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China Goes Global 2013- September 25-27, Bremen, Germany

ACQUISITIONS OF CHINESE AND INDIAN MULTINATIONALS IN EUROPE: OWNERSHIP DECISIONS AND ACCESS TO RESOURCES

Lucia Piscitello

DIG-Politecnico di Milano, Italy lucia.piscitello@polimi.it

Roberta Rabellotti

DSPS-Università di Pavia, Italy roberta.rabellotti@gmail.com

Vittoria Giada Scalera

DIG-Politecnico di Milano, Italy vittoria.scalera@mail.polimi.it

[Preliminary version. Please do not quote]

ABSTRACT Purpose: This paper investigates the ownership strategies of Chinese and Indian Multinational Enterprises (MNEs) investing in the EU countries. Design/methodology/approach: We analyze the extent to which the main motivations underlying cross border acquisitions and the characteristics at firm- and industry-level influence the decisions of Chinese and Indian MNEs about the level of commitment with the target firms when they enter more developed markets. To test our hypothesis, we develop an econometric analysis applied to Chinese and Indian acquisitions in knowledge-intensive manufacturing sectors investing in Europe between 2003 and 2011. Furthermore, with content analysis research methodology, we distinguish initiatives aiming at market- or technology-seeking. Findings: Our results confirm that Chinese and Indian MNEs acquire less control when they seek for technological competences rather then when they intend to acquire existing customer base or established brand names. In addition, we find that the characteristics at firm- and industry-level have a different impact on the ownership decisions depending on the motivation of the acquisition. Keywords: Emerging market firms, cross border acquisitions, ownership choice, technology- and market- seeking investments. .

1. INTRODUCTION The present work is about the ownership strategies of Emerging Market Multinational Enterprises (EMNEs) acquiring firms in advanced countries. More specifically, the aim of the paper is to examine to which extent the main motivation underlying the investment and the firm- and industry-specific characteristics influence the acquisition behavior of Chinese and Indian MNEs investing in Europe.

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Since the 1990s, in fact, EMNEs have been involved in a process of international expansion through outward Foreign Direct Investments (FDI), and Merger and Acquisitions (M&As) have become a more and more important component of this process (UNCTAD, 2011). In a very short time EMNEs have become global players in many industries, and have often caught up and begun to challenge the established MNEs based in advanced economies (Awate et al., 2012). This rapid and peculiar evolution has induced the recent literature to focus on better understanding the internationalization process that enables EMNEs to upgrade their knowledge and their technical capabilities in order to compete against incumbent firms (e.g. Awate et al., 2012; Di Minin et al., 2012; Li et al., 2012). Literature in international business (e.g. De Beule et al., 2013; Deng, 2009) has already emphasized that EMNEs investing in more advanced economies are motivated mainly by the access to critical intellectual resources, the acquisition of technological know-how and brands, as well as managerial and market expertise, in order to overcome their latecomer disadvantages (Child & Rodrigues, 2005; Mathews, 2006). Thus, EMNEs use international expansion in developed markets to source and acquire advanced knowledge (Mudambi, 2008), as a springboard to overcome their latecomer disadvantages and fill their technological resource void (Luo & Tung, 2007). For incorporating sophisticated assets and capabilities, MNEs may use a spectrum of organizational modes of inter-firm cooperation but FDI is possibly the most effective way (Chung & Alcacer, 2002). In particular, EMNEs largely prefer to tap into advanced market via cross-border M&As. In fact, cross-border acquisitions have been described as the most rapid and suitable way to acquire strategic capabilities to offset EMNEs’ (technological and marketing) competitive disadvantages, levering their unique competences and taking advantage of the financial exigency of the target firms, especially in advanced countries (Deng, 2009; Gammeltoft, et al. 2010). Moreover, EMNEs investing in more advanced economies face two main disadvantages, which emphasize the likelihood of failure of the acquisitions. On one hand, they suffer the so-called liability of emergingness that encompasses the challenges of coming from a developing country and can make the achievement of reputation and legitimacy very difficult (Madhok & Keyhani, 2012). On the other, the differences in the knowledge base between firms from advanced and emerging markets increase the probability that the absorptive capacity of the EMNEs is not enough to acquire and incorporate the external knowledge (Cohen & Levinthal, 1990). Within this context, the decision of the level of commitment in cross border acquisitions undertaken by EMNEs is particularly crucial for the success of the investment and the efficient acquisition of sophisticated capabilities (De Beule et al., 2013). In fact, the extent of equity ownership in cross border acquisition has critical implication for resource commitment, performance, and risk (Anderson & Gatignon, 1986). To provide evidence for our conceptual framework, we analyze foreign acquisitions of Chinese and Indian high and medium-high tech manufacturing firms that occurred in Europe during the 2003-2011 period. Drawing on two different streams of literature, namely the literature on MNE’s entry mode and the studies on EMNEs’ internationalization strategies, this work contributes to the extant literature in various aspects. In particular, to our knowledge, little is known about how Chinese and Indian firms choose their level of commitment in cross border acquisitions, and especially about the extent to which their decisions differ in case of market- or technology-seeking investments. Additionally, we provide empirical evidence on how the EMNEs aiming at accessing knowledge and technological resources take the ownership decisions in cross border M&As considering also target firm- and industry-specific characteristics. Furthermore, our study contributes to empirical research on the internationalization of EMNEs analyzing the ownership decisions of firms from the two main emerging economies, namely China and India. Instead, international business scholars have variously investigated the entry mode strategies of EMNEs mainly focusing on the general characteristics of the outward FDI (Child & Rodrigues, 2005; Rui and Yip, 2008), or on the factors that determine the choice between wholly owned subsidiary vs. a joint venture (e.g. Cui & Jiang, 2009), or taking into

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consideration only one home country overlooking the heterogeneity among the EMNEs (e.g. Cui & Jiang, 2012). Finally, our work aims at providing new empirical evidence on the strategies pursued by EMNEs for technological upgrading, considering that the issue has been mainly tackled using mainly qualitative case studies (Awate et al., 2012; Di Minin et al., 2012). The remainder of the paper is organized as follows. The next section presents our conceptual framework and testable hypotheses. The third section reports the data, descriptive statistics of this study, and the econometric models and variables employed. The fourth section illustrates and discusses the results of the econometric analysis, while the final section is dedicated to the summary of the main contributions and limitations of the paper.

2. CONCEPTUAL FRAMEWORK AND HYPOTHESES

Referring to specific case of Chinese and Indian MNEs, in this study we highlight that their entry mode choices and the ownership decisions have critical theoretical implications. Foremost, traditional FDI theories have been criticized because they have been built largely on the experience of industrialized MNEs and thus may fail to capture the unique characteristics of EMNEs (Mathews, 2006; Ramamurti, 2008). One of the key issue discussed in these studies is that EMNEs expand internationally not to exploit traditional ownership advantages, but to build a sustainable global competitiveness, leveraging their network and home country advantages (Buckley, et al. 2007). EMNEs investing in high-income countries may be involved in market- and (or) strategic asset-seeking type of FDI (Dung, 2009; Luo & Tung, 2007). More specifically, they more likely choose acquisitions when they invest in advanced economies, because of the lack of firm-embedded technological knowledge and of other strategic resources, as predicted by transaction cost economics (TCE) research and resource base view (RBV) (Anderson and Gatignon, 1986; Slangen & Hennart, 2007). This argument should be much more consistent in the case of knowledge-intensive firms (Anderson & Gatignon, 1986; Hennart & Park, 1993). However, due to a limited empirical contributions using firm-level data, it remains unclear the real motivations underlying international expansion of Chinese and Indian MNEs and the relationship with their ownership strategies ad the characteristics target firm- and industry-specific. Through M&As in developed markets with a comparative technological advantage, EMNEs can benefit from reverse knowledge spillovers (Li et al., 2012). Such an arrangement fulfils the need of EMNEs to overcome quickly their own technical and innovative weaknesses (Child & Rodrigues, 2005), although evidences highlight that they experience several problems in entering developed countries (Cuervo-Cazurra & Genc, 2008). The extant literature has suggested various arguments to motive the MNE’s decision of the equity ownership in foreign market entry and has shown that the choice of the ownership level impacts on several factors such as the effective transfer of tacit and tangible assets, the risk shared between the acquiring and the target firm, the resource commitment, and the control over the activities. Compered to non-equity forms, M&A represents the highest level of vertical integration (Van de Vrande et al., 2009), and the degree of ownership maintained in the target firm stands for the desired level of commitment in the relative foreign activity (Chari & Chang, 2009). More specifically, according to the RBV argumentation (e.g. Barney, 1991), the full acquisition of the local target allows foreign firms to access embedded knowledge and competences through the complete control over the operations (Barkerma and Vermeulen, 1998). In similar vein, TCE (Williamson, 1985) theorizes higher level of commitment in cross border acquisitions in order to minimize the transaction costs (Madhok, 1997). Nevertheless, greater level of commitment implies greater level of risk and liability of foreignness, whereas partial ownership gives the opportunity to share investments and risks mitigating the distance between the acquiring and the target firm (Anderson & Gatignon, 1986; Kogut & Zander, 1993). In this study, we rely on a stream of literature inspired by RBV and new property rights theory of the firm. According to this approach (Kim & Mahoney, 2010), the firm is described as a bundle of resources and capabilities managed by incomplete contracts between owner-

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entrepreneurs and other shareholders. The distinctive core competences of the firms are embodied in the managers and in other key employees, which make highly firm-specific investments (Rajan & Zingales, 1998). Thus, a takeover could break the original corporate equilibrium and it may disperse or even destroy the core competences embedded in the target, producing huge loss from the acquirer’s perspective. This scenario more likely comes true during full acquisitions than during partial acquisitions, considering that the former generally implies radical organizational change. When a full acquisition takes place, the acquiring firm may encounter difficulties in motivating target firm managers and employees, which may choose to underinvest in new competences, behave opportunistically and hold up the transfer of critical tacit assets such as technological or R&D knowledge, or even abandon the firm (Chen & Hennart, 2004). Cannella and Hambrick (1993) have confirmed that exit of managers after an M&A implies the loss of critical knowledge resources, which also lower the performance of the target firm. This conceptual framework is likely to be much more consistent in the case of Chinese and Indian MNEs investing in advanced countries for acquiring technological and tacit knowledge embedded in the target firms. In fact, the liability of emergingness experienced by EMNEs in advanced economies (Madhok & Keyhani, 2012), the different host country’s environment, and the limited absorptive capacity increase MNE’s costs of coordination that will be much more higher in case of transfer of technological capabilities (technology-seeking investments) than in the case of acquisition of the existing trade support assets or the local customer base (market-seeking investments). Specifically, our hypothesis is the following:

Hypothesis 1. EMNEs will acquire a lower share of equity when the motivation underlying the investments is technology-seeking rather than market-seeking.

Drawing on the argumentations developed in De Beule et al. (2013), we relate the level of commitment to uncertainty in order to explain the role of target firm- and industry-specific characteristics in the governance mode’s decisions. This theoretical framework is particularly appropriate considering our empirical setting that involves different sources of uncertainty generated by dissimilarities among partners, i.e. cultural distance and dissimilar knowledge bases, and by high turbulent environment such as the knowledge-intensive industries. In addition to the other factors, uncertainty faced by EMNEs investing in more advanced countries is not only related to the liability of foreignness due to the presence in a foreign country (Zaheer, 1995), but also to the handicap incurred simply by being from emerging countries (Madhok & Keyhani, 2012). Specifically, we consider two different dimensions of dissimilarities between the target and the acquiring firm that have been recognized as source of uncertainty, and thus may influence the choice of entry mode by EMNEs. First, we study the role of cultural distance that is particularly relevant in the case of FDI involving firms from emerging and developing countries. On the one hand, a culturally distant environment could represent an impediment to transfer intra-organizational practices and this encourages full ownership and greater control in order to enhance the power of the parent company. On the other hand, consistent with the argument of Barkema and Vermeulen (1998), MNEs in unfamiliar environments tend to share equity with a local partner in exchange for adaptation to the local environment. Accordingly to the latter argument, we recognize the importance of local resources for minimizing the uncertainty experienced by EMNEs acquiring firms in advanced countries. Thus, in order to leverage local firm’s market and environment knowledge, we expect that EMNEs facing higher uncertainty related to cultural distance will choose lower level of commitment. Second, we take into consideration the impact of dissimilar knowledge bases in the decisions of ownership by EMNEs. In fact, a transfer of routines and knowledge may be difficult in a new environment (Cohen & Bacdayan, 1994) and the firm expanding into an unrelated business may encounter several problems to take advantage of the target firm’s competences and to acquire the desired technological capabilities (Harrison et al., 1991). Thus, the transfer of competences and capabilities requires full cooperation with the parent in order to facilitate the learning process of the acquiring firm. So, leaving the seller a partial ownership may

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mitigate the problem of knowledge transfer in unrelated technological context. These difficulties may be also exacerbate in the case of EMNEs investing in unrelated business via acquisitions of firms that, compared with them, possess more sophisticated competences. Accordingly, greater differences in the knowledge bases of the target and the acquiring companies will lead to a lower level of commitment. Finally, in addition to firm’s belonging to different industries, other factors can increase acquisitions uncertainty and influence, indeed, the ownership decisions of the acquiring firm. Target firms involved in R&D-intensive industries more likely possess proprietary technologies and the acquirer typically uses the M&A as a learning opportunity and a direct channel to access the knowledge-intensive assets such as specialized human resources, innovative techniques or technological assets (Chen & Hennart, 2004). Given the importance of knowledge assets in high tech industries, a remarkable source of uncertainty has been recognized the valuation difficulties and the high risk of adverse selection drives the acquiring firms to pursue lighter level of commitment (Reuer et al., 2004). Therefore, we expect that EMNEs will be more likely to seek a lower level of commitment in the target company in R&D-intensive industries than in non-R&D-intensive industries. Thus, our second group of hypothesis may be stated as follows:

Hypothesis 2a. EMNEs will acquire a lower share of equity of the target firms when those firms are culturally distant than when they are culturally closer. Hypothesis 2b. EMNEs will acquire a lower share of equity of the target firms when those firms have more dissimilar knowledge bases than when they have similar knowledge bases. Hypothesis 2c. EMNEs will acquire a lower share of equity of the target firms when the target firms are in high (or medium-high) tech industries than when they are in less high tech industries.

Furthermore, according to the extant literature, firms’ ownership choices to access knowledge and capabilities are dependent on the type of activities, strategies and structures of the firms that are involved (for a survey, see Brouthers & Hennart, 2007). We claim that the different motivation underlying the investments, i.e. technology- vs. market-seeking, has a different impact on the relationships between the level of commitment and the target firm- and industry-specific factors. Local managers are more embedded not only in the target firm, but also in the local environment and, indeed, they are more suitable for managing relationships with local employees, suppliers or local institutions (Kogut & Singh, 1988). Therefore, it is more likely that especially in the case of market-seeking investments, the acquiring firm prefers to delegate the partial control to the local managers in order to foster the existing relations and to reduce the cultural distance with local customers and partners (Chi, 1994). The JV literature argues that firms are more likely to rely on local market knowledge in countries with greater cultural distance. Thus, we expect a negative relationship between cultural distance and level of commitment (Hypothesis 2a), but we predict that this relationship holds more likely in the case of market-seeking acquisitions than in the case of technology-seeking M&As. However, strategic asset-seeking investments occur generally among firms with less technological capabilities trying to fill their gap by acquiring innovative firms to access their resources. For EMNEs acquiring technological assets via M&As in advanced economies, the local partner has a strategic role especially if it has a dissimilar knowledge base or it is in R&D-intensive industry. Therefore, as stated in Hypotheses 2b (2c), we expect negative relationships between knowledge-base dissimilarities (targets’ R&D-intensive industry) and our dependent variable, i.e. level of commitment; but, in accordance with our previous reasoning, we claim that these relations are more likely in the case of technology-seeking acquisitions than in the case of market-seeking investments.

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3. METHODOLOGY 3.1 The sample Our hypotheses have been tested on a sample of 170 acquisitions by high tech and medium-high tech Chinese and Indian firms that occurred in the 27 European countries between 2003 and 2011. Our focus is on knowledge-intensive manufacturing acquirers. In particular, we identified high tech and medium-high tech acquiring firms as companies operating in two-digit manufacturing according to the Eurostat-OECD (2007) classification. We employed seven NACE two-digit Rev. 2 industry sectors as high tech and medium-high tech industries: pharmaceuticals (20), chemicals (21), computer, electronic and optical products (26), electrical equipment and components (27), machinery and other equipment (28), motor vehicles (29) and other transport equipment (30). Our choice has been driven by previous studies have empirically highlighted that investments for knowledge sourcing are particularly relevant in high-tech and medium high-tech manufacturing industries, especially in the case of EMNEs investing in advanced economies (Awate, et al. 2012). The data on cross-border acquisitions are obtained from BvD Zephyr and SDC Platinum. These two databases provide deal information including type of deal, date, value, degree of the ownership, and general information about the target and acquiring firm (e.g. country, region and city of origin, activities and industries of the firms). The initial sample included 230 acquisitions, 76 (33%) from China and 154 (67%) from India. To identify the final sample, other restrictions were applied. In particular, from the all the acquisition events, we excluded (1) the deals undertaken from China or India by individual or unknown investors, (2) the operations with undisclosed acquirer and/or target and (3) the investments in which the acquirer is a sovereign wealth fund (SWF) or the global ultimate owner (GUO) is not settled in China or India. Finally, acquisitions were eliminated if target firms’ data were not collected in BvD Orbis or in case we couldn’t classy the main motivation of the acquisition. The final sample includes 170 acquisitions, or 74% of the deals meeting our selection criteria. Thus after applying the selection criteria, it turns out that 53 out of 170 (31.18%) acquisitions have been undertaken by Chinese firms, and 117 (68.82%) by Indian MNEs. Table 1 provides a summary of the sample by year and by host country, reporting the distribution of the acquisition for Chinese and Indian firms, and for the total sample. Acquisitions in the sample involved 18 target European countries. Among the 170 M&As of our sample, the UK, Germany and France represent the favorite European locations for Chinese and Indian MNEs. Table 1. Home countries of the 170 acquisitions by year of the investment

Host country 2003 2004 2005 2006 2007 2008 2009 2010 2011 Total Belgium (No.) 0 1 2 4 0 1 0 2 0 10 % 0.00 12.50 9.09 17.39 0.00 3.57 0.00 11.76 0.00 5.88 France (No.) 1 3 1 1 5 1 2 4 2 20 % 12.50 37.50 4.55 4.35 19.23 3.57 16.67 23.53 7.69 11.76 Germany (No.) 1 3 3 6 4 8 0 0 9 34 % 12.50 37.50 13.64 26.09 15.38 28.57 0.00 0.00 34.62 20.00 Italy (No.) 0 0 1 1 3 5 3 2 2 17 % 0.00 0.00 4.55 4.35 11.54 17.86 25.00 11.76 7.69 10.00 Netherlands (No.) 1 0 2 1 5 0 3 1 1 14 % 12.50 0.00 9.09 4.35 19.23 0.00 25.00 5.88 3.85 8.24 Spain (No.) 0 0 3 2 2 1 0 0 2 10 % 0.00 0.00 13.64 8.70 7.69 3.57 0.00 0.00 7.69 5.88

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Sweden (No.) 0 0 1 2 0 0 1 1 1 6 % 0.00 0.00 4.55 8.70 0.00 0.00 8.33 5.88 3.85 3.53 United Kingdom (No.) 4 1 6 4 4 7 2 3 7 38 % 50.00 12.50 27.27 17.39 15.38 25.00 16.67 17.65 26.92 22.35 Others (No.) 1 0 3 2 3 5 1 4 2 21 % 12.50 0.00 13.64 8.70 11.54 17.86 8.33 23.53 7.69 12.35 Total (No.) 8 8 22 23 26 28 12 17 26 170 3.2 Dependent variable Our dependent variable, Share of equity, is the outcome of firm’s ownership decision at the time of the acquisition. 3.3 Explanatory variables 3.3.1 Technology-seeking M&A In order to classify the main motivation of the investments, we employeed the dummy variable Tech-seeking, which takes the value of 1 when the principal motivation underlying the acquisition is the access to technology and knowledge embedded in the target company, and the value of 0 otherwise (i.e. when the acquisition is primarily market-seeking). We determined the main motivation underlying each acquisition using techniques of qualitative content analysis for the categorization of textual information provided by companies’ public announcements. Relying on a deductive category application, we brought theoretical aspects of the analysis in connection with the text of the announcements (Weber, 1990). Starting from the three main FDI motivations suggested by Dunning’s eclectic paradigm (Dunning, 1993), namely market-seeking, efficiency-seeking and resource-seeking (including a subset that is known as strategic-asset-seeking), we aggregated these categories in order to fit the case of Chinese and Indian investments in advanced countries. Specifically, we adopted two main categories: technology- and market-seeking investments. The qualitative step of the analysis consisted in a methodological controlled assignment of the category to each announcement. In respect to the theory we explicitly defined the categories and identified the coding rules, determining exactly under what circumstances a text can be assigned to a specific deductive category (Weber, 1990). Our sources for public announcements and deal information were LexisNexis. Every article was read carefully by two different skilled coders to identify the main motivation of investment event, which were then hand-coded according to the coding rules previously defined. Table A (in appendix) shows the coding agenda with definitions, examples and coding rules obtained after a revision process and a formative check of reliability. Finally, we completed the procedure testing the reliability and validity of our analysis. We measure the amount of agreement or correspondence between two different coders (Neuendorf, 2002). We obtained 87% of agreement considering the 170 items assessed, which we considered a satisfactory result. Table 2 reports the distribution of the acquisitions across the two main motivations underlying the investments, distinguishing between Chinese and Indian MNEs.

Table 2. Motivations of the 170 acquisitions by MNEs’ home country

Motivation China India Total Market-seeking (No.) 29 81 110 % 54.72 69.23 64.71 Technology-seeking (No.) 24 36 60 % 45.28 30.77 35.29 Total (No.) 53 117 170

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3.3.2 Cultural distance We computed the cultural distance between China, or India, and each host country with respect to each single item by using a procedure similar to Kogut and Singh’s (1988) approach. More specifically, we calculated the cultural distance using Hofstede’s uncertainty avoidance and individualism dimensions. We measured the cultural distance using the uncertainty avoidance dimension according to the following formula:

where is the uncertainty avoidance distance between the home

country h and host country j, is the uncertainty avoidance index for the jth host country,

is the uncertainty avoidance index for hth home country, and VUA is the variance of the uncertainty avoidance index. The measure of cultural distance using the individualism dimension was also calculated in a similar fashion. Greater values of the cultural distances measures indicate greater differences between China (or India) and the target firm’s country with respect to the cultural dimension. Uncertainty avoidance has been suggested as potentially the most salient because it takes into consideration organizational and managerial characteristics (Kogut & Singh, 1988); in accordance with other studies, we also included individualism dimension that has been recognized as important (Chari & Chang, 2009; Hofstede, 1989). Furthermore, we tested other models using also the composite index of cultural distance based on Kogut and Singh’s (1988) formula, which includes all together the four cultural dimensions provided by Hofstede (i.e. power distance, uncertainty avoidance, masculinity/femininity, and individualism). Nevertheless, the aggregated index has been characterized as highly problematic because of the assumption of equivalence across the four dimensions (Shenkar, 2001), as suggested also by our results. 3.3.3 Target service sector To take into consideration that the ownership decisions may be different in the service sector (Erramilli & Rao, 1993), we included also the dummy variable Target service sector. It takes the value of 1 if the primary NACE code of the target firm is in a service sector industry (NACE two-digit Rev.2 45-96 inclusive), and 0 otherwise. Thus, 28 out of 170 (16.47%) acquisitions had a target firm operating in a service sector, whereas in 142 (83.53%) acquisitions the target was a manufacturing company. Data on the primary target industry come from BvD Orbis. Since MNEs operating in the manufacturing sector suffer endogenous uncertainty caused by different knowledge bases when they acquire target in service sector, it is likely that they will experience less level of commitment than when they acquire a manufacturing company (Barkema & Vermeulen, 1998). Therefore we expect a negative relationship between the dummy Target service sector and our dependent variable. 3.3.4 Target high and medium-high tech industry To account for the technological intensity of the target company’s industry, we introduced a dummy variable, Target tech industry, taking the value of 1 if the target company operates in a high or medium-high tech industry according to the Eurostat-OECD (2007) classification. Data on the primary target industry come from BvD Orbis. Table 3 shows the distribution of the 170 acquisitions between high and non-high tech industries and across the MNEs’ home countries and for the whole sample. We expect a negative relationship between the dummy Target tech industry our dependent variable Share of equity.

Cultural dis tance(UA) jh =(UAj −UAh )

2

VUA

Cultural dis tance(UA) jhUAj

UAh

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Table 3. Distribution of the 170 acquisitions by technology intensity of the target firm’s

industry and by MNEs’ home country Target sector China India Total Non high tech (No.) 15 22 37 % 28.3 18.8 21.76 High tech (No.) 38 95 133 % 71.7 81.2 78.24 Total 53 117 170

3.4 Control variables 3.4.1 Host country GDP growth We also controlled for the effect of host country GDP growth, introducing the variable GDP growth. Prior work (e.g. Barkema & Vermeulen, 1998) has shown that the growth of the host market may influence the mode of foreign entry: shared ownership mode may be preferred to completed acquisitions in host country with higher economic growth. We measured host country GDP growth as the annual GDP growth experienced by the target firm’s country the year before the acquisitions (data obtained from the World Bank Development Indicator database). 3.4.2 Cross-border M&As in the host country We introduced the variable Host cross-border M&As to take into consideration the relative attractiveness of the host country markets for entry by foreign firms. The international business literature has highlighted that the presence in the country markets of rivals is strategic to gain global market presence, especially in those markets considered attractive (Hamel & Prahalad, 1985). Previous studies have suggested that the relative attractiveness of the host country markets may impact on the level of commitment in cross border acquisitions (Folta, 1998). According to Chari and Chang (2009), we measure Host cross-border M&As as the percentage of the worldwide cross border M&As accounted for by cross border M&As in the target firm’s country in the year prior to the focal acquisition. Data for the measure come from UNCTAD Cross-Border M&A database. 3.4.3 Acquiring firm industry We constructed four dummies (Chemicals, Electronic, Machinery and Transport; Chemicals was used as a benchmark) to control for industry-specific effects that may influence the firm’s M&A ownership decision. The four industry classes of the acquiring firms were defined as follows: Chemicals takes the value of 1 if the acquiring firm operates in the chemical or pharmaceutical industry (NACE two-digit Rev. 2 20 and 21), and 0 otherwise; Electronic is equal to 1 if the acquirer produces electronic or electrical equipment (NACE two-digit Rev. 2 26 and 27), and 0 otherwise; Machinery takes the value of 1 if the acquirer manufactures machineries and other equipment (NACE two-digit Rev. 2 28), and 0 otherwise; Transport is equal to 1 if the acquiring firm’s manufacturing sector is automotive or transport equipment (NACE two-digit Rev. 2 29 and 30), and 0 otherwise. Thus, 60 acquisitions (35.39%) occurred in the chemical and pharmaceutical industry, 30 acquisitions (17.65%) occurred in the electronic and electrical manufacturing sector, 31 (18.24%) M&As occurred in the machinery industry, and 49 (28.82%) M&As occurred in the transport industry. Data on the primary acquirer industry come from BvD Orbis 3.4.4 Year dummies Finally, since we pool data over a 9-year period, which witnessed tremendous macroeconomic shocks, we controlled for the years of the financial crisis by adding 2 dummy variables related to the years in which acquisitions occurred (Year t for t = 2006, 2007). In this way we account

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for macro shocks that could affect the cross-border investment activities. In fact, we have been observing a general tendency of EMNEs to aggressively takeover foreign firms, leveraging on their liquidity advantage and on the home country government support and capitalizing on the financial exigency of the target firms, especially in advanced countries (Peng, 2012). 3.5 Model and methodology To test our hypothesis, we started from the following equation model: Share of equityi = β0 + β1 Tech-seekingi + β2 Cultural distancei + β3 Target service sectori +

β4 Target tech industryi + β5 Controls + εi where i=1, 2, …, 170 are the acquisition events; Share of equity is the dependent variable, which has been measured as the share of equity acquired; Tech seeking is the dummy taking the value of 1 if the acquisition was technology-seeking; Cultural distance refer to the measures of cultural distance described in the previous section; Target service sector in the dummy taking the value of 1 if the target firm was in the service sector; Controls are the described control variables described above, and ε is the error term. To test that the technology-seeking motivation underlying the acquisitions may affect differently the relationship between the share of equity acquired and the target- and industry-specific characteristics, we did further analysis. More specifically, we tested our basic equation model on the two subsamples of investments separately, namely on the subsample of technology-seeking acquisitions and on the subsample of market-seeking acquisitions, respectively. Given that our dependent variable is bounded between 0 and 1, we employed a Tobit regression model rather than an ordinary least squares regression. Prior entry mode studies have used Tobit regression for analyzing the share of equity ownership (Chari & Chang, 2009). In addition, we control for the lack of independence between observations belonging to the same acquirers, considering that some of the deals in the sample (53 observations, 31.18% of the whole sample) referred to acquisition made by the same firms. Similar to the approach used by Chari and Chang (2009), we used the cluster option that corrects for the issue by computing robust standard errors that account for observations clustered by firms. Table 4 provides the descriptive statistics of the dependent and explanatory variable, and

Table 4. Descriptive statistics

Obs Mean Std. Dev. Min Max

Share of equity 170 0.87 0.25 0.07 1

Tech-seeking 170 0.35 0.48 0 1

Cultural distance 170 2.35 1.07 0.84 5.32

Uncertainty avoidance 170 2.44 2.35 0 13.37

Individualism 170 2.79 2.22 0.02 9.09

Target service sector! 170 0.16 0.37 0 1

Target tech industry! 170 0.78 0.41 0 1

GDP growth! 170 1.81 2.75 -6.80 8.40

Host cross-border M&As! 170 0.06 0.07 0.00 0.21

Table 5 shows the correlations among these variables. We did further analysis in order to exclude multicollinearity problems.

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Table 5. Correlation matrix (1) (2) (3) (4) (5) (6) (7) (8) (9)

(1) Share of equity 1

(2) Tech-seeking -0.23 1

(3) CD -0.1402 0.2264 1

(4) UA -0.1416 -0.1359 0.0016 1

(5 IDV -0.106 0.166 0.75 -0.12 1

(6)Target serv. sector -0.193 0.20 0.092 -0.016 0.057 1

(7) Target tech ind. -0.06 0.061 -0.058 -0.14 -0.054 -0.303 1

(8) GDP growth 0.174 -0.172 -0.193 -0.09 -0.1388 -0.111 0.12 1

(9) Host CB. M&As 0.067 -0.026 -0.239 -0.582 0.1731 0.06 -0.097 0.0452 1

4. RESULTS

Table 4.1 reports the estimated coefficients of our econometric model. Specifically, we report the results obtained using the basic equation model with different measures of cultural distance: uncertainty avoidance and individualism (1), and the composite index (2). Furthermore, we tested the basic model with different measures of cultural distance on the two subsamples of investments separately, namely on the subsample of technology-seeking acquisitions (1.1 with uncertainty avoidance and individualism, and 2.1 with the composite index) and on the subsample of market-seeking acquisitions (1.2 with uncertainty avoidance and individualism, and 2.2 with the composite index), respectively. First, it can be noted that the variable Tech-seeking exhibits a negative and highly significant coefficient (p<0.01 and p<0.05), thus showing that EMNEs tend to prefer lower share of equity when the main motivation underlying the acquisitions is technology-seeking with respect to market-seeking investments. Our Hypothesis 1 is, indeed, confirmed. As regards target firm’s characteristics, we start analyzing the impact of cultural distance measures. Only Uncertainty avoidance comes out negative and significant (p<0.05), meaning that the higher the distance the lower the share of equity acquired by EMNEs, confirming our Hypothesis 2a. Also the variable Target service sector is significant (p<0.1) and it affects negatively the level of commitment of EMNEs. Thus, as predicted by our Hypothesis 2b, dissimilarities in the knowledge base of the acquiring and target firms push the first to lower level of commitment in order to favor knowledge transfer (Barkema and Vermeulen, 1998) Considering the industry-specific effect, we found that, when the target firm operates in R&D-intensive industry, the acquiring firm seeks more likely for lower level of commitment than when the target firms is in non-R&D-intensive industry. The coefficient of Target tech industry is, indeed, significant (p<0.05) and negative, supporting Hypothesis 2c. When testing the base mode on the two different subsamples, i.e. on technology-seeking (1.1 and 2.1) and on market-seeking acquisitions (1.2 and 2.2) separately, we discover that target firm- and industry-specific variables have different impact on the dependent variable. More specifically, cultural distance measured with uncertainty avoidance doesn’t impact the ownership decision when the EMNE’s acquisition is aimed at market seeking rather than technology seeking. In fact, Uncertainty avoidance is significant (p<0.1) only in model 1.2 (but not in model 1.1) indicating that it negatively affects the share of equity. Conversely, both the variable Target service sector and Target tech industry turn out to be significant (p<0.1) in model 1.1 (and 2.1), but they don’t in model 1.2 (and 2.2). In particular, in model 1.1 (and 2.1) the variables Target service sector and Target tech industry show a negative relationship with the level of commitment. In other words, when EMNEs acquire European firms for market seeking, their ownership decision is mainly driven by the uncertainty avoidance. Thus, the higher the distance the lower is the level of commitment in order to leverage the existing relationships with customers, suppliers and local institutions. On the contrary, when EMNEs acquire advanced market companies looking for the technical

12 !

competences embodied in the targets, they more likely tend to prefer low level of commitment if the partner possess unrelated and sophisticated know-how because they need to rely on the full cooperation of the partner in order to learn. Models showed in Table 6 give similar results considering our explanatory variables, except for the different measures of cultural distance. Models using the composite index of cultural distance (2, 2.1 and 2.2) show that the variable Cultural distance doesn’t come out significant, whereas, isolating the different Hofestede’s dimensions, we obtained significant coefficients that confirm our hypotheses. This result highlights the problems related to the aggregated Kogut and Singh’s (1988) index of cultural distance already observed by Shenkar (2001). Table 6. Results of the robust Tobit regression analysis with different measures of distance: uncertainty avoidance and individualism (1), and cultural distance (2)

(1) (1.1)a (1.2)b (2) (2.1)a (2.2)b

Tech-seeking -0.318*** -0.290** 0.112 0.113 Cultural distance -0.02 -0.0701 0.0021 0.0582 0.0833 0.0752 Uncertainty avoidance -0.161** -0.0406 -0.189* 0.0746 0.0824 0.101 Individualism -0.0512 -0.109 -0.0187 0.058 0.0788 0.0822 Target service sector -0.295* -0.305* -0.222 -0.289* -0.286* -0.253 0.155 0.173 0.271 0.165 0.168 0.272 Target tech industry -0.307** -0.353* -0.275 -0.233* -0.318* -0.155 0.148 0.192 0.209 0.14 0.19 0.2 GDP growth 0.0456 0.0686 0.0586 0.0607 0.067 0.0921 0.0622 0.0687 0.0849 0.0607 0.0634 0.0871 Host cross-border M&As

-0.0111 -0.0735 0.0968 0.0701 -0.0876 0.239**

0.0747 0.0883 0.126 0.0651 0.0758 0.119 Electronic! -0.148 -0.0874 -0.113 -0.07 -0.109 -0.0452 0.181 0.231 0.25 0.184 0.223 0.249 Machinery! -0.0443 -0.163 0.0161 -0.0257 -0.208 0.111 0.172 0.205 0.285 0.168 0.206 0.279 Transport -0.104 -0.15 -0.24 -0.009 -0.061 -0.108 0.148 0.227 0.215 0.146 0.228 0.211 Year yes yes yes yes yes yes Cons 1.778*** 1.439**

* 1.870***

1.680*** 1.427*** 1.726*** 0.189 0.257 0.278 0.182 0.25 0.262 N 170 60 110 170 60 110 Pseudo R-sq. 0.118 0.113 0.093 0.094 0.097 0.073

Note: Variables have been standardized. Standard errors are robust after adjusting for clustering by acquirer. Standard errors in second row;.* p<0.1, ** p<0.05, *** p<0.01. a Basic model tested using the 60 technology-seeking acquisitions b Basic model tested using the 110 market-seeking acquisitions

13 !

In order to deepen the understating of the role of cultural distance in influencing the ownership decisions of the EMNEs investing for technology seeking, we tested the base model introducing the moderation effect of cultural distance. Table 4.2 reports the results of the equation model with different measures of cultural distance: uncertainty avoidance and individualism (1), and the composite index (2). The results are similar to those of Table 4.1 as regards the explicative and the control variables. Furthermore, we found that the interaction between Tech-seeking and Uncertainty avoidance turns out to be positive and significant (p<0.1). In other words, when EMNEs acquire target firms in advanced countries aiming at acquiring their technological competences, the difficulties caused by high level of cultural distance push EMNEs to prefer full acquisitions, instead of partial shared ownership. Indeed, they can overcome the problems with little regard for the negative consequences of the acquisition for the existing corporate equilibrium (Jemison & Sitkin, 1986). Even though leaving the target with a stake in the firm helps to ensure a smooth tacit knowledge transfer, which is the main aim of technology-seeking acquisitions, the acquiring firm perceives high risk of failures and post-integration costs in case of high cultural distance, and so may choose high level of commitment. Imposing its organizational system, the acquiring firm tries to assure - at least - the acquisition of the tangible technological assets such as R&D labs and patents. Furthermore, they can use the product portfolio of the advanced target firm, and extract knowledge analyzing the underlying technological principals through reverse engineering. Table 7. Results of the robust Tobit regression analysis with different measures of distance, i.e. uncertainty avoidance and individualism (1), and cultural distance (2), and with moderation effect (1.1, 1.2, and 2.1)

(1) (1.1) (1.2) (2) (2.1) Tech-seeking -0.318*** -0.536*** -0.114 -0.290** -0.305 0.112 0.165 0.178 0.113 0.272 Cultural distance(CD) -0.02 -0.0239 0.0582 0.0887 Uncertainty avoidance (UA) -0.161** -0.238*** -0.161** 0.0746 0.0786 0.0716 Individualism(IDV) -0.0512 -0.0474 0.0168 0.058 0.0574 0.0802 Target service sector -0.295* -0.25 -0.308** -0.289* -0.288* 0.155 0.155 0.154 0.165 0.161 Target tech industry -0.307** -0.295** -0.328** -0.233* -0.233* 0.148 0.145 0.149 0.14 0.141 Tech-seeking*CD 0.00601 0.104 Tech-seeking*UA 0.0883* 0.0461 Tech-seeking*IDV -0.0659 0.0458 GDP growth 0.0456 0.0409 0.0569 0.0607 0.0602 0.0622 0.0579 0.0603 0.0607 0.0597 Host cross-border M&As -0.0111 -0.0167 -0.0162 0.0701 0.0701 0.0747 0.0708 0.074 0.0651 0.0651 Electronic -0.148 -0.148 -0.116 -0.07 -0.0713 0.181 0.18 0.18 0.184 0.186

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Machinery -0.0443 -0.102 -0.0617 -0.0257 -0.025 0.172 0.17 0.17 0.168 0.168 Transport -0.104 -0.148 -0.103 -0.00921 -0.0101 0.148 0.15 0.146 0.146 0.147 Year yes yes yes yes yes cons 1.778*** 1.793*** 1.798*** 1.680*** 1.679*** 0.189 0.193 0.193 0.182 0.182 N 170 170 170 170 170 Pseudo R-sq. 0.09 0.099 0.096 0.74 0.074

Note: Variables have been standardized. Standard errors are robust after adjusting for clustering by acquirer. Standard errors in second row;.* p<0.1, ** p<0.05, *** p<0.01.

5. DISCUSSION AND CONCLUSION In this paper we have investigated foreign acquisitions of Chinese and Indian high and medium-high tech manufacturing firms that occurred in Europe during the 2003-2011 period. In particular, we have analyzed the EMNEs’ acquisition choices as regards the ownership decisions that they assume in foreign target firms. Our results contribute to the empirical literature about EMNEs’ internationalization strategies and entry mode choices. Our findings confirm that Chinese and Indian MNEs acquire less control when they seek for technological competences rather than when they intend to acquire existing customer base or established brand names. In addition, we find that the characteristics at firm- (i.e. cultural distance and knowledge base) and industry-level (i.e. R&D-intensity) have a different impact on the ownership decisions depending on the motivation of the acquisition. In particular, when EMNEs aim at market seeking, as a result of cultural distance they acquire low level of ownership. On the contrary, when EMNEs acquire advanced market companies looking for the technical competences, they tend to prefer low level of commitment as a consequence of the partner’s dissimilar knowledge or the high specificity of the resources to be transferred. As usual, the current study has a number of limitations that provide the opportunity for future research. The major limitation steams from the scantly data about the companies in our sample. Given that it is very difficult to obtain financial and account information about the target and the parent firms, this limits the possibility to account for other target firm-specific characteristics such as the size or the R&D intensity, or to include acquiring firm-specific factors, i.e. international experience, R&D intensity, size. We also had a lot of missing values that dramatically reduced our sample. Another possible limitation derives from the data used for measuring the cultural distance. Shenkar (2001) points out that most cultural distance indexes and constructs (e.g. Kogut & Singh, 1988) oversimplify the relationship between countries, implicitly assuming the lack of corporate culture variance (e.g. Hofstede et al., 1990).

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APPENDIX Table A. Coding agenda, coding examples and coding rules

Category Definition Examples Coding rules

Technology-

seeking

M&A

The acquiring company look for

R&D capacity or outputs, innovative

products or production processes,

design facilities, patent portfolios of

local firms, and knowledge spillovers

provided by the target firm.

‘Complementary capabilities between Mahindra & GRD will

enhance the product development capabilities, provide a solid

European footprint for M&M to leverage technologies &

skillsets by harnessing the talent pool of designers and

engineers,’ [Mr Pawan Goenka, President of the Automotive

Sector of Mahindra Group] (Mahindra & Mahindra Ltd.

acquired G.R. Grafica Ricerca Design SRL in 2008)

If at least one of the aspects

cited in the definition of

Technology-seeking M&A is

mentioned as the main or the

only motivation of the

investment.

Market-

seeking

M&A

The investment is aimed at reaching

local or regional markets, often

including neighboring countries.

Underlying these types of

investments there are trade support

reasons, e.g. to access distribution

facilities, to facilitate exports, to gain

strategic positions in the value chain,

to acquire brand names.

‘The acquisition of majority stake in MSI provides immense

synergy benefits to both RSB and MSI. RSB, which exports

substantial heavy fabrications to Europe, can now have a

front-end presence in Europe to consolidate and grow its

exports. offered by RSB-MSI combine’. [Mr. S. K. Behera,

Vice Chairman of RSB Trasmissions India Ltd.] (RSB

Transmissions India ltd acquired Mechanical Supplies

International NV in 2010)

If at least one of these aspects

cited in the definition of

Market-seeking M&A is

mentioned as the main or the

only motivation of the

investment.