UNIVERSITY OF GRONINGEN
The relationship between nationality
diversity in Business Collaboration
Portfolios and nationality diversity in Top
Management Teams
Faculty of Economics and Business
Master’s thesis IE&B Yannick Kattenberg y.kattenberg@student.rug
S1992953 6/15/2015
Supervisor: dr. P. Rao Sahib Co-‐assessor: dr. T. Kohl
In this paper, it is argued that companies increase their amount of collaboration agreements in order to better cope with the more complex and faster changing business environments. Due to the fact that top management teams are both powerful within a company and important for the connection between the firm and the business environment, they are the driving force behind business partner selection. Based on data collected concerning 105 MNEs from 4 countries in 2005-2008 the relationship between nationality diversity in the top management team and nationality diversity in business collaborations portfolio is examined. Although literature suggests a preference for familiarity and similarity in business partner selection, no clear relationship is established between the nationality diversity in top management teams and business corporation portfolios.
-‐ 1. Introduction: -‐
In today’s world Multinational enterprises (MNEs) are driving globalization (Staples, 2007). Globalization is the process by which an increasing share of world production is traded internationally, and the productive systems of different countries become increasingly integrated (Cigno, Rosati & Guarcello, 2002). The current wave of globalization started soon after the end of World War II, but increases its pace in the 1980s, as rapid progress in information and transport technology stimulated the effects of trade liberalization (Krugman, 1995). Nowadays, MNEs have extended their presence all over the globe, implementing a multitude of activities for a several purposes. In doing so, MNEs have had to manage the various forces, including product, technology, market, and geographic forces, that interact and become more complex on a global scale (Luo, 2005). This raises the question if it is necessary for MNEs, in order to maximize their effectiveness in the global economy, to also globalize their boards to match this increase in complexity (Mandl, 2003).
Due to an increase in complexity and uncertainty it is hard for an MNE to make the appropriate decisions in the competitive landscape. In an MNE, it is the whole top management team (TMT), which coordinates organizational activities, the strategic process of the firm is viewed as flows of information and decisions which are derived from the characteristics and mentality of the managers (Hambrick & Mason, 1984). Focusing on the characteristics of the TMT will give us stronger explanations of organizational outcomes than when the focus is only on the individual top executive (e.g., CEO). Leadership of a complex organization is a shared activity, and the collective cognitions, capabilities, and interactions of the entire TMT enter into strategic behaviors (Hambrick & Mason, 1984). This case implies that directors are not substitutes with identical talents and abilities but that different directors have diverse and unique characteristics that create additional value (Carter, Simkins, & Simpson, 2003). One of the consequences of the more globalized economy is an increase in the number of foreign board members in MNEs originating from different markets (Staples, 2007; van Veen & Marsman, 2008).
Business alliances, an important form of building up an relationship, may be defined as collaborative efforts between two or more firms that pool their resources in an effort to achieve jointly compatible goals which they could not achieve easily by themselves (Das and Teng, 2000).
When a MNE looks for resources or competitive advantages outside the firm, the next step is to select an appropriate partner to start collaboration with. The TMT plays an important role in partner selection for collaboration, so the characteristics of the members of the TMT are essential for the collaborations the company starts with other companies. In alliances, it is important to minimize the friction between the companies engaged in the alliance (Hagel & Brown, 2005). In order to minimize the friction between two partners, one of the most important factors is trust. Trust can be a substitute for formal control mechanisms, reduce transactions costs, facilitates faster solutions for problems, and allow more flexibility in an alliance (Bierly & Gallagher, 2007). Therefore, when trust among partners is high, partners have more confidence in each other and the probability of opportunism decreases (Bierly &
Gallagher, 2007). Prior relationships between partners create trust and familiarity, so the
expectations are that if managers select a partner for collaboration they choose for what they are familiar with (Gulati, 1995; Kogut, 1989;Park & Russo, 1996). Several studies have concluded that national culture plays a large role in the attitudes and the beliefs of TMT
members, and consequently in the way of decision-making. For example, a three culture study
these factors bind entrepreneurs to their community and help explain the potential strength of investor preferences for the familiar environment (Figueiredo, Guimarães & Woodward, 2002). According to the social categorization theory, people try to identify themselves with a certain group to boost their self-esteem. It can lead to the fact, that people from a certain social group perceive persons who are not part of their social group as less trustworthy, honest and cooperative (Tajfel, 1981). Nationality is one of the factors on which a social group can be categorized. Furthermore, research shows that similarity between persons can lead to more liking and interpersonal attraction (Tajfel, 1982). With this in mind, it is likely that companies from a certain country of origin are more likely to collaborate with companies from familiar business environments. Is it possible that the nationality diversity in a TMT can affect the composition of the BCP nationality diversity, because of the factors mentioned earlier? This lead to the research question:
Does the nationality composition of the TMT of a MNE influence the nationality composition of the business collaboration?
This is illustrated by an example of the first company in the dataset. Anglo American, mining company from the United Kingdom (UK). The TMT of Anglo American in 2005 consists of 16 board members. Ten from the UK, two are from South Africa and the other four board members are from Brazil, Belgium, France and Germany. At the same time Anglo American is entering into 8 new collaboration agreements in 2006. Four with US companies and one with companies from Canada, Germany, Russia and Switzerland. On the company website of Anglo American, the UK board member S.R. Thomson react on the new collaboration agreement, with Severstal (Russia), about the expectations and goals of the collaboration (Anglo American, 2006). This reaction indicates the role of the TMT in establishing the new collaboration agreements and the relevance of comparing the diversity of the TMT, with the diversity in the collaboration agreements.
-‐ 2. Theory -‐
2.1 Role of globalisation
The two great unbundlings give a boost to international trade and globalization. The first unbundling is characterized by rapidly falling transportation costs, a trend that has been going on since the late 19th century, and it caused the end of the necessity of making goods close to the point of consumption (Baldwin, 2006). More recently, rapidly falling communication and coordination costs have fostered a second unbundling, the end of the need to perform most manufacturing stages near each other. Even more recently, the second unbundling has spread from factories to offices. Now that production and consumption are separated and the necessity of producing near each other is gone, it enables firms to produce and sell on a global scale (Baldwin, 2006). Trading firms differ substantially from firms that just serve the domestic market. Across a wide range of countries and industries, exporters have been shown to be larger, more productive, more skill- and capital-intensive, and more willing to pay higher wages than non-trading firms. Important to mention is that exporters are more productive, not as a result of exporting, but because only the most productive firms are able to overcome the costs of entering export markets (Bernard, 2007).
According to the Melitz model only the most productive firms become internationally active, because entering an export market includes costs. These costs will lower the competitiveness of the firm in the export market, because higher costs lower the profit margins. Only if the exporting firm is productive enough to overcome this disadvantage and remain competitive in the export market, despite the extra costs, it will enter the export market. Due to the absence of strategic interactions between firms, the monopolistic competition model of Melitz provides a convenient framework for the modelling of additional firm-level decisions in an open economy environment. In this environment heterogeneous firms self-select into different types of activities (Melitz, 2003). This framework can also explain why only a fraction of firms choose to become multinationals and operate foreign affiliates (horizontal FDI) or integrate with their foreign suppliers (vertical FDI).
communication (Cole, 2003). All this takes place within changing global cultural and ethical parameters, which increases the complexity of the environment in which MNE’s have to operate in (Cole, 2003).
2.2 Top management teams
The increase in complexity of the external environment MNE’s are facing with nowadays raises the amount of aspects, which should be taken into account in the determination of a new strategy. This makes strategic decision making even more difficult for international firms. This is where the role of the top management team comes in to play. These global activities by MNEs are likely to increase the range of cultures, customers, and competitors that a firm and its TMT face. Therefore, the complicated web of activities and institutions that creates opportunities for global firms also produces tremendous managerial complexity (Carpenter & Frederickson, 2001). TMTs are a group of senior executives that should be of interest for researchers who are analysing strategic decision-making in a MNE. This is because the group and its members provide an interface between the firm and its environment, and are relatively powerful, and therefore their choices and actions are likely to have an impact on the company (Carpenter, Geletkanycz & Sanders, 2004). The top team construct and team membership are often identified by using the measurement heuristic of senior hierarchical level, as indicated by title or position in the company, since individuals at higher levels are expected to have greater influence on strategic decisions (Carpenter, Geletkanycz & Sanders, 2004). On the basis of their proposition that organizations are "reflections of the
values and cognitive bases of powerful actors," Hambrick and Mason proposed,
"organizational outcomes (strategic choices and performance levels) are partially predicted
by managerial background characteristics" (Hambrick & Mason, 1984).
Carpenter, 1998). But on the other hand, TMT homogeneity promotes the collaboration that is needed to implement strategic changes, which implicates that TMT diversity would be negatively associated with strategic changes (O'Reilly, Snyder, & Boothe, 1993). Moreover, diversity can build trust and perceptions of procedural justice between a firm's product and geographic unit managers by noticing that a TMT takes different interests into account when allocating resources around the globe (Kaczmarek, 2009). TMTs that are socio-cognitively complex may be better equipped to make sense of changing international market opportunities and to "reconcile the conflicts and paradoxes" presented by globalization (Kaczmarek, 2009). Overall the TMT plays an important role in the strategic choices made by the MNE. The diversity in the TMT can be seen as a possibility for MNEs to cope with the faster changing and more complex business environment. It enables the MNE to see strategic problems from several perspectives and this will increase their adaptability, despite the fact that it would be easier to implement changes in strategies in a less diverse TMT.
2.3 Diversity in TMTs
Diversity in top management teams can be measured in many ways. An important factor that causes diversity is how much international experience the TMTs of a company have. Reuber and Fischer show that internationally experienced management teams have a greater propensity in developing foreign strategic partners and to delay less in obtaining foreign sales after start-up (Reuber & Fischer, 1997). Likewise, the heterogeneity in TMTs educational background influences the international strategic choices made by the board, because it provides an indicator of diversity of basic knowledge, cognitive processes and skills present in a TMT (Bantel & Jackson, 1989). Furthermore, the network resources available to TMTs in combination with the diversity in tenure in a TMT, is an important factor of diversity. This is because tenure is proven to be important in the way that it influences the degree to which TMT members use their networks to provide advice on international markets (Carpenter & Frederickson, 2001).
formal institution. Such effects are deeply rooted and long lasting and executives are likely to internalize and carry them along when they join a TMT in a foreign country. In this way, the formal and informal institutions of a board member’s country of origin jointly influence the board member’s field of vision, selective perception, and interpretation of strategic situations (Nielsen, 2013).
In line with the arguments above, a TMT with a broad range of diversity in nationality will bring a wide range of knowledge, and experiences with different formal and informal institutional environments. As multinational teams want to share and exchange their formal and informal experiences, because they need to collaborate to deal with the more complex environments. They engage in in-depth discussions, considerations of various alternatives and generation of new creative ideas. As a result, nationally diverse teams are better solving complex tasks and they arrive at more innovative solutions (Nielsen, 2013). With a more complex formal and informal environment due to the increase in trade after the two unbundlings, strategic choices are influenced by diversity. As well, the nationality of the board members, influences the strategic choices made by the TMT of MNEs to handle with this new more complex environment in which MNEs need to operate in.
2.4 Business portfolio of collaborations
2.4.1 Strategic Alliances
The first form of collaboration between two parties is a strategic alliance. The definition of a strategic alliance is a formal agreement between two or more business organizations to pursue a set of private and common interests through the sharing of resources in contexts involving uncertainty over outcomes (Arino, 2003). An alliance is strategic when it is the means by which a firm seeks to implement, in part or in whole, elements of management’s strategic intent (Arino, 2003). In a stable competitive environment, a firm could adapt to small differences in circumstances. Another option is to ignore the changes and decide that the disadvantages for not adapting to these circumstances are small. Collaboration is not necessary and firms can still control their own processes. In this case the penalty for firms to a loss of control is low.
This is completely different in a changeable world of rapidly globalizing markets and industries, a world of converging consumer tastes, rapidly spreading technology, escalating fixed costs and growing protectionism, which mandates alliances and makes them absolutely essential to strategy, (Ohmae, 1989). Now the disadvantages for firms that do not want to lose control over their business process and do not adapt is much higher. As a consequence, as competition becomes more global, many firms are using alliances to enter new markets, obtain new skills, and share risks and resources.
similar with and which they perceive as trustworthy, so that they will minimize the friction between the parties.
2.4.2 International joint ventures
The second aspect of the business portfolio for collaboration that is analysed is the international joint venture. These ventures involve two or more legally distinct organizations (the parents), each of which actively participates in the decision-making activities of the jointly owned entity (Geringer, 1991). If at least one parent organization is headquartered outside the joint ventures country of operation, or if the venture has a significant level of operations in more than one country, then it is considered to be an international joint venture (IJV) (Geringer, 1991). An alternative to wholly-owned subsidiaries, IJVs are commonly used by firms as a means of competing within multi-domestic or global competitive arenas. In recent times, they are perceived as strategic weapons, as one of the elements of an organization's business units network (Geringer & Hebert, 1988).
The parent firms need to create stability in order to make a joint venture successful. Instability is defined as a major change in partner relationship status that is unplanned and premature from one of both partner’s perspectives (Inkpen & Beamish, 1997). The core argument is that the instability of IJVs is associated with shifts in partner bargaining power. Shifts in bargaining power occur when one of the partners of an IJV acquires sufficient knowledge and skills to eliminate the dependency between them and their joint venture partner (Inkpen & Beamish, 1997). More knowledge and skills, means more bargaining power and less dependency. To minimize the instability in the IJV, it is important to know what you can expect from your partner, this can be achieved by greater trust and minimizing opportunism. Again, the expectations are that MNE’s will choose for collaboration partners, which with they are familiar and similar.
2.4.3 Mergers and Acquisitions
increased competition and by new market opportunities and the need to acquire complementary firm specific intangible assets, such as human resources, brand names, technologies etc. (Zademach & Rodriquez, 2009).
Although a complex strategic move, such as acquiring abroad, most likely requires substantial cognitive effort in form of conscious and deliberate information processing, the development of mental models that result from experience accumulation will typically enable the firm to decide upon and implement a suitable course of action more automatically (Nadolska & Barkema, 2007). This indicates that it will take less effort to collaborate, if companies are familiar with their partners or familiar with partners in the same country. This can be an incentive for collaboration and can lead to a decrease in diversity of the BCP. A more nationality diverse board is likely to be familiar and experienced with more different environments, which can lead to an increase in nationality diversity of the BCP. Important to take in account for cross-border M&As is the fact that national borders are also associated with factors that are likely to affect the costs and benefits of a merger. Countries have their own cultural identities and people in different countries often speak different languages, have different religions, and sometimes have longstanding feuds, all of which increase the contracting costs associated with combining two firms across borders (Erel, Liao & Weisbach, 2012). The M&A collaboration type is chosen over other collaboration types when uncertainty is high and coordination would be expensive and intensive. The expectations are that this would be the case in countries with large differences regarding to the country of origin of the focus MNE. In order to minimize the uncertainty and costs it is likely that the TMT has a preference for mergers with firms in a more familiar environment.
2.5 The link between TMT nationality diversity and BCP nationality diversity
The social categorization theory could also be an explanation for a link between the TMT nationality diversity and the nationality diversity in the collaboration agreements. In order to boost their self-esteem, people try to categorize themselves in a certain social group. The perception of people who are not in the same social group is relatively negative compared with, within social group members (Tajfel, 1981). Categorization is also a way of simplifying and ordering complexity (Tajfel, 1981). This implies that in a more complex business environment categorization by TMTs on nationality is more likely in order to simplify the environment, eventually this can lead to less diverse BCPs. The perceptions of companies, which are not in the same group as the TMT members, are relatively negative. In more diverse TMTs, the expectation is that categorization is less of an issue, which can lead to more diverse BCPs.
-‐ 3. Methodology -‐
3.1 Data
In this chapter the data collection and the methodology will be discussed. Given that this is a quantitative study of the diversity of the board members of MNEs and the diversity of the business collaboration portfolio. The data are gathered among MNEs in four European countries in the period 2005-2008. The countries, which are part of the dataset, are the United Kingdom (UK), Germany, France and the Netherlands. In total the dataset is based on 119 MNEs, of which 29 MNEs are from the UK, 30 from Germany, 24 from the Netherlands and 36 from France.
3.1.1 Data Relevance
In Europe the domestic market for MNEs is much smaller than for economic superpowers like the US or China. This implies that European MNEs, in order to keep on growing, have to operate in foreign markets. This makes it even more likely, that the nationality diversity in collaboration portfolio and the nationality diversity in board members are of greater importance in Europe. The most important and biggest MNEs can be found in the largest economies. These MNEs are the most likely to have collaboration agreements across borders and in which the nationality diversity can play a role. The most important European economies are the economies of the UK, Germany and France, so these countries are included in our dataset. Furthermore the Netherlands is included in the dataset. The Netherlands is a very open economy, and trade characterizes open economies. This implies that MNEs from the Netherlands are likely to be active across the border. This is also reflected in the TMTs of Dutch MNEs that score high on nationality diversity (van Veen & Marsman, 2008). The fact that the Netherlands is a tax haven is also a motivation to include the country in our dataset. Tax havens attract MNEs and stimulate foreign direct investment (Blanco & Rogers, 2014). This implies that nationality diversity could be important in the Dutch business environment. Lastly, the Netherlands is also included, because of personal interests.
The period 2006-2008 is a period of economic growth, just before the emergence of the Euro crisis. In this period of growth, it is more likely that MNEs look for new opportunities and try to expand their market by entering into more collaboration agreements. The period is an ideal time period to analyse if more nationality diversity in the board also implies the entering into more nationality diverse collaboration agreements. Another reason to select this time period is the fact that the TMT database that has been used consists of data about the TMTs of the selected companies in the period 2005-2007.
3.1.2 Data collection
The data relating to business collaboration portfolios of the selected MNEs are gathered from three different sources. The first source is the Orbis database. This database contains data about the joint venture activities and the merger and acquisition activities of the MNEs. The second source is the annual reports of the matching firms in the dataset. The MNE annual reports of the years 2006, 2007 and 2008 provide us with information about the business activities of the focal MNE. They provide us with information about acquisitions, joint ventures and alliances that have an impact on the financial performances. The third and last source is the company web page. The company webpage is used to gather information about business activities. Especially the press releases of the company webpage are very useful. Furthermore, not all MNEs in the original dataset are used in the analysis. The three types of MNEs that will be excluded from the dataset are MNEs, which were demerged, MNEs, which are acquired by another company or MNEs, which are merged with another MNE and do not operate under their own name anymore. This means the exclusion of 15 MNEs from our dataset. The total company list of the MNEs in our dataset is included in appendix I.
This exclusion is due to the fact that reliable data resources are not available for these companies and data collection is impossible or not reliable. Annual reports are not available anymore and/or the company webpages does not exist anymore. In some cases the companies do not have their own board anymore, which makes it impossible to use them in the dataset. The total final dataset consists out of 105 MNEs from which are 25 MNEs in the UK, 28 MNEs in Germany, 16 MNEs in the Netherlands and 36 MNEs in France.
3.2 Variable construction 3.2.1 Dependent Variable
Diversity in BCP: The dependent variable in the regression is the nationality diversity in the
business collaboration portfolio for new collaboration agreements. Diversity in this thesis is defined as the collective amount of differences among members within a social unit (Solanas, Selvam, Navarro & Leiva, 2012). In this case the social unit is the total BCP. The diversity is measured by the difference in nationalities of the companies with which the MNE has a new collaboration agreement. The nationality is determined by the partner’s country of origin. For example, if an MNE has only collaboration agreements with companies from the same country of origin, then there is no diversity. If the MNE starts a new collaboration with a company from another country not already included in the BCP, this will lead to more differences among the social unit (the BCP) and will lead to an increase in diversity.
As mentioned before, for the dependent variable, this can be explained as the collective amount of differences in nationality of the companies, with which the MNE has signed a new collaboration agreement. The BCP is a combination of all the three types of collaboration agreements, because the expectations are that the relationship between the three different types of collaboration agreements and the TMT diversity is in the same direction. In order to calculate the diversity, the Blau index is used. The Blau index is a way to calculate within-group heterogeneity, in this case, for nationality. Heterogeneity is the distribution of a population among groups in terms of a nominal parameter (Blau, 1977). Or the probability that two random selected individuals are not from the same group. In the context of this thesis: the probability that two random picked business collaborations, do not have the same country of origin. This brings us to the formula of the Blau index:
𝐵 = 1 − 𝑃!! !
!!!
The calculated diversity index is (1 − (1/3)! – (1/3)!− (1/3)!) = 0,667. In 2008, the
company has 4 new collaboration agreements with companies from Brazil, Canada and 2 companies from the USA. The diversity index is (1 − (2/4)! – (1/4)! − (1/4)! = 0,625. In
this case the (2/4)! term is corresponding with the companies from the USA, while the
(1/4)! terms are corresponding with the companies from Brazil and Canada.
3.2.2 Independent variable
Diversity in TMT: The main independent variable is the nationality diversity in board
members in the TMT. The strategic decisions are made on a top management level, which implies that the characteristics of the TMT are important for the choices made by the board (Carpenter, Geletkanycz & Sanders, 2004). One of these strategic choices is selection of the right collaboration partner. To reduce friction, risks and costs, companies and their TMT choose for what they are familiar with or with whom they can identify with (Figueiredo, Guimarães & Woodward, 2002; Tajfel, 1981). To see if this is reflected in the relationship between TMTs and their BCPs the nationality diversity in the TMT is taken as the main independent variable. According to the definition of diversity, this implies the collective amount of differences in nationality of the board members. Again, the Blau index is used. The Blau index represents the within group diversity in nationality of the board members. The heterogeneity is based upon the distribution of a population (the board) among groups in terms of nominal parameters such as ethnicity, race, language, and religion or in our case nationality (Avison & Loring, 1986). The same formula is used to calculate the Blau index:
𝐵 = 1 − 𝑃!! !
!!!
3.2.3 Control variables
Size: The first indicator to check for is size. Research gave a positive relationship between
BCP Size: The third control variable is the size of the Business Collaboration Portfolio
(BCPSize), the larger the business portfolio of an MNE; the more likely it will be that the diversity of the portfolio will increase. The driver behind this is that, a heterogeneous network will have a positive effect on product innovations and will give the MNE more opportunities to combine knowledge and technologies in a beneficial way (Nieto & Santamaria, 2007).
Industry Dummies: Dummies are included to control for industry level effects. Several
industry-fixed effects can have an effect on the diversity of the business collaboration portfolio. MNEs in R&D intensive industries are more likely to engage in collaboration and partnerships, but with diminishing propensity (Belderbos, Carree & Lokskin, 2004). A more diverse business collaboration portfolio can lead to more innovations (Nieto & Santamaria, 2007). This implies that more innovative industries will benefit from a more diverse business collaboration portfolio. Furthermore, an incentive to collaborate is to improve the competitive position through economies of scale or economies of scope. The importance of economies of scale and economies of scope are different per industry (Polenske, 2004). Some industries benefits more from collaboration than others, this can influence the number and diversity of the business collaborations of the MNEs in the different industries.
In order to make a distinction in industry fixed effects, dummy variables for five types of industries are includes in the regression, The industry types are taken from the categories of the four digit standard industrial classification (SIC code), which are: Mining and Construction (Min&Con), Manufacturing (Man), Transportation, Communication and Utilities (TranCom&Uti), Wholesale and Retail trade (Whole&Ret) and Finance, Insurance and Real Estate (FinIns&Re).
The dummy variable is 1 if the MNE is active in a certain industry category. The dummy variable is 0 if the company is not active in the industry. The base industry is the Mining and Construction sector, because this is the most common industry.
Year dummies: The last control variable is the year dummy (Year). The dummy is included
to control for time effects, the base year is 2006 and dummies are included for 2007 and 2008. 3.3 Methods
these companies that can lead to different coefficients (Hill, Griffiths & Lim, 2012). From the literature review it becomes clear that the pooled model is not suitable in this situation. Because of the fact that company-specific differences in BCP diversity and TMT diversity can exist. The fixed effects model is the standard model that incorporates this characteristic. The fixed effects model controls for company-specific, time invariant characteristics. In this case, the model will test the relationship between the predictor variables, which can be influenced by company-specific characteristics, and the outcome variables. (Hill, Griffiths & Lim, 2012) Another possible model to use for panel data is the random effects model. The random effects model also recognizes the fact that companies are randomly selected, and thus will treat the individual differences as random (Hill, Griffiths & Lim, 2012). To test if the fixed effects model or the random effects model is preferred a Hausman test will be performed.
The problem of serial correlation is controlled for by the use of clustered robust standard errors. Serial correlation means that the error variance of an individual (in this case the company), can be different in different time periods, but is constant over the individual company. To increase the efficiency of the standard errors, the clustered robust standard errors are used (Hill, Griffiths & Lim, 2012). The dataset is short and wide, which makes the use of clustered robust standard errors suitable and the use of clustered robust standard errors is also a useful robustness check.
The final regression is given by the formula:
𝑫𝒊𝒗𝑩𝑪𝑷𝒊,𝒕 = 𝜶 + 𝜷𝟏𝑫𝒊𝒗𝑻𝑴𝑻𝒊,𝒕!𝟏+ 𝜷𝟐𝑵𝒓𝑬𝒎𝒑𝒊,𝒕+ 𝜷𝟑𝑻𝒐𝒕𝑨𝒔𝒊,𝒕+ 𝜷𝟒𝑩𝑪𝑷𝑺𝒊𝒛𝒆𝒊,𝒕 + 𝜷𝟓𝑴𝒊𝒏&𝑪𝒐𝒏𝒊+ 𝜷𝟔𝑴𝒂𝒏𝒊+ 𝜷𝟕𝑻𝒓𝒂𝒏𝑪𝒐𝒎&𝑼𝒕𝒊𝒊+ 𝜷𝟖𝑾𝒉𝒐𝒍𝒆&𝑹𝒆𝒕 𝒊 + 𝜷𝟗𝑭𝒊𝒏𝑰𝒏𝒔&𝑹𝒆𝒊 + 𝜷𝟏𝟎𝒀𝒆𝒂𝒓𝒕+ 𝒖𝒊+ 𝜺𝒊𝒕
In the regression model the t=1 for t=2006, the i is the company ID with i=1 for Company=1. Note that the Diversity in TMT of the previous year is used and the error term 𝑢!" is only included in the random effects model. The models are analysed with the assistance of the STATA software package.
-‐ 4. Results -‐ 4.1 Summary Statistics
Before moving on to the regression of the data, and before determining if the diversity of nationality in the board members does have an influence on the diversity of nationality in the business collaboration portfolio’s of the MNEs, the data of the collaboration portfolio are examined. The summary statistics of the independent variable (diversity in de Business Collaboration Portfolio) and most important dependent variable (Diversity in Top Management Teams) used in the analyses are shown in table 1.
Table 1: Summary statistics for the dependent variable (BCP diversity) and the most important independent variable (TMT diversity).
N Diversity in BCP Diversity in TMT
Count: Mean (SD): Mean (SD):
Total: 315 0,603 (0,266) 0,405 (0,218)
Industry Level:
Mining & Construction 87 0,623 (0,238) 0,470 (0,230) Manufacturing 63 0,639 (0,220) 0,340 (0,220) Transport Communication & Utilities 57 0,672 (0,224) 0,337 (0,239) Wholesale & Retail trade 48 0,432 (0,3) 0,458 (0,195) Finance Insurance & Real Estate 60 0,609 (0,304) 0,401 (0,155)
Country Level:
United Kingdom 78 0,528 (0,282) 0,510 (0,175) Germany 81 0,610 (0,257) 0,220 (0,140) France 108 0,660 (0,239) 0,365 (0,195) The Netherlands 48 0,558 (0,286) 0,637 (0,113)
On the country level, the highest diversity in BCP was in France (0,660). The lowest diversity in the BCP was in the UK (0,528). This implies that French MNEs are more likely to have collaboration agreements with companies from more different countries than a MNE from the UK. A MNE from the UK will focus more on collaboration agreements with companies in countries they are familiar with. Again, a univariate T test has been used to test if the diversity in BCP is significantly different between France and the other countries in the dataset (Germany, UK, the Netherlands). The P-value of the performed T test is 0.0065, which is less than the α-level of 0.05. The conclusion is that the difference of means in BCP diversity between France and the other countries is significantly different from 0.
Furthermore, the TMT diversity is the highest in the Mining and Construction industry (0,470) and the lowest in the Transport Communication and Utilities industry (0,337). At the country level the lowest TMT diversity index is in Germany (0,220) and it is the highest in the Netherlands (0,635). This implies that industry and country factors can have an effect on the level of nationality diversity present in the TMT.
Table 2 presents the correlations statistics for the sample used in the analysis. A check for multicollinearity is performed, to check if the explanatory variables are correlated with each other. The statistics presented, do not indicate that there are signs of multicollinearity. The highest correlation is the correlation between the total assets and the number of employees, but this is only 0,42. This implies that companies that tend to have many assets do not necessarily also have many employees, or visa versa.
Table 2: Correlation statistics of the explanatory variables.
DivTMT NrEmp TotAs BCPSize Man TransComUti WholeRet FinInsRe Year 2007 Year 2008
DivTMT NrEmp* -‐0,2057 Tot As* -‐0,0980 0,4290 BCP Size 0,0655 0,0490 0,1560 Man -‐0,1395 0,0717 0,0157 0,1875 TransComUti -‐0,1478 0,0316 0,3205 0,1429 -‐0,2342 WholeRet 0,1035 0,0246 -‐0,1641 -‐0,2410 -‐0,2183 -‐0,1929 FinInsRe -‐0,0083 -‐0,0079 -‐0,3146 -‐0,0597 -‐0,2497 -‐0,2206 -‐0,2057 Year 2007 0,0077 -‐0,0092 0,0029 0,0313 0,0000 0,0000 0,0000 0,0000 Year 2008 0,0104 0,0398 0,0601 -‐0,0125 0,0000 0,0000 0,0000 0,0000 -‐0,5000
4.2 Model interpretation
In order to determine if a random effects or a fixed effects is appropriate, a Hausman test is performed. The Hausman test compares the coefficient estimates from the random effects model to those from the fixed effect model (Hill, Griffiths & Lim, 2012). On the basis of this test an appropriate model can be chosen. The Hausman test requires coefficients that are on a similar scale in order to be effective. The variables Number of Employees and the Total Assets are transformed. Number of employees is in terms of 100.000 people and the total assets are in terms of 100 million US dollars. In the Hausman test the preferred model is the random effects model and the alternative is the fixed effects model. The null hypothesis of the Hausman test is that unique errors are correlated with the regressors. This means that the null hypothesis in the Hausman test is that the random effects model is the preferred model and the alternative is the fixed effects model (Hill, Griffiths & Lim, 2012).
The Hausman test gives a chi-square (𝜒!) of 10.95 and the p-value is 0,09. With a P-value of
Table 3: The effect of the explanatory variables on nationality diversity in BCP
(1) (2)
VARIABLES The Hausman
Taylor estimates
Random Effects model
Diversity in TMT -0.0455 -0.0667 (0.0789) (0.0676) Number of Employees 0.0312 0.0301** (0.0196) (0.0126) Total Assets 0.0220 0.0180 (0.0301) (0.0209) Year dummy 2007 -0.000906 -0.00292 (0.0257) (0.0260) Year dummy 2008 -0.00333 -0.00286 (0.0258) (0.0280) BCP Size 0.0144*** 0.0209*** (0.00321) (0.00475) Manufacturing -0.0256 -0.0435 (0.0507) (0.0393)
Transport Communication & Utilities -0.00286 -0.0176
(0.0537) (0.0424)
Wholesale & Retail trade -0.150*** -0.134**
(0.0560) (0.0551)
Finance Insurance & Real estate -0.00436 -0.00615
(0.0536) (0.0582)
Constant 0.509*** 0.482***
(0.0567) (0.0476)
Observations 315 315
Number of Companies 105 105
The Hausman Taylor estimates and the Random effects estimates; (standard errors in parentheses) *** p<0.01, ** p<0.05, * p<0.1
4.3 Hausman Taylor estimator
To continue, the interpretation of the Hausman Taylor Estimator presented in the first column of table 3 will be discussed. The relationship between the dependent variable and the most important independent variable, which is challenged by the research question, is founded on the individual company level. This is illustrated with the following case.
corporate decisions include the choice for appropriate collaboration partners. After the change in governance structure the board consists out of 17 board members. In the old setting the TMT consist of one Belgian, one Italian and 15 Frenchmen. After the change of the governance structure the TMT still consists of 17 members, but in the new setting two new foreign board members were assigned. Sir Roderic Lyne from the United Kingdom and Theo Waigel from Germany joined the board. Before they joined the board, the BCP of Accor in 2006 only consisted of 16% new collaboration agreements in the UK and Germany. After reshaping the management structure and the assigning of Lyne and Waigel in 2007, 42% of Accor’s new collaboration agreements were with companies from the United Kingdom or Germany. This even increased in 2008 to a number of 66% of the BCP, which implies that there might be a relationship between the TMT diversity and the BCP diversity.
In this case the effect of nationality diversity in the TMT on the nationality diversity in the BCP is clear. However if the whole dataset is analysed the positive relationship between BCP diversity in time t is not significant with the TMT diversity in time t-1 as given by the Hausman Taylor estimates. This implies that no evidence is found for the research question formulated in the introduction.
Examining the effects of our control variables the effect of the BCP size is important. The BCP Size is positively and significantly correlated with the diversity in the BCP (P <0,01). This implies that if a company is engaged in more collaboration agreements in a certain year, this will lead to an increase in the diversity in the BCP in the same time period, concerning the nationality of the country of origin of the companies the MNE is collaborating with. In our dataset the MNEs with a large amount of new collaboration agreements in one year are: AstraZeneca (UK) in 2006 (23 collaborations), Deutsche Telekom (Germany) in 2007 (39 collaborations) and Essilor (France) in 2008 (25 collaborations). The BCP diversity indexes are respectively 0,741, 0,692 and 0,746, which are all far above the average BCP diversity index of 0,603. The other control variables for size do not have a significant effect on the BCP diversity. Not even with a significance level of p<0,1. The year dummies included in the model are insignificant, as diversity in BCP did not change a lot over the different time periods.
there is negative relationship between the nationality diversity in the BCP and the Wholesale and Retail trade dummy variable. This means that MNEs that are active in the Wholesale and Retail trade sector have significantly less diverse business collaboration portfolios in comparison with MNEs that are part of the reference industry, which is mining and construction.
4.4 The Random effects model
The second regression model is the random effect model shown in table 3. There are several differences between the random effect model and the Hausman Taylor estimates. In the random effects model the assumption is made that the individuals in the sample are randomly selected. In this research the companies are not totally random but certainly not all companies available are selected in the sample. As a consequence the individual differences are also treated as random rather than fixed (Hill, Griffiths & Lim, 2012). This can have implications for our model compared with the Hausman Taylor estimates. The random effects model is shown in second column of table 3.
Unfortunately the main independent variable is still insignificant in the random effects model. This implies, that also when we treat the individual differences as random, no evidence is provided for our main statement. There is no significant relationship between the BCP nationality diversity in year t and the TMT nationality diversity in year t-1 according to this dataset.
The main difference between the two models is the fact that one of our control variables for size, the number of employees, now has a significant effect on the BCP diversity with p<0,05. The relationship between the Number of employees and the diversity in BCP is positive, this seems likely, because larger firms tend to be more globally active and this can lead to more diversity in BCP. To examine the effect of the number of employees in our dataset, the BCP diversity index concerning the five largest and the five smallest MNEs in term of the number of employees are examined. The differences are shown in table 4 and table 5.
Table 4: Five largest companies concerning the number of employees and their BCP diversity scores. Company: Number of employees in persons: BCP Diversity:
Largest MNEs: Carrefour 458.615 0,639 Deutsche Post 428.567 0,831 Siemens 502.659 0,81 HSBC 316.646 0,705 Tesco 408.467 0,563 Average: 422.991 0,710
Table 5: Five smallest companies concerning the number of employees and their BCP diversity scores. Company: Number of Employees in persons: BCP Diversity:
Smallest MNEs: BG Group 5.003 0,315 ASML 7.614 0,426 Deutsche Borse 3.214 0,771 Tom Tom 1.884 0,676 SBM Offshore 3.015 0,116 Average: 4.146 0,461
According to these tables, it seems like the number of employees have an effect on the diversity in BCP. Large companies do have a more diverse BCP. This effect is only significant in the random effects model, in which we treat the companies as random and use the variance within and the variance between companies. It is likely that a company with many employees in one year will also have many employees in the next year. This indicates why the effect is not captured in the fixed effect model.
The control variables BCP size is still significant but the estimated effect of the BCP size on the diversity of the BCP is increased in the random effect model from 0,0144 to 0,0209. Which implies that an extra collaboration in the random effect model leads to an estimated increase in BCP diversity of 0,0209 while with the Hausman Taylor estimates, this was only an increase in BCP diversity of 0,0144.
Other industry variables do still not significantly differ from our base industry effect of the mining & construction industry.
The model of Hausman and Taylor allows for some of the explanatory variables to be related with the random effects, while the random effects model assumes that the explanatory variables are uncorrelated across individuals. Which set of estimates is better (Hausman Taylor estimates or Random effects estimates), will depend on how successful the partition into exogenous and endogenous variables is carried out, and whether the gain from having consistent estimates is sufficiently large to compensate for the increased variance of the instrumental variables estimators, because variance in the Hausman Taylor estimates is likely to be higher (Hill, Griffiths & Lim, 2012). This in combination with the choice of the level of significance (α), determines which estimates are the most appropriate estimates to use.
-‐ 5. Discussion and conclusion -‐
In the last decades international trade and globalization got an enormous boost and this increased the complexity and the uncertainty in the environment in which MNEs need to operate (Baldwin, 2006). As a consequence, the overall complexity in the business environment for MNEs will increase and they need to seek for solutions and opportunities to cope with this more complex and faster changing business environment (Luo, 2005). TMTs are of interest because they serve as a connection between the environment and the company, and are relatively powerful within a company (Carpenter, Geletkanycz & Sanders, 2004). This indicates the importance of a TMT that is able to effectively cope with these changing and more complex circumstances. Important is the level of diversity within a TMT, which can contribute to a better performance of the MNE (Kaczmarek, 2009). Diversity in terms of background, skills and networks within a TMT can contribute to the adaptability of a firm to fast changing circumstances (Bantel & Jackson, 1989). This need for higher adaptability could be a reason for the phenomenon of increased diversity in several characteristics important for TMT members active in contemporary MNEs (Staples, 2007; van Veen & Marsman, 2008). One of the aspects of interest of this research was the diversity in nationality of these board members.
several reasons like, gaining knowledge, getting access to certain resources, build up a competitive advantage or penetrating new markets (Barney, 1991; Dyer & Singh, 1998). If an MNE has made the choice to collaborate with other companies they can choose between different ways of collaboration. In this research, three ways of collaboration are discussed. The strategic alliance, the joint venture and the merger and acquisition deal. Managers have the tendency to go for what they are familiar with and they want to reduce friction in a business relationship, in order to minimize efforts and costs, to keep the relationship on a level in which collaboration is profitable (Figueiredo, Guimarães & Woodward, 2002) The social categorization category tells us that people try to identify themselves with a certain group and associate out of group members with negative characteristics, like dishonesty and laziness (Tajfel, 1981). This is why it is more likely to collaborate with companies and persons, with the same country of origin. Furthermore, similarity between persons leads to more liking and attraction (Tajfel, 1982). This gives us another reason to expect that MNEs from a certain country of origin are more likely to collaborate with other companies, which they are familiar with. This implies that more nationality diversity in the TMT can lead to more nationality diversity in BCP.
familiar institutional contexts in comparison with a MNE which has the aim to penetrate a new markets (Barney, 1991; Dyer & Singh 1998). The intention, with which a MNE starts a collaboration agreement, could be of interest in order to see if this has an effect on the relationship between nationality diversity in TMTs and nationality diversity in BCPs.
Secondly, the research results show that diversity increases if the BCP size increases, but this is in contradiction with the literature. The expectations are that MNEs and TMT members tend to choose what they are familiar with. If this is the case, more collaboration will lead to more collaboration with companies with the same country of origin and this will not increase the diversity of the BCP. Further research could use this contradiction as a basis to see what is of greater importance for the diversity in the BCP; the size of the BCP or the nationality diversity in the TMT of the previous year. Interesting would be to find out if there is a positive or negative relationship between the size of the BCP and the diversity in BCP.
This research also contributes to the role of collaboration in the strategy of a business. It gives us indications for the selection of appropriate partners for a collaboration agreement. According to this research it seems that there is no preference for partners coming from familiar countries. However, we have to take into account that in this case the three types of collaboration are analysed all together in the variable of BCP diversity, therefore it could be interesting to see if there are significant differences between strategic alliances, joint ventures and merger & acquisition deals. The relationship between the three types of collaborations and the TMT diversity is considered to be the same. It would be interesting to see, if the strength of the relationship and the direction of the relationship, is indeed the same or that differences can be identified.
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Appendix I:
List of MNEs:
Company: Country: Company: Country:
1. Anglo American United Kingdom 14. Imperical Tobacco Group United Kingdom 2. AstraZaneca United Kingdom 15. Lloyds TSB United Kingdom 3. Aviva United Kingdom 16. Marks & Spencer United Kingdom 4. BAE Systems United Kingdom 17. National Grid United Kingdom 5. Barclays United Kingdom 18. Prudential PLC United Kingdom 6. BG Group United Kingdom 19. Reckitt Benckiser United Kingdom 7. BHP Billiton United Kingdom 20. Royal Bank of Schotland United Kingdom 8. BP United Kingdom 21. Rio Tinto United Kingdom 9. British American Tobacco United Kingdom 22. SAB Miller United Kingdom 10. BT Group United Kingdom 23. Standard Chartered United Kingdom 11. Diageo United Kingdom 24. Tesco United Kingdom 12. GlaxoSmithKline United Kingdom 25. Vodafone Group United Kingdom
13. HSBC United Kingdom
Company: Country: Company: Country:
26. Adidas-‐ Salomon Germany 40. Fresenius Germany
27. Allianz Germany 41. Henkel Germany
28. Altana Germany 42. Hypo Real Estate Germany 29. BASF Germany 43. Infineon Technologies Germany
30. Bayer Germany 44. Linde AG Germany
31. BMW Germany 45. Lufthansa Germany
32. Commerzbank Germany 46. MAN Germany
33. Continental Germany 47. METRO Germany
34. DaimlerChrysler Germany 48. Munich RE Germany 35. Deutsche Bank Germany 49. RWE Germany 36. Deutsche Borse Germany 50. SAP Germany 37. Deutsche Post Germany 51. Siemens Germany 38. Deutsche Telkom Germany 52. ThyssenKrupp Germany
39. E.ON Germany 53. Volkswagen Germany
Company: Country: Company: Country:
54. Accor France 72. Michelin France
55. Air Liquide France 73. Pernod Richard France 56. Alcatel France 74. PSA Peugeot Citroen France 57. AXA France 75. PPR (Kering) France 58. BNP Paris Bas France 76. Publicis France
59. Bouygues France 77. Renault France
60. Capgemini France 78. Saint Gobain France 61. Carrefour France 79. Sanofi-‐Aventis France 62. Credit Agricole France 80. Schneider Electric France 63. EDF France 81. Societe Generale France 64. Essilor France 82. STMicroelektronics France 65. France Telekom France 83. SUEZ France 66. Gaz de France France 84. Thales Group France 67. Groupe Danone France 85. Thomson SA (Technicolor) France
68. L'Oreal France 86. Total France
69. Lafarge France 87. Veolia Environment France
70. Legardere France 88. Vinci France
71. LVMH France
Company: Country: Company: Country:
90. AEGON The Netherlands 98. Philips The Netherlands 91. Ahold The Netherlands 99. Reed Elsevier The Netherlands 92. AKZO Nobel The Netherlands 100. Royal Dutch Shell The Netherlands 93. ASML The Netherlands 101. SBM Offshore The Netherlands 94. DSM The Netherlands 102. TNT The Netherlands 95. Heineken The Netherlands 103. TomTom The Netherlands 96. ING Group The Netherlands 104. Unilever The Netherlands 97. KPN The Netherlands 105. Wolters Kluwer The Netherlands
