Internationalization of emerging economies: Empirical investigation of cross-border mergers & acquisitions and greenfield investment by Chinese firms

Ahmed, Bilal; Xie, Hongming; Ali, Zahid; Ahmad, Ilyas; Guo, Manman Article Internationalization of emerging economies: Empirical investigation of cross-border mergers & acquisitions and greenfield investment by Chinese firms Journal of Innovation & Knowledge (JIK) Provided in Cooperation with: Elsevier Suggested Citation: Ahmed, Bilal; Xie, Hongming; Ali, Zahid; Ahmad, Ilyas; Guo, Manman (2022) : Internationalization of emerging economies: Empirical investigation of cross-border mergers & acquisitions and greenfield investment by Chinese firms, Journal of Innovation & Knowledge (JIK), ISSN 2444-569X, Elsevier, Amsterdam, Vol. 7, Iss. 3, pp. 1-11, https://doi.org/10.1016/j.jik.2022.100200 This Version is available at: https://hdl.handle.net/10419/327170 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ Internationalization of emerging economies: Empirical investigation of cross-border mergers & acquisitions and greenfield investment by Chinese firms Bilal Ahmed a , Hongming Xie a,b, *, Zahid Ali c , Ilyas Ahmad d , Manman Guo e a School of Management, Zhejiang University of Technology, Hangzhou, China b School of Management, Guangzhou University, Guangzhou, China c Department of Commerce and Management, University of Malakand, Chakdara, Pakistan d Department of Economics and Business Administration, Division of Arts and Social Sciences, University of Education, Lahore, Pakistan e School of Management, Guangzhou University, Guangzhou, China ARTICLE INFO Article History: Received 17 November 2021 Accepted 2 May 2022 Available online 20 May 2022 ABSTRACT This study incorporates the eclectic paradigm and institutional theory to examine the key determinants of Chinese firms’cross-border mergers and acquisitions (M&As) and greenfield (GF) investment in advanced economies (AEs) and developing economies (DEs) during the period 2003−2016. It uses a negative binomial regression model. In terms of M&As, our findings are consistent with the growing theoretical literature on emerging market multinational enterprises (EM MNEs). However, Chinese firms’GF investments in AEs and DEs show results that are inconsistent with predictions, which means that research on GF investment requires more scrutiny and in-depth analysis. Although both economic and institutional factors affect Chinese firms’location strategies, institutions tend to play a more dynamic role in shaping the location decisions for Chinese GF investments, implying that institutional context has a greater moderating effect on the link between investment motives and GF activity. In a nutshell, one should be cautious in generalizing Chinese cross-border M&A deals to GF investments or other entry modes. © 2022 The Authors. Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Keywords: Internationalization Emerging economies M&As GF Investments China Introduction Emerging market multinational enterprises’(EM MNEs’) internationalization strategies have turned into a key topic in the international business (IB) arena (Alon, Elia & Li, 2020;Buckley, Yu, Liu, Munjal & Tao, 2016;Dikova & Brouthers, 2016). This is due to the fact that traditional internationalization frameworks might not always apply to EM MNEs (Fang & Chimenson, 2017). A number of researchers hold the view that MNEs from countries such as China do actually “deviate from the predictions of existing theories”(Alon, Child, Li & McIntyre, 2011;Cui & Jiang, 2009). As EM MNEs continue stable and growing developments in cross-border mergers and acquisitions (M&As) and greenfield (GF) investments, specifically, knowledge of the deterministic and strategic motivations for their investment requires more inquiry and debate. In the past few years, a growing body of research have looked into the locational drivers of outward foreign direct investment (OFDI) by EM MNEs (Deng & Yang, 2015; Ramasamy, Yeung & Laforet, 2012). However, there is a gap that requires a comparative analysis of this critical issue. The existing studies on OFDI, specifically on cross-border M&As and GF investments by EM MNEs, are not only inadequate but also contain some key limitations. Although comparative studies are thought to be effective in testing or generalizing Western theories and establishing new theories from EMs, they have rarely been applied in investigating cross-border M&As and GF investment by EM MNEs under various scenarios (Deng, 2013;Dikova, Panibratov & Veselova, 2019). By differentiating M&A and GF deals initiated by Chinese MNEs in various host economies, this study may advance mainstream models (i.e., the ownership, location, and internalization (OLI) theory) (Child & Rodrigues, 2005;Xu & Meyer, 2013). Moreover, the samples are primarily based on OFDI projects. Consequently, it is debatable whether one type of OFDI, such as Chinese M&As, can be generalized to others, such as GF investments. However, some critical queries have not been properly addressed in studies on EM MNEs’motivations for OFDI (both M&As and GF investments), more specifically Chinese MNEs. Of course, it remains * Corresponding author. E-mail addresses: [email protected] (B. Ahmed), [email protected] (H. Xie), [email protected] (Z. Ali), [email protected] (I. Ahmad), manmanguo@e. gzhu.edu.cn (M. Guo). https://doi.org/10.1016/j.jik.2022.100200 2444-569X/© 2022 The Authors. Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Journal of Innovation & Knowledge 7 (2022) 100200 Journal of Innovation &Knowledge https://www.journals.elsevier.com/journal-of-innovation-and-knowledge debatable whether all AEs and DEs similarly absorb OFDI. In AEs, the determinants of OFDI from emerging economies (EEs) are understood to differ from those in EEs (Hoskisson, Wright, Filatotchev, & Peng, 2013; Peng, 2017). Moreover, although institutional theory has turned out to be the leading theory in exploring OFDI by EM MNEs (Liu, Wang & Zheng, 2010), very few studies have explicitly examined the chemistry of the influences on M&As and GF investments in target locations (Dikova & Sahib, 2013). While research on Chinese M&As is rapidly expanding, the applicability of GF investment is particularly lacking in the Chinese OFDI literature (Alon et al., 2020). Recognizing these inadequacies, recent studies have called for further research into the linkages between economic elements and institutional factors, particularly in the context of EM MNEs (Nielsen, Asmussen & Weatherall, 2017). This study contributes to the existing literature on entry mode and EM MNEs by examining their motivations based on the most comprehensive country-level dataset; through an empirical examination, the study seeks to answer research questions about how M&A and GF deals are unique to specific locations. Second, we may obtain fresh insights into the OLI framework by analyzing the moderating impacts of governance indicators in different host country settings. We test our hypothesis based on an M&A and GF investment dataset from 2003 to 2016. Following Yang and Deng (2017) this study draws on the theoretical underpinnings of the OLI paradigm and institutional theory to study both macroeconomic and institutional factors of the M&A and GF activities by Chinese firms in both advanced and developing markets. Compared to earlier studies on Chinese enterprises, this study finds some variations in terms of results. For example, Ramasamy et al. (2012) and Kang (2018) reported a negative trend in the strategic asset−seeking motive for Chinese OFDI, although Buckley et al. (2007) and Kang and Jiang (2012) found no significance. However, a study on cross-border M&A and GF investment based on the same sample with the same variable (i.e., patents), but with updated data, found positive support for this argument. In short, key factors that that explain underlying country-level motivations for OFDI by Chinese enterprises are not always applicable to different entry modes. Our findings on M&As are largely consistent with the expanding theoretical literature on EM MNEs. However, studies on GF investments by Chinese firms produce results that are more inconsistent with predictions, implying that research on GF investment requires more attention and in-depth examination. Although both economic and institutional variables influence Chinese firms'location strategies, institutions typically play a more significant role in determining where Chinese cross-border M&As and GF investments occur. However, the institutional context reflected by each host government’s effectiveness seems to have a more dynamic moderating effect on the link between location decisions and Chinese GF investments when compared to cross-border M&As. More importantly, Chinese internationalization via cross-border M&A and GF investments in different sets of target markets provides unique avenues to expand current theoretical frameworks and possiblyadvancenewtheoriesonfirm internationalization and the general theory of FDI. The current research suggests that the interaction between motives and institutions largely shapes the OFDI location decision (Kang, 2018), and may produce different results for different target markets than what the mainstream strategy literature suggests. The paper is organized as follows: First, a brief review that will set the theoretical basis for the current research is presented in Section 2, which is followed by Section 3, which leads to the development of the hypothesis of this study. In Section 4, the research methodology and data are explained and described. The results are shown in Section 5, followed by a sensitivity analysis in Section 6. A brief discussion is presented in Section 7, followed by a conclusion in Section 8. Theoretical background The eclectic paradigm Dunning (1977) introduced the concept of the eclectic paradigm. In the context of MNE activity, this concept is widely used to analyze and explain the economic logic behind international production. The eclectic paradigm (Dunning, 1977,1988) asserts that firms have “ownership”or competitive advantage over other rivals, which they use in developing production in places that are suitable because of their “location”advantages. Moreover, firms maintain control over networks of assets (tangible as well as intangible) due to “internalization”advantages. The country-specific owner advantage (O-advantage) has been used to explain the rise of EM MNEs e.g., (Child & Rodrigues, 2005; Erdener & Shapiro, 2005). O-Advantages include an approach to cheap financial capital and the potential and ability to be involved in beneficial relations (Buckley et al., 2007;Dunning, 2001). Location advantages (L-advantages) are used to determine where MNEs invest, and include transport and communication costs, the spatial distribution of markets and inputs, psychic distance, and government interventions (Dunning, 1979,1988). The relative advantages of certain locations are internalized within markets. Therefore, location choice may be affected by market imperfection or failure. MNEs seek to benefit from a full return on the ownership of distinctive assets: benefits from their own technologies as well as from coordinating the utilization of complementary and mandatory assets (Dunning, 2001). This explains why MNEs choose to exploit their O-advantages overseas instead of selling them to foreign firms via market transactions (Narula, 2006). The OLI model identifies four key drivers of OFDI activities: market seeking, natural resource seeking, efficiency seeking, and strategic-asset seeking (Dunning, 1993). Some recent studies have highlighted that these motives can be applied to Chinese OFDI activities (Buckley et al., 2007;Deng & Yang, 2015;Ramasamy et al., 2012). However, these motives may vary for different destinations. For example, previous research has shown that Chinese OFDI in AEs is driven by market- and strategic-asset seeking (Yang & Deng, 2017). The OLI framework, however, provides only a partial explanation for the choice of OFDI location. The applicability of the OLI paradigm has been criticized, in the context of EM MNEs’internationalization, for disregarding the effect of organizational elements from host economies. The benefits derived from O-advantages cannot explain EM MNEs’internationalization, which is often undertaken by EM enterprises to acquire instead of exploiting new strategic assets (Mathews, 2006). The OLI framework, which is built on the notion of ownership exploration and progressive internationalization, is incompatible with these idiosyncrasies (Yeganeh, 2016). Therefore, there is a need to advance the OLI framework to integrate the institutional perspective into the eclectic model (Dunning & Lundan, 2008; Scott, 2001). Institutional theory (IT) North (1990) has observed “the humanly-devised constraints that structure human interaction.”IT sets the “rules of the game”to govern firm behavior. It has been well-recognized that institutions play a vital role in helping individuals and firms engage in market transactions and assist in the smooth functioning of market mechanisms (Meyer & Peng, 2005). Instructions from a country determine the environment for conducting business there and allow the transaction cost of doing business to be ascertained. Afirm's entry into a new country could be affected by the host country's weak institutions, such as an ineffective bureaucracy or legal system. Unpredictable and inconsistent legal enforcement in underdeveloped institutions can also make it difficult for firms to B. Ahmed, H. Xie, Z. Ali et al. Journal of Innovation & Knowledge 7 (2022) 100200 2 conduct business in these countries (Chan, 2008). EEs will be attracted to a host country with a well-enforced, predictable, transparent institutional environment (Luo & Tung, 2007; Yamakawa, Peng & Deeds, 2008). Pressure and laws from a host country's government can significantly impact a firm'sefficacy and capability (Beamish, 1993). From the perspective of the institutional environment, location choice decisions are meant to determine favorable locations with fewer institutional constraints for firms to easily adjust to the regulatory environment of a recipient economy. The quality of these institutional environments, such as sustainable economic policy, fewer ownership restrictions, safety of possessions, and non-corrupt bureaucracy, attracts MNEs and speeds up the acquisition process (Zhang, Zhou & Ebbers, 2011). Hypothesis development Market seeking Market-seeking FDI, also called "horizontal FDI," occurs when investors enter a foreign market with the purpose of expanding their sales and production. With an increase in market size, opportunities to exploit economies of scale and the efficient utilization of resources also increase through FDI (Tolentino, 2010). Several studies (Chakrabarti, 2001) identify that market size and FDI flow are positively related. Some recent studies identify an increase in the market-seek- ing motivation that drives Chinese firms and propose that a rise in this activity is probably directed at larger markets. Existing theory posits that this market-oriented horizontal FDI will be positively related to a rise in demand. Recently, some studies have identified the growing significance of market-seeking FDI by Chinese firms into developed economies as a consequence of policy liberalization (Buckley et al., 2016;Yang & Deng, 2017). Furthermore, Chinese market-seeking OFDI comprises both offensive (developing new markets) and defensive (importsubstituting and quota-hopping) initiatives (Buckley et al., 2007). The primary motivation for offensive market-seeking OFDI is market size. In an empirical study, Duanmu (2012) found that host countries’ GDPs (a fundamental measure of market size) were the most important factor in explaining OFDI (including M&As and GF investment) activities from China. Hence, we anticipate that a host country’s market size is positively associated with the number of M&As and GF investments by Chinese firms. Hypothesis 1. A host market’s size is positively associated with the number of Chinese cross-border M&As and GF investments in it. Natural resource seeking Natural resource-seeking FDI is undertaken to secure scarce resources that are costlier domestically. One of the primary motivations for FDI activity is the acquisition and security of a continuous supply of natural resources (Dunning, 1993). It is the main reason for backward vertical FDI. Backward integration to secure the supply of certain location-bound resources overseas for local utilization has been the leading driver of Chinese OFDI for the last five decades (Buckley & Casson, 2009). The main purpose of resource seeking is to supply raw materials for investing companies’downstream operations. Internalization theory stresses the significance of equity-based control in the exploitation of valuable and scarce natural resources. FDI by both developing and advanced countries is driven by the urge to get access to other countries’natural resources. Numerous studies propose that Chinese companies tend to make investments in resource-rich countries to secure continual access to fuel and other natural endowments, which are much more required for the prosperity and sustainability of the home economy (Kang & Jiang, 2012;Morck, Yeung & Zhao, 2008). Consequently, it is proposed that the number of Chinese M&As and GF investments will increase with an increase in the natural resources of a host country. Hypothesis 2. The natural endowments of host markets are positively associated with the number of Chinese cross-border M&As and GF investments in those host markets. Strategic asset seeking Many empirical studies have found evidence in favor of assetseeking motives. Deng (2009) studied some popular cases of Chinese MNEs investing in AEs and found that, in an attempt to increase their competitive advantage in the international marketplace, strategic asset-seeking has always been a primary driver of Chinese MNEs’ investment. Many companies have turned to aggressive acquisitions to gain access to innovative product technologies, well-known brands, and international distribution networks (Makino, Isobe & Chan, 2004;Nicholson & Salaber, 2013). Some previous studies on Chinese M&As and GF investment have pointed out that Chinese enterprises lag behind their Western counterparts in terms of the improvement of firm-specific advantages, particularly in organizational know-how, innovation, and distribution expertise (Cui, Meyer & Hu, 2014;De Beule & Duanmu, 2012). Consequently, Chinese people cannot develop on their own because of a comparatively weak home-country knowledge base and domestic institutional constraints (Deng, 2009). Aggressive acquisitions of firms from advanced markets could possibly compensate for their competitive disadvantages through access to novel technologies, famous brand names, and far-reaching networks (Rabbiosi, Elia & Bertoni, 2012). Moreover, a host country’s stagnant economic growth is considered a key motivator for drawing Chinese M&As as there are several developed enterprises that have been financially distressed; they sell off their strategic assets to restructure and for revival. Consequently, we propose the following hypothesis: Hypothesis 3. A host market’s strategic assets are positively related to the number of Chinese cross-border M&As and GF investments in that host market. Host institutions The OLI paradigm has been found to form the basis for the traditional determinants of Chinese OFDI in natural resources, market seeking, and strategic assets; however, economic factors alone do not adequately explain the reasons behind international acquisitions by Chinese firms. Consequently, we can assert that institutional theory may offer some relevant explanations regarding the question of why Chinese enterprises are progressively involved in OFDI in other countries. From the perspective of regulatory institutions, an MNE’s choice of a location entails ascertaining favorable destinations where constraints are less restrictive to FDI activity for the firm to adjust more readily. Previous studies have affirmed that host-country institutions strongly influence inward FDI flow (Kaufmann, Kraay & Mastruzzi, 2010). Afirm may consider it best to uplift its strategic position via alliances or joint ventures (JVs) and be less willing to opt for an M&A. This happens because, in highly-developed institutional conditions, the already-developed business atmosphere reduces possible opportunistic behaviors and ensures legal protection for market behaviors (Cui & Jiang, 2009). In contrast, in the context of underdeveloped institutions, where there is insufficient legal protection and the business environment is fragile, there is the potentially higher threat of expedience by alliance partners that considerably increases the cooperation cost to an EM MNE (Das & Teng, 2001). B. Ahmed, H. Xie, Z. Ali et al. Journal of Innovation & Knowledge 7 (2022) 100200 3 For the purpose of the current study, this research stresses host government effectiveness as a time-based boundary on OLI applications to provide additional understanding of the OFDI logic that is more prognostic of Chinese MNEs in their M&A and GF investment. While confronting EM MNE-host nation mutual pressures, firms may need to adapt in accordance with the severity of host government ineffectiveness, although acquisition may assist them to mitigate the limitations imposed by powerful actors in various settings. Consequently, we propose the following three moderating hypotheses: Hypothesis H4a. The relationship between a host market’s size and the number of M&As and GF investments by Chinese companies is negatively moderated by the host economy’s government effectiveness. Hypothesis H4b. : The relationship between a host economy’s natural resources and the number of M&As and GF investments by Chinese firms is negatively moderated by the host market’s government effectiveness. Hypothesis H4c. : The relationship between a host country’s strategic assets and the number of M&As and GF investments by Chinese firms is negatively moderated by the host market’s government effectiveness. Data, variables, and methods There are currently several studies that tend to explore Chinese firms’location choices for OFDI using panel data. A few of these, however, break their results down by entry mode (GF or M&A), making it comparatively more difficult to explain the hypothesis of this study. The current study uses several data sources to create the dataset that is used in this research. The dependent variable in this study is based on commercial databases, including SDC Platinum and the Financial Times fDi Markets. The first source is the SDC Platinum database, developed by Thomson Financial Corporation, which offers data on cumulative M&A transactions. SDC Platinum is a comprehensive database that contains information on M&A, syndicated loans, private equity, and project finance. It also provides information for identifying and monitoring deal activity, and for analyzing project trends, investment banking, comparable projects, and industry-leading league market share. The second source of data for the dependent variable is the Financial Times fDi Markets. The sample for this study is constituted based on two databases; data on GF FDI are obtained from fDi Markets, a database maintained by fDi Intelligence, which is a specialist division of the Financial Times group that tracks cross-border GF investments and encompasses all countries and industries globally since 2003. Therefore, the current dataset includes the number of cross-border investments made by Chinese firms in every recipient economy and all industries from 2003 to 2016. Researchers who have extracted data from this database have mainly projected count data models, with due regard for the validity and reliability of the value of investments. From 2003 to 2016, the current study covers all successful M&As and GF investments initiated by Chinese firms in advanced and developing markets. The third source is the Worldwide Governance Indicators database, created by Kaufmann et al. (2013), and encompasses the worldwide governance index, which includes government effectiveness. The fourth source is the World Development Indicators Database (World Bank, 2017), which offers other macro-level variables such as GDP, ratio of ore and metal exports to merchandise exports, patents, etc. A few other sources are used to obtain data for the control variables that are listed in Table 1. Dependent and independent variables Data for the dependent variable for the analysis is obtained from commercial databases, including SDC Platinum and the Financial Times fDi Markets. In this study, the dependent variable is the number of M&As and GF investments in each target country (# projects). It is calculated by the total number of completed M&A and GF deals concluded annually by Chinese companies in every recipient country. Recently, many studies have used the number of M&A transactions instead of total volume in examining EM MNEs’internationalization patterns (Deng & Yang, 2015;Dikova et al., 2019;Zhang et al., 2011). This technique shows the overall volume of M&A and GF transactions, enables the use of more accurate statistics, and ultimately boosts the validity of findings. To capture a host country's market size, the country’s GDP at constant price (GDP) is assumed to measure market breadth, which is vital for Chinese companies that carry some particular competitive edges based on competitive advantages in heterogeneous and new markets (Buckley et al., 2007). To some extent, it is evident from the literature that market-seeking motivations drive Chinese OFDI, particularly while investing in OECD or developed markets (A. A. Amighini, Rabellotti & Sanfilippo, 2013;Deng & Yang, 2015;Kolstad & Wiig, 2012), an outcome that is consistent with the mainstream FDI literature. However, a study (A. Amighini, Leone & Rabellotti, 2011)finds that market size is not always an attractive element for Chinese investors, depending on the industry or sector. Their results indicate that market size has a positive effect in the case of manufacturing FDI in developed or OECD countries, but that it tends to negatively impact resource-intensive sectors, which tend to select the poorest countries while investing in low-income economies. To capture a host country’s natural resource endowment, several researchers use the share of raw materials (fuels, ores, and metals) in total merchandise exports as a measure (Cheung & Qian, 2009; De Beule & Duanmu, 2012;Kang & Jiang, 2012), whereas some use the quantity of endowments under the earth as a yardstick that measures the prospective benefits that flow from investing in destinations with untouched natural endowments. The argument by Table 1 Variable List and Description. Variable Type Description Source M&As Dependent Number of deals SDC Platinum GF Investment Dependent Number of deals FDI Markets GDP Independent Log of GDP at constant price 2010 US$ World Development Indicators Patent Independent Total number of registered patents World Development Indicators Resources Independent Share of exports of ores and metals in GDP World Development Indicators Landlocked Control Dummy,1 if country has access to the sea CEPII BITs Control Bilateral Investment Treaties, dummy (1 yes, 0 no) UNCTAD Distance Control Log of simple distance (most populated cities, in Km) CEPII Cultural Distance Control Composite variable of Hofstede’s four cultural dimensions Hofstede’s cultural dimension Inflation Control Inflation,% consumer price index World Development Indications Government effectiveness Moderator Government effectiveness World Governance Indicators B. Ahmed, H. Xie, Z. Ali et al. Journal of Innovation & Knowledge 7 (2022) 100200 4 Kolstad and Wiig (2009) is more convincing, as it states that immediate natural resource rents would be attractive to investors instead of the potential but indefinite yield of unexplored resources. This is the reason that the share of fuel, ores, and metal exports in GDP is a relatively better proxy for a country’s natural resource endowment, as demonstrated in empirical analyses, specifically when Chinese enterprises invest in lower-income countries (Amighini et al., 2013). In terms of the strategic asset-seeking motive, empirical research on China's overall OFDI has produced contradictory results ((Buckley et al., 2007;Kolstad & Wiig, 2012). Strategic assets are typically measured by a host country’s registered patents (e.g., Ramasamy et al., 2012). This study uses a host country’s total number of registered patents (both resident and nonresident) as a measure to capture the strategic asset−seeking motive. This study follows the framework developed by Kaufmann et al. (2010) to measure the influence of target economies’ institutional environments. Government effectiveness is calculated in terms of the percentile rankings of all the economies on a scale from 0 to 100. Other variables In addition to the hypotheses previously specified, a few control variables are included. The first is a cultural variable. Cultural distance reflects the extent to which normativity affects FDI undertakings. Current research defines cultural distance as the difference between the national culture of a home economy (China) and those of host countries. It can be calculated in terms of the four cultural dimensions of power distance, uncertainty avoidance, individualism, and masculinity, as presented by Hofstede (1983). Using the scores for individual countries provided by Hofstede, (2009) and adopting the method developed by Kogut and Singh (1988), cultural distance is measured by using a composite variable that comprises the four cultural dimensions. A low score on this scale symbolizes cultural proximity, whereas a high score means a greater cultural distance between China and the host country. Distance (DIST) from a home country measures trade costs. Firms are more inclined to invest in far-away markets to avoid export costs. By contrast, studies based on the gravity model anticipate that the link between distance and FDI will be negative, as investment costs rise with distance (Kolstad & Wiig, 2012). Consequently, we additionally include a dummy (which is commonly included in gravity models) that indicates if a country has no access to the sea (LANDLOCKED), as an additional control to determine whether a host country's remoteness hinders FDI. Consistent with the above argument, Ramasamy et al. (2012) find that distance has an inverse impact on private Chinese companies, whereas it is not particularly important in the case of state-owned enterprises (SOEs). Inflation (INFL) is added as a typical indicator of economic growth. As inflation poses a higher risk to companies operating in an economy, a negative relationship between inflation in a host economy and the location choice for Chinese FDI is anticipated. In the context of China, it has been noted that high inflation does not deter investors, who consider uncertain economic situations as an opportunity to earn high returns on their investments rather than a constraint (Buckley et al., 2007). Finally, the presence of bilateral investment treaties (BITs) between China and host economies is added as a further control variable. BITs safeguard businesses against investment risk (Dixit, 2012), while in the context of China, they are more attractive to privately owned enterprises (POEs) than to SOEs. Research methods The dependent variable in this study is a count variable, that spans from zero to some positive number. Thus, since it is a nonnegative number, standard multiple regression is not suitable. Regarding the adoption of a methodology, the econometric literature proposes, in the presence of count data as a measurable statistic with a discrete response function (Greene, 2003), the adoption of a Poisson or of a negative binomial regression model, as such a model is more efficient than linear or discrete models. Negative binomial regression is preferred over Poisson regression because it allows for variation in the rate of the underlying process throughout observations based on a gamma distribution (Agresti, 2003;Dikova et al., 2019;Hilbe, 2011). Count models, meanwhile, have major flaws, such as the presence of heteroscedasticity in them and over-dispersion of data; these flaws can be overcome by modifying the models to consider the exposure of the observations to the grouping structure (Greene, 2003), which is represented, in the current study, by combinations of industries and countries. Last, a one-year lag is used for all the independent variables to avoid any potential endogeneity with the dependent variable. This yields the following model: #M&A=b 0 +b 1 Landlocked+ b 2 BITs + b 3 Distance+ b 4 Cultural distance + b 5 Inflation+ b 6 Government effectiveness + b 7 GDP + b 8 Resources + b9 Patents +mit #GF = b 0 +b 1 Landlocked+ b 2 BITs + b 3 Distance+ b 4 Cultural distance + b 5 Inflation+ b 6 Government effectiveness + b 7 GDP + b 8 Resources + b9 Patents + mit (MODEL 1) Regressions are performed separately for AEs and DEs for both M&As and GF investments deals, to enable a comparison of the results. Estimation results Tables 2 and 3show the correlation matrices for all the variables used in the M&A and GF investment settings, respectively. Overall, Table 2 Correlation Matrix for the Number of Cross-border M&As, 2003−2016. Variable 1 2 3 4 5 6 7 8 9 10 1. Total 1 2. Landlocked 0.100* 1 3. BITs 0.0387 0.0282 1 4. Distance 0.0328 0.0645 0.0915* 1 5. Cultural distance 0.0204 0.199 *** 0.156 *** 0.327 *** 1 6. Inflation 0.0927* 0.107* 0.167 *** 0.077 0.345 *** 1 7. Government 0.167 *** 0.166 *** 0.0625 0.0559 0.539 *** 0.666 *** 1 effectiveness 8. GDP (log) 0.192 *** 0.577 *** 0.0428 0.0438 0.0940* 0.028 0.0955* 1 9. Resources 0.0377 0.0759 0.0125 0.311 *** 0.0179 0.026 0.0005 0.0484 1 10. Patent 0.367 *** 0.0821 0.124 ** 0.170 *** 0.131 ** 0.133 ** 0.205 *** 0.224 *** 0.064 1 VIF −1.21 1.09 1.45 1.66 1.95 2.14 1.34 1.14 1.43 5 B. Ahmed, H. Xie, Z. Ali et al. Journal of Innovation & Knowledge 7 (2022) 100200 the independent variables do not show significant correlation with each other in bivariate relationships. All of these variables are included in the regression models. Table 4 shows the results of negative binomial regression on cross-border M&As in AE settings. Model 1 is the baseline model, which comprises only control variables and the moderator. Models 2 to 4 examine the key effects of the motivational variables on M&As. Model 5 adds all the independent variables, whereas Model 6 includes a moderator. Models 7 to 9 show the interaction effects of the moderator with different independent variables. Hypotheses 1 to 3 state, respectively, that the size of the market (GDP), resources (Resources), and strategic assets (Patent) are positively associated with the number of cross-border M&As in each host country. According to Hypothesis 1, the number of M&As in each host market is positively related to market size. As shown in Models 2 and 5, GDP is positive and significant; thus, Hypothesis 1 is supported, and shows that Chinese firms tend to increase their M&As when the size of the host market grows. Thus, the market-seeking hypothesis is borne out by the Chinese in AEs. For the natural resource motive, we used Resources as a proxy. In Models 3 and 5, natural resource is found to be positively and significantly related to the number of M&A deals. These findings support Hypothesis 2, which contends that when the host country is rich in natural resource endowments, Chinese firms are expected to increase their M&A activity. Furthermore, in Models 4 and 5, Patents positively and significantly affect the dependent variable. Our findings confirm Hypotheses 2 and 3, which claim that when the host developed country is endowed with natural resources and is strong in R&D, Chinese firms are more likely to engage in acquisition. Our study, using recent data in the context of M&As, shows that the "strategic asset-seeking" motive does lead Chinese firms to pursue M&As in AEs, in contrast to Buckley et al. (2007),whofound no significance for Patents. Table 3 Correlation Matrix for the Number of Cross-border GF Investments, 2003−2016. Variables 1 2 3 4 5 6 7 8 9 10 1. Total 1 2. Landlocked 0.0989 1 3. BITs 0.165 ** 0.018 1 4. Distance 0.141* 0.0777 0.0397 1 5. Cultural distance 0.118* 0.140* 0.221 *** 0.353 *** 1 6. Inflation 0.155 ** 0.121* 0.114* 0.00732 0.352 *** 1 7. Government effectiveness 0.301 *** 0.156 ** 0.0813 0.0679 0.444 *** 0.647 *** 1 8. GDP (log) 0.148 ** 0.329 *** 0.0253 0.0186 0.0676 0.121* 0.151 ** 1 9. Resources 0.142* 0.0714 0.0441 0.287 *** 0.00344 0.095 0.0254 0.0142 1 10. Patents 0.278 *** 0.105 0.162 ** 0.181 *** 0.135* 0.130* 0.163 ** 0.346 *** 0.0891 1 VIF −1.64 1.1 1.5 1.92 2.12 2.61 1.71 1.14 1.1 Table 4 Negative Binomial Regression Analysis of Cross-border M&As in Advance Economies, 2003−2016. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Variables Landlocked 0.657** 0.462* 0.657** 0.572** 0.473* 0.660** 0.685*** 0.664** 0.658** 0.272 0.268 0.27 0.268 0.258 0.26 0.258 0.259 0.26 BITs 0.540** 0.294 0.430* 0.420* 0.0997 0.172 0.136 0.171 0.172 0.245 0.248 0.245 0.245 0.245 0.243 0.243 0.243 0.243 Distance 0.496*** 0.636*** 0.384** 0.755*** 0.668*** 0.687*** 0.708*** 0.689*** 0.677*** 0.186 0.141 0.18 0.142 0.12 0.116 0.117 0.116 0.118 Cultural distance 0.180*** 0.217*** 0.211*** 0.230*** 0.316*** 0.315*** 0.299*** 0.318*** 0.313***  0.0574 0.0719 0.0574 0.0601 0.0719 0.066 0.0641 0.0664 0.0662 Inflation 0.0721 0.145*** 0.125** 0.103** 0.211*** 0.239*** 0.242*** 0.243*** 0.243*** 0.0499 0.0491 0.0505 0.0469 0.0447 0.0444 0.0438 0.0447 0.0455 GDP (log) 0.373*** 0.338*** 0.408*** 2.005* 0.408*** 0.405*** 0.047 0.0748 0.0729 1.039 0.0734 0.0734 Resources 0.0421*** 0.0509*** 0.0482*** 0.0493*** 0.122 0.0483*** 0.00585 0.00487 0.00485 0.00483 0.237 0.00487 Patents 2.45e-06*** 1.03e-06** 6.55E-07 4.36E-07 6.72E-07 4.07E-06 2.86E-07 4.54E-07 4.38E-07 4.63E-07 4.39E-07 1.20E-05 Government effectiveness 0.0264*** 0.5 0.0212* 0.0253*** 0.00836 0.307 0.0109 0.00876 Government effectiveness £GDP (log) 0.0169 0.0109 Government effectiveness £Resources 0.00182 0.00252 Government effectiveness £Patents 5.23E-08 1.33E-07 Constant 2.279 13.60*** 1.275 4.542*** 12.76*** 17.30*** 62.20** 16.82*** 17.03*** 1.629 1.84 1.573 1.248 2.167 2.421 29.29 2.517 2.519 Observations 192 192 192 192 192 192 192 192 192 *** p<0.01, ** p<0.05, * p<0.1. 6 B. Ahmed, H. Xie, Z. Ali et al. Journal of Innovation & Knowledge 7 (2022) 100200 Hypotheses 4a, 4b, and 4c suggest that the levels of host governments’effectiveness reduce the effects of market size (GDP), natural resources (Resources), and strategic assets (Patents) on the number of cross-border M&As in host markets, which is shown through their interaction effects. Model 6 introduces the moderator (Government effectiveness) into Model 5, while Models 7 to 9 show the interaction effect. Model 7 tests the interaction effect of the market-seeking variable (GDP) and the moderator (Govternment effectiveness £GDP). Model 8 evaluates the interaction between the natural resourceseeking variable (Resources) and the moderator (Govternment effectiveness £Resources), whereas Model 9 shows the interaction effect of the strategic-asset seeking variable with the moderator (Govternment effectiveness £Patents). In terms of interaction effects, none of the interaction variables was significant. Table 5 presents the results of a negative binomial regression analysis of Chinese enterprises’M&As in DEs. As in Table 4, Model 1 is the baseline model, while Models 2 to 4 show the individual effect of each independent variable; Model 5 incorporates all of the independent variables as baseline values for Model 6, which introduces the moderator. For the main effect of the market-seeking motive, the GDP coefficient is significant but negative in Model 2; thus, Hypothesis 1 is rejected. The Resources coefficient is positive and significant in Model 3, suggesting that natural resource endowment is a key factor in Chinese firms’undertaking of acquisitions in developing target economies. Thus, Hypothesis 2 is supported. Contrary to our predictions, the coefficient of Patents turns out to be significant but negative, which means that the number of cross-border M&As is negatively related to patents in the context of emerging economies (EEs). Based on these findings, we argue that resources are the most appealing factor for Chinese M&As in DEs. In terms of interaction effects, only the interaction between Gov. Effectiveness and Patents is negatively significant, thus supporting Hypothesis 4c that high government effectiveness in a host market negatively moderates the relationship between cross-border M&As and strategic assets. Table 6 presents the results of negative binomial regression of cross-border GF investment in AE settings. As shown in Model 2, GDP is positive and significant, providing support for Hypothesis 1, which proposes that Chinese firms are expected to increase their GF deals in response to the growing size of a target market. Therefore, the market-seeking motive hypothesis is supported in the context of AEs. Furthermore, in Model 4, Patents positively and significantly affects the dependent variable. These findings provide partial support for Hypothesis 3, which argues that Chinese enterprises are expected to increase their GF investment transactions when the host AE is rich in patents. The interaction between Resources and Govternment effectiveness is found to be negative and significant, thus supporting Hypothesis 4b, which states that the interaction between natural resources and the number of cross-border GF investment deals is negatively moderated by government effectiveness. These results are consistent with those of Kang (2018) and Kolstad and Wiig (2012), who found a similar relationship. Table 7 shows the results of the negative binomial regression analysis of Chinese firms’cross-border GF investments in DEs. Concerning the main effects of market-, resource-, and strategic assetseeking motives, none of the coefficients is significant for Chinese GF investments. These results are consistent with the findings of Deng and Yang (2015), in which none of the key variables were significant in the subsample of developing economies. The interactions between market size and Govternment effectiveness and that between strategic assets and Gov. Effectiveness are found to be negative and significant, thus supporting Hypotheses 4a and 4c, implying that government effectiveness negatively moderates the relationship between natural resources and the number of GF investments in DE settings. Table 5 Negative Binomial Regression Analysis of M&A in Developing Economies, 2003−2016. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Variables Landlocked 0.219 1.338** 0.708* 0.278 1.216** 1.935*** 1.851*** 1.937*** 1.915*** 0.407 0.527 0.422 0.413 0.5 0.441 0.455 0.441 0.443 BITs 0.609*** 1.539*** 0.638*** 0.641*** 1.525*** 1.356*** 1.396*** 1.345*** 1.308*** 0.152 0.175 0.145 0.157 0.174 0.163 0.175 0.166 0.153 Distance 0.328*** 0.690*** 0.608*** 0.287*** 1.100*** 0.591*** 0.584*** 0.597*** 0.679*** 0.0957 0.0907 0.12 0.107 0.119 0.132 0.131 0.133 0.127 Cultural distance 0.299*** 0.0756 0.479*** 0.348*** 0.0932 0.127 0.129 0.128 0.0406 0.109 0.0984 0.115 0.125 0.119 0.109 0.11 0.109 0.103 Inflation 0.0208 0.0542*** 0.0228 0.0245 0.0474*** 0.019 0.018 0.0206 0.0072 0.0184 0.0173 0.0181 0.0192 0.0166 0.0183 0.0183 0.019 0.018 GDP (log) 0.101*** 0.0838*** 0.0721*** 0.216 0.0721*** 0.0947*** 0.0236 0.0237 0.0239 0.221 0.0239 0.0222 Resources 0.0336*** 0.0282*** 0.00374 0.00379 0.0136 0.00735 0.00775 0.00638 0.00707  0.00704 0.0317 0.00722 Patents 1.77E-06 6.40e-06*** 5.90e-06*** 5.97e-06*** 6.04e-06*** 6.41e-05*** 2.03E-06 2.03E-06 2.02E-06 2.03E-06 2.07E-06 1.55E-05 Government effectiveness 0.0310*** 0.0252 0.0321*** 0.0378*** 0.00459 0.0861 0.00578 0.00492 Government effectiveness £GDP (log) 0.00212 0.00323 Government effectiveness £Resources 0.000122 0.000384 Government effectiveness £Patents 8.49e-07*** 1.87E-07 Constant 5.123*** 11.21*** 7.343*** 4.826*** 13.81*** 6.832*** 10.64* 6.786*** 7.604*** 0.774 0.99 0.943 0.848 1.038 1.403 5.983 1.408 1.352 Observations 150 138 150 150 138 138 138 138 138 *** p<0.01, ** p<0.05, * p<0.1. B. Ahmed, H. Xie, Z. Ali et al. Journal of Innovation & Knowledge 7 (2022) 100200 7 Table 6 Negative Binomial Regression Analysis of GF Investment in Advance Economies, 2003−2016. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Variables Landlocked 0.654*** 0.470* 0.646*** 0.572** 0.478* 0.734*** 0.731*** 0.762*** 0.736*** 0.246 0.246 0.246 0.252 0.253 0.257 0.257 0.257 0.257 BITs 0.433** 0.023 0.416** 0.311 0.0189 0.115 0.123 0.0774 0.115 0.196 0.199 0.198 0.196 0.203 0.199 0.2 0.199 0.199 Distance 0.127 0.300** 0.107 0.468*** 0.324** 0.348*** 0.344*** 0.369*** 0.354*** 0.168 0.128 0.17 0.133 0.131 0.125 0.124 0.123 0.128 Cultural distance 0.145*** 0.149** 0.147*** 0.178*** 0.161** 0.200*** 0.206*** 0.209*** 0.201***  0.0519 0.0681 0.0523 0.0567 0.0687 0.0609 0.0624 0.0599 0.0611 Inflation 0.0465 0.0669 0.051 0.0538 0.0722* 0.0836** 0.0848** 0.0993** 0.0813* 0.0411 0.0417 0.0417 0.04 0.042 0.0418 0.0417 0.0415 0.0426 GDP (log) 0.425*** 0.386*** 0.480*** 0.0974 0.524*** 0.482*** 0.0458 0.0716 0.0682 0.804 0.0668 0.0684 Resources 0.00654 0.00674 0.000933 0.00175 0.719*** 0.00101 0.00858 0.00798 0.00832 0.00848 0.25 0.00832 Patents 2.66e-06*** 4.07E-07 2.05E-07 1.82E-07 4.32E-07 3.48E-06 3.05E-07 5.04E-07 4.82E-07 4.81E-07 4.66E-07 1.26E-05 Government effectiveness 0.0324*** 0.141 0.0581*** 0.0332*** 0.00786 0.241 0.0127 0.00826 Government effectiveness £GDP (log) 0.00623 0.00866 Government effectiveness £ Resources 0.00769*** 0.00268 Government effectiveness £Patents 4.08E-08 1.39E-07 Constant 1.13 11.93*** 1.306 1.926 11.04*** 16.71*** 0.607 20.45*** 16.89*** 1.472 1.781 1.485 1.179 2.101 2.308 22.46 2.636 2.385 Observations 234 234 234 233 233 233 233 233 233 *** p<0.01, ** p<0.05, * p<0.1. Table 7 Negative Binomial Regression Analysis of GF Investment in Developing Economies, 2003−2016. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Variables Landlocked 0.0246 0.129 0.0246 0.0454 0.175 0.107 0.357 0.1 0.458 0.249 0.342 0.252 0.256 0.348 0.351 0.408 0.351 0.382 BITs 0.287*** 0.336*** 0.283*** 0.289*** 0.318*** 0.271** 0.165 0.267** 0.401*** 0.103 0.12 0.103 0.103 0.122 0.126 0.119 0.126 0.107 Distance 0.257*** 0.282*** 0.290*** 0.200*** 0.274*** 0.227** 0.278*** 0.227** 0.300*** 0.0617 0.0645 0.0671 0.0759 0.0936 0.101 0.0987 0.101 0.0875 Cultural distance 0.120** 0.097 0.127** 0.159** 0.127* 0.153* 0.154** 0.154* 0.197*** 0.0608 0.062 0.0612 0.0685 0.0764 0.0789 0.0704 0.0787 0.059 Inflation 0.00537 0.00684 0.00309 0.00684 0.00524 0.00141 0.00774 0.00284 0.00126 0.00787 0.00797 0.00808 0.00796 0.00826 0.00983 0.00987 0.0102 0.00965 GDP (log) 0.0143 0.0112 0.0092 0.632*** 0.00922 0.0280* 0.0165 0.0169 0.017 0.124 0.017 0.0169 Resources 0.00567 0.0048 0.00356 0.00458 0.00971 0.00392 0.0044 0.00447 0.00457 0.00458  0.0132 0.00468 Patents 1.71E-06 8.19E-07 8.67E-07 7.49E-07 7.92E-07 8.72e-05*** 1.33E-06 1.52E-06 1.52E-06 1.47E-06 1.52E-06 7.30E-06 Government effectiveness 0.0039 0.227*** 0.00477 0.0116*** 0.00323 0.0429 0.00369 0.00316 Government effectiveness £GDP (log) 0.00858*** 0.00164 Government effectiveness £Resources 9.49E-05 0.000193 Government effectiveness £Patents 1.07e-06*** 9.14E-08 Constant 4.175*** 4.040*** 4.414*** 3.723*** 4.025*** 3.412*** 12.47*** 3.346*** 4.491*** 0.495 0.737 0.529 0.609 0.87 0.997 3.223 1.006 0.918 Observations 309 298 309 309 298 298 298 298 298 *** p<0.01, ** p<0.05, * p<0.1. B. Ahmed, H. Xie, Z. Ali et al. Journal of Innovation & Knowledge 7 (2022) 100200 8