Formal institutional uncertainty and equity sought on foreign market entry: does industry matter?

Kashif Ahmed (Graduate School of Business Administration, Kobe University, Kobe, Japan)
Ralf Bebenroth (Research Institute for Economics and Business Administration, Kobe University, Kobe, Japan)
Jean-François Hennart (Department of Management, Tilburg University, Tilburg, The Netherlands)

Review of International Business and Strategy

ISSN: 2059-6014

Article publication date: 15 May 2020

Issue publication date: 11 September 2020

668

Abstract

Purpose

This study aims to examine how the effect of host country formal institutional uncertainty on the percentage of equity sought in cross-border acquisitions (CBAs) is moderated by the host country industry (i.e. targets from the technology versus those from the non-technology industry).

Design/methodology/approach

This study is based upon the legitimacy perspective of institutional theory and uses Tobit regression analysis on a sample of 1,340 CBAs.

Findings

Results show that cross-border acquirers prefer a lower equity level for targets in institutionally less developed countries and that this negative effect of the host country institutional risk on the equity percentage sought is more pronounced for technology-based targets.

Research limitations/implications

Three major limitations of the study are as follows: The data were collected from only Japanese acquirers. The study measured formal institutional uncertainty by applying only secondary data. The study used the Bloomberg Industry Classification Systems, instead of the Standard Industry Classification that has been used widely in prior studies.

Practical implications

This study shows that the industry selected has a bearing on equity sought in CBAs. Investing in institutionally less developed countries is particularly challenging when the targets of acquisition are in the technology industry.

Originality/value

To the best of the authors’ knowledge, this is the first study that investigates the moderating effects of an industry on the relationship between host country formal institutional uncertainty and the percentage of equity sought in CBAs.

Keywords

Citation

Ahmed, K., Bebenroth, R. and Hennart, J.-F. (2020), "Formal institutional uncertainty and equity sought on foreign market entry: does industry matter?", Review of International Business and Strategy, Vol. 30 No. 3, pp. 421-440. https://doi.org/10.1108/RIBS-01-2020-0005

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited


1. Introduction

Firm ownership decisions in cross-border acquisitions (CBAs) are some of the most frequently studied topics in international business (IB) studies. Scholars contend that institutional distance between home and host countries, as well as the level of formal institutional uncertainty in host countries, has to be taken into account when conducting CBAs. Accordingly, evidence shows that acquirers tend to opt for a lower equity share in their cross-border targets when the host country risk is higher (Chari and Chang, 2009). While the direct effect of institutional risk or uncertainty on the entry mode choice has been extensively investigated (Brouthers and Hennart, 2007), there are relatively fewer studies on moderating factors affecting this relationship (Ando, 2012; Arslan and Dikova, 2015; Cui and Jiang, 2012; Liou et al., 2017). The studies available show that diverse factors come into play, such as acquirer’s international experience (Ando, 2012), acquirer’s host country experience (Arslan and Dikova, 2015), state ownership of the acquirer (Cui and Jiang, 2012) and even the stage of development of home country human resources (Liou et al., 2017). Nonetheless, a neglected area of research is how the type of industry affects the acquirer’s response to formal institutional uncertainty in cross-border deals. To fill this gap in the literature, we theorize and empirically validate the relationship between host country formal institutional uncertainty and the percentage of equity sought and how this is moderated by the host country-specific industry, focusing on targets in the technology industry versus those in the non-technology industry.

The technology industry is chosen in this study for two reasons. First, technology transfer is a major reason why firms internationalize (Graebner et al., 2010), thus highlighting the practical importance of this study. Second, the technology industry is also theoretically important for our study as we focus on firms’ responses to institutional uncertainty. Cui and Jiang (2012) reasoned that if a foreign acquirer was perceived negatively in the host country for possible opportunistic behavior, it would experience greater difficulty in gaining legitimacy (Kostova et al., 2008). It would thus experience greater institutional scrutiny by local politicians during the acquisition process. These firms would be under greater threat to conform to institutional pressure, and therefore, the negative effect of institutional uncertainty on the percentage of equity sought should be more pronounced. In the context of our study, host country governments consider it essential to protect their technology firms from diffusing abroad through CBAs (Globerman and Shapiro, 2009). For this reason, cross-border acquirers seeking to acquire technological targets are perceived as a threat owing to their tendency for opportunistic behavior. Therefore, we expect that the effect of host country formal institutional uncertainty on the percentage of equity sought should be more pronounced for acquirers seeking targets in the technological sector.

To the best of the authors’ knowledge, Elango et al. (2013) is the only study showing an interaction effect between the institutional distance and technology targets in CBAs’ market entry decisions. However, their theoretical focus was on the moderating influence of the institutional distance on the relationship between the host country industry (technology versus non-technology) and the percentage of equity sought. In contrast, our study focused on the moderating effect of the industry (technology versus others) on the relationship between institutional uncertainty and the percentage of equity sought. Although the statistical test for either case relied on the interaction effect of the two variables, the underlying theoretical inquiry for both cases was entirely different. Moreover, the contextual focus of Elango et al. (2013) was limited to acquisitions in four major emerging economies, with their deals classified as technology-driven only when non-technology bidders took over technology targets. In contrast, we were more interested in the sector/industry the target was from, regardless of whether or not the bidder was from the technology industry. Also, we focused on worldwide acquisitions, albeit taken over by only Japanese firms. There were two major reasons for selecting Japanese companies for this study. First, Japan has actively borrowed from a multitude of foreign technologies to facilitate its local technological innovations (Petrescu, 2009). For this reason, we expected Japanese firms to conduct CBAs also partly to enhance their technological prowess further. Second, the geographical location of Japan in East Asia makes it easy for Japanese firms to conduct acquisitions in numerous neighboring countries even if they are not much developed institutionally such as Malaysia or Thailand. Hence, although our sample was limited to acquiring firms only from Japan, our data still had substantial heterogeneity in the institutional development on the target side. Building on the study of Elango et al. (2013), we focused on a different set of research questions in the context of Japanese CBAs.

Our study contributes to the IB literature in two ways. First, in confirming earlier findings in the IB literature about the effect of institutional uncertainty on the percentage of equity sought, this study shows that Japanese firms indeed prefer lower equity in institutionally less developed countries. Our findings are based on a comprehensive sample of 1,340 acquisitions undertaken by Japanese firms in the period 2001–2018. The long time horizon of 18 years makes us confident about the richness of our data.

Second, by building upon the legitimacy perspective of institutional theory, our study shows that firms respond to institutional uncertainty in various ways, depending on the type of target sought by the CBA bidder. More specifically, we show that the effect of institutional uncertainty on the percentage of equity sought is more pronounced when acquirers seek technology targets.

We thus contribute to the growing literature on the legitimacy perspective of institutional theory and inspire future research to consider the industry as an important and decisive element. To be more precise, our study motivates future research on examining which technology industry matters the most in influencing the relationship between host country formal institutional uncertainty and equity ownership in CBAs. We further inspire scholars to extend our study in a multi-home and multi-host country setting to provide results with greater generalizability.

After Section 1, the rest of the study is organized as follows. In Section 2, we review relevant literature, followed by the development of two hypotheses. Next, we elaborate on the methods used in this study in Section 3. We then present the results and robustness analysis in Section 4. Section 5 presents a robustness check, followed by a discussion in Section 6. The study ends with limitations and suggestions for future research avenues in Section 7.

2. Theoretical background and hypothesis development

2.1 Formal institutional uncertainty

The institutional theory deals with how social actors become isomorphic or similar to one another under the influence of institutional pressure (Aguilera and Grogaard, 2019; Dikova et al., 2010). Based on this theory, scholars argue that firms’ decisions are influenced by a diverse set of social arrangements such as rules, norms and cultural aspects prevalent in a society (Scott, 2013). Proponents of this theory emphasize the central role of institutions as they provide the rules of the game that govern firms’ economic behavior and interactions (Lahiri et al., 2014; North, 1990; Peng et al., 2008; Scott, 2005). This idea has implications for how multinational firms are viewed. For example, scholars are aware that multinational firms are influenced by both their home-county and host country institutional environments where they set up production facilities(Cui and Jiang, 2012; Enkhtaivan and Davaadorj, 2020; Grogaard et al., 2019). Thus, institutional theory has high relevance for the context of mergers and acquisitions. For example, if the host country’s institutional environment is comparatively less developed, foreign bidders will face increased uncertainties in host country locations (Oguji and Owusu, 2017; Peng, 2002, 2003).

Institutional theory suggests that CBAs are affected by broadly two types of institutions, namely, formal and informal institutions (Dikova et al., 2010; Peng et al., 2008). Formal institutions comprise organizations such as regulatory bodies, law enforcement agencies and economic reform committees (Arregle et al., 2016; Holmes et al., 2013; Yiu and Makino, 2002). In contrast, informal institutions are embedded in the social norms of society (Lahiri et al., 2014). It is widely acknowledged that less developed economies have generally less developed formal institutions. In the context of CBAs, this means that CBA bidders might face higher uncertainty in host countries with restrictive policies with respect to the acquisition, limited access to local resources, mandatory exporting requirements or unnecessary interference in operational matters (Meyer et al., 2009). Such circumstances influence the level of shares that the foreign firm seeks to acquire when taking over a target. The foreign acquirer might opt for a lower shareholding of the target in the host country for smoother operations in the post-acquisition phase, allowing the local firm to participate more actively in, and even lead, the business. Also, higher formal institutional uncertainty puts foreign firms at a disadvantage when competing against local firms (Cui and Jiang, 2012). As such, higher formal institutional uncertainty in the host country leads the bidder to seek lower equity in a cross-border acquisition. In summary, opting for a lower equity share in institutionally less developed countries minimizes cost and risk. Besides, the partial acquisition gives the acquiring firm more flexibility as it has the option of increasing shareholding later. In this way, the level of uncertainty at the beginning of a given investment can be reduced. It would seem, therefore, logical for firms to seek greater equity in institutionally higher developed markets, and lower equity in those that are less developed. Hence, our first hypothesis is as follows:

H1.

The formal institutional uncertainty level in the host country negatively affects the percentage of equity sought in a CBA.

2.2 Heterogeneous responses to institutional uncertainty

A number of IB studies include firm market entry decisions as a dependent variable and institutional distance/development as a moderating variable (Dikova, 2012; Elango et al., 2013; Ilhan-Nas et al., 2018a, 2018b; Schwens et al., 2011; Zhang et al., 2014). While acknowledging that the theoretical focus of these studies is different than ours, we find some insightful guidance in these studies. For example, in a model with firm’s market entry decision as the dependent variable, formal institutional distance/uncertainty has been shown to significantly interact with a range of variables such as the age and international experience of the Chief Executive Officer (CEO) (Dikova, 2012), host country technology industry (Elango et al., 2013), board composition and family ownership (Ilhan-Nas et al.,2018a), ownership concentration (Ilhan-Nas et al.,2018b), firm’s international experience, proprietary know-how, strategic importance (Schwens et al., 2011) and asset specificity (Zhang et al., 2014).

The literature recognizes that firms respond to institutional pressure in heterogeneous ways (Cui and Jiang, 2012; Kostova et al., 2008; Oliver, 1991). In line with this idea, Liou et al. (2017) find that the effect of formal institutional distance on the percentage of equity sought in CBAs is weaker for firms that are short of talented labor in their home countries. In other words, firms that are short of talented labor in their home countries seem to care less about the host country’s institutional distance. Ando (2012) shows that the effect of formal institutional distance on the percentage of equity sought is reduced for firms with greater international experience and also for firms with combined greater international experience and host country experience. Likewise, Arslan and Dikova (2015) also confirm that the effect of formal institutional distance on the percentage of equity sought is weaker for firms with greater host-country experience.

2.3 Legitimacy perspective of institutional theory

The legitimacy perspective of institutional theory considers varied firm responses to institutional pressure resulting from legitimizing mechanisms that the foreign firms pursue (Kostova et al., 2008). To be more specific, when multinationals face high institutional uncertainty, they try to gain legitimacy to avoid institutional pressure by local governments. In essence, multinationals seeking to enter foreign markets tend to engage in symbolic image-building. They may undertake a number of activities to bolster their image, one of which is associating themselves with other legitimate actors (Kostova and Zaheer, 1999). For example, a foreign firm could highlight its close links with its supplier firm that has already gained legitimacy in the host country or publicize its controlling shareholders who have undertaken charity activities in the host country. Hence, by leveraging the positive image of other firms or actors, multinationals try to create positive perceptions about their own future activities. Foreign firms also pursue other activities that are not binding but are seen as socially desirable (such as CSR activities) to increase their legitimacy (Tashman et al., 2019).

According to Kostova et al. (2008), diverse responses arise from these legitimizing mechanisms that are firm- and institution-specific. For example, in the presence of high institutional uncertainty, a negatively perceived firm might be able to gain limited legitimacy by actors in that country compared to a neutrally or positively perceived firm. Hence, owing to different levels of legitimacy gained by firms, there are heterogeneous responses to formal institutional distances. Generally, firms with rather limited legitimacy are under greater pressure to conform to institutional pressure as compared to firms that are able to gain relatively high legitimacy. Hence, in contrast to the classical institutional theory that assumes that all firms behave more or less in a similar way under institutional pressure, the legitimacy perspective of institutional theory recognizes that firms respond to institutional uncertainty in a somewhat heterogeneous way, depending on the amount of legitimacy gained.

2.4 Formal institutional uncertainty in technological acquisitions

In this study, we examined how firms responded to formal institutional uncertainty in CBAs, specifically contrasting technology targets with those in other industries. From the legitimacy perspective of institutional theory, CBA bidders seeking technology firms are expected to experience greater institutional pressure because of their negative image in the host countries. The literature shows that when foreign firms approach local technology firms, host-country institutions rigorously safeguard their local technologies from diffusing abroad. As technology transfer is a major motivation in technology acquisitions, host country institutions are particularly concerned about the opportunistic behavior of foreign firms and its possible negative effect on the local economy (Globerman and Shapiro, 2009). In other words, if acquirers seeking technology targets do not comply with institutional norms, host country governments are likely to take steps to prevent the target technology firms from being exploited.

In this connection, Cui and Jiang (2012) found that state-owned Chinese firms experienced greater scrutiny by host country governments when taking over foreign targets. Consequently, they had to find ways, albeit limited, to gain legitimacy while facing greater institutional uncertainty. For similar reasons, we expect that firms are more sensitive to formal institutional uncertainty when seeking technology targets compared to non-technology ones. We formulate our second hypothesis as follows:

H2.

The negative effect of host country formal institutional uncertainty on the percentage of equity sought is more pronounced for technology targets.

The conceptual model of our study is presented in Figure 1.

3. Methods

3.1 Acquisition deals

We retrieved the data for acquisition deals from the Bloomberg database. Owing to our research design and the nature of control variables, we covered only the CBAs undertaken by publicly listed Japanese acquirers. Also, we shortlisted only those transactions in which the CBA bidder did not have any ownership in the target prior to the deal and where the bidder ended up with at least 5% ownership after the completion of the deal. Furthermore, we focused only on completed transactions. Because of data limitations for control variables, we were restricted to a final sample of 1,340 transactions that were announced between 2001 and 2018.

3.2 Analytical model

We ran the following model sequentially to test our hypotheses.

Percentage of equity sought=β0+β1formal institutional uncertainty×technology target+β2formal institutional uncertainty+β3technology target+β4target size+β5acquirer size+β6acquirer experience+β7horizontal relatedness+β8cultural distance+β9host country size+β10year dummies+ε

3.3 Dependent, independent and moderating variables

The dependent variable for our study was the percentage of equity sought in CBAs. The data for this continuous variable were obtained from the Bloomberg database. Because our dependent variable, namely, the percentage of equity sought, had an upper bound of 100%, we conducted Tobit regression analysis because of its ability to consider censored-dependent variables (Greene, 1993). Hence, our study is in line with the methodology of prior studies, with the percentage of equity sought as the outcome variable (Ahammad et al., 2017; Chari and Chang, 2009; Chikhouni et al., 2017; Cui and Jiang, 2012; Cuypers and Martin, 2010; Pan, 2002).

The independent variable of our primary interest was formal institutional uncertainty in the host countries. For this variable, we first averaged governance scores in each host country based on six dimensions of the worldwide governance indicators (WGIs):

  1. voice and accountability;

  2. political stability and lack of violence;

  3. government effectiveness;

  4. regulatory quality;

  5. rule of law; and

Second, rather than measuring the governance score of only one year prior to the deal announcement, we calculated the average governance scores of three years prior to the deal announcement. For example, for an acquisition announced in 2015, the average governance score was calculated for three years, i.e. 2012–2014. This operationalization took into consideration managers’ decision-making trends over a longer time span rather than in a single year (Ahammad et al., 2017; Chari and Chang, 2009). Finally, for ease of interpretation, we reverse-coded the governance scores so that a higher score indicated greater uncertainty in the host country. In an unreported analysis, we conducted principal component analysis (PCA) of six WGI dimensions and found that all six dimensions loaded on a single factor with Cronbach’s alpha of 0.9679, explaining 86.69% of the total variability. We further received consistent regression results when we measured formal institutional uncertainty based on the PCA method.

The moderating variable, technology target, was operationalized as a dummy variable that received the value of 1 for technology targets and 0 for non-technology targets. To classify targets from the technology industry, we used the Bloomberg Industry Classification Systems (BISCs) for which the technology sector includes four industry groups:

  1. computers;

  2. office/business equipment;

  3. semiconductors; and

  4. software.

These groups have been included in high-technology industries in prior studies as well (Certo et al., 2001; Elango et al., 2013; King et al., 2008; Walter et al., 2008). In an unreported analysis, we further classified technology targets based on other sources in pharmaceuticals, aerospace, biotechnology, telecommunications and electronics owing to their high R&D intensity (NSF, 2018; OECD, 2011) and obtained consistent results.

3.4 Control variables

We controlled for several variables that had been identified in prior literature as important (Ahammad et al., 2017; Chari and Chang, 2009; Chikhouni et al., 2017). At the firm level, we included target size to control for its possible negative effect on our outcome variable (Chari and Chang, 2009). Following Ahammad et al. (2017), we operationalized target size as the natural logarithm of its market value, where market value was estimated as acquisition value/percentage of equity sought × 100. Similarly, we controlled for acquirer size as the natural logarithm of total sales (Chikhouni et al., 2017; Chari and Chang, 2009; López-Duarte and García-Canal, 2002, 2004). Additionally, we controlled for acquirer experience by operationalizing it as the number of years since the acquirer’s first acquisition in the host country (Chen, 2008; Chen and Hennart, 2004; Chikhouni et al., 2017; Mariotti et al., 2014). All these firm-level data were also obtained from the Bloomberg database.

At the industry level, prior research controlled for the effect of horizontal relatedness between acquirers and targets by checking whether both firms belonged to the same industry (Ang and Michailova, 2008; Chari and Chang, 2009; Contractor et al., 2014; Dang and Henry, 2016; Lahiri et al., 2014; Reuer and Koza, 2000; Vasudeva et al., 2018). In the same way, we operationalized horizontal relatedness as a dummy variable that received the value of 1 if the acquirer and target belonged to the same third-level industry classification as per BISC and 0 otherwise (Ahmed and Bebenroth, 2019; Waqar, 2020).

At the country level, we included two control variables. First, we controlled for the cultural distance between Japan and the host countries (Ahammad et al., 2017; Contractor et al., 2014; Chari and Chang, 2009). Following prior studies, we measured cultural distance by Kogut and Singh’s (1988) composite index based upon Hofstede’s (1980) four cultural dimensions (Arslan and Wang, 2015; Demirbag et al., 2007; Dow et al., 2016; Lahiri et al., 2014; Liang et al., 2009; Vasudeva et al., 2018). The data for this variable were obtained from Hofstede et al. (2010). Second, we controlled for host country size by operationalizing it as the natural logarithm of host country GDP (Liang et al., 2009). We measured this variable based on a three-year average prior to the deal announcement. The data for this variable were obtained from the World Bank.

Finally, as the data were from multiple years, we included year dummies in the regression analysis. Definitions, references and data sources for all variables are presented in Table 1.

4. Results

4.1 Descriptive statistics

We report the descriptive statistics and correlation matrix of our sample of 1,340 observations including 116 technology acquisitions in Table 2. We checked the variance inflation factors (VIFs) for these variables and found that the highest VIF value was 1.47, showing the absence of any multicollinearity issue. We centered all continuous variables before using them in the regression analysis.

4.2 Tobit regression analysis

The results are reported in Table 3. In Model 1, we included only control variables that explained 16.54% variation in the dependent variable. As our continuous dependent variable represented the percentage of equity sought in an acquisition deal, the significant and negative coefficient of the target size (p < 0.01) implied that the acquirer tended to choose lower equity for larger targets. Similarly, the positive and significant coefficient of horizontal relatedness (p < 0.1) implied that acquirer tended to choose greater equity in horizontally related target industries.

In Model 2, the coefficient of formal institutional uncertainty was significant and negative (p < 0.01), supporting H1. The significant and negative interaction term in Model 3 (p < 0.05) implied that the negative effect of formal institutional uncertainty on the percentage of equity sought was more pronounced for technology targets. Hence, H2 was also supported. For a better interpretation of the results of H2, we present the interaction plot in Figure 2, showing that the line for technology targets is steeper than that for non-technology firms.

5. Robustness check

We conducted a number of robustness checks in addition to our main analysis. Many studies have considered firm entry mode decision as a binary choice in which partial acquisitions are compared with full or majority acquisitions. As part of such investigations, some scholars have used a relatively high cutoff, such as 95% (Vasudeva et al., 2018; Cui and Jiang, 2012). In contrast, others have acknowledged that relatively low cutoffs, such as 50%, also matter (Dang and Henry, 2016; Demirbag et al., 2007). While embracing the benefits of using these specific cutoffs, we opted for a more robust methodology used by Chen (2008), in which a number of cutoffs were analyzed to contrast partial versus full/majority acquisitions. Therefore, we considered four cutoffs with 20% point differences: 95%, 75%, 55% and 35%.

The results of these robustness checks are presented in Table 4. In line with prior studies, we conducted logistic regression analysis for our binary outcome variable (Arslan and Wang, 2015; Chen, 2008; Chen and Hennart, 2004; Liang et al., 2009). In Panel A, all transactions with the percentage of equity sought less than or equal to 95% were classified as partial acquisitions, while all transactions with the percentage of equity sought greater than 95% represented full acquisitions. In the same way, the cutoffs of 75%, 55% and 35% were applied in Panel B, Panel C and Panel D, respectively. Throughout Table 4, partial acquisition was the reference category of our dichotomous dependent variables.

Table 4 shows that H1 was supported for all four cut-offs, while H2 received support for three out of four cutoffs. It also lent support to Dang and Henry’s (2016) advice that studies need to be cautious in their binary classification of partial versus full acquisitions. It must also be mentioned that stock exchange regulations in some countries mandate a delisting of firms if the ownership of the largest shareholder exceeds a certain cutoff. In fact, Dang and Henry (2016) mention cutoff values of certain countries ranging from 80% to 95%. Hence, in a country with a cutoff value of 80%, a deal with even 81% would be classified as full acquisition. As such, in future studies, multiple cutoffs for a comprehensive interpretation of their findings should be considered. Overall, our results were quite robust to these alternative ways of operationalizing our dependent variable.

6. Discussion

In this study, we focused on the interplay between the level of host country institutional uncertainty and the equity sough tin CBAs, comparing targets from the technology versus those from the non-technology industry. We tested two hypotheses on a sample of 1,340 deals undertaken by Japanese firms:

  1. direct effect of host country formal institutional uncertainty on the equity sought; and

  2. moderating influence of technology industry on the relationship between formal institutional uncertainty and equity sought. We discuss below our results in the light of prior literature.

First, corroborating a number of previous studies, we found that Japanese firms preferred lower equity participation in targets in institutionally less developed host countries. This finding is supported by Delios and Beamish (1999) who found that host country restrictiveness led Japanese firms to choose lower equity in their targets located in nine east and south-east Asian countries. Yiu and Makino (2002) note that generally Japanese firms prefer joint ventures over wholly owned subsidiaries in host countries with more restrictive regulatory environments. Hence, our finding supports the view that joining a local partner in a regulation-wise restrictive host country allows the acquiring firm to lessen its liability of foreignness. Similarly, based on a sample of 2,399 observations, Beamish et al. (2004) found that large institutional distance (regulative and normative) led Japanese firms to choose a lower equity level in CBAs. Likewise, based on a sample of foreign subsidiaries established in Turkey, Demirbag et al. (2007) noted that foreign firms avoided wholly owned subsidiaries when facing higher differences in the corruption index between Turkey and their home countries. In the same way, Chari and Chang (2009) showed that when the USA firms undertook CBAs in countries with higher risk (measured by the International Country Risk Guide), they chose a lower equity position in the target firms (ICRG, 2019).

Second, we found support for H2 related to host country formal institutional uncertainty with regard to technology targets. By building upon the legitimacy perspective of institutional theory (Kostova et al., 2008), we show that acquirers who sought technology targets were more sensitive than acquirers of targets in other industries. That could be because of their negative image in the host country. Thus, our findings support Kostova and Zaheer’s (1999) argument that the legitimation process is, indeed, complex. In their conceptual paper, the authors contended that host country stakeholders are usually equipped with limited information about foreign firms, in contrast to domestic firms. Hence, host country stakeholders tend to make stereotypical judgments about foreign firms by classifying them into certain groups. While cross-border bidders are often perceived negatively because of their foreignness, multinational enterprises belonging to a certain geographical region or “industry” may face an even stronger resistance (Kostova and Zaheer, 1999: 74). In other words, the legitimation process is marked with spill over effects such that the negative image attached to a certain group of organizations (based on the geographical region or industry) affects the legitimacy of other firms that are perceived to be part of that group. For example, if one specific cross-border investor exploits the agricultural sector in a given host country, other firms also seeking to enter the agricultural sector may automatically be perceived negatively by the host country actors based on negative experiences with the earlier cross-border investor. Similarly, in the context of our study, we argue that as the underlying reason for the acquisition of technology firms is often the transfer of technology back to the home country, foreign acquirers seeking technology targets face difficulty in gaining legitimacy. Host country actors are rather protective of local technology and would take steps to prevent it from diffusing abroad.

Our study provides several intriguing implications. Cui and Jiang (2012) applied the legitimacy perspective of institutional theory to investigate moderating influences of (Chinese) foreign firms’ state ownership on the relationship between host country institutional pressure and market entry choice. In contrast, we advance the legitimacy perspective of institutional theory by comparing technology targets to non-technology ones. Our results somehow contradict those of Elango et al. (2013) who found that firms would rather fully acquire technology targets at a higher formal institutional distance. However, as their study focused on acquisitions made in four major emerging economies, they rationalized their findings by suggesting that when multinationals from developed countries buy technology firms in institutionally underdeveloped countries, they aim for a greater control over the target “to protect their newly acquired technology in an unfamiliar institutional setting” (Elango et al., 2013: 513). In contrast, our analysis of Japanese outbound acquisitions supports the legitimacy perspective of institutional theory on the assumption that acquirers are more cautious when seeking technology firms.

In view of the foregoing, we therefore lend support to the strand of literature that emphasizes that in cross-border deals, industry does matter (Datta et al., 2005; Kim, 2018; Rajagopalan and Datta, 1996; Singal, 2014; Su, 2015; Perrons and Platts, 2005; Yang et al., 2007). Prior research has shown that industry is an important variable affecting CEO characteristics (Rajagopalan and Datta, 1996), organizational learning (Weerawardena et al., 2006) and patent registration (Kim, 2018). There is further literature on capital markets (Merkley et al., 2017), stakeholder management (Griffin and Koerber, 2006), labor productivity (Datta et al., 2005) and earnings management (He and Yang, 2014). In line with these studies, our investigation finds that the effect of host country formal institutional uncertainty on the percentage of equity sought in CBAs is, indeed, moderated by the nature of the industry. In other words, industry does matter, especially if the target is from the technology sector.

Our study has several practical and policy implications. First, we caution acquiring firm managers about increased institutional pressure upon buying technology targets. While host country institutional uncertainty affects all acquiring firms in general, acquirers seeking to buy technology targets may experience greater scrutiny by host country legitimating actors. Therefore, the managers of acquiring firms should be mindful of target firm mangers by addressing their concerns. On the host country side, we advise managers of technology targets to be unbiased toward cross-border acquirers. It is not necessary that all acquirers seeking to buy technology targets have opportunistic intentions. In fact, acquisition of a technology firm may produce synergistic opportunities mutually beneficial for both entities. The target firm managers may straightforwardly discuss possible post-acquisition phase issues with the acquiring firm already during the deal negotiation.

Turning to policymakers, we advise that acquiring firms should be dealt on a case-to-case basis, without a blanket policy of “ban all of them”. To be more precise, the policymakers can distinguish acquirers based on inter alia the type of technology firm sought, purpose of acquisition and planned changes in the target firm after acquisition. Considering these aspects, the policymakers can assist target firms in first devising a contract and later on in enforcing it to save host country technology firms from possible opportunistic behavior of acquiring firms.

7. Limitations and future research avenues

Like any other study, this study is not without its limitations. First, our analysis was based only on the data from Japanese firms, limiting the generalizability of our findings. Even if the authors did not find any convincing reason to worry about including only Japanese firms, future research could still consider multi-country acquirers and targets design to confirm the generalizability. Second, we measured the formal institutional variable, as many other studies did, by relying on secondary data sources (Dikova et al., 2010; Ilhan-Nas et al.,2018a, 2018b; Liou et al., 2016, 2017; Yakubu, 2020; Yiu and Makino,2002).

In line with many other studies (Ahammad et al., 2017; Ang et al., 2015; Contractor et al., 2014; Hernández and Nieto, 2015; Lahiri et al., 2014), and because of its accessibility, we chose the Worldwide Governance Indicator to measure formal institutional uncertainty. Future research could also use interview data (Antunes et al., 2019; Yang and Lutge, 2019) or a survey-based approach to take advantage of perceptual measures (Anwar et al., 2018; Yang et al., 2019). Decision-makers’ views of their environment could be investigated because it is the decision-makers’ perception of the reality – rather than the reality itself – that affects the decision (Cui and Jiang, 2012; Santangelo and Meyer, 2011). Likewise, we resorted to the BICSs for the classification of technology industry sector whereas Standard Industry Classification (SIC) has been used more often in prior studies (Certo et al., 2001; Elango et al., 2013; Hagedoorn and Duysters, 2002; King et al., 2008; Walter et al., 2008). We admit that it makes comparisons to other studies difficult. However, we argue that SIC coding to classify technology firms has not been applied consistently in prior studies. Nonetheless, in future, scholars could replicate our study using SIC-based classification coding for a comparison with our results.

Despite such limitations, this study has shown a moderating effect of technology targets on the relationship between formal institutional uncertainty and the percentage of equity sought. It is hoped that it might inspire more future research on industry heterogeneity.

Figures

Conceptual model

Figure 1.

Conceptual model

Interaction plot

Figure 2.

Interaction plot

Summary of variables

Variables Definitions References Data sources
Percentage of equity sought A continuous variable that represents the percentage of equity sought by the acquirer in CBAs Ahammad et al. (2017), Chari and Chang (2009), Chikhouni et al. (2017) Bloomberg data
Formal institutional uncertainty Reverse-coded governance score of the host countries based upon three years average prior to the deal announcement. The average governance scores in each host country is based on six dimensions of WGIs: (1) voice and accountability, (2) political stability and lack of violence, (3) government effectiveness, (4) regulatory quality, (5) rule of law and (6) control of corruption Ahammad et al. (2017) World Bank data
Technology targets A dummy variable that receives a value of one for technology targets and zero for non-technology targets Certo et al. (2001), Elango et al. (2013), King et al. (2008), Walter et al. (2008) Bloomberg data
Acquirer experience The number of years since the first acquisition in the target country Arslan and Wang (2015), Chen and Hennart (2004), Chen (2008), Chikhouni et al. (2017), Mariotti et al. (2014) Bloomberg data
Acquirer size Natural logarithm of total sales Chikhouni et al. (2017), Chari and Chang (2009), López-Duarte and García-Canal (2002, 2004) Bloomberg data
Target size Natural logarithm of the market value, where market value is estimated as acquisition value/percentage of equity sought × 100 Ahammad et al. (2017) Bloomberg data
Horizontal relatedness Dummy variable that takes the value of one if acquirer and target are from the same third-level industry (as per BICS) and zero otherwise Ahmed and Bebenroth (2019), Ang and Michailova (2008), Chari and Chang(2009), Contractor et al. (2014), Dang and Henry (2016), Lahiri et al. (2014), Reuer and Koza (2000), Vasudeva et al. (2018), Waqar (2020) Bloomberg data
Cultural distance The Kogut and Singh (1988) composite index for the difference between Japan and host country culture based on four dimensions of Hofstede (1980). These dimensions are (1) individualism-collectivism, (2) power distance,(3) uncertainty avoidance and (4) masculinity–femininity Arslan and Wang (2015), Demirbag et al. (2007), Dow et al., 2016; Lahiri et al. (2014), Liang et al. (2009), Vasudeva et al. (2018) Hofstedeet al. (2010)
Host country size Natural logarithm of host country GDP based on the average of three years prior to the deal announcement Liang et al. (2009) World Bank data

Descriptive statistics and correlation matrix

Variables Mean SD (1) (2) (3) (4) (5) (6) (7) (8)
(1) Percentage of equity sought 66.70 36.22
(2) Technology target 0.09 0.28 −0.34***
(3) Formal institutional uncertainty −0.88 0.77 0.08*** −0.07***
(4) Acquirer experience 6.10 5.80 −0.09*** −0.14*** −0.05**
(5) Acquirer size 21.89 2.33 −0.10*** −0.01 −0.02 0.57***
(6) Target size 17.96 2.43 −0.03 −0.09*** 0.02 0.43*** 0.56***
(7) Horizontal relatedness 0.28 0.45 0.05* 0.07** −0.06** −0.07** −0.14*** −0.21***
(8) Cultural distance 3.04 1.14 −0.08*** −0.09*** 0.01 −0.05* −0.02 −0.03 0.00
(9) Host country size 28.11 1.62 0.34*** −0.25*** 0.04 0.11*** −0.03 0.10*** −0.04 −0.37***
Notes:

N = 1,340 observations. ***, **, and * represent statistical significance at 1, 5, and 10%, respectively; SD = standard deviation

Tobit regression analysis

Variables Model 1 Model 2 Model 3
Formal institutional uncertainty * Technology target     −16.80**
(7.97)
Formal institutional uncertainty −23.72*** −22.50***
(2.11) (2.18)
sTechnology target 13.97** 12.78**
(5.73) (5.86)
Target size −2.25*** −3.44*** −3.38***
(0.84) (0.81) (0.81)
Acquirer size 1.21 2.39** 2.20**
(1.01) (0.96) (0.96)
Acquirer experience −0.70* −1.14*** −1.11***
(0.41) (0.39) (0.39)
Horizontal relatedness 6.69* 10.22*** 10.51***
(3.74) (3.57) (3.57)
Cultural distance 3.03** −0.64 −0.64
(1.49) (1.45) (1.46)
Host country size 15.42*** 11.59*** 11.64***
(1.13) (1.11) (1.11)
Intercept 81.28*** 66.20*** 66.50***
(10.22) (9.80) (9.79)
Year fixed effects Yes Yes Yes
Sample size 1,340 1,340 1,340
Pseudo R-squared 16.54% 24.55% 24.81%
Log-likelihood −4355.16 −4287.08 −4284.88
Notes:

The dependent variable for these Tobit regression models is the percentage of equity sought in CBAs. Standard errors are reported in parentheses. ***, **, and * represent statistical significance at 1, 5 and 10%, respectively

Logistic regression analysis

Variables Model 1 Model 2 Model 3
Panel A: partial acquisition ≤ 95%
Formal institutional uncertainty* Technology target −0.45
(0.39)
Formal institutional uncertainty −1.02*** −0.99***
(0.10) (0.10)
Technology target No Yes Yes
Controls Yes Yes Yes
Pseudo R-squared 18.59% 29.59% 29.70%
Panel B: partial acquisition ≤ 75%
Formal institutional uncertainty* Technology target −0.71*
(0.39)
Formal institutional uncertainty −1.05*** −1.00***
(0.09) (0.10)
Technology target No Yes Yes
Controls Yes Yes Yes
Pseudo R-squared 19.78% 31.89% 32.18%
Panel C: partial acquisition ≤ 55%
Formal institutional uncertainty* Technology target −0.67*
(0.35)
Formal institutional uncertainty −0.89*** −0.85***
(0.09) (0.09)
Technology target No Yes Yes
Controls Yes Yes Yes
Pseudo R-squared 19.63% 29.01% 29.31%
Panel D: partial acquisition ≤ 35%
Formal institutional uncertainty* Technology target −0.58*
(0.35)
Formal institutional uncertainty −0.68*** −0.64***
(0.09) (0.09)
Technology target No Yes Yes
Controls Yes Yes Yes
Pseudo R-squared 21.19% 26.41% 26.90%
Notes:

The binary dependent variable for these logistic regression models takes the value of 0 for partial acquisitions and 1 for majority/full acquisitions. Control variables, year dummies and intercepts are included in the regressions but have not been reported for the sake of brevity. ***, ** and * represent statistical significance at 1%, 5% and 10%, respectively. N = 1,340 observations

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Corresponding author

Kashif Ahmed can be contacted at: kashif.ahmed@stu.kobe-u.ac.jp

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