Emerging Market Multinational Enterprises engaging in Foreign Direct Investment in developed economies; overcoming the liabilities of origin by investing in Environmental and Social attributes.

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Emerging Market Multinational Enterprises engaging

in Foreign Direct Investment in developed economies;

overcoming the liabilities of origin by investing in

Environmental and Social attributes.

Name Student number Study Program Academic year Thesis circle Topic area Supervisor : S. Brekveld (Simone). : s4445201 : : MSc International Business : 2019/2020 : 2 : EMNE internationalization : prof. dr. A.U. Saka-Helmhout

Second reader : dr. F. Ciulli

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Abstract

This study examined the environmental and social pillar scores as possible determinants of the scope of internationalization of EMNEs. Also, the moderating effect of institutional distance on these determinants was tested. Data was used from 276 Emerging Market Multinational Enterprises from 22 different emerging countries. A multiple regression analysis was used to test the mentioned effects. No significant effects were found during this study. However, a great deal of these found insignificant effects are explained by the relative novelty of scores on ESG criteria and its still marginal global coverage.

Keywords: Emerging Market multinationals, emerging countries, ESG, environmental, social, institutional distance, internationalization process, scope

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2.4 Conceptual model ... 18 3. Methodology ... 19 3.1 Data ... 19 3.2 Data selection ... 20 3.3 Variables ... 21 3.3.1 Dependent variable ... 21 3.3.2 Independent variables ... 22 3.3.3 Control variables ... 24 3.4 Analytical technique ... 25 3.5 Research ethics ... 25 4. Results ... 26 4.1 Sample description ... 26 4.2 Data examination ... 27 4.2.1. Missing Data ... 27 4.2.2 Outlier Analysis ... 28 4.3 Assumptions ... 29

4.3.1. Distribution of the predictor variables ... 29

4.3.2 Distribution of the dependent variable ... 30

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4.3.4. Linearity ... 30

4.3.5. Homoscedasticity (constant variance of error terms) ... 31

4.3.6. Independence of error terms ... 31

4.3.7 Normality of the error term distribution ... 31

4.4 Multiple Regression Analysis... 31

4.5 Additional analysis ... 33

4.5.1. Model improvement ... 33

4.5.2. Robustness Check ... 33

5. Discussion ... 35

5.1 Environmental and social pillar scores ... 35

5.2 Institutional distance ... 36

5.3 Moderating effect of environmental and social scores ... 36

5.4 Control variables ... 37

6. Conclusion ... 39

6.1 Theoretical implications ... 39

6.2 Managerial implications ... 40

6.3 Limitations of this study ... 40

6.4 Directions for future research ... 42

Literature ... 44

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1. Introduction

1.1 Background

Emerging Markets are growing while growth has slowed down in developed economies (Ramamurti, 2012). Multinational enterprises (MNEs) have been rising in what are now called developed economies ever since the industrial revolution of the late 19th_{century (Guillén &} García-Canal, 2009). Theories have been built based on the choices and rationale of these MNEs. However, since the 1990s there has been a rise of MNEs from emerging markets. Emerging markets have become more relevant, but also the MNEs from these markets have become key actors in foreign direct investment (FDI) and cross-border acquisitions (UNCTAD, 2006). The definition of emerging markets can be formulated very broadly since there is a lot of heterogeneity between the emerging market countries. According to Luo and Tung (2007) emerging markets represent those countries whose national economies have grown rapidly and whose markets hold promise. The difference with developed markets is in the volatile and weak legal systems in emerging markets in comparison to developed markets. Markets are supported by their institutional infrastructure (Khanna & Palepu, 2010). This institutional development is an important feature of the well-functioning of markets since it enables buyers and sellers to access information needed to find each other and come to a right quality-price-ratio. Where developed economies have a variety of institutions to bring together these buyers and sellers and minimize market failure, these market intermediaries in emerging countries are lacking or are

underdeveloped (Khanna & Palepu, 2010; Khanna, Palepu & Sinha, 2005).

Emerging Market Multinational Enterprises (EMNEs) have had to catch up with the

internationalization of Developed Market Multinational Enterprises (DMNEs) that already started globalization a century earlier. Existing theories on internationalization were mainly developed with a focus on DMNEs since these were the dominators of the international business (IB) domain (Moghaddam, Sethi, Weber & Wu, 2014). Now that EMNEs are becoming more present in the global competitive landscape questions on the applicability of the existing theories on EMNEs are rising (Ramamurti, 2012).

1.2 Problem statement

The well-known IB theories have been very useful in explaining MNE internationalization behaviour, until the extreme rise of EMNEs, since EMNEs exhibit behaviour that is contradictory to the widely accepted patterns that were found by IB scholars based on studying DMNEs (Guillen & Garcia-Canal, 2009). EMNEs show unconventional patterns of entering foreign markets by taking bigger risks (Li, 2003). It seems logical for EMNEs to follow different paths than their competitors from developed countries since EMNEs are latecomers to the global market and have to catch up with the early movers (i.e. DMNEs). These differences may occur by entering more

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5 distant foreign markets more aggressively and at a higher speed (Johanson & Vahlne, 1990; Li, 2003). Literature mainly focusses on the asset-seeking component of EMNEs internationalization processes (see Mathews, 2002; Luo & Tung, 2007; and Ramamurti, 2009). Scholars argue that EMNEs internationalize to acquire technologies and brands from developed countries rather than exploiting the capabilities they already own (Ramamurti, 2009). This indicates the exploration of ownership advantages, rather than using firm-specific resources to build a sustainable competitive advantage (Li, 2003; Oliver, 1997)

As the main IB literature on the internationalization process of MNEs is based on the experiences and challenges faced by DMNEs there is a need for theory development in the context of EMNEs (Peng, Wang & Jiang, 2008) As can be concluded, several aspects of the internationalization process of EMNEs have remained underdeveloped and therefore it remains a much-needed research topic.

1.3 Objective

This study aims to make a contribution to the existing knowledge about the internationalization process of EMNEs. Scholars have been studying EMNE behaviour in their paths of expanding abroad, but still no complete theories have risen (Sun, Peng, Ren & Yan, 2012). Theory building on EMNEs is necessary since they show different behaviours than expected. Because of the heterogeneity of EMNEs (as a result of their roots in emerging economies) it is hard to discover one pattern of internationalization behaviour (Luo & Zhang, 2016). This study has the objective to fill part of the gap in the understanding of EMNEs’ internationalization processes, in particular on the determinants of expanding their businesses into more (institutionally) distant foreign countries.

1.4 Relevance

Luo & Zhang (2016) found that EMNEs locate their foreign establishments in both developed and less developed countries. The less developed countries are targeted because those countries have institutional environments that EMNEs are used to operate in. In that way they have a competitive advantage over their global competitors in these markets (Cuervo-Cazurra & Genc, 2008). As mentioned in section 1.2 EMNEs also tend to target less similar countries, the developed ones, for asset seeking (i.e. acquiring technologies, brands and management capabilities) (Luo & Tung, 2007). Although there has been growing scholarly attention to the reasons for EMNEs investing and competing in developed markets, not all determinants of entering developed markets have been studied extensively (Luo & Zhang, 2016). The geographic scope has been proved to be important and therefore is included in this study. Nonetheless, the determinants of the scope of internationalization into developed markets need to be studied more extensively.

An issue that concerns all MNEs that start operating in a new environment, is the unfamiliarity with it, which leads to uncertainty in that market (Marano, Tashman & Kostova, 2017). Most

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6 studies in overcoming these uncertainty problems have focussed on the expansion paths of

DMNEs (Yadav, Han & Kim, 2017). EMNEs however, face issues in their attempt to gain legitimacy in developed countries on top of the information gaps that exist for every MNE. Research has been conducted on the use of corporate social responsibility (CSR) reporting for EMNEs to overcome their legitimacy issues in developed economies, but as Marano et al. (2017) suggest, several other practices serve the same role. The role of CSR is being replaced by ESG. ESG stands for Environmental, Social, and Governance criteria and in some way measures the CSR activities more precisely with quantifiable metrics (Npower, 2019; Plaia, 2020). Since Marano et al. (2017) have studied the relationship between CSR reporting and EMNEs

overcoming their liability of origin in developed economies, this study will mainly focus on the ESG criteria to complement the existing literature.

Another important determinant of internationalization that has been studied quite a lot over the years is the phenomenon of institutional distance (Eden & Miller, 2004; Van Hoorn & Maseland, 2016). Institutional distance measures to what extent the institutions in two countries are similar or dissimilar to another (Kostova, 1999). However, research on institutional distance has focussed on the internationalization processes of DMNE yet again. It can be a determining factor in the

internationalization of EMNEs as well. The direct effect of institutional distance on EMNEs’ scope of internationalization has been studied, but in this study it is regarded as a moderating variable. The perception of firms from emerging countries in developed economies, in general, is negative. However, the engagement of a firm in corporate responsible behaviour, such as investing in environmental and social attributes, is important in developed economies. When MNEs can overcome their uncertainties from operating in an unfamiliar environment by devoting to these attributes, this effect might even be enhanced by the fact that they stem from a more distant institutional environment. EMNEs already show the unconventional behaviour of entering more distant foreign markets. This, in combination with the aforementioned, is why institutional distance is included in this study as well.

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1.5 Research Question

EMNEs seem to engage in FDI to acquire strategic assets, rather than build upon them in their internationalization process. However, from the previous subsections it can be concluded that not only the obtaining of ownership-advantages can be drivers for the internationalization of EMNEs into developed economies. As has become clear in section 1.3, this study aims to contribute to the understanding of EMNE internationalization by studying other possible determinants of entering developed markets. Therefore, the central research question of this thesis is:

What is the effect of environmental and social scores on the scope of internationalization of an EMNE into developed economies and how is this effect moderated by institutional distance?

1.6 Outline

This master’s thesis will further be structured as follows. In the second chapter hypotheses are drawn based on discussed literature in each of the sections. Subsequently, a conceptual framework is developed in which these hypotheses are represented. The methodology that is used to test the hypotheses from the second chapter is elaborated upon in chapter 3. The results of the research are shown in chapter 4 and reviewed in chapter 5. Finally, the implications and limitations of this study and directions for future research are being discussed in chapter 6.

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2. Theoretical framework

In this chapter, first, the internationalization of EMNEs into developed markets is discussed and a definition of internationalization is given (2.1). Secondly, the ESG performance of a company will be elaborated upon. Therefore different theories will be applied (2.2). The moderating variable, institutional distance, will be discussed in the third section (2.3). In both the second and the third section hypotheses will be drawn and eventually these will come together in a conceptual framework (2.4).

2.1 The internationalization path of EMNEs

Internationalization is defined by Mathews (2006, p. 41) as “the process of the firm’s becoming integrated in international activities”. This entails the firm operating across national borders in different modes of entry. However, to be able to refer to the internationalization of an MNE (or an EMNE specific) this definition has to be narrowed down. An MNE is defined as: “an enterprise that engages in FDI and owns, or in some way, controls value-added activities in more than one country” (Dunning & Lundan, 2008, p.3). This definition does not include firms that export to other countries since these do not engage in FDI. If a firm cannot effectively exercise control over its subunit, one cannot define this relationship as FDI either (Luo & Tung, 2007). The amount of control that one firm has over the other is linked to the legal ownership of the subunit (Hymer, 1960). By holding the majority of equity shares in a subunit, a firm can obtain control over it. MNEs originate from different kinds of markets. There is the common MNE from a developed market (i.e. the DMNE), of which a lot is known already. Nonetheless, MNEs from emerging markets have become of greater influence as well (Tan, 2017). Emerging markets can be defined as “low income, high growth economies that are using market liberalization as their main means of growth” (Gaffney, Cooper, Kedia & Clampit, 2014, p. 383). Hence, for the remainder of this thesis EMNEs are defined as firms from low income, high growth economies, that engage in FDI by the means of having control over their overseas subsidiaries.

Dunning (1980) argues that a firm that is going abroad should own certain firm-specific advantages. Likely because of this, Ramamurti (2012) finds it puzzling that EMNEs

internationalize at all since they are economically and technologically subordinated. A firm that operates in a foreign country has the disadvantage of being discriminated against (Hymer, 1960). Government, consumers, and suppliers most probably favour firms from their own country. This different treatment for foreign firms relative to domestic firms causes barriers to overcome. In addition to this general discrimination of foreign firms, one feature that both EMNEs and DMNEs have in common is that by going abroad they face the liability of foreignness (Zaheer &

Mosakowski, 1997). This theory assumes that firms face costs because of their unfamiliarity with the foreign environment (Zaheer, 1995). These costs come from different sources, like; higher

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9 coordination costs, unfamiliarity with the host country culture, and a lack of information networks or political influence in the host country. The latter corresponds with the governmental

discrimination of foreign firms. In order to overcome these liabilities of foreignness, firms should have competitive advantages over their foreign competitors.

Most IB studies are based on the empirical findings of developed market multinationals (DMNEs). This is because these multinational enterprises belong to the first wave MNEs and therefore are studied more extensively than EMNEs that only started internationalizing later (Li, 2003). Emerging markets have been of big importance for quite some time now, thanks to the internationalization of firms from developed countries. However, the rise of EMNEs, and especially the growth of outward FDI from these emerging economies has had a boost in the past decade (Tan, 2017). EMNEs can be different from DMNEs in significant ways as a result of their emergence in the global economy. Therefore, the two are referred to as latecomers and early-movers, respectively (Li, 2003). Two important IB theories, the OLI-paradigm and the Uppsala model, reflect the internationalization processes of DMNEs (Mathews, 2006). However, because of the differences between the two types of MNEs, IB scholars have varying opinions on whether these theories apply to EMNEs as well (Hennart, 2012; Ramamurti, 2012). To show that EMNE behaviour is different from that from DMNEs these theories are briefly outlined in the upcoming sections.

An important theory on the internationalization process of MNEs is the Uppsala model developed by Johanson & Vahlne (1990). It suggests that the development of knowledge about foreign markets and operations interplays with an increasing commitment of resources in those foreign markets in such a way that the firm increases its international involvement only gradually. The model describes two patterns of this incremental way of internationalizing. The first is

referred to as an establishment chain, in which a firm takes small steps of involvement in a foreign market (Johanson & Vahlne, 1990). From no foreign activities, via regular export activities to a sales subsidiary, and eventually to manufacturing abroad, the commitment level in the host country increases step by step. The second pattern mentioned (Johanson & Vahlne, 1990) refers to the consecutively greater psychic distance of the markets that firms enter. Psychic distance is an individual’s or collective’s subjectively perceived distance to a foreign country (Håkanson & Ambos, 2010). At first firms choose markets where uncertainty is lowest, deliberately moving to markets with which they are less familiar (Johanson & Vahlne, 1990).

A second well-known IB theory based on studying DMNEs is the eclectic or OLI-paradigm developed by Dunning (1980). This theory argues that firms can notice three kinds of advantages on the basis of which they determine the extent of expanding their businesses abroad (Dunning, 1980; 1988). The first of these three advantages is the Ownership advantage. This advantage

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10 refers to a firm possessing certain firm-specific assets that help outweigh the costs of operating in an unfamiliar environment (i.e. being competitive in the host country). Second is the Location advantage, meaning that firms will use certain resources tied to a particular foreign country. Whether or not a firm should engage in FDI is dependent on the Internalization advantage. The incentive of this advantage is avoiding uncertainty or capitalizing on market imperfections (Dunning, 1980). High transaction costs related to the uncertainty and market imperfections are avoided by having production in house, instead of outsourcing it to an external party.

As Hennart (2012) points out, the OLI-paradigm explains that firms survive in a foreign country by exploiting their ownership advantages. However, lots of EMNEs have invested abroad with the intention to acquire technology, brands, and managerial experience and therefore are not motivated by the exploitation, but the acquisition of these ‘advantages’ (Hennart, 2018; Luo & Tung, 2007). EMNEs do not only differ by the grounds for internationalizing but also their speed and scope of internationalization differ significantly from that of DMNEs (Madhok & Keyhani, 2012). Where the Uppsala model explains different stages of internationalization, EMNEs take bigger and more daring steps into foreign markets. Vernon (1979) argues that FDI flows from developed into emerging countries and not vice versa. Here, again, EMNEs have shown to act contradictory to what was expected by entering distant developed economies instead of economies that are more similar to their own (Madhok & Keyhani, 2012).

As mentioned before, there is no consensus among IB scholars on whether the OLI paradigm can be applied to EMNEs as well. Ramamurti (2012) points out the two main points of view in literature. The first is that EMNEs are a new kind of MNE and these differ in such significant ways from DMNEs, that new theory building is necessary. The other point of view argues that existing theories are sufficient for explaining EMNEs’ behaviour. A third point of view is added by suggesting that studying EMNEs can help extend IB theories to fit them too (Cuervo-Cazurra, 2012; Ramamurti, 2012). For example, the ownership advantages of Dunning’s (1980) OLI-paradigm, are different for firms from developed and developing economies (Li, 2003; Dunning, Kim & Park, 2008). EMNEs at first seem to lack the technology, brand, and management

advantages that form the basis of the paradigm (Ramamurti, 2012). The fact that EMNEs miss the ‘traditional’ ownership advantages does not automatically mean that they lack their own, unique competitive advantages (Dunning et al., 2008). Nonetheless, Dunning et al. (2008) remark that these ownership advantages are less of a determination of outward FDI than they were for DMNEs. Another point of attention is brought up by Li (2003), who argues that existing theories also fail to explain how EMNEs as latecomers catch up with DMNEs as early movers. Therefore, it can be concluded that existing IB theories cannot be blindly adopted and applied to EMNEs.

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2.1.1 Scope of internationalization into developed economies

The internationalization of a firm is referred to by Lu & Beamish (2001) as international diversification. In line with the former is the definition for what is regarded as the scope of internationalization in this study: the number of developed countries a firm has expanded its business to, by the means of having subsidiaries in those countries (Crick, 2009; Felzensztein, Ciravegna, Robson & Amorós, 2015). In this study the scope is narrowed down to the number of developed countries to make a clear distinction in EMNEs’ drivers of internationalization into diverse areas of the world (Felzensztein et al., 2015). Developed economies as target countries are interesting since EMNEs have shown to establish subsidiaries in these countries, besides their expansion into other emerging countries (Athreye & Kapur, 2009). As mentioned in the first chapter, IB scholars have already explored several reasons for EMNEs to internationalize. Nonetheless, still lots of possible influences and enablers of internationalization remain undiscovered. Therefore, in the next sections, other determinants of the scope of internationalization of EMNEs into developed economies will be theorized upon.

2.2 ESG criteria

The (firm-specific) ownership advantages that are needed to be at odds with your rivals in the global market seem to be missing for EMNEs at first (Dunning, 1980). There might, however, be certain firm or country-specific assets that enable firms from emerging economies to go global anyway. As will be further elaborated in this section, ESG performance of a firm can be seen as a firm-specific asset as well.

Any firm that starts doing business abroad, operates in an unfamiliar environment (Zaheer, 1995). As mentioned in the previous section, this is known as the liability of foreignness. MNEs face costs due to the cultural, political, and economic differences in the foreign country whereas domestic firms do not (Zaheer & Mosakowski, 1997). An additional issue for MNEs is that of legitimacy. Legitimacy can be defined as “a generalized perception or assumption that the actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions” (Suchman, 1995, p. 574) or as “the acceptance of the organization by its environment” (Kostova & Zaheer, 1999, p. 64). A subunit of an MNE must gain both internal (i.e. within the MNE) and external legitimacy (i.e. in the host environment) (Kostova & Zaheer, 1999). As MNEs are confronted with more and more diverse stakeholders, it is harder to gain legitimacy in all contexts they are operating in (Park, 2018). On top of the liabilities of foreignness, EMNEs face liabilities of origin (Bartlett & Goshal, 2000; Yu & Liu, 2016). These liabilities of origin are exclusive to EMNEs and rise from the fact that stakeholders in developed countries have a negative opinion about firms from emerging countries. This is due to the fact that emerging markets are considered underdeveloped and immature concerning both their institutional development and infrastructure, and factor market development (Hoskisson,

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12 Wright, Filatotchev & Peng, 2013; Park, 2018). Bartlett & Ghoshal (2000) also point at the

institutions in the developing countries that are regarded as insufficient. An example of the reasons why developed market stakeholders have negative perceptions about emerging markets, that of poor product quality and safety regulations (Yu & Liu, 2016). Since home country institutions do not provide sufficient information on EMNEs, it is difficult for stakeholders in developed

countries to get a truthful and clear image when evaluating EMNEs (Marano et al., 2014). Considering this, the battle for EMNEs in gaining legitimacy in developed countries becomes even harder (Held & Berg, 2014; Park, 2018).

Firms from emerging markets, to overcome the liabilities of foreignness and origin, seek guidelines and expectations from global institutions to comply with (Kostova, Roth & Dacin, 2008). In this way they can gain legitimacy everywhere in the world. As argued by Marano et al. (2014), engaging in CSR is a globally accepted legitimate practice. CSR is aiming at creating social value by reducing negative and enhancing positive impacts on the world (Araki, 2013). By engaging in CSR, the negative image that stakeholders in developed economies have of an EMNE can be extenuated (Marano et al., 2014). Liabilities of origin are partly being taken away when EMNEs distance themselves from their home countries in this way. CSR practices go further than mainstream corporate activities and according to Qureshi, Kirkerud Theresa and Ahsan (2019) withhold corporate sustainability progression and ESG disclosure. ESG ratings are defined as “evaluations of a company based on a comparative assessment of their quality, standard or performance on environmental, social or governance issues” (SustainAbility, 2018, p. 4). These factors have become more and more important partly due to their use by the United Nations and Global Reporting Initial (Galbreath, 2013). ESG provides the globally accepted institutions EMNEs seek to comply with (Bassen & Kovács, 2008; Kostova et al., 2008). This can help EMNEs gain legitimacy in developed countries.

From the case study by Yu and Liu (2016) it appeared that a Chinese firm that invested in their possessions of ESG attributes had managed to reduce negative perceptions in local society. It also appeared that investing in CSR practices is a primary approach to achieve society’s

acceptance (Sharma, 2019; Yu & Liu, 2016). In the same way, investing in ESG reduces the liability of origin that EMNEs face when expanding to developed economies. There will be a smaller chance of rejection by host countries when EMNEs score higher on ESG criteria since these are becoming increasingly important for the valuation of firms (Bassen & Kovács, 2008). That in turn, would make it more likely for an emerging market firm to internationalize into a developed economy.

Zaheer (1995) stressed the importance of firm-specific resources, like scoring high on ESG, as providers of sustainable competitive advantage. This implies the competitive advantage of a firm

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13 investing in ESG. Even though this study focusses on gaining legitimacy rather than attracting investments, the fact that high rated ESG firms are demanded increasingly in developed economies, where this demand is only scarce for poor ESG performers, emphasizes the importance of ESG in business once again (Nordea, 2017).

2.2.1 Environmental pillar score

The environmental pillar of ESG captures three different topics on which firms are evaluated: Resource Use, Emissions, and Innovation (Refinitiv, 2019). Firms are rated on their performance on all three themes (THB Asset Management, 2019). To be more specific, climate change, energy and water use, and carbon emissions are conditions to consider regarding a firm’s environmental performance (Galbreath, 2013). This performance can be in reducing the negative impact on the environment, but also includes seizing opportunities for positive change. Environmental

performance is becoming increasingly important for both the public (i.e. customers and suppliers) and authorities in Europe (Nordea, 2017). Because of this growing relevance of ESG, there is an opportunity for EMNEs in this field. The environmental demands in emerging countries are subordinated in comparison to mature economies (Dunning & Lundan, 2008; Garcia, Mendes-Da-Silva & Orsato, 2018). This is related to emerging markets being regarded as immature and therefore EMNEs struggling with the liability of origin. Which can be vanquished by investing in ESG. Several stakeholders, like consumers and stockholders, respond positively to firms that make an effort to reduce the environmental damage they cause (Yadav et al., 2017). This, in turn, can lead to EMNEs gaining legitimacy in developed economies, when they score high on their environmental performance pillar. The presence of environmental attributes in a firm can provide the competitive, firm-specific advantage of a firm that lies at the basis of setting up an

establishment in a foreign market (Dunning, 1980; IFC, 2018). From this reasoning, the following hypothesis is drawn:

➢ Hypothesis 1: Scoring high on environmental criteria of ESG has a positive effect on EMNEs’ scope of internationalization into developed economies.

2.2.2 Social pillar score

The social aspect of CSR has become a hard-to-define phenomenon over the years since it encompasses more and more issues (Morsing & Schultz, 2006). The social attributes of ESG affect almost all companies in all industries because there barely are any limits to it (THB Asset Management, 2019; Morsing & Schultz, 2006). Nevertheless, the broad social pillar of ESG is composed of four topics: Workforce, Human Rights, Community, and Product Responsibility (Refinitiv, 2019). This also includes fair trade principles, product safety, gender equality, and healthy and safe workplaces (Galbreath, 2013). Again, firms are rated on all of them, which ultimately leads to a combined score for social performance. This social performance can gain legitimacy for a firm when it is perceived as positive by stakeholders. As Yu and Liu (2016)

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14 argue, product quality and safety regulations are a burden for the acceptance of EMNEs in

developed economies. When EMNEs score high on the social pillar, they distance themselves from their home country and overpower their liability of origin in doing so. This is substantiated by the indication that ESG is a leading indicator for quality improvement (Nordea, 2017). As in the previous section, social attributes can be regarded as contributing to the ownership advantages of a firm that are seen as a necessary first step towards FDI (Dunning, 1980; IFC, 2018). Hence, the following hypothesis is drawn:

➢ Hypothesis 2: Scoring high on social criteria of ESG has a positive effect on EMNEs’ scope of internationalization into developed economies.

2.3 Institutional environment

“Institutions are the humanly devised constraints that structure political, economic, and social interaction. They consist of both informal constraints (sanctions, taboos, customs, traditions, and codes of conduct), and formal rules (constitutions, laws, property rights).” (North, 1991). New Institutional Economics claims that institutions derive from the phenomenon that information is never fully complete (Harriss, Hunter & Lewis, 1995). This lack of information brings about uncertainty in transactions and to reduce this uncertainty institutions are formed. Uncertainty does not only encompass the price of goods, but also the intentions of other actors or future conditions that one does not know about (Bates, 1995). Institutions are thus created as a result of market failures, which arise when the market equilibrium lacks to hold. According to Scott (2001) institutions can be distinguished by the means of three different pillars: a regulative, normative, and cognitive pillar. Regulatory institutions attempt to influence behaviour by establishing rules and attaching incentives, sanctions, and punishments to those rules (Scott, 1995). These are therefore carried out by the regulatory institutions which may consist of state actors like

governmental and political bodies (Voinea & Kranenburg, 2017) The normative pillar introduces a prescriptive, evaluative, and obligatory dimension (Scott, 1995). Forms of normative pillars are beliefs, expectations, codes of conduct, norms, and values (Scott, 1995; Voinea & Kranenburg, 2017). The cultural-cognitive pillar consists of shared conceptions that constitute the nature of social reality (Scott, 1995). This leads to the creation of frames through which entities make meaning of things. This can take the form of taken-for-granted practices, priorities, paradigms, models of reality, shared belief systems, and expectations of generally accepted appropriate behaviour (Voinea & Kranenburg, 2017). Besides the distinction in three pillars, institutions can be distinguished in formal (i.e. laws, regulations, and contracts) and informal institutions (i.e. values and belief systems) (Dunning & Lundan, 2008; North, 1990). It can be concluded that the regulative pillar corresponds to formal institutions and that the normative and cultural-cognitive pillars correspond to informal institutions (Table 1).

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15 Degree of Formality (North, 1990) Examples Supportive Pillars (Scott, 1995)

Formal Institutions Laws Regulative (coercive) Regulations

Rules

Informal Institutions Norms Normative

Culture (Cultural-) Cognitive Religion

Table 1: Pillars of institutions (Voinea & Kranenburg, 2017)

Institutional forces affect the processes and decision making within organizations, which entails organizations abiding by the rules of the game set by institutions (DiMaggio & Powell, 1983; Hoskisson, Eden, Lau & Wright, 2000; Meyer & Rowan, 1977). Meyer and Rowan (1977) refer to institutions as rationalized myths and organizations conform to these to gain legitimacy, resources, stability, and enhanced survival prospects. Organizations will earn legitimacy by conforming to the rules and beliefs that prevail in certain environments and that will enable them to survive (Xu & Shenkar, 2002). Institutional environments are complex to comply with since they exist of multiple task environments, multiple institutional pillars, multiple resource providers, and multiple stakeholders (Kostova & Zaheer, 1999). Furthermore, an environment that consists of weak institutions increases transaction costs and makes it harder to establish new business

relationships (Meyer, 2001). For MNEs this is even more complicated since they have to deal with institutions in both their home and host country (Kostova & Zaheer, 1999; Meyer, Mudambi & Narula, 2011; Xu & Shenkar, 2002). Where the goal for firms is to gain legitimacy by conforming to institutional pressures, failure to do so has the opposite effect.

The role of institutions is to reduce transaction and information costs by reducing uncertainty and setting up stable structures to facilitate exchange (Hoskisson et al., 2000). Organizations are bound to the formal and informal institutions in the country they operate in. In developed

economies these institutions are well established. Firms operating in these environments have the advantage to suffer as little as possible from market failure, because of the existence of

intermediaries that provide the information that is necessary to complete transactions (Khanna & Palepu, 2010). However, this is different for firms operating in emerging economies. Even though emerging countries have developed some institutions, they are missing the key sources to

minimize market failure (Khanna & Palepu, 2010; Mair, Martí & Ventresca, 2012). This phenomenon is referred to by Khanna et al. (2005) as institutional voids. Institutional voids indicate “the absence of specialized intermediaries, regulatory systems, and contract-enforcing mechanisms in emerging markets” (Khanna et al., 2005, p.63). It can thus be concluded that the quality of institutions in developed markets is higher than in emerging markets and that

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16 voids in the home markets of EMNEs are part of their negative image in developed countries that needs to be fixed (Cuervo-Cazurra & Ramamurti, 2014).

2.3.1 Institutional distance

Institutional distance measures the difference or similarity between two countries regarding the regulatory, normative, and cultural-cognitive institutions (Kostova & Zaheer, 1999). This distance affects the required adjustments a firm has to make when operating in a foreign country. The more institutionally distant the host country is from the home country, the more adjustments firms will have to make, and the more difficult it will be for firms to understand and interpret the host country's institutional demands (Kostova & Zaheer, 1999). This relates to the liabilities of

foreignness addressed in section 2.1, which are a source of competitive disadvantage (Shirodkar & Konara, 2016). The perceptions of EMNEs in their developed host countries are quite negative, even though their home countries’ markets have moved up from being centrally planned

economies to market-oriented economies. The people in developed host countries are experiencing EMNEs and DMNEs as different because of for example the late liberalization of the emerging markets and the institutional differences that still exist between emerging and developed

economies. As mentioned in the previous section, MNEs have to deal with different institutional pressures because they operate in different institutional environments. This becomes even more difficult when the institutional distance between countries increases (Eden & Miller, 2004; Xu & Shenkar, 2002). MNEs, by engaging in FDI, have a high level of engagement with their local contexts (Meyer et al., 2011). Thereby MNEs face the complexity of multiple embeddedness, as they have to balance the forces from local institutions with those of fitting in the overall structure of the MNE. This can also be described as “the tension between the need for global integration, on the one hand, and local adaptation, on the other hand” (Kostova & Roth, 2002). Dikova, Sahib & Witteloostuijn (2010) have conducted research that showed that MNEs face difficulties in dealing with institutional distance. The more distant the institutions from home and host countries are, the more likely it seemed that an acquisition was abandoned. As discussed in section 2.1, MNEs are classified as such because of their engagement in FDI, which entails the acquisition of foreign firms as subsidiaries. It can be concluded that overall, institutional distance is assumed to have a negative effect on foreign acquisitions and therefore negatively affects the path of firms’

international expansion (Chao & Kumar, 2010).

Although, in general, institutional distance is assumed to have a negative effect on the internationalization process of MNEs, this does not necessarily seem to apply to EMNEs (Luo & Tung, 2007). MNEs, in general, tend to enter foreign markets that are more distant from their home market less aggressively than host markets with a smaller institutional distance (Gaffney, Karst & Clampit, 2016; Pan & Tse, 2000; Xu & Shenkar, 2002). EMNEs, on the other hand, seek value in acquisitions in developed countries, because the institutions in their home country push

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17 them away (Aybar & Ficici, 2009). For EMNEs the institutional distance can be beneficial since certain dimensions of it can compensate the institutional voids that they face in their home markets (Gaffney et al., 2016; Khanna & Palepu, 2006). This phenomenon of EMNEs, that originate in weak institutional environments internationalizing in economies with strong institutional environments through making acquisition, is called upward FDI (Aleksynska & Havrylchyk, 2013). The conclusion can therefore be drawn that the difference in institutions between developed and emerging economies is mainly positive for EMNEs and therefore has a positive influence on their scope of internationalization.

2.3.2 Institutional distance as a moderator

All over the world there are increasing geopolitical risks, threats of terrorism, and extreme weather patterns, which leads to global markets becoming more volatile (Ramamurti & Williamson, 2019). Because of the interdependencies of markets and globalization, this affects both developing, emerging, and developed economies. EMNEs, since they originate from countries with volatile institutions, have more experience in dealing with these situations. Hence, they have developed more adequate capabilities to cope with uncertain circumstances than DMNEs who emerged in stable markets (Ramamurti & Williamson, 2019). As Zaheer (1995) stated, firms need to have organizational capabilities that provide a sustainable competitive advantage. Now that even developed markets face uncertain situations EMNEs can develop competitive advantages over their developed-market competitors. As logically follows from the foregoing, the competitive advantage of EMNEs over DMNEs is their ability to deal with institutional voids, that even arise in mature markets now.

Overall countries are becoming more developed, and together with that institutions become more important (Dunning & Lundan, 2008). These institutions are covering both environmental and social issues such as the protection of employees, non-discrimination, and environmental and health protection measures. These institutions still are more likely to be reliable in mature

economies and hence, the value of acting in compliance with institutions in those countries is extra valuable (Dunning & Lundan, 2008). Madhok and Keyhani (2012, p. 31) describe the situation clearly: “there is a credibility and legitimacy deficit in the eyes of host country stakeholders, who become even more circumspect due to insufficient or missing knowledge of foreign EMNE firms, their quality and safety standards, and the like”. However, differences in institutional quality also exist between the developed countries and these differences can be substantial as well

(Bruinshoofd, 2016). The liability of origin of EMNEs is stressed more as the host country becomes more advanced in its institutional quality (Madhok & Keyhani, 2012). Investing in environmental and social attributes already was assumed to have a positive effect on overcoming the liability of origin of EMNEs. In line with the previous, the positive effect of environmental

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18 and social attributes might be stronger when institutional distance increases, even solely regarding developed economies. From this the following hypotheses are drawn:

➢ Hypothesis 3a: Institutional distance positively moderates the relationship between the score on environmental attributes and EMNEs’ scope of internationalization into developed economies.

➢ Hypothesis 3b: Institutional distance positively moderates the relationship between the score on social attributes and EMNEs’ scope of internationalization into developed economies.

2.4 Conceptual model

The concepts and relations discussed in this chapter are visualized in the conceptual model in figure 1. Complementary to these relations, the formulated hypotheses are displayed.

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3. Methodology

This chapter will elaborate on the data that has been used to conduct this research. The first two sections will discuss the selection and collection of the data. In the third section, the

operationalization of the different variables is clarified. In section 4 of this chapter the analytical tool will be explained. Lastly, research ethics with regard to this study will be assessed.

3.1 Data

For carrying out this study, data is used to test the hypotheses formulated in chapter two. The data used is derived from three different databases and an extra database for one of the control

variables. The first database, Orbis, contains business information on approximately 200 million companies worldwide. Orbis’ reports are based on companies’ annual reports and turned into standardized formats, which makes it easy to work with (Bureau van Dijk, 2020). Private or inside company information is indirectly provided through indicators, because of which Orbis can provide rich and reliable information on companies from every country in the world. In gathering the data, it was particularly difficult to gain access to data on ESG variables in emerging countries. It is important to create a time frame in which EMNEs have the time to expand their businesses. It was found that most ESG scores were available for the year 2017. However, in order to gather reliable results it seems logical to use data from an earlier year to create a sufficient time frame. The data for the dependent variable, the scope of internationalization, should be from a year later in time than that of the independent variables (Hair, Black, Babin & Anderson, 2014). The data for the scope of internationalization will, therefore, be as measured in 2019. This is the last available year that has been completed and therefore is the most suited to provide valid results.

The data on the ESG scores is derived from the ESG (Asset4) database, compiled by Thomson Reuters. This database provides information on all different elements of ESG, but at the same time company size and transparency biases are minimal (Refinitiv, 2019). As mentioned before, most data on ESG variables for EMNEs seemed to be available for the year 2017. However, it takes time for firms to set up an establishment in a different country. Even though the institutions in developed economies are stronger than in EMNEs’ home countries, acquisitions regularly are not settled within short notice. The timeframe to set up an establishment would be too short when data from 2017 this year would be used. Another point of attention is the fact that overall there is not much ESG data available. However, regarding the availability of the data on ESG criteria, the decision was made to use data from 2015. This data will be reliable and firms have had time to set up an establishment before 2019.

Finally, data has to be retrieved for the moderating variable, institutional distance. The World Bank Group has created the World Governance Indicators (WGI) Project, which reports on governance indicators in six dimensions for over 200 countries in the period from 1996 to 2018

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20 (WGI, n.d.). Institutional Distance scores are based on the scores on the World Governance Indicators of the World Bank for each country in 2015.

3.2 Data selection

The focus of this study lies on the internationalization of EMNEs into developed countries. To be able to collect data from EMNEs, it is necessary to make clear what the country of origin of these firms should be. Therefore, it is important to have an overview of country classification. Both the classification of countries as emerging and the classification of countries as developed are relevant.

Morgan Stanley Capital International (MSCI) also is a provider of ESG Indexes to provide information to institutional investors (MSCI, 2020-a). Although their data is not used for this study, their emerging markets index is still of high relevance. According to MSCI, 26 countries are classified as emerging countries, and these are spread across 5 regions of the world (MSCI, 2020-b). However, to be certain of the identification of countries as emerging the MSCI classification is compared to another. FTSE Russell is a global provider of analytics and data solutions, also used by institutional investors from all over the globe (FTSE Russell, 2020). FTSE Russell makes a distinction in Advanced Emerging and Secondary Emerging markets (FTSE Russell, 2018). From the FTSE index 25 countries are classified as emerging. A reason for this discrepancy might be that this index is from 2018, while the MSCI website was updated in 2020. However, since the data collected is from the year 2015, the corresponding emerging countries from both indexes will be regarded as emerging countries for this study (Table 2). MSCI has included both Argentina and Korea in the classification as emerging countries, where FTSE has not. Also, FTSE has included Kuwait in the classification of emerging countries, while MSCI has not. These three countries are not included as emerging countries in this study.

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21 Emerging countries

Americas Europe, Middle East & Africa Asia Brazil Chile Colombia Mexico Peru Czech Republic Egypt Greece Hungary Poland Qatar Russia Saudi Arabia South Africa Turkey

United Arab Emirates

China India Indonesia Malaysia Pakistan Philippines Taiwan Thailand

Table 2 (FTSE Russell, 2020; MSCI, 2020-b)

3.3 Variables

3.3.1 Dependent variable

The aim of this study is to get more insight in the internationalization process of EMNEs into developed economies. As mentioned in chapter two, for a firm to be called an MNE it has to engage in FDI (Dunning & Lundan, 2008). Hennart (2012) clarifies this by opposing it to

exporting or licensing, meaning that a firm needs to control an establishment in a foreign country to be an MNE.

Regarding the direction of outward FDI from EMNEs, they do the unexpected by entering more distant rather than more similar foreign markets. In this study the scope of

internationalization into developed countries will be measured as the number of foreign subsidiaries a firm has in the countries that over the last years have proved to be targeted by EMNEs. Based on different studies these countries are located in Western Europe, North America, Australia, and New-Zealand (Aleksynska & Havrylchyk, 2013; Buckley, 2018; Kim, Hoskisson & Lee, 2014; Shirodkar & Konara, 2016). In the Orbis database there is a category called Western Europe. In this category Greece and Turkey are also included. However, these countries are classified as emerging countries for the purpose of this study and therefore are excluded as developed countries. Besides these two, another four countries were excluded from the

classification of developed countries. In Orbis, the countries Gibraltar, Monaco, San Marino, and Vatican City were classified as Western European countries. These countries were either not known in the database by the World Bank or the scores were not complete. As a result, it is not possible to calculate the institutional distance between these and the emerging countries. Therefore, these countries were excluded as developed countries as well.

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3.3.2 Independent variables

The independent variables are hypothesized to explain the variance in the scope of internationalization of EMNEs into developed economies.

3.3.2.1 Environmental attributes

The Asset4 ESG database (by Thomson Reuters) consists of all different measures of ESG variables. For the purpose of this study only the combined scores of the environmental and social pillars of ESG will be looked at. As elaborated upon in chapter two, one of these pillars is the environmental pillar. The environmental pillar is measured by firms scoring on three different categories: Resources Use; Emissions; and Innovation. The descriptions of these scores can be found in table 2.

Environment Pillar Score Weighted average relative rating of a company based on the reported environmental information and the resulting three environmental category scores.

Resource Use Score A company’s performance and capacity to reduce the use of materials, energy, or water, and to find more eco-efficient solutions by improving supply chain management.

Emissions reduction Score

A company's commitment and effectiveness towards reducing environmental emissions in the production and operational processes. Environmental

Innovation Score

A company’s capacity to reduce the environmental costs and burdens for its customers, and thereby creating new market opportunities through new environmental technologies and processes or eco-designed products.

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23 3.3.2.2 Social attributes

The second ESG pillar that will be studied in this thesis is the social pillar. For the social pillar, in the Asset4 database, the firm is scored on measures in the following categories: Workforce; Human Rights; Community; and Product Responsibility. The descriptions of these scores can be found in table 3.

Social Pillar Score Weighted average relative rating of a company based on the reported social information and the resulting four social category scores. Workforce Score A company’s effectiveness towards job satisfaction, healthy and safe

workplace, maintaining diversity and equal opportunities, and development opportunities for its workforce.

Human Rights Score A company’s effectiveness towards respecting the fundamental human rights conventions.

Community Score A company’s commitment towards being a good citizen, protecting public health and respecting business ethics

Product Responsibility Score

A company’s capacity to produce quality goods and services integrating the customer’s health and safety, integrity and data privacy

Table 3: Social Pillar Score descriptions (Refinitiv, 2019)

3.3.2.3 Moderating variable: Institutional distance

Van Hoorn & Maseland (2014) have already investigated institutional distance by using the six indicators and therefore the six indicators are assumed to be a reliable measure for institutional quality and consequently for the measure of institutional distance in this study. The WGI project scores countries on the following six institutional indicators: Voice and Accountability; Political Stability; Government Effectiveness; Regulatory Quality; Rule of Law; and Control of Corruption. The data that to measure the institutional distance for this study is from 2015, as the data from the other independent variables is too. For both the emerging and developed countries that are included in this study, the WGI project scores were obtained (Appendix 1A & 1B)

To construct the institutional distance variable, the scores on the indicators have to be mutated. Institutional distance is defined by Kostova (1999) as the extent of similarity or dissimilarity between the regulatory, normative, and cognitive institutions of two countries; the difference in institutional scores of both the home and the host country of the EMNE. As probably the host country score will be higher, this score is taken and the home country score is subtracted from it. The score left is the institutional distance between the EMNE’s home country institutional environment and that of the country where this EMNE has an establishment. The institutional distance scores between the emerging and developed countries can be found in Appendix 1C.

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3.3.3 Control variables

To make sure that this study can be regarded as valid and reliable, control variables are used (Field, 2018). The first variable that was intended to be used as a control variable is firm size. This variable is studied extensively and has proved to influence the internationalization scope of firms (Dass, 2000). Bigger firms generally possess more resources than smaller firms, which enables these bigger firms to take risks. As risks are associated with the internationalization of firms, the size of the firm could have an effect on the scope of internationalization (Johanson & Vahlne, 1990). Often the proxy that is used for firm size is the number of employees of a firm. However, for the acquired dataset there was not enough data available on the number of employees in the Orbis database. A new control variable was added instead, that has the same arguments for using it. Operating revenue has been used as a proxy for firm size before and also shows the possession of a higher level of resources (Al-Khazali & Zoubi, 2005). However, since operating revenue of the firm is not the most common proxy for firm size and confusion is to be avoided, the control variable will be (called) operating revenue instead of firm size (Al-Khazali & Zoubi, 2005). The data for this variable is provided by Orbis with the turnover of the EMNE (in USD) in the year 2015.

The other control variable has been tested and proved to be of influence on the scope of internationalization of EMNEs. Marketing intensity will be used as a control variable. The higher the marketing intensity of a firm, the more likely it is that a firm possesses capabilities for building a strong brand name and identity in the host country (Aulakh, Kotabe & Teegen, 2000). These capabilities also enable firms to capture value through better customer research (Ren, Eisingericht & Tsai, 2015). To assess this control variable, data on Sales, General, and Administrative

expenses (SGA) and turnover (each in the currency of the country of origin of the EMNE) is manually derived from Morningstar. To calculate the marketing intensity of the firm the SGA expenses are divided by the turnover and multiplied by 100. This thesis uses the SGA in 2015 as a proxy for marketing expenses since not all firms in emerging countries have provided advertising expenses as a separate journal entry in their annual reports.

It was intended to use R&D intensity as a control variable in this study as well since the previous students have studied this variable as being a predictor of the scope of

internationalization. However, the Orbis database did not have enough available data on this variable for the acquired dataset. The Morningstar website that was used for deriving the data for the variable ‘marketing intensity’ did not provide enough useful information on R&D expenses either. Therefore it was decided upon not to use this variable as a control variable anymore.

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3.4 Analytical technique

The analytical technique that is used to test the hypotheses formulated in chapter 2 is a multiple regression analysis. Multiple regression analysis assesses the degree and character of the

relationship between the dependent and independent variables by examining the magnitude, sign, and statistical significance of the regression coefficient for each independent variable (Hair et al., 2014). When conducting a multiple regression analysis, only metrically scaled independent and dependent variables can be incorporated. This is the case for the dependent variable (scope of internationalization) and independent variables (Environmental and Social scores; institutional distance; operating revenue; marketing intensity).

The general form of multiple regression is as follows: Υ = β0 + β1Χ1 + β2Χ2 + …+ βkΧk + Ɛ In this equation Y represents the dependent variable and β0 represents the intercept (Hair et al., 2014). Furthermore, the X’s in the equation represent the independent variables used in the research of which the corresponding β’s represent the slope of their coefficients. The Ɛ at the end of the equation is a representation of the random error term. When applying the variables

incorporated in this study to the general form, it looks as follows:

Υ = β0 + β1C1_OpRev + β2C2_Mint+ β3X1_escore + β4X2_sscore+ β5M_id + β6intX1_M + β7intX2_M

To be certain that the results that are obtained by the study are representative, certain assumptions of the analysis have to be met (Hair et al., 2014). If assumptions are substantially being violated, this was corrected for.

3.5 Research ethics

The data that is used for this study is derived from the Orbis Database, The Thomson Reuters (EIKON) database, the WGI project of the World Bank Group, and the Morningstar website. The researcher did not collect the data herself and therefore has had no influence on the way the data was collected initially. However, both Orbis, Thomson Reuters, and the World Bank Group are well esteemed and acknowledged organizations for the collection of data. For the control variable marketing intensity data had to be derived manually for each EMNE, but this was done with precision and checked for all search results of an EMNE. Therefore, it is assumed that this data was collected in an ethical and responsible manner. In terms of privacy, the data collected for this study will cause no harm to the parties (firms) involved. The researcher guarantees that the data in this study is not manipulated.

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4. Results

In this fourth chapter, the assumptions of general and multiple regression analysis will be tested and the results of the multiple regression analysis will be discussed. Before that, a description of the sample will be given and a missing data and outlier analysis will be performed.

4.1 Sample description

It was found that there is still very little data available on the E, S (and G) scores of firms from emerging and developing countries. This study intended to build upon the studies of students from previous years at the Radboud University. To do this it would have been be useful to use the same set of companies. However, when checking for available E and S scores for these companies it was found that there was more than 60% missing data, which left under one hundred companies in the sample. This sample size would have been too small to conduct a multiple regression analysis and therefore it was chosen to compile an entirely new dataset (Hair et al., 2014). This dataset is compiled based on the selection of companies from emerging countries (as mentioned in section 3.2), that had

subsidiaries in developed economies in 2019. To be able to extract data from the Asset4 database the ISIN number for these companies had to be known. Still a lot of companies remained, but the actual search for companies with available E and S scores left a much smaller sample of 296 companies with subsidiaries in developed economies (which they could control). Though this has to be considered when drawing conclusions, the amount of missing data on the independent variables would otherwise be extremely high.

Table 5 represents all the countries that were included in the sample of this study with the number of subsidiaries they had in the selected developed economies. There were only two countries that were marked as emerging countries in section 3.2 that are not represented in the sample, which are Pakistan and Peru. These countries are not from the same parts of the world and therefore this is not regarded as problematic. It is remarkable that some countries are very highly represented whereas others are barely represented by the number of EMNEs. The majority of EMNEs in the sample is from Taiwan,

accounting for 21,3% of the total sample. Also, South Africa, China, and Brazil are represented a lot with representations in the sample of 14,9%, 11,5%, and 10,1%, respectively.

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Country Number of EMNEs Percentage

United Arab Emirates 5 1,7

Brazil 30 10,1 Chile 8 2,7 China 34 11,5 Colombia 7 2,4 Czech Republic 1 0,3 Egypt 2 0,7 Greece 8 2,7 Hungary 2 0,7 India 3 1,0 Indonesia 2 0,7 Mexico 5 1,7 Malaysia 13 4,4 Philippines 3 1,0 Poland 10 3,4 Qatar 3 1,0 Russia 24 8,1 Saudi Arabia 2 0,7 Thailand 13 4,4 Turkey 14 4,7 Taiwan 63 21,3 South Africa 44 14,9 Total 296 100,0

Table 5: Sample summary

The sample size that is used for multiple regression analysis is of great influence and under the control of the researcher (Hair et al., 2014). The sample size is important because of its effect on both the statistical power and the generalizability of the results. The minimum ratio of observations to variables is 5:1, but ratios of 15:1 or 20:1 are preferred. This study contains 5 independent variables (control variables included) and therefore the preferred sample size should at least be 100. The preliminary sample contains 296 EMNEs and therefore is large enough to perform the analysis.

4.2 Data examination

4.2.1. Missing Data

The retrieved dataset for this study has to be checked for missing values on one or more variables. This is done by looking at the missing values for each of the variables. As can be seen in Appendix 2A, there is only a small amount of missing data for the two control variables. As explained in section 3.2, it was expected that there would be no missing data for the main variables of this study. The missing data consists of 3 and 11 missing values out of a total set of 296 companies for operating

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28 revenue and marketing intensity respectively. Both represent a percentage of missing values of less than 5%. Furthermore, it is useful to look into the number of cases with missing data for each variable (Hair et al., 2014). The table in Appendix 2B shows that no EMNE has missing data for more than one variable.

According to Hair et al. (2014), after looking into the extent and patterns of the missing data, the degree of randomness of the data has to be assessed and for that two tests were conducted. First, two t-tests were performed for the dichotomous variables that were created to distinguish the missing and the non-missing data of operating revenue and marketing intensity. The results of these t-tests are presented in Appendices 2C and 2D. Only in one instance, where groups were formed on marketing intensity and compared on institutional distance, showed a significant difference between the groups. However, because differences were found only in this one instance, the effects are of marginal concern (Hair et al., 2014). Furthermore, Little’s MCAR test was executed. This test has a significance level of .766 (p = .766), which indicates a nonsignificant difference between the observed missing data pattern and a random pattern (Appendix 2E). From this, it can be concluded that the missing data is ‘missing completely at random’ (MCAR).

Since the data is found to be MCAR, listwise deletion is allowed (Hair et al., 2014). Although Hair et al. (2014) state that this is not preferable due to restrictions on sample size other methods become more preferable. However, the missing data in the sample represents <5%, which is less than the 15% threshold set by Hertel (1976), and deleting listwise maintains the consistency in the data. The sample size also is still big enough so that this would not have too much effect and that this imputation method is the most commonly used method among researchers (Kwak & Kim, 2017). I believe that because of the aforementioned, listwise deletion of missing data is suited for this study as well. The list of companies deleted is presented in Appendix 2F.

4.2.2 Outlier Analysis

Outliers are values of variables that are distinctly different from the other values for a specific variable. Usually, outliers are the outstanding high or low values on the variable, or it can be a unique combination of values across several variables that make the observation stand out from the others. It is important to look at outliers since these can skew and peak the data (Field, 2018). When looking at the descriptive statistics of all of the variables in the sample (Appendix 3A) it can be seen that the dependent variable (Y_No_of_subsidiaries) and a control variable (C1_operating_revenue) have values beyond the critical value of | 3 |. The obvious outliers of these variables are graphically represented in the boxplots in Appendix 3B.

However, for detecting the more subtle outliers in the dataset, the data was converted to z-scores. These can be used as a benchmark since these z-scores express scores in terms of a distribution with a mean of 0 and a standard deviation of 1 (Field, 2018). If z-scores are calculated they should fall

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29 within a range of | 3 | in order to not be considered an outlier (Hair et al., 2014). The threshold could go up to four and therefore cases with z-scores between 3 and 4 are reconsidered before they are removed. In Appendix 3c outliers for each of the variables are shown based on their z-scores exceeding the threshold of | 3 |. For each variable, the question of whether the outliers are indeed peculiar is to be answered in a different way. It was found that for the scope of internationalization 4 outliers were found. For the first three it is found that these EMNEs have over 100 subsidiaries in developed economies. Since it was explained in chapter 2 that it is quite bold for EMNEs to move to developed economies For one single EMNE to have this many subsidiaries, is regarded to be unlikely. Therefore, these outliers are deleted from the sample. Operating revenue was found to have 5 outliers based on the converted z-scores. However, only EMNEs with the two highest z-scores seem to be particularly peculiar. The other two EMNEs still score below 4, which, according to Hair et al. (2014) can be the extended threshold for sample sizes of over 80 EMNEs. The last variable that showed outliers based on z-scores was marketing intensity. Looking at the original values for these EMNEs instead of the z-scores shows that their marketing intensity is higher than 75%. Which, indeed, seems unlikely high. Taking all of this into account 5 EMNEs (Appendix 3D) were deleted from the sample (since one EMNE was an outlier for both scope of internationalization and operating revenue). After the deletion of the EMNEs with outliers, the sample size is 276, which is still more than the 100 samples needed to conduct the multiple regression analysis.

4.3 Assumptions

Before a multiple regression analysis can be conducted several assumptions have to be tested and met (Hair et al., 2014). These assumptions represent the requirements of the underlying statistical theory. First the assumptions will be tested that, according to Hair et al. (2014), affect every univariate and multivariate statistical technique. Afterwards, the assumptions specific to multiple regression analysis are tested. These assumptions represent the requirements specific to the underlying statistical theory of multiple regression analysis.

4.3.1. Distribution of the predictor variables

The predictor variables are the independent variables established in the conceptual model, including the moderating variable and the control variables. All these variables are metrically scaled and therefore the normality for these variables needs to be assessed (Hair et al., 2014). For assessing the normal distribution of these variables, several techniques are available. Graphical representations of normality can be given using a histogram or a probability-probability plot (Field, 2018). In addition to assessing these graphical analyses, there are also statistical tests to assess normality (Hair et al., 2014). One often-used test is that of skewness and kurtosis of which the values should lie < | 3 | for a variable to be normally distributed. For both the main independent variables data is normally distributed regarding the skewness and kurtosis (Appendix 4A), which is also displayed in the graphical representations (Appendices 4B & 4C). Looking at the p-plots and histograms for the other three

Emerging Market Multinational Enterprises engaging in Foreign Direct Investment in developed economies; overcoming the liabilities of origin by investing in Environmental and Social attributes.