Emerging market heterogeneity: the where and why of EMNE cross-border acquisitions

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Emerging market heterogeneity: the where and why of

EMNE cross-border acquisitions

B.M. (Bart) Postma

S2556375

University of Groningen

Faculty of Economics and Business

Email: b.m.postma@student.rug.nl

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Emerging market heterogeneity: the where and why of EMNE cross-border acquisitions ABSTRACT

Emerging market multinationals are increasingly competing with developed market multinationals in the global market. International business literature has found significant differences in their internationalization behaviour, however its focus on comparing emerging and developed markets has created a dichotomous view of the world that oversimplifies the complexity of the world and disregards the variety among emerging markets. The current study addressed this oversight by investigating the heterogeneity in internationalization behaviour of EMNEs by comparing the ‘where’ and ‘why’ of their cross-border investments. Studying a sample of 3272 cross-border acquisitions conducted by 2256 unique EMNEs from Brazil, Russia, India, China, South Africa, Indonesia, Malaysia, the Philippines and Thailand between 2004 and 2018, we carried out OLS regression analyses and found support for heterogeneity in the motives and barriers that drive the location choice of cross-border investments. We found support for market-seeking and strategic asset-seeking motives for some of the home countries, and for the negative effects of both linguistic and geographic distance on the likelihood of investment in a host country. The results of this study highlight the importance of understanding emerging markets as a heterogeneous group and the need to extend research on emerging market internationalization beyond China (and India). Future research could elaborate on this study by exploring changes in internationalization behaviour over time as home countries and institutions develop and investigate whether our results hold true for other modes of entry used in cross-border investments. Investigation into which home country characteristics explain the differences among emerging markets would also be a logical next step.

Keywords: Emerging market multinationals, outward FDI, location choice, OFDI motives,

institutional distance.

INTRODUCTION

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competitive landscape (Luo and Tung, 2018) and are increasingly competing with developed market multinationals (DMNEs) (Buckley et al., 2016a). OFDI flows from emerging markets have significantly increased over the years, especially from China and India (De Beule and Duanmu, 2012). In light of their growing role in international business (IB), it is unsurprising that these markets have recently attracted significant scholarly attention (Marano, Tashman and Kostova, 2017).

The IB literature strongly focuses on the contrast between developed and emerging markets (Hernandez and Guillén, 2018; Luo and Zhang, 2016). Some scholars (Ramamurti, 2009) argue that over time EMNEs will become more similar to DMNEs as they mature (Fleury, Fleury and Olivieira, 2018). However, the literature indicates that EMNEs’ internationalization patterns (e.g. location choice, mode of entry) do not seem to adhere to those suggested by existing internationalization theories (Ramamurti, 2012). Classic internationalization theory suggests that firms internationalize incrementally as cross-country differences complicate doing business abroad (Johanson and Vahlne, 1977), thereby increasing the cost of doing business. Yet, EMNEs make bold and risky investments in distant markets at an unprecedented pace (Deng, 2012). Moreover, a recent literature review (Li, Quan, Stoian and Azar, 2018) found that EMNEs differ from DMNEs in their location choices for internationalization. However, this dichotomous perspective of emerging versus developed market firms overlooks the complexities of the world and fails to take into account the heterogeneity among emerging markets (Jindra, Hassan and Cantner, 2016).

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A popular topic of study related to OFDI location choice are the drivers of and barriers to these investments (Luo and Zhang, 2016). A unique feature of EMNE internationalization is the rapid and radical nature of their cross-border investments to both developed and less developed countries (Deng, 2012). The former can be explained by asset, capability and market-seeking to compensate for the lack of firm-specific advantages (e.g. technological expertise or strong brands) of EMNEs, which have been a crucial factor determining the success of DMNEs’ internationalization (Jindra et al., 2016), whereas the latter can likely be explained by EMNEs’ experience in how to do business in similar (institutional) environments (Cuervo-Cazurra and Genc, 2008). The underlying idea is that cross-national differences, or distance, makes it harder and more costly for firms to do business abroad (Berry, Guillén and Zhou, 2010). Accordingly, internationalization theory posits that firms internationalize to countries at close proximity and incrementally make their way to countries at greater distance as the gain international experience (Beugelsdijk et al., 2018; Johanson and Vahlne, 1977), thereby influencing location choice.

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We focus on four countries in this region that show strong economic growth (IMF, 2019) and are internationally oriented (OECD, 2018); Indonesia, Malaysia, Thailand and the Philippines.

This research tries to make a few contributions to the literature. The main aim of this paper is to challenge the dichotomous view of developed versus emerging markets and the underlying assumption of homogeneity among emerging markets. This will be done by conducting a comparative study on the location choice of OFDI from EMNEs originating from nine different home countries, thereby looking at the differences among those markets, which have largely been treated as a uniform group in the literature (Jindra et al., 2016; Peng et al., 2018). Second, we add to the literature by researching the drivers of EMNE cross-border investments based on classic internationalization motives, covering a 15-year time period in which emerging markets have grown to become more important players on the world stage of international business. Third, this research aims to contribute to the literature by investigating the effects of institutional distance on OFDI location choice and making comparisons across different home countries. Determining how emerging markets differ from each other in their internationalization behaviour provides an important first indication for future research on how the particularities of home countries play a role in explaining these differences.

The remainder of this paper is structured as follows. First, EMNE internationalization is discussed, followed by a more elaborate theoretical background on OFDI location choice and factors influencing location choice decisions. Next, we present the methodology followed by the results section. Lastly, we end the paper by the results, limitations and areas of future research and concluding on the main findings of this research.

THEORY AND HYPOTHESES DEVELOPMENT Emerging markets and EMNE internationalization

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presence of these firms on the world stage has spurred an increasing amount of papers published on the internationalization behaviour of these firms (Kostova, Marano and Tallman, 2016; Luo and Zhang, 2016; Luo and Tung, 2018).

Nevertheless, how these firms manage to successfully internationalize and compete despite suffering from competitive disadvantages and weak institutions in their home countries, is still unclear (Marano et al., 2017). Classic internationalization theory posits that firms make foreign investments to exploit their firm-specific advantages, such as their technology, brands or marketing (Ramamurti and Singh, 2009). However, EMNEs have weaker firm-specific advantages compared to DMNEs (Cuervo-Cazurra and Genc, 2008). Accordingly, the prerequisite of (domestically developed) firm-specific advantages in order to internationalize may not suffice to explain the internationalization behaviour of EMNEs (Kim and Aguilera, 2016). Furthermore, recent research increasingly indicates that there is heterogeneity among internationalization of EMNEs (Lu, Liu, Wright and Filatotchev, 2014; Peng et al., 2018).

Location choice

One of the key decisions that firms need to make when internationalizing is the location choice of their cross-border investments (Li et al., 2018). The location choice refers to the where and why of firm internationalization (Goerzen, Asmussen and Nielsen, 2013). A widely used theory to research OFDI and location choice is Dunning’s (1977, 1993) OLI framework (Demirbag and Glaister, 2010). The OLI framework (or eclectic paradigm) explains the internationalization of firms based on three potential sources of advantage; ownership, location and internalization. Together, it covers the ‘why, where and how’ of internationalization decisions.

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Research on EMNE entry modes shows that mergers and acquisitions are favoured establishment modes for EMNEs, accounting for nearly one third of emerging market OFDI (Rabbiosi, Elia and Bertoni, 2012), which could reflect EMNEs aiming to acquire ownership advantages.

Second, location specific (L) advantages explain where firms will invest to undertake value-adding activities (Dunning, 1977, 1993). Location advantages, or country-specific advantages comprise of endowments such as natural resources, cheap labour, strong institutional environments or good infrastructure (Blomkvist and Drogendijk, 2016; Dunning and Lundan, 2008). A firm can either utilize these locational advantages to exploit their ownership advantages or, in the case of EMNEs, develop or acquire ownership advantages (Dunning, 1977, 1993). Existing literature shows that the characteristics of the environment (e.g. resource endowments) are of great importance for firm strategy (Kim and Aguilera, 2016). An important assumption that is made regarding location-specific advantages is that they are location-bound, but available for all firms (Dunning and Lundan, 2008). Location-specific advantages are thus country-specific, whereas ownership advantages are firm-specific (Hennart, 2012). Host countries that possess such advantages over the domestic market and over other foreign markets are hence expected to attract more FDI.

Last, internalization specific (I) advantages explain how firms internationalize in terms of entry mode. Firms can enter foreign markets using a wide variety of ownership structures, however the main modes, in order of increasing level of control, are contracts, joint ventures and wholly owned subsidiaries (Brouthers and Hennart, 2007). In line with internalization theory and transaction cost theory, internalization specific advantages posit that firms will engage in FDI when transaction and coordination of arm’s length relationships exceed the costs of internalization (Dunning, 2000).

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Motives Market-seeking

Firms that are driven by market-seeking motives invest abroad because they want to profit from the potential of the market by serving the local market (Buckley and Munjal, 2017). In order to get a foothold in the local market, EMNEs often enter through acquisition of local firms, which provides a quick way to gain control over for example distribution channels or brands (Buckley and Munjal, 2017). In particular, host countries that have a large base of potential new customers for the firm are attractive to invest in as such markets make it easier to realise economies of scale (Kang and Jiang, 2012). Likewise, high economic growth can attract FDI as it will lead to growth in demand (Kang and Jiang, 2012). Previous research on the location choice of Chinese EMNEs shows that many Chinese EMNEs are driven by market-seeking motives (e.g. Buckley et al., 2007). We therefore hypothesize that the potential of the host country market attracts OFDI from EMNEs.

Hypothesis 1

Host country market potential is positively related to the number of acquisitions done by EMNEs.

Natural resource-seeking

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resources abroad to keep up with the economic development of the home country. Accordingly, countries with a comparative advantage in natural resource endowments are expected to attract OFDI from EMNEs.

Hypothesis 2

Host country natural resource endowments are positively related to the number of acquisitions done by EMNEs.

Efficiency-seeking

Existing papers on EMNE internationalization and OFDI motives often ignore the efficiency-seeking motive (e.g. Buckley et al., 2007; Drogendijk and Blomkvist, 2016; Kang and Jiang, 2012). Given the large low-cost labour force, EMNEs can enjoy relatively low labour costs compared to developed markets (Buckley et al., 2007). Accordingly, as opposed to DMNEs, efficiency-seeking is often considered the least important motive for EMNEs to internationalize (Li et al., 2018). However, there is some research on efficiency-seeking of EMNEs. For example, Anwar and Mughal (2017) found that South African firms invest abroad to relocate their production facilities to countries with lower tariffs and lower labour costs. Similar indications of efficiency driven OFDI were found for Russian EMNEs (Anwar and Mughal, 2015) and Chinese EMNEs (Cheung and Qian, 2009). Furthermore, Amal and Tomio (2012) argue that Brazilian EMNEs cross-border investments are market-seeing, whereas Asian EMNEs are more driven by efficiency-seeking and resource-seeking motives, indicating there might be variety among emerging markets in their motives.

Wages in emerging markets have increased, narrowing the wage gap with developed countries (Baldwin, 2012). Accordingly, we expect that EMNEs might invest in less developed countries that offer wages that are (even) lower than domestic wages. Hence, we hypothesize that EMNEs take into account labour costs in potential host countries when making a location choice for their cross-border acquisitions.

Hypothesis 3

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Strategic asset-seeking

Strategic asset-seeking refers to OFDI aimed at accessing critical assets such as knowledge, managerial know-how or (patented) technology (Buckley and Munjal, 2017). Strategic assets are valuable resources and capabilities that can lead to a competitive advantage (Deng, 2009). These assets can thus help EMNEs overcome their latecomer disadvantage and compete more effectively with competitors, in particular those originating from developed markets (Luo and Tung, 2007). Furthermore, obtaining strategic assets through acquisitions can help EMNEs gain legitimacy (Deng, 2009).

Empirical studies have found that EMNEs make cross-border investments in developed markets to get access to brand names, patented technology or distribution networks (Deng and Yang, 2015). A recent example of an EMNE acquiring strategic assets is Geely’s acquisition of Volvo, which gave Geely access to Volvo’s technology and brand name, which Geely in turn used to sell luxury cars in the growing Chinese auto market (Reuters, 2010). Similarly, Lenovo acquired IBM’s PC division in 2005, making Lenovo the third largest supplier of computers globally (Deng and Yang, 2015).

In a research on the motives of Chinese OFDI, Buckley et al. (2007) did not find significant results for a strategic asset-seeking motive between 1984 and 2001. However, in a more recent paper, Buckley and colleagues (2016b) found support for the former but not the latter motive when looking at the effects of China’s ‘Go Global’ policy (which launched concurrently to their WTO membership in late 2001) on the location choice and volume of OFDI between 2001 and 2011. Blomkvist and Drogendijk (2016) argue that EMNEs start off with a cost-based competitive advantage but, as they develop, will move towards more knowledge intense parts of the value chain, e.g. R&D to improve their global competitiveness. It thus seems that EMNEs are aiming to compete globally by developing or acquiring strategic assets that have made DMNEs successful through cross-border investments. Accordingly, we argue that EMNEs will seek strategic assets abroad to upgrade their (technological) capabilities.

Hypothesis 4

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Institutional theory

Institutional theory is one of the most frequently used frameworks in IB literature to explain the behaviour of firms (Kim and Aguilera, 2015; Luo and Zhang, 2016; Peng, Wang and Jiang, 2008; Xu and Meyer, 2013). Accordingly, the notion that institutions matter for doing business (abroad) is widely recognized (Van Hoorn and Maseland, 2016). Simply put, institutions are the context in which (international) business takes place (Fainshmidt, Judge, Aguilera and Smith, 2018). North (1990) posits that institutions are the ‘rules of the game’ in an (institutional) environment. More formally, he defined institutions: “humanly devised constraints that structure political, economic and social interaction” (North, 1990: p97). Institutions shape the behaviour and decision-making of firms as firms need to adapt to the institutions in terms of (political, social and legal) rules that apply in that institutional environment (Buckley and Munjal, 2017).

Institutions encompass formal institutions and informal institutions that reduce uncertainty and costs by setting certain expectations of behaviour in an institutional environment (Peng and Meyer, 2011). Formal institutions are documented, authoritative rules, such as the laws and regulations in a country, which are enforced through the legal system (Marano et al., 2016; Peng and Meyer, 2011) and are also known as the regulative institutional pillar (Scott, 1995). Informal institutions on the other hand consist of the normative and cognitive pillars of institutions (Scott, 1955). The former constitutes of norms, values and beliefs and guides what is deemed moral and ethical (Holmes, Miller, Hitt and Salmador, 2013), whereas the latter reflects internalized and unconscious assumptions, values and beliefs that influence firm behaviour (Scott, 1995).

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Distance

The concept of distance, i.e. the dissimilarities between two countries, has been used to study a wide variety of phenomena in IB (Ambos and Håkanson, 2014). Cross-country differences complicate internationalization (Martín and Drogendijk, 2014). Accordingly, scholars have introduced and studied numerous types of distance, inter alia institutional (Berry et al., 2010; Xu and Shenkar, 2002), geographic, administrative (Ghemawat, 2001), cultural (Brouthers and Brouthers, 2001; Kogut and Singh, 1988, Shenkar, 2001) and psychic distance (Chikhouni et al., 2017; Johansson and Vahlne, 1977; Dow and Karunaratna, 2006; Håkanson and Ambos, 2010). Zaheer, Schomaker and Nachum (2012) even posit that international management is the management of distance given the many types of distance firms face when internationalizing.

Distance between countries increases uncertainty in foreign markets and thereby increases the liability of foreignness (Azar and Drogendijk, 2014; Jiang, Holburn and Beamish, 2014). Zaheer (1995, p343) defines liability of foreignness as “all additional costs a firm operating in a market overseas incurs that a local firm would not incur”. Operating in an unfamiliar environment leads to learning costs in understanding the environment and adapting to ‘the rules of the game’ (i.e. the institutions) of the local environment (Buckley and Munjal, 2017). Accordingly, when home-host country distance is larger, the costs of doing business abroad are higher (Berry et al., 2010).

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In this research, we will focus on both types of distance. First, given the transparency of formal institutions, cross-country differences are easier to compare (e.g. comparing government policies or laws). This is also known as administrative and political distance (Ghemawat, 2001). Second, while not as transparent as formal institutions, informal institutions are also expected to play an important role in the location choice of OFDI. To a great extent, these informal institutions are captured by cultural distance.

The concept of institutional distance was introduced by Kostova (1999) as the difference between the institutions of two countries. The distance in institutions between home and host country influences the investment choices of firms (Xie, Reddy and Liang, 2017). Dissimilarities in institutions will discourage firms to invest in a potential host country (Jiang et al., 2014). Accordingly, it has been argued that institutional distance affects location choice (Shirodkar and Konara, 2017). Low institutional distance (i.e. home and host country are similar) reduces the extent to which firms suffer from liability of foreignness, thereby reducing the cost of doing business in these target countries (Hitt, Franklin and Zhu, 2006). Accordingly, internationalization theory (Johanson and Vahlne, 1977) posits that firms should internationalize incrementally, increasing home-host country distance over time as liability of foreignness increases with distance (Zaheer, 1995).

EMNEs originate from weaker institutional environments (in comparison to DMNEs) and are more familiar with the way business is done in such (challenging) institutional environments that are characterized by institutional voids (Cuervo-Cazurra and Genc, 2008). For example, firms that deal with corruption and political risks in their home country might not be deterred by corruption and political risks in a host country as they know how to conduct business in such an environment (Cuervo-Cazurra, 2018). In contrast, firms originating from a home country in which such behaviour is uncommon (i.e. developed markets) may incur high costs of doing business in such an environment due to their liability of foreignness in an environment with institutional voids such as corruption, given their unfamiliarity with the ‘rules of the game’ (Cuervo-Cazurra, 2012). Therefore, we hypothesize that institutional dissimilarity (i.e. distance) between the (administrative and political systems of) home and host country has a negative effect on the likelihood on the number of acquisitions done in that host country.

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and Munjal, 2012). West (2004) argues that language is closely related to cultural values and influences ways of thinking. This is similar to Hofstede’s (1980) definition of culture as the ‘collective programming of the mind’. West’s (2004) findings indeed suggest that linguistic distance is a good measure for cultural distance. Ghemawat (2001) argues that, ceteris paribus, trade between two countries will be three times higher when the countries share a common language. Existing literature has found evidence for the importance of (common) language in reducing transaction costs in doing business (e.g. Doh, Bunyaratevej and Hahn, 2009). Accordingly, we hypothesize that linguistic distance reduces the likelihood of EMNE cross-border acquisitions in a host country.

Hypothesis 5

Formal institutional distance has a negative effect on the number of acquisitions done by EMNEs in a host country.

Hypothesis 6

Linguistic distance has a negative effect on the number of acquisitions done by EMNEs in a host country.

DATA AND METHOD Sample

For this comparative study, we investigated the OFDI behaviour of EMNEs from nine different home countries. We selected the home countries based on a few criteria. First, they had to be an emerging market with sufficient OFDI. We incorporated the BRICS countries (i.e. Brazil, Russia, India, China and South Africa), as these countries account for roughly 60 percent of OFDI from emerging markets (World Bank, 2017). In addition, we included a number of Southeast Asian emerging markets because of their rapid growth and diversity (Oehmichen, 2018). Asia is characterized by great diversity in culture, governmental structures and economic development (Barkema et al., 2015), which has been overlooked in the existing literature (Witt and Redding, 2013). Based on these criteria, the following home countries were included in this study; Brazil, Russia, India, China, South Africa, Indonesia, Malaysia, Philippines and Thailand.

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data sources; the World Bank Database (World Bank, 2019b), the psychic distance dataset (Dow and Karunaratna, 2006), the ILOSTAT (International Labour Organization, 2019) and lastly the CEPII database (Mayer and Zignago, 2011). After combining and cleaning the data from the different sources, a final sample size of 3272 OFDI deals conducted by 2256 unique EMNEs in 95 host countries remained. Table 1 below shows an overview of the distribution of the cross-border acquisitions (in the total sample) across the different home countries. For a year-on-year overview of the cross-border acquisitions per home country, see appendix A2.

Table 1

Home country Total number of

acquisitions

Unique number of EMNE

Average number of acquisitions per EMNE Brazil 195 123 1.59 Russia 745 422 1.77 India 879 546 1.61 China 782 627 1.25 South Africa 308 208 1.48 Indonesia 74 50 1.48 Malaysia 658 425 1.55 Philippines 74 45 1.64 Thailand 129 96 1.34 BRICS total 2909 1926 1.54 Southeast Asia total 935 616 1.50 Grand total 3844 2542 1.52 Method

Variables and measures Dependent variable

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originating from the nine home countries mentioned previously. Third, we focused on deals among companies, thus both acquiror and target both the acquiror and target had to be firms, thereby excluding for example governments. Last, we specified that the investments had to be (cross-border) acquisitions, excluding other modes of entry such as mergers or joint ventures.

The mean number of acquisitions over the years 2004-2018 was 27.97, with a standard deviation of 60.59 and N = 130. The range of the dependent variable was 426, with a minimum of 1 and a maximum of 427.

Independent variables

For all independent variables with time-series data, a one-year time lag was applied to account for the time between an investment decision and the completion of the cross-border acquisition.

Formal institutional distance. We measured formal institutional distance using the

World Governance Indicators (WGI), developed by the World Bank (2019a), following a few recent papers (Li, Li and Shapiro, 2012; Håkanson, Ambos, Schuster and Leicht-Deobald, 2016; Shirodkar and Konara, 2017). Given the large number of host countries in our sample, geographic coverage of data was an important criterion in selection measures. The WGI dataset is comprehensive as it comprises of year-on-year data (since 1996) of over 200 countries and territories.

The WGI consist of six dimensions; Voice and Accountability, Political Stability and No Violence, Government Effectiveness, Regulatory Quality, Rule of Law and lastly, Control of Corruption. As our sample spans fifteen years of data, we calculated the average values of each of the six dimensions for the years 2003 to 2017.

Linguistic distance. We measured linguistic distance between home and host country

using data from Dow and Karunaratna’s (2006) psychic distance dataset. Common language facilitates communication cross-country and reduces the negative effects of cultural and psychic distance (Buckley et al., 2012; Doh et al., 2009). Linguistic distance was measured as a composite variable based on three 5-point scale items, assessing the difference in dominant languages between two countries, Countryi and Countryj, the prevalence of countryi’s dominant language(s) in countryj and vice versa (i.e. prevalence of countryj’s language(s) in countryi). In this study, countryi stands for home countries and countryj stands for host countries.

Market-seeking. We used GDP (million constant 2010 US dollars) to capture the

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effects of long-term market potential on location choice. GDP per capita was measured as GDP divided by the total population of a country and GDP growth was measured as the annual percentage growth rate of GDP. Buckley et al. (2007) found that market growth has a positive effect on market-seeking investments (Buckley et al., 2007). The data for both measures were collected from the World Bank (2019b). We then calculated the means of each of the three measures over the years 2003-2017.

Natural resource-seeking. To test the effects of natural resource-seeking on location

choice, we measured natural resource endowments using two different measures; fuel exports and ore and metal exports, consistent with previous research (Buckley et al., 2016b; Kolstad and Wiig, 2012; Slangen and Beugelsdijk, 2010). Both measures are a percentage of a country’s total merchandise export, and both were retrieved from the World Development Indicators (World Bank, 2019b). For both measures, we averaged the yearly exports to calculate the mean fuel and ores and metal exports percentages between 2003 and 2017.

Efficiency-seeking. We proxied the efficiency-seeking motive by looking at the labour

productivity in a host country, as production becomes more efficient when labour productivity increases (Chen and Yeh, 2012). Labour productivity was measured as GDP (at constant 2010 US$) divided by the number of employed people. Employed people covered all people of working age that had paid employment or were self-employed. Data on (annual) labour productivity per country was collected from the ILOSTAT database (International Labour Organization, 2019).

Strategic asset-seeking. Strategic asset-seeking motives were proxied by the total

number of patents registered in a host country, both by residents and non-residents, in line with previous research (Buckley et al., 2007; Quer et al., 2017). Data was retrieved from the World Development Indicators from the World Bank Database (2019b). Again, the average number of patents registered was calculated by calculating the mean patent registrations in a country in the years 2003 to 2017.

Controls

Tax haven status. Over the years, corporate income tax rates have declined across the

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Geographic distance. Following Buckley et al. (2007) and Kolstad and Wiig (2012),

we controlled for geographic distance between home and host country. We operationalized geographic distance as the distance between the capitals of the home and host countries in kilometres. Our data was collected from the CEPII GeoDist database developed by Mayer and Zignago (2011).

Analytical approach

Exploratory correlation and reliability analyses

We first ran an exploratory correlation analysis on the OFDI from all nine home countries. As the distance variables (i.e. the main independent variables formal institutional and linguistic distance and the control variable geographic distance) differ per home country, they were omitted from this analysis. Three of the proxies for OFDI motives (i.e. GDP, labour productivity and patents) were significantly correlated with our dependent variable (number of acquisitions). For ore and metal exports, a weakly negative, but nonsignificant correlation was found. Notably, we find a strongly significant, near perfect positive correlation (.99, p < .05) between labour productivity and our alternative measure for market potential; GDP per capita. GDP per capita is GDP divided by total population and labour productivity is calculated as GDP divided by number of employed people. Accordingly, GDP per capita was omitted from further analyses.

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Table 2: Correlations between dependent and independent variables

M SD 1 2 3 4 5 6 7 8 9 All home countries 1. Tax havens1 _{.06 .24 1} 2. Number of acquisitions 27.97 60.59 .28** 1 3. Host GDP 497440.76 1595828.48 -.03 .66** 1 4. Host GDP per capita 15097.77 19700.19 .50** _.38** _.28** ₁ 5. Host GDP growth 4.14 2.28 -.11 -.17* _{-.15 -.46}** ₁

6. Host ore & metal exports

9.18 15.07 -.11 -.08 -.10 -.19* _.20* ₁

7. Host fuel exports 17.02 25.72 -.10 -.10 -.07 -.02 .00 -.16 1 8. Host labour productivity 32848.88 40527.19 .51** .36** _.28** _.99** -.48** -.20* -.02 1 9. Host patents 15637.41 74427.56 -.04 .44** _.85** _{.15 .01 -.08 -.09 .14 1} 1 _{Dummy coded, 0 = no, 1 = yes}

** p < .01, * p < .05

Correlation analyses per home country. Next, exploratory correlation analyses were

run for each separate home country to incorporate the distance variables for each home country (appendix C1 to C9). Across the different home countries and the independent variables, we observed variance in the strength of correlations observed. Given the number of home countries and number of independent variables, we only highlight the most notable correlations below.

Correlations between number of acquisitions and geographic distance were negative for all countries, though only of moderate strength (-.34) for Brazil, Russia and Thailand. Moderate negative correlations were found between formal institutional distance and number of acquisitions for China (-.41) and weak negative correlations were found for India, Indonesia, Philippines and Thailand. Correlations between linguistic distance and number of acquisitions were negative and strongly significant (p < .01) for all nine home countries, with notably strong negative correlations for Malaysia (-.74) and Russia (-.61).

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correlated to the number of acquisitions for any of the home countries. Third, moderate positive significant correlations were found between labour productivity and number of acquisitions for China (.37) and India (.32). Last, moderate significant correlations were found between host country patent registrations and number of acquisitions for the Philippines (.60), India (.50), China (.44) and Thailand (.30).

Factor analyses

Formal institutional distance. Strong, significant positive correlations were found

between the six dimensions of the World Governance Indicators (p < .01). Subsequent reliability analysis showed that internal consistency was very high (Cronbach’s α = .97). Accordingly, we conducted a principal components factor analysis with Varimax rotation to reduce the number of dimensions. One factor was extracted with an eigenvalue > 1, accounting for 87.2 per cent of variance. Next, we measured formal institutional distance by calculating the absolute difference between the scores of two countries, in line with Håkanson et al. (2016).

Table 3: Factor analysis Worldwide Governance Indicators

Construct and item wording Factor Loadings

Rule of Law .99

Government effectiveness .97

Control of Corruption .97

Regulatory Quality .96

Voice and accountability .87

Political stability and no violence .84

Regression analyses

OLS regression analyses. To test our hypotheses (H1 to H6), we ran OLS regression

analyses. First, we ran a regression analysis using the entire sample, followed by separate regression analyses for each of the nine home countries. As the three distance variables (i.e. institutional, linguistic and geographic) are based on home-host country pairs, they were not included in the first regression analysis.

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variables GDP and Patents for the China model) exceeded a value of 5. Commonly used cut-off values for multicollinearity are VIF < 5 (e.g. Sheather, 2009), whereas others consider VIF < 10 an acceptable cut-off value (e.g. Hair, Black, Babin and Anderson, 2013; Kutner, Nachtsheim and Neter, 2004). Accordingly, our VIF values seem acceptable within these bounds and we expect multicollinearity issues to be limited in in this research.

Table 4: Multicollinearity statistics for OLS regressions

VIF All

countries

BRA RUS IND CHN ZAF IDN MYS PHL THA

Tax haven 1.49 1.57 .64 1.56 1.77 1.58 1.56 1.69 1.55 1.56 Geographic distance n/a 1.37 .78 1.13 1.20 1.35 1.32 1.32 1.20 1.31 Formal institutional distance n/a 1.93 .25 2.79 4.03 1.28 3.58 1.17 3.48 2.64

Linguistic distance n/a 1.25 .88 1.20 1.37 1.28 1.15 1.25 1.19 1.24

GDP 4.28 4.57 .23 4.72 6.30 4.66 4.47 4.41 4.78 4.42

GDP growth 1.42 1.62 .60 1.75 1.68 1.64 1.78 1.70 1.83 1.75

Ore & metal exports 1.10 1.12 .84 1.16 1.16 1.16 1.11 1.11 1.14 1.19

Fuel exports 1.06 1.08 .85 1.08 1.12 1.15 1.12 1.11 1.11 1.07

Labour productivity 2.01 2.89 .22 3.38 4.35 2.25 3.91 2.21 3.78 3.28

Patents 4.01 4.47 .25 4.39 5.99 4.56 4.23 4.16 4.54 4.21

BR = Brazil, RUS = Russia, IND = India, CHN = China, ZAF = South Africa, IDN = Indonesia, MYS = Malaysia, PHL = Philippines, THA = Thailand

RESULTS

First, the results of the regression analysis of the full sample will be presented to test hypotheses 1 through 4. As the variables regarding distance (i.e. control variable geographic distance and main independent variables formal institutional and linguistic distance) are home country specific, hypotheses 5 and 6 were not tested using the full sample. Second, the results of the analyses for each home country subsample will be presented. An overview of the step by step models of the OLS regression analyses including all home countries can be found in appendix D1.

Regression analysis full sample. In the full sample model, we tested hypothesis 1 to 4

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Table 5: Regression table all home countries

Model 1 Model 2

Steps and variables Beta SE Beta SE

Intercept (5.54) (11.37) 1 Control Tax havens1 _{.27** (21.83) .30** (18.42)} 2 Main effects GDP 1.08** (.00) GDP growth .02 (2.01)

Ore & metal exports .00 (.26)

Fuel exports -.04 (.15) Labour productivity -.03 (.00) Patents -.47** (.00) F 9.80** 22.80** Degrees of freedom 122 116 R Square .07 .58 Δ R Square .07** .51**

1 _{Dummy coded, 0 = no, 1 = yes}

Standard errors are reported between parentheses

** p < .01, * p < .05, † _{p < .10}

Hypothesis 1 stated that host country market potential is positively related to the number of acquisitions done by EMNEs. Host country GDP had a strong significant positive effect (b = 1.08, p <.01) on the number of acquisitions done, supporting hypothesis 1. No significant effect was found for GDP growth, our additional variable for market-seeking motives we used as a proxy for future market potential of a host country. No significant effects were found for natural resource-seeking (b = .00 for ore and metal exports and b = -.04 for fuel exports) and for efficiency-seeking (b = -.03). Thus, hypothesis 2 and 3 were not supported. Patent registrations, our proxy for strategic asset-seeking, had a significant (p <.01), but contrary (b = -.47) effect to what was predicted in hypothesis 4. Lastly, our control variable ‘tax haven’ is found to have a positive significant effect on the number of acquisitions.

Important to note is that not all home countries are equally represented in the full sample, as can be seen in table 1 presented earlier in the methods section. This may cause the results of this OLS regression to be skewed as Russia, India, China and Malaysia accounted for over 75 per cent of the total number of acquisitions in the sample.

Regression analyses per home country. Next, we ran regression analyses per home

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Table 6: Regression analyses per home country (Full model)

Variable BRA RUS IND CHN ZAF IDN MYS PHL THA

Model 3 Intercept (1.85) (4.03) (5.63) (7.86) (4.13) (1.51) (6.59) (.73) (1.44) Controls Tax havens1 _-.01 (1.91) .19* (4.27) .04 (5.74) .18 (6.68) -.08 (3.28) .27* (1.33) .23* (6.67) .12 (.65) .27 (1.22) Geographic distance -.40** (.00) -.13 _(.00) -.04 _(.00) -.15 † (.00) -.15 _(.00) -.17 _(.00) -.07 _(.00) -.10 _(.00) -.24* _(.00) Main effects Formal institutional distance -.07 (1.05) -.26 † (2.24) .10 (3.18) .23 (2.67) .10 (1.73) .27 (.73) .02 (3.26) .16 (.26) .08 (.68) Linguistic distance -.15 (.72) -.59** _(1.73) -.13** _(1.25) -.30** _{(6.52) (.73)} -.33** -.29** _(1.05) -.64** _(4.41) -.21** _(.14) -.31 _(1.42) GDP .62** (.00) .47** _(.00) 1.42** _(.00) 1.12** _(.00) .39 † (.00) .19 (.00) -.11 _(.00) .98** _(.00) .51 (.00) GDP growth .05 (.26) -.21* (.59) .03 (.82) -.08 (.90) -.08 (.44) .13 (.02) .07 (.89) .10 (.09) .09 (.17) Ore & metal exports .06 (.03) .03 (.08) .00 (.10) .07 (.11) .01 (.06) .03 (.01) .01 (.11) -.01

(.01) -.09 (.02) Fuel exports -.05 (.02) -.06 (.04) -.00 (.06) -.05 (.06) .00 (.03) .09 (.00) .00 (.06) .06 (.16) -.00 (.01) Labour productivity .04 (.00) .10 (.00) -.08 (.00) -.20 (.00) .03 (.00) -.15 (.00) .02 (.00) -.15 (.00) -.10 (.00) Patents -.20 (.00) -.24 _(.00) -.71** _(.00) -.60** _(.00) -.21 (.00) -.10 _(.00) .28* _(.00) -.25 † (.00) -.14 _(.00) F 6.18** 9.34** 40.66** 12.08** 2.78** 3.32** 14.72** 8.06** 5.91** Degrees of freedom 80 80 80 80 80 80 80 86 80 R square .44 .54 .84 .60 .26 .29 .65 .27 .43 Δ R Square .21** .13** .68** .35** .07 .03 .05 .09** .15**

BR = Brazil, RUS = Russia, IND = India, CHN = China, ZAF = South Africa, IDN = Indonesia, MYS = Malaysia, PHL = Philippines, THA = Thailand

1 _{Dummy coded, 0 = no, 1 = yes}

Standard errors are reported between parentheses ** p < .01, * p < .05, † _{p < .10}

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effects of host country GDP on the number of cross-border acquisitions done by EMNEs from South Africa (b = .39). Our additional test for the effect of future market potential (proxied using GDP growth) on location choice was only significant for Russia, however the effect was negative (b = -.21).

No significant results were found for the posited natural resource-seeking motive, thus rejecting hypothesis 2. A potential reason for this result is that the substantial variety in host countries in our sample, ranging from developed to developing markets. A more focused set of host countries, e.g. host countries in Africa would have more likely yielded significant results. For example, Blomkvist and Drogendijk (2013) focused specifically on African host countries, finding support for natural resource-seeking motives of Chinese OFDI in Africa.

Hypothesis 3 tested the effect of efficiency-seeking motives by looking at the labour productivity of host countries. No support was found for hypothesis 3, i.e. efficiency-seeking motives did not significantly drive the location choice of EMNE cross-border acquisitions in our study. As the home countries in our sample are still emerging, they still enjoy relatively low wages domestically; though in recent years wages have gone up in these countries (Baldwin, 2012) we do not see this reflected in the results of our analyses.

Strategic asset-seeking was tested in hypothesis 4 by proxying the availability of technological know-how through resident and non-resident patent registrations in a country. Hypothesis 4 was supported for Malaysia (b = .28, p < .05). Furthermore, strong significant effects (p < .01) were found for India (b = -.71) and China (b = -.60), however the direction of the effect was opposite to what was hypothesized. Lastly, a weak significant (p < .10) negative effect (b = -.25, p < .10) was found for the Philippines.

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Our final hypothesis proposed that linguistic distance between home and host country would negatively impact the number of acquisitions. We found strong support (p < .01) for this hypothesis for all home countries except Brazil and Thailand, for which the effects were negative, but nonsignificant. Effect sizes for Malaysia and Russia were largest (b = .64 and -.59 respectively) and smallest for India (b = -.13).

Lastly, we found some significant effects for the control variables in the final models of our regression analyses. We found significant positive effects (p < .05) for host country tax haven status on number of acquisitions done by EMNEs from Malaysia, India and Russia (effect sizes respectively b = .27, b = .23 and b = .19). Geographic distance had a significant negative effect on Brazilian cross-border acquisitions (b = -.40, p < .01). Furthermore, significant negative effects were found for EMNEs from Thailand (b = -.24, p < .05) and China (b = -.15, p < .10).

DISCUSSION AND CONCLUSION Discussion

Previously, the IB literature has acknowledged that the internationalization behaviour of EMNEs and DMNEs differs from each other, as also supported in a recent literature review by Li and colleagues (2018). However, this dichotomous view oversimplifies the variety observable in internationalization behaviour among emerging markets (Peng et al., 2018). Our research shows that there is variation in the motives and location choices of EMNEs originating from different emerging markets.

Following Dunning’s (2000) OLI framework, we looked at four classic internationalization motives; market-seeking, natural resource-seeking, efficiency-seeking and strategic asset-seeking. The location specific (L) advantages (e.g. market size or strategic assets) of a country can attract OFDI. Following the same logic, there is also variety among the (locational advantages of) home countries, which impacts the strategy of firms (Kim and Aguilera, 2016). For example, natural resource-seeking or efficiency-seeking motives will not be as pronounced if there is an abundance of resources or unskilled low-cost labour available domestically.

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these existing findings to EMNEs from other emerging markets. However, the effect sizes vary across home countries, with effect sizes for India and China being particularly large, which is surprising given the size of their domestic market. Large markets make it possible to reach economies of scale and provide a market for selling goods (Buckley et al., 2007; Deng and Yang, 2015), which allows for gaining a cost advantage.

Furthermore, we find that the EMNEs in our study are not significantly driven by natural resource-seeking in their location choices as effect sizes are small and nonsignificant across the different home markets. Especially for China, this result was surprising, as China is known for investing in Africa to access natural resources (World Bank, 2013), given China’s limited natural resource endowments. However, in comparison to China, the other home countries have larger natural resource endowments. For example, fuel exports as a percentage of total merchandise exports exceeds 60 percent for Russia and nears 30 percent for Indonesia. Ore and metal exports account for more than a quarter of South Africa’s total merchandise exports. In contrast, both ore and metal exports and fuel exports contribute only marginally (1.5 and 1.8 percent respectively) to China’s merchandise exports.

A second explanation for these results may be our choice to focus on only cross-border acquisitions rather than total OFDI flows, thus omitting other entry modes such as joint ventures, mergers and greenfield investments (i.e. buying land and building new facilities) and brownfield investments (i.e. purchasing or leasing facilities). However, acquisitions may not necessarily be the preferred mode of entry for EMNEs. For example, brownfield investments may be a more appropriate mode of entry when a firm wants to acquire inimitable resources such as a network or political connections of a target firm, while the target firm has weak managerial and technological assets (Lebedev et al., 2014). This could be the case in developing countries with large natural resource endowments as local firms lack technological knowledge and capabilities and cannot extract the raw materials themselves, but do possess a network and political ties that give them access to these natural resources, which are often controlled by the state (Buckley et al., 2017).

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Further, we tested an internationalization motive that has largely been ignored in research on EMNE internationalization; efficiency-seeking. As emerging markets have developed over the years, we expected efficiency-seeking to become one of the drivers of the EMNE cross-border investments as domestic labour costs have gone up. Buckley et al. (2008) similarly argued that efficiency-seeking will become increasingly important for EMNEs when they get more involved internationally and operate on a bigger scale. Furthermore, efficiency-seeking becomes more important when competing globally and wanting to move up the value chain (World Bank, 2016). However, moving up the value chain might also lead to a change in the competitive advantage that EMNEs try to pursue, moving away from competing on costs and increasingly competing on quality. Our findings suggest that, for cross-border acquisitions between 2003 and 2017, EMNEs from multiple home countries are not driven by efficiency-seeking motives. Future research is needed to investigate whether this is explained by the availability of low-cost labour at home or by EMNEs moving up the value chain and competing on quality instead of costs.

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of OFDI. This implies that researchers should be cautious when making generalizations on the internationalization behaviour of EMNEs from different home countries. Further, it warrants the need for comparative studies on emerging markets.

Moreover, we looked at the effects of distance on the location choice of these EMNEs. We hypothesized that formal institutional distance and linguistic distance would negatively affect the number of acquisitions done by EMNEs. Furthermore, we controlled for the effect of physical (i.e. geographic) distance between the capitals of the home and host country. We find some interesting differences among the home countries, which we will elaborate upon below.

The hypothesized negative effect of formal institutional distance was only marginally significant for Russia and nonsignificant for the other home countries. Taken into consideration the strong support we found for the market-seeking motives of EMNEs, we argue that the effects of host market potential outweigh the liability of foreignness faced by EMNEs in institutionally distant host countries. Quer et al. (2012) similarly found that Chinese EMNEs are less risk averse in their OFDI than DMNEs. However, the majority of the host countries that are both institutionally distant from emerging markets and integrated in the world economy, will have more established and developed institutions compared to the home countries of EMNEs. In turn, these countries provide a favourable investment climate that attracts OFDI as political risk is low, which in turn increases market stability and lowers costs of doing business (Buckley et al., 2016a; Peng et al., 2008). In contrast, countries that are institutionally distant in the other direction are characterized by more risk due to their dysfunctional and poorly established institutions. There is however limited evidence for OFDI flows from EMNEs to such markets. For example, Chinese EMNEs have invested in risky locations such as Syria, Iraq and Sudan to access their natural resource endowments (Buckley et al., 2017). However, acquisitions in these countries are relatively rare. In line with Chikhouni et al. (2017) and Håkanson and Ambos (2010), we posit that the direction of distance should be taken into account for EMNEs, as they internationalize into both stronger and weaker institutional environments. However, we can conclude that for EMNEs, their internationalization motives outweigh the effects of formal institutional distance, and that this holds true across different home countries. This is also supported by Malhotra et al. (2011), who found that EMNEs are inclined to disregard cultural distance when host country market potential is high.

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to highlight that the effect sizes differ across the nine home countries in our study. This implies that emerging markets are not homogeneous and future research should be careful in making generalizations across emerging markets. The fact that we did not find a significant effect for Thailand and Brazil further supports this implication. Appendix A2 shows that the number of cross-border acquisitions from Thailand is, compared to the other emerging markets, relatively small and almost all the acquisitions from Thailand were conducted in recent years (after 2010). Furthermore, appendix B1 shows that the majority of the cross-border investments from china are done within the (Southeast) Asia region, as supported by the significant negative effect of geographic distance for Thailand. It thus seems that Thailand is relatively new to internationalizing (through cross-border acquisitions) compared to the other home countries in our sample. Similarly, geographic distance had a significant negative effect on Brazilian EMNEs location choice. We also see this reflected in appendix B1, as many of the host countries are geographically close as they are located on the South American continent.

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Limitations and future research directions

Our main objective of this study was to make cross-country comparisons on the OFDI behaviour of EMNEs from various emerging markets. Accordingly, we did not take into account within-country differences among EMNEs. This was also not possible due to a limitation in how our data was structured. As we used a count variable of cross-border acquisitions at the country level, we only had data on the number of acquisitions between home-host country pairs. Using a different data structure would allow for more elaborate analyses, such as incorporating a multilevel model. A multilevel model would also allow running a regression analysis for all home countries simultaneously using a random intercept (and optionally a random slope) rather than comparing separate regression analyses. This would also make it possible to make comparisons across EMNEs in different industries. For example, natural resources may be more important for a manufacturing company, whereas strategic assets could be more important in the technology/IT sector.

Moreover, we took a cross-sectional approach in this study as our main goal was to indicate that there is variety in the internationalization behaviour of firms depending on their country of origin. However, future research could take into account the development of the home countries. Some scholars (Ramamurti, 2009) argue that over time EMNEs will become more similar to DMNEs as they mature (Fleury, Fleury and Olivieira, 2018). Taking a longitudinal approach would allow to research the (changes in the) importance of internationalization motives over time. Additionally, future research should elaborate on our study by investigating the idiosyncratic characteristics of the home country institutions of emerging markets.

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Conclusion

The main aim of this study was to make comparisons across emerging markets, as the IB literature has largely treated these markets as a homogeneous group despite their unique and varying home country institutions. Using a sample of 3272 cross-border acquisitions conducted by EMNEs from nine home countries into 95 host countries over the period 2004-2018, we researched the why and where of OFDI from emerging markets. More specifically, we investigated the location choice of OFDI by looking at the drivers of and barriers to cross-border acquisitions. Our findings show that there are differences among the cross-cross-border investments of EMNEs from different home countries; in line with the scant literature comparing emerging markets with emerging markets (e.g. Quer et al., 2017).

Investigating four classic internationalization motives; market-seeking, natural resource-seeking, efficiency-seeking and strategic asset-seeking as well as multiple measures of cross-country distance, we find that market-seeking significantly impacted the location choice for many of the home countries in our sample, though not for every home country. Moreover, there was notable effect size variety between countries. Natural resource-seeking and efficiency-seeking motives were found not to be significant for EMNE cross-border acquisitions. Furthermore, we expected EMNEs to seek strategic assets abroad as a catching-up or ‘springboard’ strategy to overcome their latecomer disadvantage (Luo and Tung, 2007). The hypothesized effect was only found for Malaysia. Surprisingly, a strong negative effect was found for both India and China. Further, we find that formal institutional distance does not significantly affect the location choice of EMNEs’ cross-border acquisitions, implying that location-specific advantages outweigh dissimilarities in formal institutions. In contrast, we do find strong support for the negative effects of linguistic distance for most, but not all emerging markets.

Overall, it is important to emphasize that there is heterogeneity in the internationalization behaviour of EMNEs originating from different emerging markets. Future research should move beyond the simplified dichotomous view comparing emerging and developed market (firms). Instead, emerging markets should be treated as a heterogeneous group of countries and future search should investigate more in-depth which domestic particularities explain the variance in internationalization behaviour among these countries.

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REFERENCES

Amal, M., and Tomio, B. T. (2012). Determinants of Brazilian Outward Foreign Direct Investment (OFDI): A Host Country Perspective. In Presented paper in the 16th annual meeting of the National Association of Post Graduate Research (ANPAD), Brasilia Ambos, B. and Håkanson, L. (2014). The concept of distance in international management

research. Journal of International Management, 20(1), 1-7

Anwar, A.I. and Mughal, M.Y. (2015). Determinants of Russian Firms’ Merger and Acquisition Activities. Economics Bulletin, 35(4), 2382-2394

Anwar, A.I. and Mughal, M.Y. (2017). Out of Africa? Locational determinants of South African cross-border mergers and acquisitions. Applied Economics, 49(33), 3263-3279 Athreye, S., and Godley, A. (2009). Internationalization and technological leapfrogging in the

pharmaceutical industry. Industrial and Corporate Change, 18(2), 295-323

Azar, G. and Drogendijk R. (2014). Psychic distance, innovation and firm performance. Management International Review, 54(5), 581-613

Baldwin, R., (2012). Global supply chains: Why they emerged, why they matter, and where they are going, CEPR Discussion Papers 9103, 1-33

Barkema, H. G., Chen, X. P., George, G., Luo, Y., & Tsui, A. S. (2015). West meets East: New concepts and theories. Academy of Management Journal, 58(2), 460-479

Beugelsdijk, S., Kostova, T., Kunst, V.E., Spadafora, E. and van Essen, M. (2018). Cultural distance and firm internationalization: a meta-analytical review and theoretical implications. Journal of Management, 44(1), 89-130

Berry, H., Guillen, M., and Zhou, N. (2010). An institutional approach to cross-national distance. Journal of International Business Studies, 41(9), 1460–1480

Blomkvist, K. and Drogendijk, R. (2013). The impact of psychic distance on Chinese outward foreign direct investments. Management International Review, 53(5), 659-686

Blomkvist, K. and Drogendijk, R. (2016). Chinese outward foreign direct investments in Europe. European Journal of International Management, 10(3), 343-358

Buckley, P.J., Clegg, L.J., Cross, A.R., Liu, X., Voss, H. and Zheng, P. (2007). The determinants of Chinese outward foreign direct investment. Journal of International Business Studies, 38(4), 499-518

33

Buckley, P.J., Forsans, N. and Munjal, S. (2012). Host-home country linkages and host-home country specific advantages as determinants of foreign acquisitions by Indian firms. International Business Review, 21(5), 878-890

Buckley, P.J. and Munjal, S. (2017). The role of local context in the cross-border acquisitions by emerging economy multinational enterprises. British Journal of Management, 28(3), 372-389

Buckley, P.J., Munjal, S., Enderwick, P. and Forsans, N. (2016a). Do foreign resources assist or impede internationalisation? Evidence from internationalisation of Indian multinational enterprises. International Business Review, 25(1), 130-140

Buckley, P.J., Yu, P., Liu, Q., Munjal, S. and Tao, P. (2016b). The institutional influence on the location strategies of multinational enterprises from emerging economies: evidence from China’s cross-border mergers and acquisitions. Management and Organization Review, 12(3), 425-448

Brouthers, K.D. and Brouthers, L.E. (2001). Explaining the national cultural distance paradox. Journal of International Business Studies, 32(1), 177–189

Brouthers, K.D. and Hennart, J-F. (2007). Boundaries of the firm: insights from international entry mode research. Journal of Management, 33(3), 395-425

Chari, M. and Acikgoz, S. (2016). What drives emerging economy firm acquisitions in tax havens? Journal of Business Research, 69(2), 664-671

Chen, C. and Yeh, C. (2012). Re-examining location antecedents and pace of foreign direct investment: Evidence from Taiwanese investments in China. Journal of Business Research, 65(8), 1171-1178

Chen, Y.Y., and Young, M.N. (2010). Cross-border mergers and acquisitions by Chinese listed companies: A principal–principal perspective. Asia Pacific Journal of Management, 27(3), 523–539

Cheung, Y. and Qian, X. (2009). Empirics of China’s outward foreign direct investment. Pacific Economic Review, 14(3), 312-341

Chikhouni, A., Edwards, G. and Farashahi, M. (2017). Psychic distance and ownership in acquisitions: direction matters. Journal of International Management, 23(1), 32-42 Cuervo-Cazurra, A. (2012). Extending theory by analysing developing country multinational

companies: solving the goldilocks debate. Global Strategy Journal, 2, 153-167

34

Cuervo-Cazurra, A. and Genc, M. (2008). Transforming disadvantages into advantages: developing-country MNEs in the least developed countries. Journal of international business studies, 39, 957-979

De Beule, F., and Duanmu, J. L. (2012). Locational determinants of internationalization: A firm-level analysis of Chinese and Indian acquisitions. European Management Journal, 30(3), 264-277

Demirbag, M. and Glaister, K.W. (2010). Factors determining offshore location choice for R&D projects: a comparative study of developed and emerging regions. Journal of Management Studies, 47(8), 1534-1560

Deng, P. (2009). Why do Chinese firms tend to acquire strategic assets in international expansion? Journal of World Business, 44(1), 74-84

Deng, P. (2012). Accelerated internationalization by MNCs from emerging economies: determinants and implications. Organizational Dynamics, 41, 318-326

Deng, P. and Yang, M. (2015). Cross-border mergers and acquisitions by emerging market firms: A comparative investigation. International Business Review, 24(1), 157-172 Doh, J.P., Bunyaratavej, K. and Hahn, E.D. (2009). Separable but not equal: The location

determinants of discrete services offshoring activities. Journal of International Business Studies, 40(6), 926-943

Dow, D. and Karunaratna, A. (2006). Developing a multidimensional instrument to measure psychic distance stimuli. Journal of International Business Studies, 37(5), 578-602 Dunning J.H. (1977). Trade, Location of Economic Activity and the MNE: A Search for an

Eclectic Approach. In: Ohlin B., Hesselborn P.O. and Wijkman P.M. (eds) The International Allocation of Economic Activity. Palgrave Macmillan, London

Dunning, J.H. (1993). Multinational Enterprises and the Global Economy. Addison-Wesley: Wokingham

Dunning, J.H. (2000). The eclectic paradigm as an envelope for economic and business theories of MNE activity. International Business Review, 9, 163-190

Dunning, J.H. and Lundan, S. (2008). Multinational Enterprises and the Global Economy. Edward Elgar: Cheltenham, U.K

Estrin, S., Ionascu, D., and Meyer, K. E. (2007). Formal and informal institutional distance, and international entry strategies. William Davidson Institute Working Paper No. 728 Fainshmidt, S., Judge, W.Q., Aguilera, R.V. and Smith, A. (2018). Varieties of institutional

35

Fleury, A., Fleury, M.T. and Oliveira, L. (2018). The evolution of studies on emerging market multinationals: what is new. In 6th Copenhagen conference on ‘emerging multinationals’: outward investment from emerging economies, Copenhagen, Denmark, 11-12

Gammeltoft, P., Pradhan, J.P. and Goldstein, A. (2010). Emerging multinationals: home and host country determinants and outcomes. International Journal of Emerging Markets, 5(3/4), 254-265

Gaur, A.S., Ma, X. and Ding, Z. (2018). Home country supportiveness/unfavorableness and outward foreign direct investment from China. Journal of International Business Studies, 49(3), 324-345

Ghemawat, P. (2001). Distance still matters: The hard reality of global expansion. Harvard Business Review, 79(8), 137–147

Goerzen, A., Asmussen, C.G. and Nielsen, B.B. (2013). Global cities and multinational enterprise location strategy. Journal of International Business Studies, 44(5), 427-450 Hair, J. F., Black, W. C., Babin, B. J., and Anderson, R. E. (2013). Multivariate data analysis:

Pearson new international edition. Pearson Higher Ed.

Håkanson, L. and Ambos, B. (2010). The antecedents of psychic distance. Journal of International Management, 16(3), 195-210

Håkanson, L., Ambos, B., Schuster, A. and Leicht-Deobald, U. (2016). The psychology of psychic distance: Antecedents of asymmetric perceptions. Journal of World Business, 51(2), 308-318

Hennart, J-F. (2012). Emerging market multinationals and the theory of the multinational enterprise. Global Strategy Journal, 2(3), 168-187

Hernandez, E. and Guillén, M.F. (2018). What’s theoretically novel about emerging-market multinationals? Journal of International Business Studies, 49(1), 24-33

Hitt, M.A., Franklin, V. and Zhu, H. (2006). Culture, institutions and international strategy. Journal of International Management, 12(2), 222-234

Hofstede, G. (1980). Culture and organizations. International Studies of Management & Organization, 10(4), 15-41

36

IMF (2019). Real GDP growth: annual percent change. Retrieved from:

https://www.imf.org/external/datamapper/NGDP_RPCH@WEO/OEMDC/WEOWO RLD/DA/ADVEC/AS5

International Labour Organization (2019). ILOSTAT, ILO modelled estimates. Retrieved from: https://ilostat.ilo.org

Jiang, G.F., Holburn, G.L.F. and Beamish, P.W. (2014). The impact of vicarious experience on foreign location choice. Journal of Management, 20(3), 345-358

Jindra, B., Hassan, S.S. and Cantner, U. (2016). What does location choice reveal about knowledge-seeking strategies of emerging market multinationals in the EU? International Business Review, 25(1), 204-220

Johanson, J. and Vahlne, J-E. (1977). The internationalization process of the firm: A model of knowledge development and increasing foreign market commitments. Journal of International Business Studies, 8(1), 23–32

Kang, Y., and Jiang, F. (2012). FDI location choice of Chinese multinationals in East and Southeast Asia: Traditional economic factors and institutional perspective. Journal of World Business, 47(1), 45-53

Kim, J.U. and Aguilera, R.V. (2016). Foreign location choice: review and extensions. International Journal of Management Reviews, 18(2), 133-159

Kogut, B. and Singh, H. (1988). The effect of national culture on the choice of entry mode. Journal of International Business Studies, 19(3), 411-432

Kolstad, I. and Wiig, A. (2012). What determines Chinese outward FDI? Journal of World Business, 47(1), 26–34

Kostova, T., Marano, V. and Tallman, S. (2016). Headquarters-subsidiary relationships in MNCs: Fifty years of evolving research. Journal of World Business, 51, 176-184 Kostova, T. (1999). Transnational transfer of strategic organizational practices: a contextual

perspective. The Academy of Management Review, 24(2), 308-324

Kostova, T. and Zaheer, S. (1999). Organizational legitimacy under conditions of complexity: the case of the multinational enterprise. Academy of Management Review, 24(1), 64-81 Kutner, M., Nachtsheim, C. and Neter, J. (2004). Applied Linear Regression Models. 4th ed.

Irwin: McGraw-Hill

Lebedev, S., Peng, M.W., Xie, E. and Stevens, C.E. (2015). Mergers and acquisitions in and out of emerging economies. Journal of World Business, 50(4), 651-662