Multinationality and Institutional Diversity:
Implications for MNE Performance.
June 30th 2014 Sabrina Karskens: 5975786 Final Version Master Thesis
Business Studies: International Management Supervisor: Dr. Niccolò Pisani
Abstract
The relationship between multinationality and performance has been a central theme that has been highly debated in international business research. Many factors influence this relationship and determine the nature of this relationship. In this study, the influence of institutional diversity in both the home and primary host region on the association between multinationality and performance is investigated using data relative to the 2013 Fortune Global 500 companies. The underlying relationship is found to be linear, demonstrating that the benefits associated with internationalization exceed the costs of coordination and liabilities of foreignness. No moderating effect is found for the home region and primary host region institutional diversity on the relationship between multinationality and performance. However, the areas of governmental and legal institutional diversity in the home region are found to negatively moderate the relationship between multinationality and performance. This research contributes to the understanding of the relationship between multinationality and performance and the factors that influence this relationship.
Keywords: Multinationality, Performance, Geographic Scope, Institutional Diversity, Home Region, Primary Host Region.
Table of Contents
Introduction………..……….………....….p. 5 Literature Review……….………..……p. 8 - The Relationship between Multinationality and Performance………..…...p. 8 - Home Region……….……….………p. 12 - Host Regions………...……..……..p. 14 Theoretical Framework……….……….….…..p. 18 - The Shape of the Relationship between Multinationality and Performance..p. 18 - Institutional Diversity………..……….…….…….p. 19 Methods..………...……….….………....p. 28 - Data Collection……….…………..p. 28 - Measures……….…p. 29 - Statistical Model……….………p. 31 Results………...……….……….p. 33 Discussion………...……….……..…….p. 40 - Academic Relevance……….….p. 42 - Managerial Implications……….p. 43 - Limitations………....……..p. 43 Conclusion………...……….…………..p. 46 References………..………….…………...….p. 48 Appendices……….……….…..………..…p. 57
List of Tables and Figures
Figure 1. The conceptual model……….…….p. 7 Figure 2. Home region institutional diversity, MNE geographical scope and
performance………p. 24 Figure 3. Primary host region institutional diversity, MNE geographical scope and performance……….……p. 26 Figure 4. The conceptual model………..……p. 27 Figure 5. Fortune Global 500 companies divided by industry………p. 32
Table 6. Descriptive statistics: means, standard deviations and correlations….…p. 34 Table 7. Geographic scope, institutional diversity and MNE performance………p. 35 Table 8. Geographic scope, areas of institutional diversity and MNE
Introduction
The relationship between multinationality and performance has always represented one of the core issues addressed in international business (IB) research and still represents a central them in the field. Multinationality is defined as the extent to which firms operate in international environments by investing in assets and/or controlling activities outside their home country and is also referred to as internationalization (Cantwell & Sanna-Randaccio, 1992). Research has been reporting mixed finding on the relationship between multinationality and performance. Whether there is a systematic association between internationalization of MNEs and their performance is central to the entire field (Glaum & Oesterle, 2007). Answering this question is of importance because it is a major element of all the contributions to the theory of foreign direct investment (FDI) and other theories of foreign market entry (Glaum & Oesterle, 2007).
Empirical studies that have been conducted over the last 30 years have shown mixed results as to how multinationality is related to performance (Contractor, 2007). A positive, negative, or no relationship, are among the results of this body of research (Yang & Driffield, 2012). However, a substantial amount of research has provided support for a positive relationship between internationalization and performance (Goerzen & Beamish, 2003). With regard to this positive relationship, some have found internationalization to have a positive and linear association with firm performance, others have found a positive relationship until a point of inflection is reached (Goerzen & Beamish, 2003).
These conflicting empirical findings could be the result of incomplete theorization about the full range of benefits and costs to implement an
internationalization strategy. When internationalization increases, this may increase benefits but also costs, which could influence the shape of the relationship curve between multinationality and performance (Zaheer, 1995). Building on extant research, this thesis precisely examines the association between multinationality and performance.
A positive relationship between multinationality and performance may be influenced by progresses in technology, an increase in trade and the resulting globalization (Banalieva & Dhanaraj, 2013). However, according to Rugman & Verbeke (2004) we still live in a regionalized rather than globalized world. In their study of the Fortune Global 500 firms, more than 80% of the companies are characterized by a home region focus, defined as the propensity of a firm to expand within the home region as opposed to outside the home region (Banalieva & Dhanaraj, 2013). Qian, Li, Li & Qian (2008) also confirm that the world is in a regionalized state, and show that a home region focus positively affects performance. Additionally Qian, Khoury, Peng & Qian (2010) also corroborate the finding that increased internationalization within the home region increases performance. Extant research thus confirms that geographic scope influences MNEs’ performance. To understand whether and under which circumstances a home region (versus global) focus results in a higher performance is the object of the present study.
In researching the performance implications of regionalization and globalization, the topography of institutional landscapes and the appreciation of their diversity should also be taken into consideration (Jackson & Deeg, 2008). Institutional diversity is the variation in the institutional environments across countries (Banalieva & Dhanaraj, 2013) and represents a critical factor in determining internationalization costs. Thus it is of great importance in determining the shape of
the relationship curve between multinationality and performance. When differences between the home and host region institutional diversity are not taken into consideration, the logic behind the chosen strategy concerning internationalization will remain incomplete. Accordingly, this study investigates the moderating effect of institutional diversity variables on the relationship between multinationality and performance. The conceptual model of this study is presented in Fig. 1.
The outline of this thesis is as follows. First, an overview will be presented of the current state of research on the relationship between multinationality and performance, and the influence of home region and host region on this relationship. Next, hypotheses are developed concerning the shape of the relationship curve between multinationality and MNE performance, and the relevance of institutional diversity as part of this relationship. Then the adopted methodology for this study will be discussed and the results of the empirical analysis will be presented. Finally, the discussion, implications of the findings, limitations and conclusion of this study will be discussed.
Literature Review
The Relationship between Multinationality and Performance.
Dunning (1980) introduced the eclectic paradigm as a framework for companies to determine when foreign direct investment (FDI) is beneficial. The framework outlines the motivation and the benefits for companies when becoming a multinational. Among the advantages of multinationality are the ability to conduct arbitrage, exploit market failures and combine firm-specific assets (FSAs) with country-specific assets (CSAs) (Yang, Martins & Driffield, 2013, Dunning, 1980). The relationship between multinationality and performance has been researched and corroborated by many researchers, each with their own addition to this relationship.
Pangarkar (2008), for example, found a positive relationship for small- and medium sized enterprises, which demonstrated the generalizability among different firm sizes of the multinationality-performance (M-P) relationship. Kotabe, Srinivasan & Aulakh (2002) demonstrated the positive relationship between multinationality and performance in a longitudinal study, through the moderating effect of companies’ Research & Development (R&D) skills and marketing capabilities. Thomas & Eden (2004) concluded that whether there is a positive relationship or not, depends on the time of the performance measurement.
While the costs of multinationality may outweigh the benefits at certain stages of the internationalization process, extensive research shows that in the long-run multinationality leads to a positive performance. Contractor (2007) corroborated these findings and explained the effect that Thomas & Eden (2004) found by concluding that a positive relationship exists, but this might become negative in the initial stage
because of the investment a company has to make, or becomes negative in a more advanced stage because of the possibility to over-internationalize.
The focus on the advantages of multinationality often causes the disadvantages to be disregarded. The disadvantages mostly consist of the costs involved in multinationality, such as the liabilities of foreignness and newness (Lu & Beamish, 2004). Liabilities of foreignness are the additional costs companies face when operating abroad (Zaheer, 1995). According to Zaheer (1995) there are four types of these additional costs, namely the costs directly associated with spatial distance, costs due to unfamiliarity with the host-country, costs resulting from a lack of legitimacy and economic nationalism in the host country, and costs from sales restrictions in the host country.
Lu & Beamish (2004) argue that the liabilities of foreignness increase the costs of governance and coordination for companies, which can be overcome by experiental learning. But when firms operate in an increasing number of countries and/or regions, these governance and coordination costs escalate to the point that the costs surpass the benefits, with a decline in profit as a result. Therefore, they conclude that an increasing degree of multinationality at later stages has a negative influence on performance. This negative relationship is corroborated by several studies including Chen & Tan (2012), who investigated the effect of internationalization on the performance of Chinese firms. They conclude that the best performance is reached within the China home country and a positive but declining performance is achieved within the Greater China home region. However, a negative performance occurs when internationalizing outside the home region, which leads them to conclude that the relationship between multinationality and performance is negative.
Despite numerous studies proving the presence of a direct relationship between multinationality and performance, Hennart (2007) argues that this relationship does not exist. After researching more than 100 empirical studies, he concludes that they have failed to produce robust results to prove this relationship. He believes that scholars have complicated the functional form and the measurement of the variables as a reaction to the failure to produce evidence for this relationship. The reasons for firms to internationalize that scholars mention in these studies are to exploit economies of scale, greater access to resources, more flexibility, and opportunities to learn. Hennart (2007) argues that from a transaction cost perspective it is not necessary to go abroad to exploit economies of scale. It is also not necessary to have many affiliates in order to get better and more flexible access to resources. He also argues that learning from abroad is not a common driver for international expansion. Accordingly, there is no direct relationship between multinationality and performance in his opinion.
Researchers are not only in disagreement about the existence of a positive, negative or non-existent relationship between multinationality and performance, but also about how to measure this relationship. Multinationality can be measured in multiple ways, namely by geographic scale and geographic scope. The geographic scale of multinationality is the extent to which a MNEs activity is dependent on foreign markets (George, Wiklund & Zahra, 2005) and represents the depth of internationalization (Rugman & Oh, 2013). A larger geographic scale of MNEs international activities will allow firms to acquire market share rapidly (Bartlett & Ghoshal, 1998).
When measuring multinationality by geographic scope, the breadth of international geographic expansion of the firm is captured (Goerzen & Beamish,
2003). Geographic scope denotes the number of countries in which a MNE conducts it’s business and highlights the international geographic reach around the globe (George, Wiklund & Zahra, 2005, Lu & Beamish, 2001). A broad geographic scope may enable greater performance from intangible resources, diversify risks and achieve market power (Tallman & Li, 1996). In this thesis the focus of our research will be on breadth of multinationality, which makes geographic scope the suitable option to measure multinationality.
Previous research that studied geographic scope by looking into the differences between a home region orientation and global orientation has mostly been supportive of a better performance for companies that adopt a global orientation. Elango (2004), for example, expected to find a positive home region-performance relationship, but instead had to conclude that a greater home region orientation reduced performance for MNEs. This effect has been corroborated by Delios & Beamish (2005) who found that Japanese MNEs with a home region orientation were the worst performers in their sample.
As more research is being conducted, we are becoming more knowledgeable about the moderating variables that explain why and when the relationship between multinationality and performance is positive, negative or non-existent. Findings regarding the relationship between multinationality and performance have been mostly supportive of a positive relationship, but research also suggests that there are several disadvantages that should not be disregarded. In this respect, the home- and host region represent crucial factors in determining the advantages and disadvantages in the relationship between multinationality and performance.
Home Region
The focus of this thesis will be on the regional level given that latest research provides support for the notion that most cross border activity takes place at the regional level (Rugman & Verbeke, 2004, Qian, Li, Li & Qian, 2008, and Banalieva & Dhanaraj, 2013). The home region is the region that shapes the firms’ identity in a culturally, normatively and operationally manner or by it’s founding (McGahan & Victer, 2010). Also the home region is often identified with the geographical area where the company has its headquarters. The home region can affect the performance of a firm because it influences the firms core operations and their corporate activities. MNEs attribute their performance to the influence of the home region, and may export these influences into host regions (McGahan & Victer, 2010).
However, there have been claims that managers need to frame their strategies in manners that are not constrained by geographical boundaries. A home-country or regional strategy would limit companies in this globalizing world. This implicates that the home region will have less relevance as a source of competitive advantage in this increasingly globalized economy (Hawawini, Subramanian & Verdin, 2004). This view is supported by Rugman & Oh (2013). They suggest that the conventional focus on the country-level of analysis needs to be supplemented by analysis on the regional level and industry level. Hawawini, Subramanian & Verdin (2004) agree that country level analysis is not very relevant, but they do conclude that global industry effects are increasingly more important.
Many researchers do not support the claim that the home country is no longer of importance. McGahan and Victer (2010) researched the importance of home-country effects on corporate profitability for firms with varying degrees of
multinationality. Their results suggest that MNEs may use the experience from their home country to develop FSAs that are not easily imitated by others firms. The development of these FSAs is facilitated by the home country institutions, according to Bobillo, López-Iturriaga & Tejerina-Gaite (2010). The contribution of the home region to the development of FSAs demonstrates the influence the home region has on the relationship between multinationality and performance.
Yang & Driffield (2012) conducted a meta-analysis of 54 papers that researched the multinationality-performance relationship and demonstrate that the home region is not only of importance for the development of FSAs. They found that companies from Non-US firms faced lower returns on their international investments than US firms. Non-US firms appeared to have a U-shaped multinationality-performance relationship. This means that they first suffer losses because of the initial investment before their performance increases. US firms are less likely to suffer these losses in the early stages of their internationalization. This difference in performance between firms from different home countries is also explained by Brock, Yaffe and Dembovsky (2006). They researched the relationship between internationalization and performance in the context of knowledge intensive firms. Their results also indicate that there is a positive relationship between multinationality and performance, but this performance differs for companies from the United States versus companies from Europe. Performance is inverse U-shaped for American firms and U-shaped for European firms. Because US-firms usually have more experience expanding to more offices in the U.S. before going abroad, their curve is different from European firms that usually have less experience with expanding the company in their home country before going abroad. Thus the size of the home country impacts the experience that is obtained before internationalizing.
The home region is of utmost importance in determining company performance when internationalizing for multiple reasons. Amongst these reasons are the FSA development in the home region which determines the performance of a company when internationalizing and the size of the home region in which a company can gain experience before expanding into a host region.
Host Regions
Whether performance gains from FDI differ with respect to the choice of location made by MNEs represents an important question for academics and practitioners (Yang, Martins & Driffield, 2013). The differences between regions can manifest itself along many dimensions like government, legal, economic and regulations (Banalieva & Dhanaraj, 2013). These differences can create market imperfections, which can be exploited by international companies (Caves, 1971). Since internationalizing companies explore market imperfections in host regions, the characteristics of host regions are of importance to the relationship between multinationality and performance.
Verbeke & Brugman (2009) suggest that a difference between the performance in the home and the host region is not to be expected per se. A firm will only go abroad if the expected performance is higher than that of domestic investments. As a consequence a MNE will select the geographic configuration and entry mode that will best fit the FSAs of the company. According to them this implies that multinationality itself is not able to enhance performance. The optimal alignment of the geographic choice and the companies’ FSAs therefore influences the
relationship between multinationality and performance. This highlights the importance of the chosen host region on this relationship.
The benefits and costs of internationalization need to be taken into account when one has to decide where to conduct FDI. The benefits of internationalizing include the access to economies of scale and scope, reduction of revenue fluctuation, risk spreading and an increase in market power (Lu & Beamish, 2004). The costs of internationalizing are determined by the liability of foreignness and newness, trade barriers, and the coordination and administration of differences between countries (Gomes & Ramaswamy, 1999). All these cost of internationalization increase the risk for a MNE. Ghemawat (2001) states that companies routinely exaggerate the attractiveness of foreign markets. The size of untapped markets dazzles managers into losing sight of the difficulties of pioneering in new markets. Complexities and risks mostly consist of costs created by distance. This does not only constitute the geographic distance dimension but also cultural, administrative and economic distance dimension. The risk of choosing a host region will increase considerably when the distance on one or more of these four dimensions has a strong presence.
The risk connected to the investment in a particular host region can be diminished by spreading these risks across multiple host regions, as demonstrated by Mauri & Figueiredo (2012). They researched how the performance variability of a MNE is affected by the strategic patterns that are used to expand abroad. According to them firms have higher performance variability over time when they have a higher exposure to risk. Firms that have less risk are more predictable and therefore more financially stable. Managers and investors make decisions based on risk. They conclude that performance variability decreases when the activities of a company are geographically dispersed. This suggests that there is less risk for a company when
there is an increasing degree of multinationality, which could be beneficial for performance.
There is also a difference in benefits and risks from FDI between developing and developed regions. In developing regions firms can benefit from low-cost production factors, but there are also costs associated with adaption to the environmental differences. In developed regions companies will find demanding customers with a larger purchasing power but also strong competition (Qian, 2000). Yang, Martins and Driffield (2013) focused on the role of location in the relationship between multinationality and performance. According to them investing in developing regions will increase risk, but this offers a larger performance improvement than investing in developed regions. Companies can however invest in both developed and developing regions, as is explained by Qian et al. (2008). They demonstrate that firms from developed regions maximize performance when they diversify into a moderate number of developed regions and a very limited number of developing regions.
Besides general benefits, costs and risk, a host region also needs to meet the specific requirements of a company. Different locations have different advantages to offer. Demirbag & Glaister (2010) examined the determinants in company location decisions. Their findings indicate that determining factors for a location choice at the regional level are R&D wage difference and knowledge infrastructure between the home and host region, engineering and science talent pool size and political risk of the host region. They emphasize that at the firm level important location determining factors are the international experience with R&D projects and prior research in the host region but also the R&D wage level and the availability of science and engineering talent in the home region.
The choice of a host region location is of crucial importance in determining the company performance. The benefits, risks and the company specific requirements in relation with the government, legal, economic and regulatory dimensions are all determining location factors in deciding a host region. Also the home region determines what the requirements are of a host region. Therefore both home and host region have to be taken into consideration when researching the relationship between multinationality and performance.
Theoretical Framework
The Shape of the Relationship between Multinationality and Performance.
Moderating variables like the costs of internationalization create complexity in assessing the relationship curve between multinationality and performance. The current findings with regard to this relationship have been mostly supportive of a positive association between multinationality and performance, but the shape of this relationship is still the subject of debate.
One of the research directions on international diversification hypothesized and concluded that the relationship between multinationality and performance is positive and linear (Tallman & Li, 1996). This indicates that firm performance becomes increasingly positive in a linear matter as they expand internationally. But more recent research has found this relationship to be moderated by other factors and to have a U-shaped curve instead of linear (Kotabe et al, 2002).
The U-shape indicates that firms initially experience negative performance due to start-up investments and a lack of international experience. After this initial negative performance, firms tend to gain international experience. This international experience will lead a company into a positive performance (Ruigrok & Wagner, 2003).
Another research direction argues that the relationship between multinationality and performance is inverted U-shaped rather than linear or U-shaped (Chao & Kumar, 2010, Gomes & Ramaswamy, 1999). They suggest that performance of internationalization is positive in the early stages. However, this positive effect is overtaken in time by the costs of coordination of widely dispersed international
operations. When the level of multinationality increases to a high degree, the company is forced to adopt more costly and complex organizational structures and organizational control (Thomas & Eden, 2004). This will raise costs associated with liabilities of foreignness, and at that point in time the curve will become negative (Zaheer, 1995). In other words the liabilities of foreignness increases the costs of governance and coordination for companies. Lu & Beamish (2004) demonstrate that when firms operate in increasingly more countries, the governance and coordination costs will escalate to a point where the costs will surpass the benefits with a decline in profit as a result.
A MNE with a global focus will have a better chance to show a positive performance than a firm with a home region focus, but the benefits of a global focus may be overtaken by the costs due to liability of foreignness when multinationality becomes too high. Therefore, building on the previous contributions of Chao & Kumar (2010) and Gomes & Ramaswamy (1999), the relationship between multinationality and performance is expected to have an inverted U-shape.
Hypothesis 1: The relationship between a firm’s geographic scope and performance has an inverted-U shape.
Institutional Diversity
MNEs operate in diverse business environments, and are challenged to adapt to diverse institutions across countries and regions. They bring home region characteristics, such as routines, capabilities and standard practices, but operate in host regions where different constraints and opportunities may exist (Jackson & Deeg,
2008). Institutions are of utmost importance to determine a business strategy and understand the performance across national borders. Institutions may be defined as the “rules of the game” (North, 1990). Institutional diversity is the variation in the institutional environments across countries (Banalieva & Dhanaraj, 2013). A high institutional diversity means that there are considerable differences among the institutions of the countries in that region, and a low institutional diversity means that there are few considerable differences. Without institutions, transactions between firms would become too costly and risky and business dealings would only be undertaken with already known and trusted parties (Wan & Hoskisson, 2003). Institutions are also critical in determining the costs of internationalization that are derived from the concept of liability of foreignness as explained by Zaheer (1995). These internationalization costs from the host region are costs stemming from economic nationalism and the lack of legitimacy of foreign firms, and the costs from the home region stemming from restrictions of sales to certain countries. This highlights the relevance of both the home and host region institutional diversity as demonstrated by Banalieva, Santoro & Ruihua Jiang (2012). They expected firms to perform better when they have a regional focus rather than a global focus. However, they found that this relationship between geographic scope and performance depends on the degree of regional integration in the home region. Regional trade agreements are intergovernmental treaties that countries use to agree on more advantageous trade and investment relationship than with non-signatory partners. These regional trade agreements cause a higher degree of regional integration and drastically lower the institutional diversity between countries. Contrary, a lower regional integration implies a high institutional diversity and in this case a global focus is more beneficial than a regional focus. This finding is corroborated by Banalieva & Dhanaraj (2013).
They found that firms seek to minimize the institutional diversity by locating in host countries that have a similar institutional environment to their home country. MNEs show a significant negative home region orientation when institutional diversity in the home region is high.
Institutional diversity has been a relatively neglected factor in the large amount of research regarding the multinationality-performance relationship. Concepts that are related to institutional diversity have however been studied. For example, de Jong & van Houten (2014) studied the importance of cultural diversity on the relationship between multinationality and performance. They found a positive impact of multinationality on performance for MNEs that operate in culturally similar countries, and a negative impact for MNEs that operate in culturally diverse countries. The culture in a country is embedded in the institutions, and is therefore closely related to institutional diversity. Goerzen & Beamish (2003) extended this to the influence of country environmental diversity. They found country environmental diversity to be negatively associated with performance. Country environmental diversity includes culture, economic development and politics, which are all concepts related to institutional diversity. Both de Jong & van Houten (2014) and Goerzen & Beamish (2003) demonstrate that a high diversity in the concepts related to institutional diversity are not beneficial for performance. Assuming that a high diversity in previously mentioned concepts also implies a high diversity in institutions, a low degree of institutional diversity in the host region will then be more beneficial for MNEs. Wan & Hoskisson (2003) however argue that internationalizing to a host region with a low institutional diversity might prove to be troublesome for MNEs from institutional weak environments. They researched home-country effects in the relationship between multinationality and performance. The strength or
weakness of institutions in the home country determines home country-effects. When home country institutions are weak, internationalization would not hurt, it would however not provide substantial benefits either, because most of these firms lack global competitiveness. This again demonstrates the importance for firms to have access to institutions that facilitate the development of FSAs that determine the success of internationalization. This, however, also illustrates that MNEs from countries with weak institutions face more difficulty to adopt a global focus. Adopting a global focus when institutional diversity in the home region is high might not always be possible when the home region institutions are weak.
Institutional diversity is a broad concept that incorporates many dimensions. Chao & Kumar (2010) split institutional diversity into a regulatory and a normative dimension. The regulatory dimension comprises of laws, constitutions and property rights, and the normative dimension relates to cultural elements. They found the regulatory dimension to have a negative effect on the relationship between multinationality and performance, while the normative dimension had a positive effect.
This demonstrates that institutional diversity is quite complex. It consists of several dimensions that could influence the relationship between multinationality and performance in different ways. In this thesis institutional diversity will therefore be divided into five separate constructs being government, legal, areas, economic and regulatory. Government includes expenditures, taxes and enterprises. Legal consists of legal structure and security of property rights. Areas and economic include access to sound money and freedom to trade internationally. Regulatory covers the regulation of credit, labor and business (Gwartney, Lawson, Park, Edward, Rugy & Wagh, 2002).
Institutional diversity plays a role throughout the entire existence of an MNE. Governance hazards resulting from institutional diversity such as the risk of asset expropriation at less than full market value, constraints on the pursuit of business opportunities, weak enforcements of contracts and many more, can prove to be destructive for companies (Zhou & Poppo, 2010). In general, spatial proximity makes the home region an attractive location for internationalization. However, when home region institutional diversity is high, alternative global markets characterized by a relatively lower institutional diversity become more attractive. Thus, a global focus may prove to be even more beneficial than a home region focus when institutional diversity in the home region is high. It is therefore hypothesized that:
Hypothesis 2: Firm's home region institutional diversity positively moderates the relationship between firm's geographic scope and performance.
In Fig. 2 a two-by-two contingency framework is displayed that is helpful in illustrating the underlying logic behind hypothesis 2. Following this logic, there are several predictions that arise. All else equal, with low institutional diversity in the home region, MNEs with a global focus are hypothesized to have a lower performance than if the institutional diversity in the home region is high. Additionally, when institutional diversity in the home region is low, MNEs with a home region focus are hypothesized to have a higher performance than if the institutional diversity in the home region is high.
When institutional diversity in the home region is high, a home region focus leads to a worse performance than if the institutional diversity is low. This reduction in performance will occur because institutional diversity can result in, for example, governance hazards, constraints on the pursuit of business opportunities and weak enforcements of contracts (Zhou & Poppo, 2010). Additional costs will have to be made to overcome the challenges of a different institutional environment. These costs tend to become high and will lower performance. It could therefore be beneficial to adopt a global focus and conduct business in a region outside of the home region where the institutional diversity is low.
When the institutional diversity in the home region is low it will be beneficial to conduct business within the home region, since conducting business outside the home region will be less beneficial due to higher costs than within the home region.
foreignness and costs of coordination (Zaheer, 1995). Low institutional diversity in the home region will therefore be more beneficial when a home region focus is adopted, because of these higher costs of coordination and liabilities of foreignness associated with a global focus.
A similar approach is adopted to examine the institutional diversity in the primary host region. The right choice of a primary host region will be influenced by the institutional diversity in host regions and will impact MNE performance. With a global focus, high institutional diversity in the chosen host region will lead to a worse performance than if the institutional diversity were low due to governance hazards, constraints on the pursuit of business opportunities and weak enforcements of contracts (Zhou & Poppo, 2010). In that case a home region focus will then be more beneficial than a global focus. However, when the institutional diversity in the chosen host region is low, a global focus will be more beneficial because of the access to economies of scale, scope and new resources. These benefits will be greater than the liabilities of foreignness and coordination costs and offer better performance than a home region without access to economies of scale, scope and new resources. It is therefore hypothesized that:
Hypothesis 3: Firm's primary host region institutional diversity negatively moderates the relationship between firm's geographic scope and performance.
In Fig. 3 a two-by-two contingency framework is displayed that is helpful in illustrating the underlying logic behind hypothesis 3. Following this logic, there are several predictions that arise. All else equal, with low institutional diversity in the primary host region, MNEs with a global focus are hypothesized to have a higher performance than if the institutional diversity in the home region is high. Additionally, when institutional diversity in the primary host region is low, MNEs with a home region focus are hypothesized to have a lower performance than if the institutional diversity in the home region is high.
The conceptual model introduced in Fig. 1 can now be complemented by the implications of an inverted U-shaped relationship curve (hypothesis 1), a positively moderating effect of home region institutional diversity (hypothesis 2) and a negatively moderating effect of the primary host region institutional diversity
Methods
Data Collection
To test the conceptual model in Fig 4, a cross-sectional research analysis is conducted. Data is gathered using companies from the Fortune Global 500 list. Companies in the Fortune Global 500 list are ranked on their revenues for the fiscal year 2012. The Fortune Global 500 companies are very appropriate for this study because the organizations are required to publicly disclose annual reports and financial information.
Information about the Fortune Global 500 companies is gathered from the Orbis database for the fiscal year 2012. Orbis provides detailed financial and accounting information from the largest firms across the world. The records of each company provide data about the sales, assets, subsidiary locations, return on assets, return on equity, number of employees, firm age and industry. MNE performance and several control variables will be measured using this data.
The information from Orbis is supplemented by information from the annual reports of each company. From the annual report information about the geographic location of the sales and assets is retrieved to determine the geographic scope. Secondary data from the Fraser Index is used to measure the home region institutional diversity. The index measures how institutions differ between countries in the areas of government, legal, access to sound money, economic and regulatory institutions (Banalieva & Dhanaraj, 2013). Together these areas constitute the institutional diversity.
information on return on assets (ROA) have also been excluded. With the exclusion of these firms, the final sample consists of 323 MNEs. Descriptive statistics of the surveyed companies are described in Fig. 6.
Measures
Multinationality is determined by geographic scope. The geographic scope is measured by calculating the global focus of a company, which is derived from dividing the sales in the home region by the total amount of sales and subtracting this from 1. The global focus of a company is greater when the calculated ratio is higher. Additionally the quadratic term of geographic scope is calculated. The calculated ratios are standardized by calculating the Z-scores in SPSS, which are used to calculate the quadratic term of geographic scope.
Regions are based on the segments of the United Nations (UN) country classification as is demonstrated in Appendix 2. They are divided into four regions, Asia and Oceania, Europe, Americas and Other.
Performance is determined by using the return on assets (ROA), which measures the earnings before tax divided by the total assets, using the 2012 financial data (Qian et al, 2008). ROA measures a MNEs ability to reap profits from their invested assets (Banalieva & Dhanaraj, 2013) and is also used in previous studies to measure MNE performance (Contractor et al. 2007). When ROA is calculated over only one year, there can be annual transient errors in the data such as a high degree of fluctuation. Therefore, an additional average variable for ROA over a five-year period from 2008 till 2012 is calculated. Some other studies have used return on sales (ROS) as a measure for firm performance instead of ROA. However, the correlation between
ROA and ROS is very high (r = 0.91) and has generated similar research results (Hitt et al. 1997). Therefore, in this thesis performance is measured by using ROA.
Home region and host region institutional diversity are measured using the Fraser Index of Economic Freedom of the World that is co-published by CATO-institute, the Fraser institute and several think tanks worldwide (Gwartney, Hall & Lawson (2010). Institutional diversity is fairly stable over time, therefore data gathered in 2010 is applicable and used in this research. The Fraser Index measures the degree to which the policies and institutions of countries are supportive of economic freedom. Five areas are being measured: government size, legal system and property rights, access to sound money, economic and regulatory institutional diversity. All these areas are given a score between 0 and 10, with a higher score meaning a better quality of that area. For each area there is also an overall score for institutional diversity, with a higher score for a better quality of institutional diversity (Banalieva & Dhanaraj, 2013). In Appendix 1 an elaborate description of all 5 areas can be found. Regional institutional diversity is expressed by the calculation of the variation coefficient, which is calculated by dividing the standard deviation of the distribution by its mean. A higher variation coefficient is indicative of a greater regional institutional diversity (Pfeffer & Langton).
Several control variables are included in the model. The size of the firm is the first control variable because larger MNEs are typically more capable of exploiting economies of scale, which will result in higher ROA and sales (Chao & Kumar, 2010). Firm size is measured by the number of employees, and additionally by the average number of employees over the five-year period from 2008 till 2012. The second control variable is firm age because research has shown that the age of the firm influences short and long-term performance (Galbreath & Galvin, 2008). Firm
age is measured by the date of incorporation. The third control variable is the type of industry, using industry information from ORBIS, because there is a considerable variation in terms of profit between industries (de Jong & van Houten, 2014). Dummy variables for each industry in the sample are operationalized and included in the analysis to account for industry level effects. The 19 industry categories are divided into 5 larger groups of industry dummy variables, namely machinery and electrical products, processed materials, basic living resources, private services and governmental services. In Fig. 5 the Fortune Global 500 companies are divided by industry category. The fourth control variable is the scale of multinationality. In many articles the scale of multinationality is measured by the ratio of foreign sales to total sales (Yang & Driffield, 2012., Qian et al, 2008., Banalieva & Djanaraj, 2013., Banalieva, Santoro & Jiang, 2012). In this thesis multinationality is measured by geographic scope, therefore the measurement of multinationality by scale has been added as a control variable.
Statistical Model
A Mann Whitney-U test is conducted to further examine the difference in performance between a home region orientation and a global orientation. Hierarchical multiple regressions are used to test:
- The relationship curve between geographic scope and MNE performance (Hypothesis 1).
- The moderating effect of home region institutional diversity on the relationship between geographic scope and MNE performance (hypothesis 2).
relationship between geographic scope and MNE performance (Hypothesis 3).
Fig. 5 Fortune Global 500 companies divided by industry.
Industry Percentage of companies in this
industry 1. Machinery, equipment, furniture, recycling 15.4 % 2. Wholesale & retail trade 12.2 %
3. Banks 11.8 %
4. Chemicals, rubber, plastics, non-metallic products
10.8 %
5. Insurance companies 10.0 %
6. Other services 8.0 %
7. Primary sector 6.6 %
8. Gas, water, electricity 5.0 %
9. Post & telecommunications 4.2 % 10. Metals & metal products 4.0 % 11. Food, beverages, tobacco 3.6 %
12. Transport 3.4 %
13. Construction 2.0 %
14. Industrial company 1.0 %
15. Publishing, printing 0.6 %
16. Textiles, wearing apparel, leather 0.6 % 17. Hotels & restaurants 0.6 %
18. Education, health 0.2 %
Results
With regard to the performance as the dependent variable, geographic scope as the independent variable and the four control variables, the descriptive statistics are presented in Fig. 6. Means, standard deviations and bivariate correlations have been calculated for all variables. With regard to the control variables, the average company age is established at 71 years based on the average year of founding in 1943, and the average firm size at 116.594 employees. Information about the percentage of companies per industry is presented in Fig. 5. The 5 dummy variables representing the 19 industries did not prove to be problematic in the correlation and regression model. The correlation between home region institutional diversity and host region institutional diversity is high with (r = .95) which indicates multicollinearity. Therefore home region and primary host region institutional diversity variables cannot be tested together in the same model, only separately. All other correlations are below .7 and are therefore non-problematic according to Pallant (2011). All variables are therefore retained.
A Mann-Whitney U test is conducted to test whether global focused companies have a higher ROA, than home region focused companies. The test shows that MNE global focus results in significantly more ROA than MNE home region focus (U = 8685, p < 0.001). This indicates that global focused companies perform significantly better than home region focused companies when moderating variables are not taken into consideration.
Table. 6 Descriptive statistics: means, standard deviations and correlations.
Correlation is significant at the 0.05 level (2-tailed) * Correlation is significant at the 0.001 level (2-tailed) **
Five hierarchical regression models are evaluated using Ordinary Least Square (OLS) analysis. The control variables are entered in the first model, the independent variable geographic scope is entered in the second model and both geographic scope and geographic scope squared are entered in the third model. The moderating variable home region institutional diversity and the interaction term home region institutional diversity * geographic scope are entered in the fourth model. The moderating variable primary host region institutional diversity and the interaction term primary host region institutional diversity * geographic scope are entered in the fifth model.
In Table 7 the various model fit parameters show that the R^2 improves from 9 percent explanatory power in model 1 (F = 5.00 with a p-value < 0.01), to 13 percent explanatory power in model 5 (F = 4.45 with a p-value < 0.01).
Also in Fig. 7 the results of the hierarchical regression models are reported. In model 2 and 3, the models with geographic scope and geographic scope squared, there is no evidence for an inverted U-shaped relationship curve between geographic scope and performance. This demonstrates that hypothesis 1 is not supported. The results show evidence for a linear relationship between geographic scope and performance in model 2 and 3. This suggests that the relationship between geographic scope and performance is linear when moderating variables are not taken into consideration.
Mean S.D. 1 2 3 4 5 6
1. ROA 5,14 6,82
2. Geographic Scope 0,33 0,26 .19** 3. Home region institutional
diversity 0,07 0,02 -.08 -.27** 4. Host region institutional
diversity 0,07 0,02 -.09 -.24** .95**
5. MNE Age 1943 53,82 .07 -.10 .15** .15** 6. MNE Size 116594 116172 .02 .18** -.08 -.07 -.06
Table. 7 Geographic scope, institutional diversity and MNE performance. The models are measured with the dependent variable performance (measured by ROA).
p < 0.10; * p < 0.05; ** p < 0.001***
Standardized coefficients for all models. Values in parentheses are standard errors. Model 1 Model 2 Model 3 Model 4 Model 5 Independent Variable Geographic scope 0.178*** (1.419) 0.149** (1.542) 0.184 (4.597) 0.200 (4.408) Geographic scope squared 0.073 (0.396) 0.060 (0.397) 0.070 (0.400) Moderating Variables Home region institutional diversity - 0.020 (21.382) Home region institutional diversity * Geographic scope - 0.047 (60.562) Primary host region institutional diversity - 0.056 (21.525) Primary host region institutional diversity * Geographic scope - 0.075 (60.401) Control Variables MNE age 0.074 (0.007) 0.092 (0.007) 0.082 (0.007) 0.083 (0.007) 0.092 (0.007) MNE industry Included Included Included Included Included
MNE Size - 0.002 (0.000) - 0.034 (0.000) - 0.036 (0.000) - 0.024 (0.000) - 0.033 (0.000) Multinationality scale 0.141** (0.411) 0.134** (0.406) 0.138** (0.406) 0.133** (0.406) 0.131** (0.407) N 323 323 323 323 323 R^2 0.097 0.124 0.127 0.129 0.136 Adj. R^2 0.077 0.103 0.103 0.099 0.106 F-stat 5.00 5.72 5.22 4.30 4.45 P-value .000*** 0.000*** 0.000*** 0.000*** 0.000***
When the moderating variables are added in model 4 and 5, there are no significant results for geographic scope and geographic scope squared, therefore no conclusion can be drawn about the shape of the relationship when moderating variables are taken into consideration. Hypothesis 1 is therefore not supported.
In Fig. 7 there are no significant results for the moderating effect of home region institutional diversity and primary host region institutional diversity, nor for the interaction term with geographic scope. Therefore it cannot be concluded that home region- and primary host region institutional diversity moderate the relationship between geographic scope and performance. This demonstrates that hypothesis 2 and 3 are not supported. Because the correlation between home region and primary host region institutional diversity is high (r = .95), the combined effect of home region- and primary host region institutional diversity cannot be analyzed.
One of the control variables, the scale of multinationality has a significant result in all 5 models. This demonstrates that the scale of multinationality has a significant relationship with MNE performance.
In fig. 8 the results for the additional analysis of the 5 separate areas, which together constitute institutional diversity, are reported for the home region and primary host region. The separate areas are described elaborately in Appendix A. Home region institutional diversity areas and the interaction term between that area of institutional diversity and geographic scope have been inserted in model 4, 5, 6, 7 and 8. Primary host region institutional diversity areas and the interaction term between primary host region institutional diversity and geographic scope have been inserted in model 9, 10, 11, 12 and 13. The different areas have been inserted one by one. This means that first area 1 and the interaction term are inserted in model 4, area 2 and the interaction term are inserted in area 3. This has been done for all separate areas.
The interaction term of the home region governmental institutional diversity area significantly (p = 0.066) moderates the relationship between the geographic scope and performance at the < .10 level. Also, the interaction term of the legal home region institutional diversity area significantly (p = 0.069) moderates the relationship between geographic scope and performance at the < .10 level. Surprisingly the governmental and legal institutional diversity in the home region moderate the relationship between multinationality and performance negatively. None of the other home region institutional diversity areas demonstrate significant results, nor do any of the primary host region institutional diversity areas. It can therefore be concluded that only the governmental and legal areas of institutional diversity in the home region impact the relationship between multinationality and performance.
Table 8. Geographic scope, areas of institutional diversity and MNE performance. The models are measured with the dependent variable performance (measured by ROA).
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Independent variable Geographic scope 0.171** (1.394) 0.147** (1.504) 0.288** (2.712) 1.656** (21.505) 0.106 (2.872) 0.668 (9.080) - 0.857 (19.089) Geographic scope squared 0.063 (0.390) (0.077) (0.386) 0.082 (0.387) 0.069 (0.401) 0.091 (0.393) 0.091 (0.396) Moderating variables: areas of home region institutional diversity Government - 0.021 (5.365) Government * Geographic scope - 0.214* (13.933) Legal - 0.026 (63.247) Legal * Geographic scope - 1.532* (158.531) Acces to sound money - 0.022 (14.839) Acces to sound money * Geographic scope 0.050 (39.633) Economic - 0.037 (57.200) Economic * Geographic scope - 0.548 (129.174) Regulatory 0.030 (101.707) Regulatory * Geographic scope 1.003 (227.685) Control Variables Age 0.072 (0.007) 0.088 (0.007) 0.079 (0.007) 0.075 (0.007) 0.073 (0.007) 0.078 (0.007) 0.065 (0.007) 0.064 (0.007) Size 0.007 (0.000) - 0.024 (0.000) - 0.026 (0.000) - 0.006 (0.000) - 0.006 (0.000) - 0.027 (0.000) - 0.012 (0.000) - 0.015 (0.000) Industry Included Included Included Included Included Included Included Included Multinationality scale 0.140** (0.408) 0.132** (0.403) 0.136** (0.404) 0.133** (0.399) 0.135** (0.399) 0.138** (0.407) 0.142** (0.400) 0.143** (0.401) N 322 322 322 322 322 322 322 322 R^2 0.097 0.124 0.127 0.153 0.155 0.128 0.153 0.148 Adj. R^2 0.078 0.103 0.103 0.124 0.126 0.098 0.124 0.119 F-stat 5.00 5.72 5.22 5.27 5.34 4.27 5.24 5.06 P-value 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000***
p < 0.10; * p < 0.05; ** p < 0.001*** Standardized coefficients for all models. Values in parentheses are standard errors.
Model 9 Model 10 Model 11 Model 12 Model 13 Independent variables Geographic scope 0.141 (2.601) 0.067 (9.718) 0.163 (3.013) 0.068 (7.057) 0.311 (14.773) Geographic scope squared 0.079 (0.396) 0.080 (0.396) 0.069 (0.403) 0.078 (0.397) 0.069 (0.403) Moderating variables:
Areas of primary host region institutional diversity Government - 0.092 (5.770) Government * geographic scope - 0.021 (14.547) Legal - 0.106 (32.527) Legal * geographic scope 0.069
(73.633)
Acces to sound money - 0.040
(15.673) Acces to sound money *
geographic scope - 0.033 (42.423) Economic - 0.097 (47.262) Economic * geographic scope - 0.080 (102.753) Regulatory - 0.042 (74.906) Regulatory * geographic scope - 0.173 (204.416) Control Variables Age 0.090 (0.007) 0.084 (0.007) 0.089 (0.007) 0.080 (0.007) 0.090 (0.007) Size - 0.032 (0.000) - 0.034 (0.000) - 0.035 (0.000) - 0.034 (0.000) - 0.035 (0.000) Industry Included Included Included Included Included Multinationality scale 0.129** (0.406) 0.131** (0.406) 0.134** (0.407) 0.134** (0.406) 0.134** 0.407) N 322 322 322 322 322 R^2 0.140 0.138 0.133 0.136 0.133 Adj. R^2 0.109 0.107 0.102 0.105 0.103 F-stat 4.57 4.51 4.31 4.41 4.33 P-value 0.000*** 0.000*** 0.000*** 0.000*** 0.000***
Discussion
The results of this research demonstrate that there is a positive and linear relationship between multinationality and performance when moderating variables are not taken into consideration. No associations between the moderating variables home region and primary host region institutional diversity are found. The areas of governmental institutional diversity and legal institutional diversity in the home region negatively moderate the relationship between multinationality and performance. These results are the opposite of what is hypothesized because the hypothesis represent an inverted U-shaped relationship between multinationality and performance and a positive moderating effect of the home region institutional diversity areas.
The linear association between multinationality and performance indicates that firm performance becomes increasingly positive as firms expand internationally. This demonstrates that the disadvantages associated with internationalization such as the costs of coordination, liabilities of foreignness and trade barriers, do not diminish company performance as suggested by Lu & Beamish (2004). The benefits of internationalization that include access to economies of scale and scope, reduction of revenue fluctuation, risk spreading and an increase in market power (Lu & Beamish, 2004) exceed the additional costs associated with internationalization. This results in a linear relationship between multinationality and performance, which is in line with previous research findings of Tallman & Li (1996); Hitt et al. (2006) and Ruigrok & Wagner (2005). Tallman & Li (1996) explain this by stating that losses due to overexpansion should be mitigated by highly developed skills at managing international companies in a sample of multinational firms and by the typical gradualism of internationalization. Chao & Kumar (2010) focused on the moderating
effect of institutional distance on the Fortune Global 500 and found a very weak inverted U-shaped relationship between multinationality and performance. According to them this implies that the posited relationship for the Fortune Global 500 companies was primarily positive and linear, as is found in this research.
The negative moderating effect of governmental and legal institutional diversity in the home region suggest that, all else equal, performance will be higher when a home region focus is adopted compared to a global focus when institutional diversity in the home region is high. When institutional diversity in the home region is low, performance will be lower with a home region focus instead of a global focus. This is the opposite of what is hypothesized, namely a positive moderating effect of home region institutional diversity.
Wann & Hoskisson (2003) argue that internationalizing to host regions with a low institutional diversity would not provide substantial benefits when the institutions in the home region are weak, because firms from weak institutional diversity environments lack global competitiveness. Therefore, an explanation of the negative moderating effect of home region institutional diversity may be weak institutions in the home region. Nonetheless, this does not explain the negative moderating effect of home region institutional diversity for companies from strong institutional environments.
The research results may suggest that diversity in the institutional environment can also be beneficial. As explained earlier, the differences between regions can manifest itself along multiple dimensions like government, legal, access to sound money, economic and regulatory (Banalieva & Dhanaraj (2013). But when the institutional diversity is high within a region, these differences exist between countries within a region instead of between regions. The differences between
countries within a region can create market imperfections that can be exploited by international companies (Caves, 1971). These companies adopt a home region focus and benefit from market imperfections within a region that may be caused by institutional diversity. When institutional diversity in the home region is low, there might not be many opportunities to exploit market imperfections in the home region, which makes a global focus more suitable for exploiting market imperfections in other regions.
Academic Relevance
This research adds to the body of research in International Business that is conducted to explain whether there is a systematic relationship between multinationality and performance. The relationship between multinationality and performance is a major element of all the contributions that are made to theories of foreign market entry and FDI (Glaum & Oesterle, 2007). The hypotheses in this study are not supported, but the results of this research nonetheless add to the understanding of this relationship and its moderating variables. Research conducted over the last 30 years provided results that supported a positive, negative or no relationship (Contractor, 2007). A substantial amount of this research has however provided support for a positive relationship (Goerzen & Beamish, 2003), which is supported by this study. The positive linear relationship between multinationality and performance suggest that the costs of internationalization are outweighed by the benefits of multinationality. This study also contributes to the understanding of moderating variables, such as the home region governmental and legal institutional diversity, that influences the relationship between multinationality and performance.
Managerial Implications
The results of this study have important implications for managers that need to decide on a strategy of internationalization. Internationalization will be beneficial for all MNEs, but exactly how beneficial internationalization will be is determined by the home region regulatory and legal institutional diversity. Managers deciding on a strategy when internationalizing must therefore take this into careful consideration. The results indicate that not all companies will experience the same benefits when internationalizing. MNEs from regions with high institutional diversity may benefit more from a home region focus than MNEs from regions with low institutional diversity. MNEs from regions with low institutional diversity may benefit more from a global focus. Therefore, with regard to MNE performance, the home region regulatory and legal institutional diversity should be taken into account when management decides on an internationalization strategy.
Limitations
The study has several limitations. There are no significant results for the moderating effect of home and primary host region institutional diversity. The correlation between the home region and primary host region institutional diversity is high in this research. Therefore home region institutional diversity and primary host region institutional diversity cannot be analyzed in the same model. A larger amount of data can be gathered and/or different measures of institutional diversity can be adopted in future research to enable that both home region and host region institutional diversity
can be analyzed in the same model. This would provide more insight into the moderating effect of institutional diversity in the home and primary host region than testing them separately.
Also, this study can unfortunately not explain the presence of bi-regional firms, tough this could be of interest for future research. Bi-regional firms have at least 20% of their sales in two regions, but not more than 50% of their sales in any one region (Rugman & Verbeke, 2004). In this thesis the focus is on MNEs that are either home region or host region focused. This intermediary form of internationalization can be taken into consideration in future research.
Cross-sectional data is used to research the hypotheses. This means that data has been gathered at one point in time, which does not provide a good basis for establishing causality. With cross-sectional data it can be concluded that the variables are related, but it is difficult to determine causality between the independent variable and dependent variable. The same challenge arises for the moderating variables home region and primary host region institutional diversity. Cross-sectional data is also sensitive to confounding factors. For example, the recent financial crisis that started in 2008 can also affect the relationship between multinationality and performance. This crisis may have had a negative influence on the performance, and the adopted geographic scope. Future research should include longitudinal data so causality can be established better and confounding factors can be controlled for.
Five aspects of institutional diversity are taken into consideration in this study, however cultural diversity is not considered. Cultural diversity has been demonstrated to be an important moderating variable in the research of de Jong & van Houten (2014). Ghemawat (2001) describes several types of distance that influence the success of internationalization in his CAGE model. He argues there are four types of
distance: geographical, economic, administrative and cultural distance. Elements of institutional diversity have been covered by economic and administrative distance in this thesis, while the geographic and cultural distances have not been covered. To optimize the research analysis of the relationship between multinationality and performance one should take cultural and geographic diversity into consideration in addition to institutional diversity.
