EMNE heterogeneity explored: the uniqueness of Chinese EMNEs’ OFDI

The growth of outward foreign direct investment (OFDI) originating from emerging markets has attracted significant scholarly attention (Marano et al., 2017). The international business (IB) literature has strongly focused on juxtaposing emerging and developed markets (Luo and Zhang, 2016). A literature review (Li et al., 2018) suggests that emerging market multinationals (EMNEs) and developed market multinationals (DMNEs) indeed differ in their location choice for cross-border investments, supporting this contrast. However, a dichotomous perspective portraying emerging versus developed market firms implicitly assumes homogeneity among either set of firms and fails to consider heterogeneity among emerging markets (Jindra et al., 2016).

This assumption of homogeneity of countries seems incongruent with a key characteristic of IB scholarship: the relevance of context. It is widely acknowledged that country-level context and embeddedness of firms in these environments are key for firm behavior (Kostova et al., 2020). Yet, the literature on EMNEs is primarily dominated by single-country studies on China (see also the recent review by Ott et al., 2023), apart from a few notable exceptions based on, for example, Indian (Kumar et al., 2020), Brazilian (Carneiro et al., 2018) and Russian (Annushkina and Trinca Colonel, 2013) samples. In contrast, comparative studies across emerging markets are relatively scant (Luo and Zhang, 2016). Hence, much of what we know about (the OFDI behavior of) EMNEs is based on Chinese EMNEs. Paul and Benito (2018) reviewed the literature on emerging market OFDI, finding that 61% of studies focused on China. Likewise, Bıçakcıoğlu-Peynirci (2023) finds that the lion’s share (35 out of 71 or 49%) of papers on EMNE internationalization focuses on China. This China-centric focus overlooks the variance among emerging market home country institutional environments.

It is questionable that Chinese firms are representative for EMNEs at large. Chinese EMNEs show distinctive, high-risk behavior (Quer et al., 2019) and differ in response to cultural and political risk compared to Indian EMNEs (Quer et al., 2017). Further, differences in state support between Brazilian and Chinese firms reveal the effect of policy differences, which may influence internationalization strategies (Carlos Zalaf Caseiro and Masiero, 2014).

This suggests that the internationalization behavior of EMNEs may depend on country-of-origin idiosyncrasies (Deng and Yang, 2015). EMNEs are subject to the institutional environment(s) in which they are embedded, which consequently influences their strategic decision-making (Gaur et al., 2018). Indeed, recent research reiterates the need to more seriously consider home country institutions as drivers of firm internationalization (Ndofirepi, 2024). Accordingly, some scholars argue that EMNEs from different emerging markets may differ in their OFDI behavior, implying non-uniformity among emerging market firms (Jindra et al., 2016). Yet, the literature has primarily focused on China, making the country the poster child of emerging markets, leading to improper generalizations of empirical results (Ott et al., 2023). We intend to test this critical contention and undertake a comparative study of the internationalization patterns of EMNEs originating from different emerging markets.

Using a sample of 5,299 cross-border acquisitions by 3,390 EMNEs from China and 20 other emerging markets in 136 host countries between 2000 and 2019, we research the influence of various home country institutions and assess whether and how Chinese EMNEs differ from EMNEs from other emerging markets in terms of their cross-border acquisitions’ location choice.

The main objective of this paper is to shed light on the heterogeneity among emerging markets and how this affects firm internationalization. Doing so, we challenge the assumption that China is representative for emerging markets and that the behavior of Chinese EMNEs is representative for EMNE behavior in general. Empirically, there is scant research that compares emerging markets and the behavior of EMNEs from different countries with each other, despite the differences in institutions that affect strategic decisions of firms from diverse settings (Gaur et al., 2018). We consider different dimensions of home country institutions: the state, financial markets, human capital and social capital (Fainshmidt et al., 2018), positing that institutional systems are key determinants in shaping OFDI location choice behavior. We find that, indeed, several dimensions of home country institutions significantly affect the location choice of EMNE foreign direct investments. Specifically, we find that human capital development reduces the distance of foreign investments, whereas social capital increases the distance. Contrary to our expectations, we find no support for significant differences between Chinese EMNEs and other EMNEs, but we do observe variance in location choices of firms from different types of institutional systems. In particular, our results reveal that EMNEs originating from state-led institutional systems, including Chinese firms, choose more distant locations for their cross-border acquisitions compared to EMNEs from other institutional systems.

Our study makes several important contributions to the IB literature. First, building upon a configurational approach to institutions, we provide a more nuanced view on the effect of (home country) institutions on location choice behavior. Second, our findings reveal heterogeneity among emerging markets, indicating a need to move beyond a dichotomous world view of developed versus emerging markets. This provides important implications for theory, like the need to use a “richer” conceptualization of institutions, and the consideration to be careful using China as representative for emerging markets in general. A key implication for managers in EMNEs who decide on internationalization is, therefore, to not carelessly mimic the behavior of industry leaders from other countries but to build on the unique institutional configuration in their home country (DiMaggio and Powell, 1983).

EMNEs are increasingly internationalizing (Chikhouni et al., 2017) and contribute to global economic growth (Tang and Buckley, 2022). A large portion of these investments originate from China, especially since the introduction of the “Go Global” policy in the early 2000s (Gammeltoft and Cuervo-Cazurra, 2021). In 2016, the scale of China’s OFDI far exceeded that of other large emerging markets such as Russia or India, and China has become the most important trade partner for many countries worldwide (Ramamurti and Hillemann, 2018).

The internationalization of EMNEs in and of itself is not new; however, their rapid expansion through OFDI is a relatively recent phenomenon (Gammeltoft and Cuervo-Cazurra, 2021) which alters the global competitive landscape (Li et al., 2018). Some researchers suggest that EMNEs internationalize into other emerging markets to benefit from their experience with lesser developed institutional environments (Cuervo-Cazurra and Genc, 2008; Tang and Buckley, 2022; Holburn and Zelner, 2010), whereas others highlight that EMNEs internationalize into developed markets to acquire ownership advantages which they cannot (yet) develop themselves (Jindra et al., 2016), commonly through aggressive acquisitions (Liang et al., 2021). Accordingly, we focus on the location choice of EMNE cross-border acquisitions when formulating our hypotheses.

Institutions matter for the behavior of multinational enterprises (MNEs) and institutional theory has grown into a key perspective in IB literature (Jackson and Deeg, 2019) and internationalization. Institutions shape the way business is conducted and constrain the economic activity in the environment (Nielsen et al., 2017). They play a pivotal role in determining which (firm) behavior is considered legitimate (Liou et al., 2016).

In the context of emerging markets, the institutional environment can both support and restrict the internationalization of MNEs. Governmental support such as preferential access to resources (Gammeltoft and Cuervo-Cazurra, 2021) and liberal OFDI policies (Li and Ding, 2017) can aid EMNEs. Ramamurti and Hillemann (2018) highlight how Chinese Government policies have created advantages that helped Chinese firms be more competitive, for example by upgrading infrastructure, building industrial parks, improving education and investing in R&D. Institutional voids, that is, the lack or underdevelopment of institutions (Doh et al., 2017) in the home country, can help EMNEs develop capabilities to function in challenging but similar institutional environments (Gammeltoft and Cuervo-Cazurra, 2021). Conversely, institutional voids can also make it more challenging to do business and push firms to escape the home country environment in favor of institutionally distant but more developed host markets (Cuervo-Cazurra et al., 2018; Nayyar and Prashantham, 2021). The (lack of) institutional development of the home country may thus facilitate and constrain internationalization decisions.

The institutional context also explains why EMNEs internationalize rapidly and relatively early (Liou et al., 2016). To date, research has, however, primarily focused on host country institutions (Estrin et al., 2016) and institutional differences or distance (Mingo et al., 2018) to explain internationalization strategies. Host country institutions may serve as a pull factor for EMNEs (Liou et al., 2016) because of locational advantages such as natural resources. Distance, that is, the (perception of) dissimilarities between two countries (Ambos and Håkanson, 2014) is generally considered a negative factor which leads to higher costs of doing business abroad (Berry et al., 2010) and, therefore, forms a determinant of firm strategy (Quer et al., 2017).

The home country context has been less seriously explored as a determinant of EMNE internationalization and has only recently received more attention in the IB literature (Cuervo-Cazurra et al., 2021; Li and Ding, 2017). While emerging markets share the characteristics of fast growth, structural changes and significant market potential despite institutional weaknesses, they are not necessarily homogeneous (Luo and Tung, 2007). The idiosyncrasies of the home country institutions have a profound impact on firm strategy and performance (Wu and Chen, 2014; Peng et al., 2008). We study how the home country context, and in particular diversity of home country institutions among emerging markets, influences EMNE location choice.

IB research frequently studies institutions based on differences in institutional quality (i.e. strong versus weak institutions) and by extension the distance in quality between home and host country is seen as a “difference in degree” (Jackson and Deeg, 2008, 2019). However, this provides a limited view on the heterogeneity that lies within institutional contexts. Comparative institutionalism (Jackson and Deeg, 2008, 2019) suggests taking into account the variety in institutional dimensions and configurations, focusing on “difference in kind” instead, as drivers of economic activity (Jackson and Deeg, 2019). Consistent with this idea, we adopt the Varieties of Institutional Systems (VISs) framework (Fainshmidt et al., 2018) for studying the institutional environment of emerging markets, which comprises five key institutional dimensions: the state, financial markets, human capital, social capital and corporate governance. The VIS framework explores institutions of 68 understudied economies (Fainshmidt et al., 2018), including China, making it an excellent theoretical framework to study the effects of home country institutional dimensions on economic activity, in this case EMNE internationalization behavior ^[1].

Role of the state: As firms operate in institutional environments, they are faced with institutional pressures that determine legitimate or appropriate behavior (DiMaggio and Powell, 1983). Behaving according to the norms helps in gaining legitimacy from powerful actors, such as the government (Han et al., 2018). The government is a key actor, as it can provide firms with unique advantages that they can leverage when expanding abroad. First, firms may benefit from knowledge about host countries provided by the home country government, and second, the government may provide preferential treatment, such as tax exemptions, or financial resources when firms invest in line with government policies (Lu et al., 2014). For example, China’s Belt and Road initiative has stimulated and aided Chinese firms to invest particularly in countries with fragile, relatively underdeveloped institutional environments (Sutherland et al., 2020). Some governments, indeed, play a highly active role, whereas other governments provide less direct support, such as through tariffs or subsidies to protect certain industries or by providing interpersonal connections and networks (Zhang and Whitley, 2013). Beyond that, trade agreements and relationships between governments could provide indirect support (Hoskisson et al., 2013), thereby affecting the location choice decision for cross-border investments. As such, we posit:

Role of financial markets: Financial markets refer to the available sources within an institutional system through which firms can raise capital. Beyond the potential governmental (financial) support for local firms, the financial infrastructure may differ in different economies. For example, Brazil has invested in developing financial institutions through, for example, the state-owned Brazilian Development Bank and the Banco do Brazil, which allowed Brazilian firms to be more competitive in their cross-border acquisitions (Hoskisson et al., 2013). Fainshmidt et al. (2018) highlight that state-led institutional systems (e.g. China) are also often bank-based, with state-owned and private banks being the primary source of capital. Underdeveloped financial markets in the home country may instead pose limitations to foreign expansion for firms (Buitrago and Barbosa Camargo, 2020). Lack of financial capital may leave firms to be relatively small and act more opportunistically, whereas abundance of capital would lead to a more collaborative nature of firm interaction and more networking (Fainshmidt et al., 2018; Whitley, 1999). In turn, this may influence the available location choices for firms’ cross-border expansions. Hence, we hypothesize:

Role of human capital: Human capital (via education) is a driver of economic growth (Glaeser et al., 2004). Human capital refers to how knowledge is formed and how the labor market functions within a society (Fainshmidt et al., 2018). How much knowledge is available within an institutional system is reflected in aspects such as literacy rates, life expectancy and education levels (Fainshmidt et al., 2018). Human capital depends on the development of economic institutions, which are necessary for the development of an education system, and in turn skilled talent, leading to increased labor productivity (Chan et al., 2024). Rabbiosi et al. (2012) highlight how the availability of human and technological capital in the home country increases the absorptive capacity of firms and the ways in which they learn, leading to more explorative investments. We, therefore, posit that the human capital available in the home country institutional environment affects where EMNEs will invest:

Role of social capital: Social capital refers to societal trust, both between actors in the institutional system and in the system itself (Fainshmidt et al., 2018), respectively, interpersonal and institutionalized trust (Witt and Redding, 2013). Prior research (Brouthers and Brouthers, 2003) has linked trust and trust propensity (i.e. the belief that trusting others leads to better outcomes, even if they may not be reliable) to entry mode decisions, suggesting that it has a positive effect on choosing joint ventures over wholly-owned entry modes.

Interestingly, many developed markets have a high level of institutionalized trust, with a functional system that prevents opportunistic behavior through formal institutional systems. For example, intellectual property protection not only promotes domestic research and development efforts of firms but also has a positive impact on OFDI (Buitrago and Barbosa Camargo, 2020). In contrast, Witt and Redding (2013) note strong variance in the levels of institutionalized trust, but consistently high levels of interpersonal trust, focused on networking and relationships among Asian business systems, such as the Chinese. Moreover, they highlight the high presence of business groups and family control in firms resulting from trust structures in these countries (Witt and Redding, 2013). Firms may expand to countries in which they can leverage these business networks. Trust plays a key role for economic activity and relationships and can lead to higher efficiencies, better decision-making and reduction of transaction costs (Ertug et al., 2013). When formal or institutionalized trust is stronger, one could reasonably expect that lower transaction costs and increased efficiency allow firms to expand to more distant locations. Thus, we hypothesize:

Above, we developed hypotheses about the influence of institutional diversity on location choices of EMNEs. We, thus, made claims about how the institutions in a home country may influence firms’ internationalization behavior. China and Chinese firms have received significant attention in the emerging market literature (Luo and Zhang, 2016; Liang et al., 2021) and have often been used for testing theories about EMNEs. We argue that China is a specific and unique institutional environment compared to other emerging markets and that the behavior of Chinese EMNEs is not necessarily representative for EMNEs at large for several reasons.

Many Chinese firms, for example, enjoy financial support from the government, allowing them to take more risk when internationalizing (Quer et al., 2017). State direction, in general, is an aspect that sets China apart, with the five-year plans for (expansion) strategies (Buckley et al., 2007). “State direction over firms (whether formal or informal) is likely to generate a signature in the locational pattern of outward investment that would not be predicted by the general theory of FDI, which assumes that firms are profit maximisers” (Buckley et al., 2007, p. 514). Similarly, the Chinese Government’s investment efforts in infrastructure across Africa has benefits for the bilateral relations between China and recipient countries (Luo and Tung, 2007). These bilateral relations can reduce the risk for Chinese EMNEs investing abroad (Buckley et al., 2018); for example, the foreign aid investment programs in Africa have improved political and diplomatic ties (Luo and Tung, 2007). Together, these factors may result in Chinese EMNEs making atypical location choices (Buckley et al., 2018). Ramamurti and Hillemann (2018) highlight government-created advantages as one of the unique aspects of Chinese OFDI. It is, therefore, imperative to conduct more research on how the home country institutions shape the strategies of EMNEs operating within the Chinese context (Tse et al., 2024). As a start, we test the claim that Chinese EMNEs make different location choices than EMNEs from other countries, reflecting China’s unique home country institutional context:

Our sample consists of cross-border acquisitions by EMNEs from the “E20+China” list ^[2], representing 21 emerging markets that contributed 47% to worldwide gross domestic product (GDP) in 2024 (Casanova and Miroux, 2024). The list was compiled based on several criteria (Casanova and Miroux, 2024): First, advanced economies according to IMF, countries with < 1.5 million inhabitants or countries with data older than three years according to the World Bank were excluded. Second, a weighted score is calculated based on GDP per capita (40% weight), global trade volume (20% weight), poverty levels (20% weight) and extreme poverty level (20% weight). The 20 largest remaining economies (by nominal GDP) and China were selected for the list.

Our data on cross-border acquisitions came from Bureau van Dijk’s Zephyr. First, we selected completed-confirmed and completed-assumed deals between January 1, 2000 and December 31, 2019. Second, we only included investments originating from E20+China. Third, we removed investments into tax havens, which we identified using two measures; those listed by Tørsløv et al. (2023)^[3] and countries with a population of less than one million ^[4]. Fourth, we only included deals from companies, excluding investments made directly by governments and individual investors, resulting in a sample size of 5,299 investments (Table 1) conducted by 3,390 EMNEs.

The dependent variable location choice was operationalized as the weighted geographic distance between home and host country, taking into account population distribution across cities within countries as opposed to simple geographic distance. Geographic distance is commonly used in internationalization studies (Buckley et al., 2007; Kolstad and Wiig, 2012). We retrieved data from the CEPII GeoDist data (Mayer and Zignago, 2011). Geographic distance had a mean value of 5,960.58 and standard deviation of 2,484.74.

The role of the state was operationalized using the POLITY2 scores from the Polity5 data set (Marshall and Gurr, 2020). The polity scores are measured from −10 (strongly autocratic) to 10 (strongly democratic). Authoritarian regimes may impose more direct control and are more likely to make use of favoritism, as opposed to democratic regimes which rely more on predictability, public policy and lobbying (Marshall and Elzinga-Marshall, 2017). The range for polity score in our sample was −7–10, with a mean score of 4.68 (closed anocracy) and a standard deviation of 5.72.

To operationalize the role of financial markets, we took into account the financial structure of the country by looking at the size and proportion of bank- and stock-based capital to the total market size. We created a scale from 0 to 100, with 0 being bank-based financial systems and 100 being market-based systems, which was calculated as follows: Stock market capitalization as % of GDP/(stock cap + bank assets as % of GDP) * 100. The mean value for financial structure was M = 54.37 with a standard deviation SD = 20.97 and a range of 9.60–97.76.

Increases in human capital result in a more skilled and efficient workforce, ultimately leading to a positive change in labor productivity. As such, we proxy human capital as labor productivity, measured as GDP (at constant 2010 US$) divided by the number of employed people. Employed people includes all individuals of working age who are either self-employed or engaged in paid employment. We collected the data from the ILOSTAT database (International Labour Organization, 2021).

We measured the role of social capital using the World Governance Indicators (World Bank, 2021). Specifically, we used the “Rule of Law” dimension, ranging from −2.5 (weak) to 2.5 (strong). Rule of Law captures “perceptions and views of the extent to which agents have confidence in and abide by the rules of society, in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence” (Kaufmann and Kraay, 2024, p. 5). This is in line with existing literature measuring trust in society (Witt and Redding, 2013). Values ranged from −1.10 to 1.43, with a mean value of −0.16 and standard deviation 0.48.

We controlled for a range of host country and home–host dyad variables based on existing location choice literature.

We control for host country GDP and GDP growth, measured as GDP in million constant 2010 US dollars and the annual percentage growth rate of GDP at market prices (World Bank, 2021) as a proxy for market size, which is positively related to foreign investments (Lu et al., 2014).

We control for host country natural resources, as these may attract foreign investments. Firms may invest to access natural resources abroad as inputs for production (Buckley et al., 2007). Natural resources were measured using fuel exports and ore and metal exports (as % of merchandise exports), consistent with previous research (Kolstad and Wiig, 2012). Data were retrieved from the World Development Indicators (World Bank, 2021).

We include host country labor productivity, measured as GDP (constant 2010 US$) divided by the number of employed people as higher labor productivity in the host country may make firms invest for efficiency-driven purposes. We used data from ILOSTAT (International Labour Organization, 2021) to proxy investments with efficiency purposes.

Host country critical assets such as knowledge, managerial knowhow and technology (Buckley and Munjal, 2017) may attract EMNEs to acquire such assets abroad to overcome their latecomer disadvantage (Li et al., 2012). We use the total number of patents registered in a host country, in line with previous research (Buckley et al., 2012; Quer et al., 2017) to measure this. We used data from the World Development Indicators (World Bank, 2021).

We control for home–host country differences, by taking into account linguistic distance (Dow and Karunaratna, 2006). Informal institutions are largely captured by culture, which consists of multiple aspects such as languages, ethnicities, religions and social norms (Ghemawat, 2001). This measure is based on three five-point scale items; assessing the difference in dominant languages between two countries, Country_i and Country_j, the prevalence of country_i’s dominant language(s) in country_j and the prevalence of country_j’s dominant language(s) in country_i (Dow and Karunaratna, 2006).

Finally, we include colonial ties using a dummy variable to determine whether there has ever been a colonial link between home and host country (1 = yes and 0 = no), based on the CEPII database (Mayer and Zignago, 2011).

As data for our independent and control variables were available annually, we synchronized them with the investments based on the year of each investment to accurately measure their impact.

All analyses and data cleaning actions were conducted using Stata/SE 18. We conducted our analyses in two stages. First, we specify a cross-sectional, three-level hierarchical linear mixed model for investments (_i) nested within firms (_j) and firms nested within home countries(_k). The unit of analysis is the individual cross-border investment. While some firms made multiple investments during the time span of our data, the individual investments are treated as independent location choice decisions. The hierarchical structure accounts for random effects at the firm (_j) and home country (_k) levels.

Model 1 tests H1a–H1d:

where LocationChoice_ijk represents the weighted geographic distance (in kilometers) between home country (k) and host country for cross-border investment (i) by firm (j); $ß0$ is the intercept; $ß1$ to $ß11$ are fixed-effect coefficients; and u_k + v_jk + ε_ijk are random effects at the country, firm and acquisition level; i, j, k, t index investments, firms, home countries and time.

Model 2 follows the same Model specification with the addition of an extra dichotomous variable China (1 = China, 0 = other home country) for the China effect.

The descriptive statistics and correlation analysis are reported in Tables 2 and 3. We checked for multicollinearity and concluded that it is unlikely to affect our results as the mean variance inflation factor for our full model was 2.71.

In addition, we conducted an independent samples t-test to compare the (weighted) geographic distance between Chinese (M = 8,601.75 and SD = 117.00) and non-Chinese firms (M = 5,417.99 and SD = 64.53). The results show a significantly higher mean geographic distance for investments made by Chinese firms, (t[5185] = −20.95 and p = 0.00).

Table 4 reports the results of our main analysis to test H1a through H1d. H1a tests the impact of the state on geographic distance, but we find no significant effect (B = −214.35 and p = 0.23). Financial structure likewise shows a negative but insignificant effect on geographic distance (B = −90.14 and p = 0.34). We find support for H1c, finding a significant negative effect (B = −749.38 and p = 0.00) for human capital on EMNEs’ location choices, suggesting that higher levels of human capital result in location choices at smaller geographic distance. Our analysis further finds a significant positive effect for social capital (B = 414.16 and p = 0.02), in line with H1d, indicating that higher social capital increases geographic distance of cross-border acquisitions. We controlled for various host country indicators, finding a significant effect for host country pull factors. We highlight two key results here, namely a significant positive effect for host country GDP (B = 2,027.55 and p = 0.00) and a significant negative effect for host country patent registrations (B = −895.08 and p = 0.00). Finally, we took into account colonial ties and linguistic distance and found a significant negative effect for linguistic distance (B = −216.00 and p = 0.00).

Next, we turn to our comparative analysis (Table 5), where we posit that Chinese EMNEs show different internationalization behavior in comparison to other EMNEs. The China dummy shows a positive, but insignificant effect (B = 1,760.36 and p = 0.21), indicating, contrary to our expectations, that Chinese EMNEs do not significantly differ in their location choice compared to other EMNEs in our sample.

To test the robustness of our findings, we also ran the models using several alternative measures. Our results remained robust under these circumstances. For our measure of the role of the state (H1a), we used government expenditure as a percentage of GDP, finding a similar negative, but likewise insignificant effect. Next, we used Human Capital Index data from the World Bank (2021), which accounts for the effects of health and education on labor productivity, to measure human capital, yielding a significant but slightly bigger negative effect, however, based on a much smaller sample (∼n = 500). Finally, replacing our measure for linguistic distance with “common official language” from the CEPII database yielded a robust effect confirming the main result.

Confronted with a lack of significance for our contention that Chinese firms make different location choices than other EMNEs, we continued to explore the heterogeneity of EMNEs and ran an additional analysis in which we grouped the home countries based on their institutional system as categorized by Fainshmidt et al. (2018). In particular, we compared state-led systems, including China, to other institutional systems. Interestingly, the results of this analysis show that investments conducted by EMNEs from state-led systems make significantly more distant cross-border acquisitions (B = 1,107.31 and p = 0.09), confirming our main claim that heterogeneity among emerging markets plays a role in the location choices of EMNEs’ foreign investments (Table 6).

This study set out to research how home country institutions affect location choice behavior, in particular challenging the assumption that Chinese EMNEs are representative for EMNEs in general. Drawing on institutional theory and more specifically the VIS framework (Fainshmidt et al., 2018), our statistical analyses do not confirm our hypothesis that Chinese firms make distinct strategic choices. However, our findings do provide insights into the importance of the heterogeneity among emerging markets based on their institutional systems and the influence these have on the internationalization strategies of embedded firms. These findings answer calls to take institutional variation more seriously (Witt and Redding, 2013) and highlight how such variation affects the way of doing business (Jackson and Deeg, 2019).

Our results show that different dimensions of institutional systems matter for firm strategy. We find that EMNEs from emerging markets with higher levels of human capital invest in countries which are geographically nearer. This could reflect a change in the motives behind EMNE cross-border acquisitions. Historically, strategic asset-seeking has been a key motive for EMNEs to internationalize. Elia and Santangelo (2017), however, posit that EMNEs’ motives have shifted from “catching up” to being driven by stronger “national innovation system,” which reflects knowledge development in a country. This may explain the negative effect for host country patents, a control variable included to proxy strategic asset-seeking motives, given the prominence of this motive in the EMNE literature (Luo and Tung, 2007).

We find a positive effect for the institutional dimension social capital. Higher trust leads to more cooperative and trusting decisions (Ertug et al., 2013). While not tested here, Ertug et al. (2013) also highlight the importance of perceived trustworthiness by partners as a relevant factor. Future research could examine the discrepancies between trust and perceived trust.

The non-significant finding for the role of the state may reflect the dual nature of state involvement. The effect of state capitalism and government involvement on strategic choices of firms are key topics in the literature on emerging markets (Gaur et al., 2018). Lu et al. (2014) find that government support increases the likelihood of entering a specific host country, reducing the need for experience for risk mitigation. State involvement could, however, negatively affect the perception from host countries and target firms (Wright et al., 2021).

When it comes to our challenge to IB literature to take China as the poster child example for EMNEs in general, we find no significant difference between Chinese firms’ location choices and those of other EMNEs. However, on a configurational level, EMNEs from state-led institutional systems do make significantly more distant cross-border investments than EMNEs from other institutional systems. This result signals that our configurational approach to institutions is a first step in increasing our understanding of the influence of institutional variation on EMNE behavior.

The comparative capitalisms literature has long suggested an approach in which institutions are compared as different in kind (e.g. bank-based vs equity-based), as opposed to different in degree (e.g. institutional distance) to provide a richer comparison (Jackson and Deeg, 2008). Institutions should be seen as not only constraints of firm behavior but also providing opportunities for firms, such as the government support to EMNEs’ internationalization strategies. This may be reflected in our findings regarding the different internationalization behaviors of EMNEs from state-led versus other institutional systems.

Our study contributes to the IB literature by highlighting the role of home country institutions in shaping the location choice behavior of EMNEs. First, we demonstrate that some dimensions of the home country institutional environment influence location choice, when controlling for host country and home–host dyad factors. Second, our study aimed to challenge the dominant role of Chinese EMNEs in the literature. While a focus on China has been pivotal in our understanding of these relatively new players on the global competitive market, it has also led to a myopic focus on China as being representative for EMNEs in general. Such a view does not align with the institutional variance found among emerging markets and, therefore, may have misguided our understanding of EMNE behavior, ignoring the diversity and expansion behavior of these EMNEs. Our findings, in particular that firms from state-led institutional systems (such as China) invest in acquisitions further away than firms from other institutional systems, illustrate the relevance of studying the home country institutional context as a determinant of cross-border investment decisions and challenges the implicit assumption of uniformity among emerging markets and EMNEs.

Our research also has important implications for policymakers and managers. Policymakers could foster human and social capital more by increasing investments in these institutional domains as our results show these dimensions influence investment behavior of firms. They may also wish to provide additional support to internationalizing firms to align with economic goals (e.g. promoting investment in certain locations based on diplomatic ties). Foreign policymakers may also leverage their country’s relative strengths in certain institutional dimensions to attract inward FDI.

Our findings imply that EMNE managers should be wary to just mimic strategies of Chinese EMNEs, because of their global success. While firms may learn from industry leaders, they should consider firm-specific and home country institutional advantages to develop successful internationalization strategies.

Our paper has several limitations, which provide avenues for future research. First, our data end in 2019; future research can explore the effects of more recent developments like the pandemic, geopolitical tensions and trade wars.

Second, we used only secondary data and had to rely on proxies for certain variables. For example, for human capital, we considered more direct measures such as advanced education attainment or literacy rates, but these variables significantly reduced our sample. We, therefore, selected measures for which we had more complete information for our sample. Future research on (home country) institutions can benefit from supplementing secondary data sources with primary data. This would enable researchers to provide a richer analysis of the effects of institutional dimensions on firm behavior.

Third, we focused on cross-border acquisitions as this entry mode is popular among EMNEs. Future studies may include other investments such as Greenfields or brownfields. Brownfield investments may be preferred over acquisitions by firms with weak managerial and technological assets but a strong network or political ties (Lebedev et al., 2015). Future research could explore the prevalence of different investment types based on home country institutions.

Fourth, future research could elaborate on the specific investment behavior of state-owned enterprises, especially wholly owned or majority owned SOEs. State ownership can bring about additional challenges, for example, scrutiny related to national security (Cuervo-Cazurra et al., 2018) and may influence the strategic decisions, like location choice behavior of firms.

Finally, we focused on country-level data to capture differences between EMNEs from different emerging markets, providing relevant macro level insights. Accordingly, we did not take into account potentially relevant factors on lower levels, such as the industry (e.g. technology intensity) and firm level (e.g. firm experience or network embeddedness). We see this study as an important starting point for a wider discussion on the variety among emerging markets and EMNEs and stimulating attention to other emerging markets than China.

We set out to improve our understanding of the variation of home country institutional context in shaping location choices of EMNEs and to test the representativeness of the behavior of Chinese EMNEs for EMNEs in general. Our findings show that human and social capital are important determinants of location choice. No statistical support was found for significant different location choices made by EMNEs from China; however, we find that EMNEs from state-led systems invest in more distant locations. Our results highlight the need to move from a simplified dichotomous view of emerging versus developed markets to a more fine-grained breakdown of different institutional systems to explain firm behavior.

The authors would like to thank the Editor-in-Chief, for his guidance and constructive feedback throughout the review process. Authors also thank the two anonymous reviewers for their valuable and insightful comments, which greatly helped us to improve the paper.