 .004 . .047 .054 100 100 100 100 100 100 100 100 .212* -.090 -.075 .088 .351** .199* 1 .125 .034 .372 .459 .385 .000 .047 . .215 100 100 100 100 100 100 100 100 .277** -.421** -.398** .002 .112 .193 .125 1 .005 .000 .000 .984 .267 .054 .215 . 100 100 100 100 100 100 100 100 Pearson Correlat Sig. (2-tailed) N Pearson Correlat Sig. (2-tailed) N Pearson Correlat Sig. (2-tailed) N Pearson Correlat Sig. (2-tailed) N Pearson Correlat Sig. (2-tailed) N Pearson Correlat Sig. (2-tailed) N Pearson Correlat Sig. (2-tailed) N Pearson Correlat Sig. (2-tailed) N Percentage of foreign in the board Ln_Assets-foreign in millions of $ Ln_Sales-foreign in millions of $ Ln_Employees-foreign millions Ln_Affiliates-foreign Transnationality Index Internationalization In

Industry in which the company operates -based on UNCTAD list

Percentage of foreigners in the board Ln_Assets-f oreign in millions of $ Ln_Sales-for eign in millions of $ Ln_Employe es-foreign

in millions Ln_Affiliates-foreignTransnationality IndexInternationalization Index

Industry in which the company operates -based on UNCTAD list

Correlation is significant at the 0.01 level (2-tailed). **.

Correlation is significant at the 0.05 level (2-tailed). *.

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hypothesis five and six are supported by this phenomenon. Moreover, the correlation between foreign assets, foreign sales, foreign employees and foreign affiliates are quite strong. For example, foreign assets have significant correlation with foreign sales (Pearson Correlation 0.687, sig 0.000) and foreign employees (Pearson Correlation 0.320, sig 0.001). Otherwise, Transnational Index has significant correlation with Internationalization Index (Pearson Correlation 0.199, sig 0.047).

In order to control these DOI variables, Table 13-15 shows more detailed information about regression analysis.

Model R R Square Adjusted R Square Std. Error of the Estimate 1 .338(a) .114 .096 .20529 Table 14 ANOVA (b) Model Sum of

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Table 15 Coefficients (a)

Unstandardized Coefficients

Standardized Coefficients

(Constant) -.075 .102 -.735 .464 Transnationality Index .004 .001 .268 2.752 .007 1 Internationalizati on Index .002 .001 .158 1.623 .108

The results stated that the independent variable-DOI is quite well explaining

nationality diversity of TMTs. The F-test is significant with 0.003 and R and R square are 0.338 and 0.114 separately. Moreover, in the T-test the correlation between

Transnational Index and nationality diversity is significant with (p=0.007, n=100). However, we can see the Internationalization of Index (p=0.108, n= 100) has a lower correlation with nationality diversity. As a result, Transnational Index seems to be a better predictor than Internationalization Index. Therefore, Hypothesis 5 and 6 is accepted. The DOI has positive impact with nationality diversity of TMTs.

This study indicates a weak positive significant correlation between TMT nationality diversity and TNCs’ Internationalization Index and a strong positive significant correlation between TMT nationality diversity and TNCs’ Transnational Index. Previous research states that TMT nationality diversity is affected by a firm’s DOI. On one hand, the companies that have more nationality diversity in their TMTs would avoid some foreign risk and get more international opportunities (Tihanyi et al, 2000). On other hand, the firm has more internationally active need local market and

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which includes foreign sales ratio, foreign assets ratio, foreign subsidiaries ratio and foreign employees’ ratio. As mentioned before, the transnational index is accounting the combination of foreign sales ratio, foreign assets ratio and foreign employee’s ratio. Furthermore, the Internationalization Index is accounting the foreign affiliate ration for TNCs. The results in my study have some differences with pervious studies. One possible reason would be that since TNC has one affiliate in the country, the market information is easy to accessible, no matter how many affiliates they have in the country. Therefore, largest TNCs’ do not need to have more foreign affiliates.

Multivariate test of hypotheses

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Table 16 Regression analyses with percentage of foreign as dependent variable

Model 1 Model 2 Model 3 (Constant) Beta P Beta P Beta P

Assets-Total -0.146 0.337 -0.245 0.264 -0.219 0.319 Sales-Total -0.189 0.293 -0.843 0.008 -0.568 0.100 Employees-Total 0.002 0.989 0.002 0.992 -0.124 0.528 Affiliates-Total 0.051 0.641 0.048 0.820 0.006 0.979 Assets-foreign 0.183 0.332 0.151 0.423 Sales-foreign 0.669 0.015 0.441 0.136 Employees-foreign 0.007 0.970 0.124 0.549 Affiliates-foreign -0.082 0.712 -0.022 0.923 US, Canada -0.121 0.254 Europe -0.021 0.924 Asia -0.178 0.545 Adj.R-square 0.059 0.111 0.119 F-value 2.542 2.546 2.214 Df 4 8 11 P-value 0.045 0.015 0.020 N 100 100 100

The results state that the explained variance which is low shows the R-square is only 0.059. We can take a look at Firm size characteristic: it shows that although total employees and total affiliates are not significant, both of them are impacting

nationality diversity in the right direction. Moreover, the variables of total assets and total sales are irrelevant to the explanation of nationality diversity.

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affiliates have a bit decline in its strength. Only the total employees are not changed. Additionally, the total sales and foreign sales are significant with nationality diversity of TMTs.

Finally, model 3 has introduced country characteristics in the regression analysis. I have added US/Canada, EU and Japan as dummies to Model 3 (See Model 3 in Table 16). The explained variance has a little rise, up to 0.119. The parameters of beta in model 2 are also marginally affected by the new input of country variables. The influences of all variables are reducing except the foreign employees. The result implies that parts of their influence are more concentrated in some certain countries than in others. The foreign employee has increased slightly. This stresses that the characteristics of countries and country of origin don’t influence foreigners’ access to TMTs in this research.

This result is opposite to the viewpoint of Van Veen and Marsman, (2008) who has concluded that country effects seem to be stronger than company effects. The factors of different regions are not adding a substantial increase in the explanatory power of the model.

The possible reasons would be bigger sample size and different classifications of the country of origin for TNCs. On one hand, Veen and Marsman (2008) have analyses 363 MNCs in their research. However, this research used world’s top 100

non-financial TNCs as sample companies. It is quite different from each other. These 100 TNCs are the biggest corporations in the world, which played a vital role in the global economy. They are operating in many different markets, holding many foreign employees for their international activities. As argue before, these 100 TNCs have highest DOI in the world.

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Conclusion

This paper tests the effect of firm size, DOI and country of origin on the nationality diversity in world’s top 100 TNCs. Although, many theories have provided deep understand about nationality diversity of TMTs on MNCs. However, our knowledge for world’s top TNCs is still limited.

My research is based on the world’s top 100 non-financial TNCs, which has 74 companies employing two-tier board structure and 21 companies employing one-tier board structure. Among these 100 companies, 22 American companies form the first biggest group. France, United Kingdom and Germany companies are taking the lead in the European countries. Moreover, 10% of the total sample companies are not having foreigner in their board. In addition, the biggest group of companies has two foreigners in their board, which accounting for 14％ of the total sample companies.

Furthermore, these 100 TNCs are consisted of 1726 managers on their board. In my research, 3.4% of the managers’ nationalities could not be determined. That means 1668 managers are remained in the sample. Only 452 managers in the sample are working abroad, which means a total of 26.19% foreigners are found among the total group of 1668 managers. This number is higher than many researches which have been done before. For example, the research on 363 EU based MNCs in 2005 was found only 14.9% of executives are foreigners in their board (van Veen & Marsman, 2008). Considering that these world’s top 100 TNCs have a high degree of Internationalization and firm size, the board nationality diversity of these TNCs is quite high.

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research which are mainly used to explain board nationality diversity of the MNCs from developed countries. They are degree of Internationalization, firm size and country of origin.

In this paper, the Firm size will be operational in four ways: total assets, total sales, total employees and total affiliates. Otherwise, the DOI will be operational in two ways: The Transnational index and ratio of Internationalization index. On one hand, the Internationalization index is calculated by the number of foreign affiliates divided by the total number of affiliates. On the other hand, the Transnationalized index is calculated as the average of the three ratios: foreign assets to total assets, foreign sales to total sales and foreign employment to total employment.

The results of my study show that the hypothesis 1, 2, 5, 6 are supported. These are the positive relations between total assets, total sales, Internationalization Index, Transnational Index and nationality diversity.

On one hand, there is no positive relation between total employees, total affiliate and nationality diversity, which is inconsistent with former research. For example, van Veen & M Marsman (2008) found positive relations between total employees and nationality diversity.

One possible reason toexplain the lack of a significant correlation between TMT nationality diversity, total employee and total affiliates is that the larger firm is not so internationally active. For example, Although British Petroleum Company Plc is the second largest TNC in sample companies; it only has 97100 employees in total. However, Toyota is the third largest TNC, which has 299394 employees in total. Therefore, the TNCs do not have to employ more employees and do their business in a wide variety of countries.

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Marsman (2008), who has concluded that the TMTs nationality diversity on MNCs is strongly affected by the country of origin. The factors of different regions are not adding a substantial increase in the explanatory power of the model 3, which means the region factors do not influence TMTs nationality diversity of world’s top 100 TNCs.

The possible reasons would be the different classifications of the country of origin for TNCs. Veen and Marsman (2008) have classified the country of origin for MNCs by single countries, such as German, The Netherlands and UK. However, in this research, the sample is very difficult to assort them by single country. I have classified it by regions such as US, EU and Japan in my study. Because different country come from one region could have different governance regime. It might be the reason why the result in my research is opposite to Veen and Marsman ’s.

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Limitation & Further Research

In this section, the limitations of my research and some interesting things for further research will be discussed.

Limitations

The first limitation of my research is the sample size which only consists of 100 Transnational Corporations. It seems to be insufficient to test all 7 independent variables and one control variable with this sample. So the variable ratio (8 variables in 100 companies) seems to be too low.

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The third limitation would be the decision of definition about TMTs in TNCs. In my research, I decide that TMT consist of non/executive board of one-tier companies and both non/executive and supervisory board of two-tier companies. The selection for TMTs of listed companies made it possible to collect relevant data of 1726 managers. However, some researchers have proved that supervisor board will have more foreigner than executive board. So, the companies which consist of two-tier structure will have more foreigner in their TMTs than the companies which only consist of one-tier structure. It might make the bias in my research.

Further Research

First of all, my study is just a beginning for the research of board nationality diversity of world’s top 100 non-financial TNCs. It will be more interesting to do a follow-up research in the near future. Then these 100 TNCs are the biggest corporations in the world. It has played a vital role in the global economy. They are operating in many different markets. After that, they have held biggest percentage of foreign assets, employees, sales and affiliates in the world. As the results for my study, these world’s top 100 non-financial TNCs are have high DOI. And the DOI of these TNCs and TMTs nationality diversity of TNCs are positively related. All the evidence suggests that they will achieve higher DOI and may have higher nationality diversity of TMTs in the near future. In order to develop their business, these TNCs also might conduct more Cross border M&As to achieve higher DOI of company in the near future. So, the further research might test the relation between Cross border M&As and TMTs nationality diversity of TNCs.

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single country of origin. It might clearly classify the difference between each country. And the results will be more reliable.

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Appendix

Appendix A

(1) Assets-Total

Model R R Square Adjusted R Square Std. Error of the Estimate 1 .288a .083 .074 .20777 Table 8 ANOVAb

Model Sum of Squares df Mean Square F Sig.

Regression .383 1 .383 8.882 .004a Residual _4.231 ₉₈ _.043 1 Total _4.614 ₉₉ Table 9 Coefficientsa Unstandardized Coefficients Standardized Coefficients

(Constant) _1.171 _.302 _3.873 _.000

1

Ln_Assets-total in millions of

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(2) Sales-Total Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .295(a) .087 .077 .20736 ANOVA (b) Model Sum of

Regressio n .400 1 .400 9.308 .003(a) Residual 4.214 98 .043 1 Total 4.614 99 Coefficients (a) Unstandardized Coefficients Standardized Coefficients

(Constant) 1.018 .245 4.149 .000 1 Ln_Sales-tot al in millions of $ -.069 .023 .295 3.051 .003

A Dependent Variable: Percentage of foreigners in the board

(3) Employees-Total

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Model R R Square Adjusted R Square Std. Error of the Estimate 1 .158(a) .025 .015 .21427 ANOVA (b) Model Sum of

Regressio n .115 1 .115 2.499 .117(a) Residual 4.499 98 .046 1 Total 4.614 99 (4) Affiliates-Total Coefficients (a) Unstandardized Coefficients Standardized Coefficients

(Constant) .701 .272 2.574 .012

1

Ln_Employees-total in millions

-.037 .024 .158 1.581 .117

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Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .014(a) .000 -.010 .21696

Predictors: (Constant), Ln_Affiliates-total

ANOVA (b)

Model

Sum of

Regressio n .001 1 .001 .020 .888(a) Residual 4.613 98 .047 1 Total 4.614 99 Coefficients (a) Unstandardized Coefficients Standardized Coefficients

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Model R R Square Adjusted R Square Std. Error of the Estimate 1 .311(a) .097 .059 .20946 ANOVA (b) Model Sum of

(Constant) 1.123 .340 3.301 .001 Ln_Assets-total in millions of $ -.041 .046 -.146 -.888 .377 Ln_Sales-total in millions of $ -.044 .042 -.189 -1.058 .293 Ln_Employees-total in millions .000 .031 .002 .014 .989 1 Ln_Affiliates-to tal .014 .030 .051 .468 .641

A Dependent Variable: Percentage of foreigners in the board

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ANOVA (b)

Model

Sum of

(Constant) .825 .368 2.242 .027 Ln_Assets-total in millions of $ -.068 .061 -.245 -1.125 .264 Ln_Sales-total in millions of $ -.198 .073 -.843 -2.706 .008 Ln_Employees-t otal in millions .000 .051 .002 .010 .992 Ln_Affiliates-tot al .013 .057 .048 .228 .820 Ln_Assets-foreig n in millions of $ .059 .061 .183 .976 .332 Ln_Sales-foreign in millions of $ .167 .067 .669 2.485 .015 Ln_Employees-f oreign in millions .002 .043 .007 .037 .970 1 Ln_Affiliates-for eign -.018 .049 -.082 -.371 .712

a Dependent Variable: Percentage of foreigners in the board

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ANOVA (b)

Model

Sum of

Regressio n 1.000 11 .091 2.214 .020(a) Residual 3.614 88 .041 1 Total 4.614 99 Coefficients (a) Unstandardized Coefficients Standardized Coefficients

(Constant) .837 .369 2.265 .026 Ln_Assets-total in millions of $ -.061 .061 -.219 -1.003 .319 Ln_Sales-total in millions of $ -.134 .080 -.568 -1.664 .100 Ln_Employees-t otal in millions -.029 .053 -.124 -.553 .582 Ln_Affiliates-tot al .002 .058 .006 .026 .979 Ln_Assets-foreig n in millions of $ .049 .061 .151 .805 .423 Ln_Sales-foreign in millions of $ .110 .073 .441 1.505 .136 Ln_Employees-f oreign in millions .027 .044 .124 .602 .549 Ln_Affiliates-for eign -.005 .050 -.022 -.097 .923 US, Canada -.057 .050 -.121 -1.148 .254 EUROPE -.015 .208 -.021 -.073 .942 1 ASIA -.133 .220 -.178 -.607 .545

The potential determinates of nationality diversity in Top Management Teams in the World’s Top 100 non-financial TNCs

Master Thesis for International Business and Management