Country uncertainty, power distance, and payment methods in acquisitions

ABSTRACT

This study examines the impact of country-specific uncertainty on the choice of payment method in international acquisitions. Our results show a negative association between the level of target country-specific uncertainty and cash transactions. Specifically, when the host country experiences a high level of country uncertainty, acquirers are more likely to choose non-cash transactions in which acquiring firms can issue their own equity to the target firm as part or all of the purchase consideration of the deal. The result is robust to alternative tests and analysis of subsamples. We also find that differences in uncertainty between host and home countries are informative of bidders’ payment choices. Further, we find that the negative relation between target country-specific uncertainty and cash payment weakens when there are larger differences in power distance between host and home countries. Our findings provide recommendations for policy-making bodies, and have implications for firm managers making corporate restructuring decisions.

KEYWORDS:

• Country uncertainty
• acquisitions
• payment methods
• institutional environment
• power distance

JEL CLASSIFICATIONS:

• G34
• G32
• G38
• D81

1. Introduction

Cross-border acquisitions have been a popular form of foreign direct investment (FDI) in recent years. The value of cross-border acquisitions grew dramatically after the Global Financial Crisis (GFC), reaching US$694 billion in 2017 (UNCTAD 2018). Engaging in cross-border acquisitions is a key strategic and structural decision that enables bidders to exercise their growth opportunities, expand into international markets and leverage their core competences and competitive advantages (Vermeulen and Barkema 2001; Ghauri and Buckley 2003; Cheng and Leung 2004; King et al. 2004; Rossi and Volpin 2004; Chen 2008; Haleblian et al. 2009; Bhagat, Malhotra, and Zhu 2011; Claessens and Yurtoglu 2013; Kim and Lu 2013; Dang et al. 2018). Cross-border acquisitions also bring additional risk considerations owing to varying cross-country institutional settings, and the choice of payment method has been proposed as one means for bidding and target companies to potentially manage these risks (Fuller and Glatzer 2003). Prior research has focused on country-level structural and regulatory attributes such as the legal framework, information transparency, investor protection, and corporate governance mechanisms and their association with acquisition payment decision-making (Huang, Officer, and Powell 2016). In this paper we extend this literature by considering the role of two broad institutional factors, namely country-specific uncertainty associated with the economic and political environments, and the power distance relationship between bidding and target companies, which directly impact on the magnitude of potential acquisition benefits and their achievement and, from this, acquisition risk and method of payment choice.

Acquisition risks for acquirers can come in various forms including the uncertainty associated with estimating synergies and the potential subsequent target mis-valuation, completion risk due to regulatory concerns, low bid premium levels or control impediments such as target company founder or blockholder share interests (Barkema, Bell, and Pennings 1996; Reuer, Shenkar, and Ragozzino 2004). These risks can be mitigated to some degree through extensive due diligence work or carefully planned execution strategies. External risk factors associated with economic activity, government policy settings or financial market conditions at the country-level are much more difficult, or impossible, to directly influence at the transaction level, while they may strongly affect acquisition gain and success outcomes (Chen 2008; Dang et al. 2018). Further, since cross-border acquisitions are more complex in nature due to the country-specific uncertainty which arises when dealing with a target firm in a foreign market, choice of payment alternatives (i.e. cash vs. non-cash) has increasingly important implications for the likelihood of completion and subsequent success of the acquisition (Morosini, Shane, and Singh 1998; Huang, Officer, and Powell 2016). Target firms and shareholders are likewise expected to consider the level of political and economic uncertainty in the bidder’s home market when negotiating or evaluating acquisition offers. Specifically, a high level of uncertainty about a foreign bidder’s equity value and underlying post-acquisition performance will lead to target shareholders increasingly preferring all-cash or cash-dominant offers (Fuller and Glatzer 2003; Dutta, Saadi, and Zhu 2013). This could further be reinforced by the unattractiveness of potentially owning bidding company shares that are listed on an international stock exchange, making a share exchange acquisition less likely to succeed. However, stock payment will also share part of the acquiror’s synergy gains from the acquisition if they end up being realized in the cross-border setting.

Economic uncertainty or lower levels of economic development will impact on the ability of acquirers or merged firms to capitalize on underlying business product or service demand, control or reduce cost levels and implement integration strategies based on infrastructure or innovation motives (Nguyen and Phan 2017; Bonaime, Gulen, and Ion 2018; Cao, Li, and Liu 2019; Sha, Kang, and Wang 2020). Political uncertainty will influence the certainty of asset ownership and control and potentially discourage new investment in host country operations (Julio and Yook 2012). Financial market weaknesses or inefficiency will impact on both the pool of available debt and equity capital and increase the costs of obtaining external financing, which will place potential restrictions on the degree of post-acquisition restructuring activity or new investment. Hence, it is expected that the nature of country-specific economic, political and financial uncertainty will be correlated with underlying acquisition risk and, through that channel, with the choice of payment method.

The nature of the underlying relationship between the acquiring and target companies is also expected to be associated with the perceived level of uncertainty or risk of an acquisition and the subsequent method of payment preferences (Chakrabarti, Gupta-Mukherjee, and Jayaraman 2009; Li et al. 2013; Di Guardo, Marrocu, and Paci 2016). If the bidder is more dominant or has a higher degree of authority and/or the target firm is subservient relative to the bidder, such as based on cultural dimensions like power distance between the bidder and the target (Hofstede 1980), then the bidder may be able to impose their payment method preferences on the target firm. Evidence of such subservience, however, is also likely to increase the probability of acquisition bid success and aid in the bidder’s ability to execute post-acquisition integration objectives and strategies which may mitigate wider concerns about the impact of external factors (such as economic or political uncertainty) on the achievement of acquisition benefits. Thus, we propose a potential link between country-specific uncertainty and cultural distance in determining the choice of payment method.

Using a probit regression framework and a comprehensive sample of domestic and cross-border acquisitions in 58 target countries for the period from 2000 to 2015, we assess the effect of country-specific uncertainty on the payment decision in acquisitions. In addition to the general level of country uncertainty, we examine the impact of differences between home and host country uncertainty on the choice between equity and cash payment terms and the effect of the power distance between home and host markets on payment decisions.

Our results show a negative relation between the level of target country-specific uncertainty and cash acquisition transactions. Specifically, when the host country experiences a high level of country uncertainty, acquirers are more likely to choose non-cash payment methods, instead favoring payment in terms of their own equity. This result is robust to alternative testing procedures and subsamples. We also find that the preference for stock payment is more prevalent in high-uncertainty host countries. Our findings are consistent with the existence of information asymmetry between host and home countries, and incremental target country-specific uncertainty representing a key additional element of information asymmetry having explanatory power for bidder firms’ choice to employ stock as an acquisition payment method to mitigate risk. Regarding the economic significance of the findings, a one-standard-deviation increase in the target country-specific uncertainty (0.52) causes a reduction of approximate 2.76 percentage points [=0.52*(−0.053)] in cash payment likelihood, which is around 6.41% [=0.52*(−0.053)/0.43] of the average cash payment propensity across the sample.

Further, we examine whether the interaction between host country uncertainty and the power distance with the home country affects acquirers’ payment choices. The choice of power distance value (PDV) as the explanatory variable is motivated by arguments that cultural distance provides challenges to a firm’ internationalization process. Cultural distance may alleviate the exposure of acquirers to target country-level uncertainty via building stronger internal resilience and reducing sensitivity to market-level shocks (Lucey and Zhang 2010; Li et al. 2013). Previous research has also supported investors’ home bias pattern, according to which targets show less enthusiasm for unfamiliar foreign stocks in cross-border acquisitions (Coval and Moskowitz 1999; French and Poterba 1991; Grinblatt and Keloharju 2001). Such an effect can also apply to domestic firms where a large ‘culture gap’ exists. Power distance represents attitudes of entities or individuals towards hierarchy versus equality, with high power distance being associated with recognized differences in authority and the existence of autocratic or centralized decision-making and authority, whereas lower power distance is consistent with greater equality and a coordinated approach to decision-making (Hofstede 1980). At the acquisition level, greater power distance between acquiror and target countries, and particularly for target countries with higher power distance traits, would increase the likelihood of target firms and shareholders accepting the payment method proposed by bidders. In the case of lower power distance difference, we may expect to see greater consultation between the two parties in determining the preferred acquisition payment terms and/or target firms may be less willing to accept stock payment from foreign bidders due to overseas market listing and information asymmetry concerns. Thus, both bidding and target companies are expected to integrate country-level uncertainty and cultural considerations in determining their payment preferences in cross-border acquisitions.

Empirically examining this integration, our findings show that the negative relation between target country-specific uncertainty and cash payment likelihood becomes weaker when there are larger differences in power distance between host and home countries.¹ This is consistent with bidders’ method of payment decision-making being less dependent on target country uncertainty considerations when targets are increasingly exposed to a hierarchical cultural environment, suggesting that bidders may be more confident of successful post-acquisition integration and the achievement of acquisition synergies when targets have relatively higher power distance traits.

This paper makes three main contributions to the literature. While various past empirical and theoretical studies focused on firm-level determinants of the payment method in acquisition deals (Ayers, Lefanowicz, and Robinson 2004; Faccio and Masulis 2005; Alshwer, Sibilkov, and Zaiats 2011; Uysal 2011; Boateng and Bi 2013), there is no clear-cut evidence in terms of the effect of country-level institutional environments of target companies on the choice of payment method in acquisition deals. Our findings strengthen prior evidence by Huang, Officer, and Powell (2016), Nguyen and Phan (2017), and Bonaime, Gulen, and Ion (2018), by identifying the importance of country-level uncertainty on the bidders’ payment method choices. The findings of this study provide new evidence explaining the motivations for the trade-off between cash payment and non-cash payment and document the link between host country uncertainty and acquirers’ risk management strategies.

Second, this study enhances our understanding of how risk arising from acquisitive activity in foreign markets with high levels of uncertainty can be alleviated and yields a better understanding of the advantages of non-cash deals in mitigating information asymmetry associated with acquisitions. Understanding the implications of country uncertainty on the payment strategy in cross border acquisitions will allow managers to conduct their acquisitions more efficiently.

Third, this study provides new evidence for the choice between cash and non-cash payment on the basis of one specific dimension of cultural distance, namely power distance value, which, to the best of our knowledge, has not been detailed extensively in prior studies. In the context of acquisitions, a pertinent issue is the acquirer-target role hierarchy (Nahavandi and Malekzadeh 1993). As a start, we focus on PDV due to its immediate ramifications for how acquirers and targets take action in response to a structural condition as part of any acquisition. This is known as the acquirer-target hierarchical role relationship (Hambrick and Cannella 1993; Nahavandi and Malekzadeh 1993; Huang, Zhu, and Brass 2016). According to Hofstede (1980), PDV indicates the degree to which a national culture acknowledges hierarchy or equality as a standard of social life. We show that power distance is important in determining the choice of payment method in acquisitions, and particularly that cultural distance can moderate the role of country-level uncertainty in determining acquisition payment choice preferences. Thus, consideration of target (host) country cultural aspects is important in acquisition planning and relatively greater emphasis of target firms on higher power distance (respect for hierarchy and authority) appears to extend to the acquisition arena and to enhance bidders’ expectations in relation to achievement of acquisition objectives.

The rest of the paper is structured as follows. Section 2 discusses the related literature and hypothesis development. A description of data and methods is reported in Section 3. Section 4 presents the empirical results and Section 5 concludes with a discussion of the implications of our findings.

2. Literature background and hypothesis development

2.1. Institutional environment and acquisitions

When considering the possibility of foreign market entry, it is necessary for the firm to take into account the effect of the foreign country’s social, legal, economic and political framework. This is particularly true for international investment activities in the form of cross-border takeovers, for which there is a high risk of information asymmetry as well as problematic differences in national culture, regulations, institutional arrangements, accounting standards, and tax rules (Shimizu et al. 2004; Dikova, Sahib, and Van Witteloostuijn 2010; Dikova and Sahib 2013; Lawrence, Raithatha, and Rodriguez 2021).² Similar extant literature emphasizes the important role of institutional characteristics in merger and acquisition activities (Bris and Cabolis 2008; Bhagat, Malhotra, and Zhu 2011; Barbopoulos, Paudyal, and Pescetto 2012; Lebedev et al. 2015; Dang et al. 2018). These studies suggest that higher levels of cross-border activity are likely in countries which have higher quality investor protection. Such institutional arrangements include transparency in accounting and information disclosure and strong protection of minority shareholders. Put differently, foreign bidders are generally disadvantaged relative to domestic bidders due to asymmetry of institutional environment, host country-specific uncertainty, culture, and economic conditions. To ensure investment efficiency, bidders will prefer to seek out targets in more transparent, low-risk markets.³

2.2. Method of payment determinants

There has been strong consensus in the literature that one of the vital aspects of the risk management strategy adopted by firms in the acquisition process is the choice of payment method. Hansen (1987), Wansley, Lane, and Yang (1987), Fishman (1989), and Yook, Gangopadhyay, and McCabe (1999) argue that the payment method choices in acquisition deals are based on asymmetric information, with bidders showing a preference for non-cash payment when there is considerable target valuation uncertainty. Specifically, the risk of bidder overpayment is mitigated by using non-cash payment (e.g. stock) as any losses ascribed to overpayment will be shared by post-acquisition acquiring and target shareholders.

Prior literature has focused primarily on firm- and acquisition-level attributes as determinants of method of payment decision-making in both domestic and cross-border acquisition transactions. The two predominant methods of payment employed are cash or stock, either individually or as a combination of the two components. Historically cash has been the most popular consideration used, however, payment via bidder stock has become increasingly common and especially during periods of stronger sharemarket performance (Travlos 1987; Shleifer and Vishny 2003; Alexandridis, Mavrovitis, and Travols 2012; Boone, Lie, and Liu 2014). Method of payment choice in acquisitions has been closely associated with signaling, valuation and financing concepts, with cash payment, which typically implies funding via debt sources, perceived to indicate confidence in acquisition outcomes and benefits, bidding firm undervaluation and the existence of financing capacity and flexibility. Stock payment, on the other hand, reflects negative news regarding acquisition certainty and gains, likely bidder firm overvaluation in the market and bidding firms being financially constrained (Rhodes-Kropf and Viswanathan 2004; Alexandridis, Antoniou, and Zhao 2008; Feito-Ruiz, Fernandez, and Menedez-Requejo 2015).

Prior research has identified method of payment decisions to be related to bidding company attributes including size, agency and governance environment, valuation, capital structure and ownership with cash more likely to be used as the acquisition method of payment if bidders are larger and have stronger governance structures, undervalued stock, greater financing flexibility, higher equity costs, and more concentrated and family ownership structures (Martin 1996; Rhodes-Kropf and Viswanathan 2004; Rossi and Volpin 2004; Faccio and Masulis 2005; Basu, Dimitrova, and Paeglis 2009; Martynova and Renneboog 2009; Teti, Dallocchio, and Currao 2020). Attributes of targets which are correlated with method of payment choice include firm size and relative target to bidder size, underlying risk and listing status (Chang 1998; Faccio, McConnell, and Stolin 2006; Capron and Shen 2007; Officer, Poulsen, and Stegemoller 2009; Boone, Lie, and Liu 2014; Huang, Officer, and Powell 2016) and acquisition-specific determinants of method of payment choice that have been identified include acquisition size, full or partial bid structure, bidder toeholds, and the industry match between bidding and target firms. Prior studies focusing on the cross-border acquisition environment have extended these determinants to include both bidder and target country-level investor protection, governance and legal and transparency environments and indicators (La Porta et al. 1998; Rossi and Volpin 2004; Martynova and Renneboog 2009; Huang, Officer, and Powell 2016). Huang, Officer, and Powell (2016) show that transparency, robustness of corporate governance mechanisms and institutional quality of the target country are key factors affecting the payment method in cross-border acquisitions. When the target country has poorer corporate governance mechanisms, weaker regulatory support for minority shareholders or less transparency, cross-border acquisitions tend to be riskier for bidders.

This paper proposes that broader country-level economic and political uncertainty and the cultural relationship between bidder and target firms directly influence acquisition risk and decision-making, including method of payment choice.

2.3. Country uncertainty

Worries about economic, financial and political uncertainties have expanded worldwide since the GFC period of 2007–2009. Recent International Monetary Fund country reports confirm that such uncertainty is a key determinant of weaker economic performance in many countries.⁴ Increasing concern about uncertainty has stimulated global scholars to build indices to measure economic and political uncertainty not only for a set of developed countries but also for a large set of developing countries. Ahir, Bloom, and Furceri (2018) construct a World Uncertainty Index (WUI) with the aim of measuring economic and political uncertainty for a large set of developed and developing countries. The index reflects the frequencies of the word ‘uncertainty' (and its variants) in the Economist Intelligence Unit (EIU) country reports for 143 countries from the first quarter of 1996 onward.⁵ On a global scale, the WUI rises quickly and reaches high values around major economic, financial and political events, namely the 9/11 attacks, the SARS pandemic, the second Gulf War, the Eurozone debt crisis, El Nino, Europe border-control crisis, the UK’s Brexit referendum and the most recent U.S. presidential elections.

Ahir, Bloom, and Furceri (2018) argue that their index is superior to existing measures of economic policy uncertainty because it is based on a single source having specific topic coverage - economic and political developments. Concerns about the accuracy, ideological bias and consistency of the World Uncertainty Index are tackled through a standardized process and structure. Both of these factors improve the comparability of the World Uncertainty Index across countries.⁶ Extant literature employs this index to proxy for cross-country policy and economic uncertainty in a number of preceding studies (Boubaker and Nguyen 2019; Constantinescu, Mattoo, and Ruta 2019; Gozgor et al. 2019; Baker et al. 2020; Cheung, Steinkamp, and Westermann 2020). We use this index as a proxy for the broad country uncertainty variable.

2.4. Hypothesis development

Our first hypothesis is based on literature relating to the link between country-specific uncertainty and the choice of payment method. At the country level, when it comes to the possibility of foreign market entry, firms must overcome the ‘liability of foreignness’ (Zaheer 1995; Barkema, Bell, and Pennings 1996; Zahra, Ireland, and Hitt 2000; Martin and Salomon 2003; Eden and Miller 2004; Johanson and Vahlne 2009; Zhu et al. 2012; Casillas, Barbero, and Sapienza 2015; Doornich 2018). The ‘liability of foreignness’ emphasizes the social cost of conducting business in foreign countries, resulting from the dissimilarity of national governance systems (i.e. institutional distance) between the acquirer and the target (Eden and Miller 2004) or from host-country institutional characteristics (Brouthers and Brouthers 2001; Lopez-Duarte and Vidal-Suarez 2013). As per Kaufmann, Kraay, and Mastruzzi (2011), country-level governance is the tradition and institutions by which authority is exercised in a country. Empirical studies of the performance of cross-border acquisitions show that institutional distance is a critical determinant. Accordingly, scholars have examined its effects on the abnormal returns of acquirers (Chari, Ouimet, and Tesar 2009; Gubbi et al. 2010; Ellis et al. 2017), deal completion or abandonment (Dikova, Sahib, and Van Witteloostuijn 2010; Zhou, Xie, and Wang 2016), and target premiums (Weitzel and Berns 2006; Bris and Cabolis 2008).

Besides the difference between the home and the host nation, the absolute level of country governance is also of paramount importance to cross-border acquisition activities. Specifically, Kwok and Reeb (2000) propose a downstream hypothesis, which states that downstream multinational firms are subject to increased risk. To be more specific, downstream institutions provide less economic and political stability, thereby exacerbating the investor’s currency and governance risks. In addition, in a downstream environment, assets and investments are more difficult for the bidder to exploit, thus raising their financial risk. These considerations are demonstrated by several studies investigating the behavior of acquirers when encountering less institutionally developed environments. In particular, Rossi and Volpin (2004) suggest that the size and volume of acquisition deals are much lower in host countries with poorer institutional environments, ineffective shareholder protection, and weak information transparency. In other words, poor national governance may influence the transparency of the acquisition process, which reduces the confidence of bidders, thereby reducing acquisition volume. Recent studies highlight that political instability gives rise to increases in investment and stock volatility (Pastor and Veronesi 2012; Julio and Yook 2012; Boutchkova et al. 2012), affects intrinsic values of acquired firms (Bhagwat, Dam, and Harford 2016), increases completion time and trading costs, reduces post-acquisition synergies (Nguyen and Phan 2017), and affects investment decisions of acquirers (Gulen and Ion 2016). Hence, acquirers tend to delay acquisition deals when political instability is high (Bhagwat, Dam, and Harford 2016).

Regarding the influences of country-level uncertainties on the payment method chosen in acquisition deals, prior studies focus on the risk of adverse selection which arises due to information asymmetry between acquiring and acquired firms. When the bidder is subject to a shortage of sufficient or precise information about the target (Reuer, Shenkar, and Ragozzino 2004; Chari and Chang 2009), the acquirer encounters the risk of adverse selection (i.e. overpayment) arising from a low degree of accuracy in evaluation of the target’s value or excessive transaction costs throughout the negotiation phase. Mukherji et al. (2013) argue that asymmetric information is the primary source of overpayment risk, particularly because of the misvaluation of intangible assets. As a result, acquirers prefer stock payment as acquiring firm shareholders bear the bulk of the risk arising from the overvaluation of target companies in cash financed deals (Hansen 1987; Rappaport and Sirower 1999; Huang, Officer, and Powell 2016).⁷

In an international context, internal risk factors are exacerbated by external uncertainties such as the ‘liability of foreignness’ (Barkema, Bell, and Pennings 1996; Eden and Miller 2004; Aybar and Ficici 2009). Such external risks result from differences in institutional environments between the home and host countries, or from the poor quality of the institutional environment in the host country, which heightens information asymmetry and complexity (Dutta, Saadi, and Zhu 2013). One recommended method to tackle such information asymmetry is to retain local shareholders which can be achieved by using stock as a payment method (Reuer, Shenkar, and Ragozzino 2004; Kang and Kim 2008). Additionally, in accordance with the concept of home bias in investment portfolios and overseas listing decisions, investors seem to be hesitant to hold the securities of companies with which they are not familiar. Further, Chan, Covrig, and Ng (2005) show that investors may even demonstrate foreign bias by shifting their investment portfolios towards certain foreign markets on the basis of the level of similarity of the institutional environment, economic development or other institutional factors which alleviate the problem of information asymmetry. Such a practice has been found to apply in foreign listing decisions in which firms prefer foreign markets having geographical proximity or other familiar characteristics, clarifying the inclination of issuers in the US, a country with a good country-level governance system, to cross-list in Canada, the United Kingdom, and several nations in Europe which also possess good corporate governance. Thus, in the domain of cross-border acquisitions, when there is a significant institutional distance between home and host country, acquirers are expected to generally prefer non-cash payment methods. When the target is located in a country with poor governance, the future cash flow uncertainty is increased by the low quality of the institutional environment and acquiring firms will prefer to hold on to cash for precautionary reasons. In addition, Nguyen and Phan (2017) indicate that a poor institutional environment can worsen firms’ financial constraints and intensify the likelihood of default, making external financing expensive. Consequently, it is more difficult and costly for acquirers operating in weaker governance systems to obtain external funds to make payment for acquisition deals. Following these arguments, concerns about acquisition cost, the magnitude of potential synergy gains and the likelihood of acquisition success owing to the existence of information asymmetry and greater levels of economic and political uncertainty in target countries is expected to increasingly motivate bidding firms to share risk by electing to employ part or full stock exchange payment terms. Based on this expectation, we formulate the following hypotheses:

H1: There is a negative relationship between target country-specific uncertainty and the probability of cash payment method choice.

H2: There is a negative relationship between the difference in the levels of country-specific uncertainty across the host and home countries and the probability of cash payment method choice.

Prior studies have also examined the impact of differences between national cultures on acquisitions. Schoenberg (2000) and Denison, Adkins, and Guidroz (2011) find that national cultural distance presents a significant challenge for cross-border acquisitions. Accordingly, a firm engaged in a cross-border acquisition faces the issue of double-layered acculturation, referring to the cultural distances at both the country and corporate level (Barkema, Bell, and Pennings 1996). Specifically, cultural distance is found to act as a hindrance to the integration and capability transfers at the organizational and national culture levels.⁸ Acquirers and targets have to combine both levels of cultural differences. Siegel, Licht, and Schwartz (2011) point out in the case of increases in cultural distance, that it seems more difficult to deal with stakeholders of acquired firms and monitor subsidiary management. Also, negotiations tend to be more complex and costlier which ultimately increases the likelihood of abandonment of the deal. Therefore, as a result of acquiring culturally distant target firms, when acquirers are aware of potential problems during the integration process, they are expected to equip themselves with a diverse set of practices and routines necessary to operate successfully in a new environment (Morosini, Shane, and Singh 1998; Chakrabarti, Gupta-Mukherjee, and Jayaraman 2009). Since cultural values instruct managers to make decisions towards risk and return (March and Shapira 1987; Li et al. 2013; Maung et al. 2021⁹), diversity in managerial risk-taking and opportunity recognition may also be influenced. Specifically, what might be seen as risk by managers in the acquirer’s country may be treated as opportunity in the target’s. Therefore, as a result of acquiring culturally distant targets, acquiring firms’ internal resilience may be strengthened against market uncertainty. In addition, Lucey and Zhang (2010) report that cultural distance leads to lower levels of market co-movement; therefore, with cultural distance, not only can acquirers build stronger internal resilience, but also alleviate their sensitivity to market-level shocks. These arguments suggest that cultural distance may have varying influences on bidder firm approaches to cross-border acquisitions and particularly how cultural dimensions may interact with wider uncertainty associated with target environments, and further in the case of cross-border acquisitions where difference in cultural distance likely exist.

Motivated by this discussion, we examine whether and how cultural distance influences the relation between country-specific uncertainty and the choice of payment method in acquisitions. We focus on Power Distance Value (PDV), which is a dimension of cultural distance that shows interactions of people originating from different cultures. Specifically, following Hofstede (1980), PDV refers to the degree to which a national culture acknowledges hierarchy or equality as standard in a society. Specifically, low PDV cultures do not consider hierarchies important in social interactions. In other words, individuals are highly indifferent to hierarchical positions and endeavor to treat each other in an equal manner. However, in high PDV cultures that hold hierarchies as the norm, individuals are seriously concerned about distinction in hierarchical positions such that higher-positioned individuals expect those in lower positions to follow their requests, and those in lower positions have natural inclination to show respect for higher-positioned individuals. The impact of PDV also pervades formal organizations, as illustrated in cross-culture studies of hierarchical relationships in the workplace (Gelfan et al. 2007; Tsui, Nifadkar, and Ou 2007). Specifically, in comparison with managers in low PDV cultures, it is likely that those in high PDV cultures derive their power from their formal hierarchical positions (Earley 1999), utilize their formal power to get control over subordinates (Fu and Yukl 2000), and expect their subordinates to follow obediently (Wade-Benzoni et al. 2002). Conversely, subordinates in low PDV cultures tend to regard themselves as peers with the administrators and look for interactions on an equal basis. By way of an illustration, Leung, Su and Morris (2001) suggest that harsh supervisory criticisms are likely to be adversely received by subordinates in low PDV cultures, whilst they are stimulated by empowerment (Hui, Au, and Fock 2004).

Despite the fact that the research on PDV is often carried out with regards to within-culture interactions, in which managers and subordinates come from a high PDV or a low PDV culture, essential insights of PDV can be extended to interactions across cultures including cross-culture interactions resulting from cross-border acquisition deals (Schoenberg 2000). PDV seems to affect individuals in a consistent manner when they interact with those from outside their own nation. Accordingly, Laurent (1983) shows how the stability of national culture affects the normative view of managers. Lubatkin et al. (1998) explain that during the integration process, acquirers in Britain and France consistently embrace control mechanisms reflecting their unique national heritage regardless of the domicile of targets (i.e. domestic or foreign), or how many years since completion of the acquisition. From these studies, it can be concluded that the interaction between acquiring and acquired companies is likely to reflect their respective PDVs (Teerikangas and Very 2006; Sarala and Vaara 2010).

We use PDV as the basis for developing an associated framework for the interactions between bidders and targets in acquisitions both generally and specifically in relation to method of payment decision-making and incorporating the underlying preferences of bidding and target firms and shareholders. We propose two potential perspectives in this regard which have contrasting hypothesis predictions for the underlying relation between country-specific uncertainty and method of payment choice. Firstly, if the PDV in target countries is higher than the PDV in bidder countries (effectively the difference between target PDV and bidder PDV being positive), then the acquirer-target role hierarchy (Nahavandi and Malekzadeh 1993)¹⁰ would be consistent with the bidder being in a stronger bargaining position or position of authority relative to the target firm, on average, and the target likely acquiescing to the bidder’s method of payment preference. Extending from this, however, such a power distance relationship between bidding and target companies may also reduce likely target firm resistance in acquisitions and provide for a smoother integration process assuming acquisition success. This would subsequently increase bidder expectations about the achievement of potential acquisition benefits and generally reduce perceived acquisition risk, which may increase their willingness to use cash rather than stock as the method of payment choice. As such, we propose that the power distance between target and bidder firms in acquisitions, assuming alignment with country-level PDVs, has an influence on the relation between country-specific uncertainty and acquisition method of payment choice, however, this association could either strengthen or weaken the suggested negative underlying relation. This leads to our third hypothesis as follows:

H3: The negative relation between target country-specific uncertainty and the likelihood of cash payment method choice is associated with the difference in power distance between the target (host) and bidder (home) countries.

3. Research design

3.1. Data and sample

To test our hypotheses, we analyze a comprehensive sample of domestic and cross-border acquisitions during the period between 2000 and 2015 across 58 target countries. The acquisition deal and firm-level data are collected from the Thomson Reuters SDC Platinum and Worldscope databases, respectively. For country-level characteristics, data for analysis are obtained from Policy Uncertainty, Fraser Institute, Transparency International, Worldwide Governance Indicators (World Bank) and World Development Indicators (World Bank). Following the literature, we require companies to seek a stake in listed target companies no less than 5% and hold at least 5% after the deal is completed (Bris and Cabolis 2008). Also, to address sample selection bias, we exclude observations in which multiple firms acquire the same company on the same day. For an acquisition to remain in the sample, the transaction value must be at least US$1.0 million, and target firms are publicly listed and have financial data available in the Worldscope database. After screening criteria and deleting observations with missing values, the final sample comprises 78,926 completed acquisition deals conducted in 58 target countries.¹¹

3.2. Variable construction

To examine our first hypothesis on the relation between target country-specific uncertainty and payment method choice, we conduct Probit regressions examining the association between the payment method variable (CASH) as the dependent choice variable and the target country-specific level of uncertainty. We control for deal specific variables, as well as country-level variables as discussed. Specifically: (1) $\begin{array}{r}CAS{H}_{i,t}={\beta }_0+{\beta }_{1}CU{I}_{j,t-1}+{\beta }_{2}CONTROL{S}_{t-1}+{\gamma }_s+{\delta }_t+{\Omega }_g+{\zeta }_{i,t}\end{array}$(1) where,

* Dependent variable (CASH_i,t)

This variable takes on the value of one if an acquisition is paid by cash and zero if it is paid by acquirer’s stock or a mix of cash and stock payment for transaction i in year t.

* Independent variable (CUI_j,t-1)

This index indicates country-specific uncertainty developed by Ahir, Bloom, and Furceri (2018) and defined using the frequency of the word ‘uncertainty' in the quarterly Economist Intelligence Unit country reports for target country j in year t-1. The index is associated with greater economic policy uncertainty, stock market volatility, risk and lower GDP growth. A higher number means higher uncertainty and vice versa.

We also employ alternative measures of country-specific uncertainty to check the robustness of our findings. First, we employ the levels of corruption control (CORRCTRL) and of corruption perceptions (CORRPER) as proxies for country-specific uncertainty. There is a strong positive correlation between country political uncertainty and the level of corruption.¹² In addition, previous studies find that firms’ strategic decisions depend, to a considerable extent, on the level of corruption in the host country (Weitzel and Berns 2006; Godinez and Liu 2014; Di Guardo, Marrocu, and Paci 2016; Jain, Kuvvet, and Pagano 2017). Since the purpose of control of corruption is to improve institutional transparency, we utilize control of corruption (CORRCTRL) and corruption perceptions (CORRPER) as alternative observable proxies for country uncertainty (Nguyen and Phan 2017).¹³ Second, we use national elections (ELECTION) as another proxy for country-specific uncertainty. The ELECTION variable is a dummy that equals 1 if country i holds a presidential (or parliamentary) election in year t, and 0 otherwise. We also use the Economic Policy Uncertainty Index (EPU) as another proxy for country-specific uncertainty. This index is constructed by Baker et al. (2016) and combines three underlying components, including newspaper coverage of policy-related economic uncertainty, the number of federal tax code provisions set to expire in future years, and the disagreement among economic forecasters.

* Control variables (CONTROLS_t−1)

In addition to the key explanatory variables, we control for a number of factors that could potentially affect the payment method choices (Faccio and Masulis 2005; Nguyen and Phan 2017). Firstly, we include variables capturing deal-specific characteristics. RELATED is an indicator variable receiving the value of one if the target and acquirer are in the same operational area and zero otherwise.¹⁴ SHARESOUGHT, which may also affect payment choice, is the percentage of the target firm’s shares sought by the bidding firm. CROSS-BORDER is an indicator variable taking on the value of one if the bidder and target firm are not from the same country and zero if they come from the same country.¹⁵ TOEHOLD is the proportion of target equity held by the bidder before the acquisition.¹⁶ lnDEALVALUE is the value of the transaction and is presented in natural logarithm of US$ million.¹⁷

Secondly, regarding firm-level characteristics, following the current literature (Dutta, Saadi, and Zhu 2013; Cao, Li, and Liu 2019), we control for firm size (SIZE), relative size (RELSIZE), growth potential (MB), and high-tech acquisitions (HTECH).¹⁸ We also control for bidder resource liquidity or financial flexibility using the operating cash flow to total assets ratio (CF/TA) for the financial year prior to acquisition announcement. It is expected that bidders with greater financial capacity (or which are less financially constrained) are relatively more likely to use cash as the method of payment.

Third, we include variables representing country-level characteristics. When examining the effects of country-level uncertainty on payment choices, it is necessary to consider the institutional environment and economic conditions of both the target and the bidder country (Ayers, Lefanowicz, and Robinson 2004). The first country-level control variable refers to the quality of economic freedom (ECOFREE).¹⁹ This study uses the index published by the Fraser Institute, which measures the degree of economic freedom of a country in five dimensions: (1) Size of government, (2) Legal structure and security of property rights, (3) Access to sound money, (4) Freedom to trade internationally, and (5) Regulation of credit, labor, and business. The index is valued from 0 (lowest level of economic freedom) to 10 (highest value of economic freedom).²⁰ We also employ the TAX variable to proxy for the tax level on income and capital gains at the country-level. This variable is taken from Global Financial Development Database.²¹Further, payment method decisions might be affected by the wealth and purchasing power of consumers in the economy, measured by the market capitalization to GDP (MCGDP). All country-level control variables are winsorized at the 1% and 99%. For certain variables, we use Target or Acquirer prefixes to indicate whether the variable is for the target country or bidder country. In order to reduce potential endogeneity, we employ one-year-lagged country-specific uncertainty measures and one-year-lagged control variables in all regressions, and to control for year, industry, and geographic effects, we respectively include year (δ_t), industry (γ_s), and host country geographic location (Ω_g) fixed effects in all model specifications.²²

4. Regression results

4.1. Sample description

Table 1 reports the summary of deal-specific characteristics (Panel A) and the institutional environment of target countries included in our data (Panel B). Panel A shows that in 29.97% of the deals the bidder acquires a related target, while 43.11% of deals use cash payment and 39.84% are cross-border. On average, bidders seek 54.08% of the target’s equity ownership and the mean transaction value is US$443.97 million. We also present the data by the quartile of target countries, ranked by the number of deals. The data show that deals are concentrated in target countries in the top quartile (71,028 out of 78,926 deals, or 89.99%). The results further show that deals in top-quartile countries are more likely to involve non-related targets and use cash payment. Panel B presents the summary of the target country’s institutional variables. This shows that target countries in the top quartile are subject to lower levels of corruption, have higher qualities of economic freedom, and face higher tax rates, though these countries exhibit higher country uncertainty. They also exhibit lower GDP growth rates, on average.

Table 1. Deal-specific characteristics and institutional environment of target country.

Target country	Number of deals	Related	Cash	Mean toehold	Mean share sought	transaction value (US$ million)	Cross-border
Panel A: Deal-specific characteristics
Top quartile	71,028	26.44%	44.12%	27.25%	58.14%	259.85	24.75%
2nd quartile	4,855	29.19%	38.86%	30.17%	52.23%	451.65	51.28%
3^rd quartile	2,117	46.13%	36.08%	28.19%	56.40%	402.22	59.21%
Bottom quartile	926	44.27%	35.63%	30.25%	56.27%	330.93	72.34%
All countries	78,926	29.97%	43.11%	29.95%	54.08%	443.97	39.84%
Panel B: Target country institutional characteristics
Target country	CUI	CORRCTRL	CORRPER	ECOFREE	TAX	GDP Growth
Top quartile	0.710	1.082	6.963	7.241	41.07%	3.25%
2nd quartile	0.563	0.856	5.052	6.932	28.62%	3.06%
3rd quartile	0.599	0.904	4.722	7.008	26.54%	4.85%
Bottom quartile	0.607	0.569	5.004	6.915	26.11%	3.11%
All countries	0.648	0.758	5.898	7.150	29.00%	3.38%

Note: This table reports deal-specific characteristics and institutional environment of target country. Panel A shows the percentage of deals in related industries, deals that use cash payment, the mean percentage of shareholding stake by the acquirer in the target firm at the time of the bid announcement, the mean percentage of the target firm’s shares sought by the bidding firm, the mean transaction value, and cross-border deals. We present the data by the quartile of target countries, ranked by the number of deals. Panel B reports target country institutional characteristics, including Country Uncertainty index (CUI), Control of Corruption (CORRCTRL), Corruption Perceptions index (CORRPER), Quality of Economic Freedom (ECOFREE), Tax levels (TAX), and GDP growth.

In Table 2, we present summary statistics of the selected variables for each target country according to the classification of their economies (developed or developing) and for the whole sample across the 58 target countries. On average, for the sample as a whole, acquisition transactions have an industry relatedness ratio of 30% and a cash payment ratio of 43%. The highest average transaction value for developed markets is in the Netherlands at $1633 m, with the biggest mean deal size in emerging markets being Russia at $710 m.²³ Moreover, the average percentage of equity sought in target companies from developed economies is quantitatively similar to those in emerging markets (54.59% relative to 53.46%), though there are significant differences across the countries. For instance, the average percentage of shares sought in the United States is 90.03%, while an average of only 27.28% of shares are sought in Portuguese acquisitions.

Table 2. Summary statistics by target country.

Target Country	No. deals	Related	Cash	Mean toehold	Mean share sought (%)	Transaction value (US$ mil)	Cross- border	CUI	CORRCTRL	CORRPER	ECOFREE	TAX
Panel A: Developed markets
United States	24243	0.32	0.42	0.04	90.03	404.82	0.16	0.71	1.51	7.51	8.19	0.53
Japan	7285	0.26	0.65	0.32	44.40	112.78	0.07	0.71	1.37	7.55	7.69	0.48
Canada	6150	0.41	0.30	0.11	82.32	170.58	0.25	0.52	1.98	8.13	8.09	0.54
South Korea	5651	0.12	0.42	0.19	46.37	88.96	0.11	1.62	0.50	6.17	7.47	0.28
Australia	5520	0.29	0.37	0.14	68.41	104.25	0.27	0.43	1.97	8.51	8.06	0.65
Singapore	2170	0.19	0.51	0.28	60.11	106.44	0.33	0.33	2.18	9.08	8.74	0.32
United Kingdom	1662	0.25	0.55	0.25	72.55	841.22	0.32	0.82	1.85	7.94	8.18	0.37
New Zealand	1110	0.28	0.31	0.16	72.63	69.42	0.45	0.48	2.24	9.03	8.40	0.53
France	960	0.26	0.52	0.43	42.71	571.19	0.33	0.75	1.37	6.97	7.34	0.25
Taiwan	920	0.36	0.33	0.21	57.13	135.64	0.32	0.52	0.67	6.37	7.53	0.07
Israel	660	0.23	0.30	0.15	66.33	105.11	0.45	0.85	0.94	6.14	7.21	0.31
Germany	625	0.32	0.54	0.45	45.57	525.26	0.37	0.26	1.82	7.57	7.66	0.16
Norway	460	0.18	0.44	0.54	40.76	245.01	0.33	0.62	2.10	8.15	7.55	0.30
Italy	451	0.31	0.37	0.44	41.45	971.56	0.26	0.79	0.32	4.84	7.29	0.33
Sweden	361	0.22	0.54	0.39	60.74	311.26	0.36	0.63	2.23	8.72	7.54	0.15
Spain	337	0.22	0.40	0.30	33.20	809.75	0.28	0.64	1.04	6.33	7.53	0.41
Switzerland	227	0.42	0.51	0.40	42.83	1410.44	0.37	0.48	2.01	8.06	8.33	0.20
Netherlands	211	0.28	0.62	0.27	61.77	1633.47	0.43	0.44	2.11	8.33	7.70	0.25
Denmark	164	0.39	0.45	0.35	57.11	451.54	0.33	0.71	2.37	8.90	7.85	0.40
Belgium	145	0.25	0.52	0.43	49.33	578.03	0.45	0.37	1.33	6.95	7.39	0.36
Finland	120	0.25	0.39	0.44	44.17	292.23	0.42	0.27	2.36	9.03	7.87	0.20
Portugal	115	0.34	0.54	0.52	27.28	388.15	0.31	0.52	1.11	6.40	7.38	0.22
Austria	85	0.21	0.48	0.48	36.20	647.78	0.54	0.54	1.67	7.52	7.71	0.27
Ireland-Rep	72	0.20	0.59	0.16	72.11	1530.25	0.49	0.61	1.57	7.11	7.96	0.38
Luxembourg	50	0.42	0.49	0.35	58.16	3856.27	0.78	0.71	1.42	7.16	7.33	0.29
Iceland	22	0.23	0.29	0.34	40.52	357.14	0.21	0.58	2.25	8.36	7.79	0.29
Panel B: Developing markets
China	4911	0.24	0.36	0.19	50.27	114.11	0.21	0.32	−0.45	4.33	6.26	0.23
India	2821	0.26	0.42	0.28	47.85	88.11	0.40	0.28	−0.41	4.49	6.44	0.41
Malaysia	2443	0.23	0.63	0.29	62.93	75.24	0.19	0.57	0.20	5.52	6.73	0.47
Thailand	1191	0.28	0.31	0.33	48.15	69.24	0.27	0.77	−0.31	4.37	6.69	0.34
South Africa	1143	0.26	0.45	0.17	69.55	155.12	0.29	1.19	0.27	5.02	6.82	0.51
Indonesia	1021	0.33	0.35	0.23	56.13	95.10	0.51	0.81	−0.75	4.25	6.55	0.35
Mexico	809	0.37	0.30	0.19	77.52	359.20	0.55	0.96	−0.37	3.75	6.77	0.30
Vietnam	556	0.27	0.76	0.22	38.11	29.23	0.38	0.59	−0.55	3.68	6.20	0.35
Philippines	543	0.33	0.31	0.24	55.86	118.24	0.30	0.72	−0.62	3.82	7.07	0.40
Brazil	399	0.38	0.44	0.52	40.01	549.85	0.28	0.87	−0.03	4.32	6.14	0.25
Russian Fed	396	0.34	0.26	0.41	35.27	710.26	0.23	0.76	−0.99	3.41	6.45	0.04
Kuwait	291	0.25	0.30	0.20	54.06	100.14	0.40	0.76	−0.11	4.37	6.34	0.03
Poland	290	0.23	0.41	0.45	40.89	117.11	0.47	0.53	0.57	5.41	7.05	0.13
Egypt	290	0.32	0.34	0.25	52.47	205.62	0.46	0.76	−0.64	3.85	6.15	0.26
Turkey	239	0.31	0.56	0.34	35.50	276.18	0.42	1.05	−0.07	4.49	6.75	0.17
Utd Arab Em	231	0.25	0.33	0.11	67.06	209.00	0.63	0.63	1.04	6.24	7.59	0.00
Greece	215	0.34	0.31	0.36	50.27	445.22	0.52	0.30	0.01	4.11	7.01	0.21
Saudi Arabia	166	0.21	0.26	0.23	53.88	99.71	0.53	0.41	−0.11	4.48	4.78	0.20
Chile	141	0.31	0.48	0.41	41.14	278.53	0.31	0.22	1.47	7.20	7.73	0.28
Morocco	125	0.35	0.42	0.30	48.32	261.17	0.56	0.30	−0.36	3.90	6.08	0.27
Kazakhstan	121	0.44	0.35	0.21	60.73	400.93	0.61	0.27	−0.96	3.61	6.40	0.34
Peru	115	0.33	0.45	0.40	39.67	130.22	0.45	1.01	−0.33	4.04	7.50	0.29
Nigeria	92	0.38	0.46	0.13	61.64	371.53	0.52	1.41	−1.14	2.75	6.21	0.31
Jordan	85	0.25	0.33	0.12	54.72	55.57	0.47	0.29	0.16	5.49	7.34	0.11
Panama	83	0.44	0.32	0.10	82.67	163.38	0.68	0.53	−0.31	3.75	7.40	0.07
Argentina	83	0.37	0.28	0.41	33.22	357.16	0.52	1.46	−0.36	3.63	6.23	0.15
Puerto Rico	63	0.26	0.52	0.05	89.94	229.11	0.70	0.68	0.72	−0.01	NA	NA
Qatar	61	0.36	0.28	0.23	52.53	226.21	0.50	0.12	0.63	5.52	4.18	0.29
Bulgaria	61	0.33	0.30	0.25	56.11	95.94	0.68	0.56	−0.34	4.33	6.49	0.19
Venezuela	58	0.47	0.84	0.35	57.17	473.20	0.24	1.22	−0.87	3.25	5.26	0.03
Colombia	54	0.35	0.41	0.48	48.42	377.25	0.42	0.90	−0.29	3.99	6.21	0.20
Czech Republic	53	0.30	0.47	0.42	52.72	424.48	0.72	0.77	−0.15	4.24	6.45	0.15
DEV	59,776	0.28	0.45	0.31	54.59	645.62	0.34	0.62	1.64	7.60	7.78	0.33
EMG	19,150	0.31	0.41	0.28	53.46	249.68	0.45	0.68	−0.12	4.32	6.52	0.24
GLB (Total)	78,926
GLB (Mean)		0.30	0.43	0.29	53.95	420.34	0.40	0.65	0.64	5.73	7.07	0.28
GLB (Std.Dev)		0.44	0.49	0.38	37.42	141.48	0.42	0.52	0.89	1.88	0.76	0.13

Note: This table reports deal-specific characteristics and institutional environment of target country for developed and developing market subsamples over 2000-2015. The acquisition-related variables (Related, Cash and Cross-border) are proportions expressed in decimal form and index values are provided for the country-level institutional attributes (CUI, CORRCTRL, CORRPER, ECOFREE, and TAX).

Regarding the institutional environment variables, the mean of the overall country uncertainty variable for the whole sample is 0.65. South Korea experiences the highest degree of country uncertainty (1.62) for the developed markets, while Argentina exhibits the largest developing country uncertainty level (1.46). It is not surprising that, on average, developed market economies have higher institutional transparency and control of corruption (1.64) compared to emerging market economies (−0.12).

Table 3 presents descriptive statistics for the two modes of payment, namely cash and non-cash. The results show that the greater the level of uncertainty in the host country (higher Target CUI), the lower the probability of a transaction being paid by cash. Results from Table 3 also show mean values for the quality of corruption control in host countries are higher for the cash-mode payment sub-sample (higher Target CORRCTRL and Target CORRPER). In addition, cash payment is more common in host countries with more developed financial markets (bigger Target MCGDP), but less likely in host countries with higher economic freedom (larger Target ECOFREE). Regarding factors related to the characteristics of the deal, cash transactions have a lower mean deal value (DEALVALUE), seek less target firm equity (SHARESOUGHT), and exhibit higher pre-bid ownership (TOEHOLD).

Table 3. Descriptive statistics.

	Cash				Non-Cash
	(1)				(2)
Variable characteristics	N	Mean	Med	SD	N	Mean	Med	SD	Diff (2)-(1) t-ratio
Target CUI	33,218	0.617	0.529	0.452	45,708	0.659	0.504	0.538	3.37***
Target CORRCTRL	33,196	1.075	1.218	0.822	44,563	1.053	1.222	0.917	−4.56***
Target CORRPER	33,196	6.774	7.315	1.802	44,489	6.407	7.295	1.886	−6.58***
Target EPU	27,750	111.591	117.121	39.884	36,283	114.775	119.400	39.958	9.99***
Target ECOFREE	32,463	7.607	7.630	0.721	42,885	7.699	7.846	0.825	3.46***
Target MCGDP	32,528	1.030	1.004	0.474	42,907	1.022	1.041	0.466	−3.33***
Target TAX	33,218	0.454	0.487	0.130	42,722	0.468	0.511	0.150	3.58***
Target SIZE	31,642	12.303	12.310	3.214	33,125	12.215	12.241	3.098	−1.19
Target RELSIZE	31,642	0.339	0.342	0.128	33,125	0.314	0.307	0.144	−1.88*
Target MB	31,642	1.894	1.775	0.085	33,125	1.709	1.771	0.082	−1.62
Target HTECH	33,218	0.316	0.002	0.462	45,708	0.281	0.002	0.439	−7.89***
Acquirer CUI	33,218	0.721	0.665	0.518	45,708	0.692	0.598	0.547	−5.36***
Acquirer CORRCTRL	33,196	1.165	1.377	0.825	44,563	1.194	1.404	0.870	3.26***
Acquirer CORRPER	33,196	7.017	7.502	1.715	44,477	7.002	7.395	1.841	−0.82
Acquirer ECOFREE	31,907	7.724	7.841	0.758	42,625	7.779	7.965	0.793	8.16***
Acquirer MCGDP	31,886	1.057	1.010	0.481	41,596	1.050	1.042	0.450	−0.62
Acquirer TAX	31,857	0.457	0.471	0.135	41,442	0.463	0.476	0.124	9.52***
Acquirer SIZE	31,642	15.746	15.642	4.031	31,642	15.018	15.067	4.988	−2.24**
Acquirer CF/TA	31,642	0.058	0.054	0.007	31,642	0.042	0.044	0.003	−2.11**
Acquirer HTECH	32,360	0.251	0.000	0.431	45,240	0.245	0.000	0.430	−1.34
Diff CUI	33,218	−0.008	0.000	0.284	45,708	0.003	0.000	0.276	3.27***
Diff ECOFREE	31,907	−0.072	0.000	0.581	42,625	−0.099	0.000	0.626	−6.15***
Diff MCGDP	31,886	−0.024	0.000	0.352	41,596	−0.032	0.000	0.333	−2.89***
Diff TAX	31,857	0.009	0.000	0.078	41,442	0.003	0.000	0.081	−9.65***
RELATED	33,218	0.274	0.000	0.442	45,211	0.286	0.000	0.407	1.92*
TOEHOLD	33,218	0.215	0.000	0.411	45,211	0.151	0.000	0.350	−22.17***
SHARE SOUGHT (%)	33,218	61.958	69.742	37.006	45,211	70.886	100.000	35.642	36.15***
CROSS-BORDER	33,218	0.241	0.000	0.421	45,211	0.244	0.000	0.425	0.41
DEALVALUE ($mil)	33,218	224.137	19.004	921.332	45,211	283.015	19.462	1563.023	5.585***

Note: This table reports the summary statistics of the variables in our data for two groups: Cash and Non-Cash paid acquisitions. Country-level variables include CUI, CORRCTRL, CORRPER, ECOFREE, MCGDP, and TAX. Deal-specific and firm-level characteristics: RELATED, TOEHOLD, SHARE SOUGHT, CROSS-BORDER, DEALVALUE, SIZE, RELSIZE, MB, CF/TA, and HTECH. Difference variables (Diff) indicate the difference between the target and acquirer countries. For variable definitions, see the Appendix A. We use Target or Acquirer prefixes to indicate whether the variable is for the target country or bidder country. Symbols ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

4.2. Country-specific uncertainty and payment methods in acquisitions

To examine the first hypothesis on the influence of host country uncertainty on payment choice, we estimate Equation (1) for the whole sample and report the results in Table 4.²⁴ Model [1] shows that the coefficient for the target country-specific uncertainty (CUI) variable is negative and statistically significant (beta coeff = −0.053 and p < 0.01). This result supports hypothesis (H1) that the target country-specific uncertainty is negatively related with cash payment decisions. Put differently, the negative and statistically significant coefficient for the Target CUI variable suggests that acquiring firms are less likely to offer cash payment if deals take place in host countries with higher levels of uncertainty. The results when controlling for deal-specific, firm-level, and country-level characteristics (Models [2] through [4]) also confirm our findings.

Table 4. Country-specific uncertainty and payment methods in acquisitions.

Variables	(1)	(2)	(3)	(4)
Target CUI	−0.053***	−0.071***	−0.201***	−0.179***
	(−5.52)	(−6.92)	(−16.22)	(−14.08)
RELATED		0.043***	0.043***	0.052***
		(4.06)	(3.75)	(4.31)
TOEHOLD		0.106***	0.055***	0.041***
		(8.05)	(4.07)	(3.00)
SHARE SOUGHT		−0.004***	−0.004***	−0.004***
		(−28.16)	(−29.12)	(−29.41)
lnDEALVALUE		0.030***	0.032***	0.032***
		(12.70)	(13.55)	(13.27)
CROSS-BORDER		−0.011	0.011	0.001
		(−1.18)	(1.07)	(0.15)
Target SIZE		0.067	0.067	0.072
		(1.15)	(1.15)	(1.23)
Target RELSIZE		0.013**	0.012**	0.013**
		(2.04)	(2.04)	(2.04)
Target MB		0.008*	0.008*	0.008*
		(1.85)	(1.85)	(1.85)
Target HTECH		0.034***	0.051***	0.034**
		(2.87)	(4.11)	(2.39)
Target ECOFREE			−0.132***	−0.130***
			(−7.42)	(−4.98)
Target MCGDP			0.270***	0.181***
			(16.75)	(7.80)
Target TAX			0.052	0.271***
			(0.76)	(3.11)
Acquirer SIZE				0.084**
				(2.23)
Acquirer CF/TA				1.126***
				(3.25)
Acquirer ECOFREE				−0.035
				(−1.04)
Acquirer MCGDP				0.134***
				(6.11)
Acquirer TAX				−0.346***
				(−4.55)
Acquirer HTECH				0.058**
				(2.41)
Fixed-effects	YIG	YIG	YIG	YIG
Obs	78,926	75,524	68,411	64,767
Pseudo R^2	0.032	0.047	0.067	0.089

Note: This table reports the results from Probit regressions of the payment method variable on the target country’s level of uncertainty. The dependent variable, CASH, is an indicator variable taking on the value of one if an acquisition is paid by cash and zero if it is paid by acquirer’s stock or a mixed cash and stock payment. The main test variable is the level of target country-specific uncertainty (CUI). Control variables include: (i) Deal-specific and firm-level characteristics: RELATED, TOEHOLD, SHARE SOUGHT, CROSS-BORDER, lnDEALVALUE, SIZE, RELSIZE, MB, CF/TA, and HTECH; (ii) Country-level variables: ECOFREE, TAX, and MCGDP. The empirical model is as follows: (1) $\begin{array}{r}CAS{H}_{i,t}={\beta }_0+{\beta }_{1}CU{I}_{j,t-1}+{\beta }_{2}CONTROL{S}_{t-1}+{\gamma }_s+{\delta }_t+{\Omega }_g+{\zeta }_{i,t}\end{array}$(1) We use Target or Acquirer prefixes to indicate whether the variable is for the target country or bidder country. See the Appendix A for variable definitions. In all model specifications, we also include year, industry, and geographic location dummies to control for year (Y), industry (I), and geographic (G) effects, respectively. These models are chosen after checking for multicollinearity and model specification error. z-ratios are shown in parentheses. Symbols ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

The economic significance of the results is also evident. Taking Model [1] as an example, a one-standard-deviation increase in the target country-specific uncertainty (0.52) causes a reduction of approximate 2.76 percentage points [=0.52*(−0.053)] in cash payment likelihood, which is around 6.41% [=0.52*(−0.053)/0.43] of the average cash payment propensity across the sample.

A number of tests are employed to verify the robustness of the results. First, we estimate the effect of the country-specific uncertainty variable on payment choice for different subsamples of target firms from countries with different economic conditions, specifically firms in developed and developing markets. Accordingly, we carry out initial analysis of the impact of target country uncertainty, in association with economic development levels, on the acquirer’s payment choice. This represents an initial assessment of whether the nature of the relationship between host country uncertainty and payment choice in acquisitions differs across countries with diverse levels of economic development. We estimate Equation (1) separately for target firms from developed and developing markets, and report the regression results in Models [1] and [2] of Table 5 Panel A. The coefficient for the variable Target CUI is consistent with Table 4 for the developed markets subsample but does not hold for the developing target countries subsample. This suggests that higher levels of host country uncertainty only lower the probability of acquisitions being paid by cash in developed markets. According to Ahir, Bloom, and Furceri (2018), country uncertainty spikes around major economic, financial and political events and tends to be more synchronized within developed economies and between economies with tighter trade and financial linkages. These conflicting findings may also suggest that acquisition risk is perceived to be inherently greater in developing countries which determines method of payment preference independent of specific levels of economic and political uncertainty observed around the time of acquisition announcements. In general, these results provide further support for our first hypothesis that higher uncertainty in host countries reduces the likelihood of cash-financed takeovers.

To verify our previous findings, we also examine the robustness of our results by using the subsample of non-U.S. firms, and excluding the GFC period. The reasoning behind the former test is to check if our results are driven by U.S. firm sample selection bias. In Table 5 Panel A, Equation (1) is re-estimated for the subsample of non-U.S. markets with the regression results reported in Model [3]. The negative coefficient observed for the Target CUI variable supports the earlier full sample results, suggesting that target country uncertainty remains a significant determinant of payment choice in takeovers regardless of the location of the target firm (i.e. in U.S. or non-U.S. markets). In addition, there is a possibility that our regression results are impaired by the 2007–2008 GFC event, which had a major impact on the functioning of world capital markets for listed firms. Therefore, another test is conducted in which firm-year observations over the 2007–2008 period are removed. The revised results are presented in Models [4] and [5]. We observe that the sign of the coefficient for Target CUI remains negative and statistically significant, suggesting that whether we include the GFC period or not has does not contaminate our main results. We have further examined the relationship between policy uncertainty and cash payment choice in domestic and cross-border subsamples separately, and reported the results in Models [6] and [7] of Panel A. We find that the negative relationship between target country-specific uncertainty and the probability of cash payment method choice still holds for both domestic and cross-border subsamples.

Table 5. Results for subsamples.

	Panel A: The target country’s level of policy uncertainty across subsamples
	DEVELOPED	DEVELOPING	NON-US	CRISIS	NON-CRISIS	DOMESTIC	CBMA
Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	−0.204***	−0.028	−0.226***	−0.157***	−0.218***	−0.086***	−0.033**
	(−12.57)	(−1.14)	(−16.79)	(−6.83)	(−13.99)	(−6.80)	(2.14)
Firm-level controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Target country controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Acquirer country controls	Yes	Yes	Yes	Yes	Yes	No	Yes
Fixed-effects	YIG	YIG	YIG	YIG	YIG	YIG	YIG
Obs	52,255	12,421	43,107	9,711	55,056	42,897	21,870
Pseudo R^2	0.095	0.086	0.068	0.089	0.105	0.092	0.086
	DEVELOPED		DEVELOPING		NON-US		CRISIS		NON-CRISIS
	High	Low	High	Low	High	Low	High	Low	High	Low
Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
Panel B: The target country’s level of policy uncertainty across subsamples when uncertainty is high and low
	−0.499***	0.214***	−0.081	0.212*	−0.377***	0.449***	−0.517***	0.512***	−0.355***	0.428***
Target CUI	(−20.46)	(2.89)	(−1.60)	(1.93)	(−16.88)	(8.02)	(−11.39)	(3.22)	(−15.05)	(7.15)
Firm-level controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Target country controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Acquirer country controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Fixed-effects	YIG	YIG	YIG	YIG	YIG	YIG	YIG	YIG	YIG	YIG
Obs	29,929	25,325	4,796	7,625	21,194	21,913	6,276	3,435	26,527	28,529
Pseudo R^2	0.095	0.066	0.080	0.057	0.092	0.054	0.075	0.050	0.089	0.067

Note: This table reports the results from Probit regressions of the payment method variable on the target country’s level of policy uncertainty for subsamples (Panel A) and when uncertainty is high and low (Panel B). Our subsamples include target firms operating in developed versus developing markets and non-US firms ([1] through [3]). We also limit our sample to the crisis and non-crisis years of our overall sample period ([4] and [5]). We further test the relationship between policy uncertainty and cash payment for domestic and cross-border subsamples separately ([6] and [7]). In Panel B, for each acquisition announcement year in the sample period, we sort target firm country into two groups (high and low policy uncertainty) based on the median value. The target country’s level of policy uncertainty for each acquisition announcement year is defined as high if the uncertainty value of this year is higher than the median uncertainty of the sample in a given year. The dependent variable, CASH, is an indicator variable taking on the value of one if an acquisition is paid by cash and zero if it is paid by acquirer’s stock or a mixed cash and stock payment. The main test variable is the level of target country-specific uncertainty (CUI). Control variables include: (i) Deal-specific and firm-level characteristics: RELATED, TOEHOLD, SHARE SOUGHT, CROSS-BORDER, lnDEALVALUE, SIZE, RELSIZE, MB, CF/TA, and HTECH; (ii) Country-level variables: ECOFREE, TAX, and MCGDP. See the Appendix A for variable definitions. In all model specifications, we include the deal-specific, target firm and acquirer firm control variables and year, industry, and geographic location dummies to control for year (Y), industry (I), and geographic (G) effects, respectively. Note we only report the results for the main variable of interest (Target CUI) in the table. These models are chosen after checking for multicollinearity and model specification error. z-ratios are shown in parentheses. Symbols ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

Table 6. Multinomial Logit regression for the three subsamples (cash, stock and mixed payment).

	Cash vs. Stock			Mixed payment vs. Stock
Multinomial Logit Regression	Coef.	SE	z	Coef.	SE	z
Target CUI	−0.045	0.021	−2.14	0.072	0.021	3.31
RELATED	0.015	0.020	0.75	−0.015	0.020	−0.75
TOEHOLD	0.053	0.027	1.92	−0.043	0.028	−1.55
SHARE SOUGHT	0.000	0.000	0.65	0.001	0.000	4.14
lnDEALVALUE	0.005	0.005	1.07	−0.006	0.005	−1.21
CROSS-BORDER	−0.077	0.025	−3.03	0.055	0.025	2.17
Target SIZE	0.034	0.031	1.11	0.002	0.002	1.03
Target RELSIZE	0.014	0.007	1.78	0.001	0.001	1.24
Target MB	0.009	0.008	1.02	−0.003	0.002	−1.32
Target HTECH	−0.027	0.027	−0.98	0.002	0.027	0.08
Acquirer SIZE	0.093	0.040	2.31	0.011	0.007	1.63
Acquirer CF/TA	1.075	0.291	3.69	0.095	0.046	2.13
Acquirer HTECH	0.017	0.026	0.64	−0.035	0.027	−1.32
Target country controls	Yes
Acquirer country controls	Yes
Fixed-effects	YIG
Obs	64,767
Wald χ^2	2266.91***
Pseudo R^2	0.046
Stock payment sample is the base outcome

Note: This table presents the multinomial Logit regression results of the relation between country uncertainty and payment methods decisions. The dependent variable, METHODS, takes the value of zero for stock payment (base), one for cash, and two for mixed cash and stock payment. The main test variable is the level of target country-specific uncertainty (CUI). Control variables include: (i) Deal-specific and firm-level characteristics: RELATED, TOEHOLD, SHARE SOUGHT, CROSS-BORDER, lnDEALVALUE, SIZE, RELSIZE, MB, CF/TA, and HTECH; (ii) Country-level variables: ECOFREE, TAX, and MCGDP. See the Appendix A for variable definitions. We also include year, industry, and geographic location dummies to control for year (Y), industry (I), and geographic (G) effects, respectively. The LR Chi-squared value confirms the significance and the explanatory power of the estimated specification. Symbol *** indicates significance at the 1%level.

We are also concerned that the causal relation between country-specific uncertainty and payment choice might vary when uncertainty is high and low. Accordingly, we use the yearly median of the target country’s uncertainty index (CUI) measures as the cut-off value and classify firms into two further categories based on high (low) uncertainty as those with an above- (below-) median uncertainty index (CUI). We then examine how the association between the levels of target country uncertainty and payment choice differs between developed and developing markets. We estimate Equation (1) separately for target firms with high and low uncertainty from developed economies and those from developing markets, and present the regression results in Models [1] to [4] of Table 5 Panel B. We find that the negative relationship between target country-specific uncertainty and the probability of cash payment method choice still holds when target countries experience high level of uncertainty; whereas the sign of CUI becomes positive when uncertainty is low. The impact of country-specific uncertainty on the acquirer’s payment choice is significantly stronger when the target firms are located in a developed market. This result supports our first hypothesis and suggests that, in an environment of high policy risk, acquirers tend to avoid offering cash; whereas, when investing in target countries with low policy uncertainty, acquirers are increasingly likely to offer cash payment.

We also compare the results for low and high uncertainty in non-U.S. markets and for the GFC and non-GFC periods. The comparisons in Table 5 Panel B show that the estimated coefficient for Target CUI is negative and statistically significant for the target firms operating in non-US high uncertainty markets. For the subsamples of crisis and non-crisis periods, our results remain unchanged, suggesting that the negative relationship between target country-specific uncertainty and the probability of cash payment method choice exists during periods of relatively high uncertainty.

4.3. Robustness checks

In this section, we attempt to provide further, and more definitive, evidence of the impact of the target country-specific uncertainty on deal-level payment decision-making. We focus on other methodologies, such as the multinomial model, and a set of alternative proxies for a country uncertainty. Finding associations between these proxies and acquisition payment methods would provide strong evidence supporting our main hypothesis.

First, we employ the multinomial logit regression method to verify our previous findings.²⁵ Table 6 presents the results of the association between country uncertainty and payment method decisions. The dependent variable, METHODS, takes the value of zero for stock payment (base), one for cash, and two for mixed cash and stock payment. The main test variable is the target country’s uncertainty index (CUI). The results show that the coefficient estimate on CUI is negative and statistically significant at the 5% level for the cash offerings model. This result supports the prediction from hypothesis H1 and indicates that acquiring firms in high-uncertainty target countries are more likely to avoid making cash acquisition offers. We also find that the coefficient estimate on CUI is positive and statistically significant for the choice of mixed payment over stock offerings.

Secondly, we focus on the level of corruption control as a proxy for country uncertainty. The current literature finds an impact of corruption on international acquisitions and suggests that firms’ strategic decisions depend, to a considerable extent, on the level of corruption in the host country (Weitzel and Berns 2006; Godinez and Liu 2014; Di Guardo, Marrocu, and Paci 2016; Jain, Kuvvet, and Pagano 2017). Since the purpose of control of corruption is to improve institutional transparency, we utilize control of corruption (CORRCTRL) and corruption perceptions (CORRPER) as observable alternative proxies for country uncertainty. Accordingly, the CORRCTRL variable proxies for the level of control of corruption and captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as the capture of the state by elite groups and private interests. This variable is obtained from the Worldwide Governance Indicators (World Bank). Its value ranges from −2.5 to +2.5, where the higher the index, the less corruption is indicated. The CORRPER variable relates to the level of corruption perception. It ranks 180 countries and territories by their perceived levels of public sector corruption according to experts and businesspeople, and uses a scale of 0–100, where 0 is highly corrupt and 100 represents a very low level of corruption. We collect this variable from Transparency International. We report regression results in Panel A of Table 7. The results show that the coefficient for Target CORRCTRL is positive and significant at the 1% level and, hence, strongly supports our first hypothesis (H1) that the probability of cash payment increases with greater corruption control (less uncertainty). The findings when using the variable Target CORRPER also confirm our findings. Accordingly, acquirers tend to prefer cash payment in host countries with stronger institutional environments, more transparency, stronger control of corruption and better perception of corruption. Additionally, we observe that the coefficients for representations of very high corruption control (both Target CORRPER² and Target CORRCTRL²) are negative and significant at the 1% level, which implies that the relationship between the degree of corruption control and the probability of cash-financed takeovers increases at a decreasing rate.

Table 7. Robustness check with alternative independent variables

Variables	(1)	(2)	(3)	(4)
Panel A: Institutional transparency
Target CORRCTRL	0.669***	0.743***
	(14.92)	(15.17)
Target CORRCTRL^2	−0.173***	−0.179***
	(−12.98)	(−13.62)
Target CORRPER			0.296***	0.327***
			(9.54)	(9.87)
Target CORRPER^2			−0.020***	−0.021***
			(−8.86)	(−8.86)
Target country controls	No	Yes	No	Yes
Acquirer country controls	No	Yes	No	Yes
Fixed-effects	YIG	YIG	YIG	YIG
Obs	68,411	64,767	68,411	64,767
Pseudo R^2	0.056	0.071	0.044	0.069
Variables	(1)	(2)
Panel B: National Elections
Target ELECTION	−0.082***	−0.046**
	(−5.16)	(−2.40)
Target country controls	No	Yes
Acquirer country controls	No	Yes
Fixed-effects	YIG	YIG
Obs	68,411	64,767
Pseudo R^2	0.046	0.072
Panel C: Economic Policy Uncertainty
Target EPU	−0.001***	−0.002***
	(−4.97)	(−6.75)
Target country controls	No	Yes
Acquirer country controls	No	Yes
Fixed-effects	YIG	YIG
Obs	58,033	54,982
Pseudo R^2	0.052	0.056

Note: In this table, we employ a number of alternative proxies for a country’s uncertainty. In Panel A, we use the levels of corruption control (CORRCTRL) and of corruption perceptions (CORRPER) as proxies for country-specific uncertainty. The CORRCTRL variable relates to the levels of control of corruption and captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as capture of the state by elites and private interests. The CORRPER variable relates to the levels of corruption perceptions by perceived levels of public sector corruption according to experts and business people. In Panel B, we use national elections (ELECTION) as another proxy for country-specific uncertainty. The ELECTION variable is a dummy that equals 1 if country i holds presidential (or parliamentary) election in year t, and 0 otherwise. In Panel C, we use Economic Policy Uncertainty Index (EPU) as another proxy for country-specific uncertainty. This index is constructed by Baker et al. (2016) based on combining three types of underlying components, including newspaper coverage of policy-related economic uncertainty, the number of federal tax code provisions set to expire in future years, and the disagreement among economic forecasters. The dependent variable, CASH, is an indicator variable taking on the value of one if an acquisition is paid by cash and zero if it is paid by acquirer’s stock or a mixed cash and stock payment. Control variables are as given in the previous table. See the Appendix A for variable definitions. We use Target or Acquirer prefixes to indicate whether the variable is for the target country or bidder country. In all model specifications, we include deal-specific, target firm and acquirer firm control variables and year, industry, and geographic location dummies to control for year (Y), industry (I), and geographic (G) effects, respectively. Similar to Table 5, we only include the results for the main variable(s) of interest. z-ratios are shown in parentheses. Symbols *** and ** indicate significance at the 1% and 5% levels, respectively.

Next, we employ another test of robustness by using national elections (both presidential and parliamentary) as a proxy for country risk. The pioneering studies of Julio and Yook (2012) and Boutchkova et al. (2012) find that national elections and related economic policies serve as appropriate settings to observe the impact of political uncertainty on firms’ investment and equity trading. These studies indicate that, during election years, firms reduce investment expenditures until the electoral uncertainty is determined. From the bidders’ perspective, election years can increase information asymmetry and adverse selection. Following Bhattacharya et al. (2017), we create a dummy variable, ELECTION, that equals 1 if country i holds a presidential (or parliamentary) election in year t, and 0 otherwise. We re-estimate Equation (1) and report the regression results in Panel B of Table 7. The negative coefficient observed on the Target ELECTION variable supports the earlier full sample results, suggesting that target country uncertainty remains a significant predictor of payment choices in takeovers.

Finally, we re-estimate Equation (1) using the Economic Policy Uncertainty Index variable (EPU) as a proxy for country uncertainty (Sha, Kang, and Wang 2020).^26,27 This index is constructed by Baker et al. (2016) and combines three types of underlying components, including newspaper coverage of policy-related economic uncertainty, the number of federal tax code provisions set to expire in future years, and the disagreement among economic forecasters. We present the results in Panel C of Table 7. The results support our main prediction and show that coefficient estimates on EPU are negative and statistically significant across all specifications. This indicates that acquiring firms planning to acquire a target firm operating in target country with high economic policy uncertainty are less likely to elect to use cash as the method of payment.

4.4. Country-specific uncertainty difference and payment methods in acquisitions

In this section, we examine the influence of the difference in country-specific uncertainty between the target and acquirer on the payment method offered in acquisitions. We postulate that the greater the disparity in country-specific uncertainty between target and acquirer countries, the lower the probability of transactions being financed by cash. Our regression model is as follows: (2) $\begin{array}{r}CAS{H}_{i,t}={\beta }_0+{\beta }_{1}DiffCU{I}_{j,t-1}+{\beta }_{2}CONTROL{S}_{t-1}+{\gamma }_s+{\delta }_t+{\Omega }_g+{\zeta }_{i,t}\end{array}$(2) where, the dependent variable, CASH, takes on the value of one if an acquisition is paid by cash and zero otherwise. The main test variable is the difference in country-specific uncertainty between home and host countries (Diff CUI). CONTROLS_t-1 is a set of deal-level, firm-level, and country-level control variables reported with a one-year lag. In all model specifications, year (δ_t), industry (γ_s), and geographic (Ω_g) effects are also controlled. These models are chosen after checking for multicollinearity and model specification error.

We report the regression results in Table 8. We find that the Diff CUI variable is negatively associated with the likelihood of cash-financed deals, which supports the extant literature in that cash-financed deals are less likely to occur when there is a significant gap in country uncertainty between host and home countries. This result supports hypothesis (H2) and suggest that the extent of the difference in uncertainty levels between target and bidder countries, and not just the nature of target country-specific uncertainty, is associated with the preference for cash payment in acquisition deals.

Table 8. Policy uncertainty difference and payment methods in acquisitions.

Variables	(1)	(2)	(3)
Diff CUI	−0.042***	−0.045***	−0.045***
	(−2.74)	(−2.91)	(−2.91)
RELATED		0.042***	0.058***
		(4.21)	(5.19)
TOEHOLD		0.108***	0.074***
		(8.21)	(4.96)
SHARE SOUGHT		−0.004***	−0.004***
		(−28.32)	(−27.13)
lnDEALVALUE		0.031***	0.037***
		(12.73)	(13.37)
CROSS-BORDER		−0.006	0.018
		(−0.58)	(1.27)
Target SIZE		0.016	0.016
		(1.22)	(1.22)
Target RELSIZE		0.008	0.007
		(1.07)	(1.07)
Target MB		0.003	0.003
		(0.55)	(0.55)
Target HTECH		0.016*	0.016*
		(1.68)	(1.68)
Acquirer SIZE		0.047*	0.044*
		(1.85)	(1.82)
Acquirer CF/TA		1.079**	1.082**
		(2.33)	(2.35)
Acquirer HTECH		0.059**	0.064**
		(2.44)	(2.48)
Diff ECOFREE			−0.052**
			(−2.35)
Diff MCGDP			−0.009
			(−0.46)
Diff TAX			0.372***
			(5.11)
Fixed-effects	YIG	YIG	YIG
Obs	78,926	68,411	64,767
Pseudo R^2	0.028	0.043	0.059

Note: This table reports the results from Probit regressions of the payment method variable on the difference in uncertainty between the target and acquirer countries. The dependent variable, CASH, is an indicator variable taking on the value of one if an acquisition is paid by cash and zero if it is paid by acquirer’s stock or a mixed cash and stock payment. The main test variable is the level of target country-specific uncertainty (CUI). Control variables include: (i) Deal-specific and firm-level characteristics: RELATED, TOEHOLD, SHARE SOUGHT, CROSS-BORDER, lnDEALVALUE, SIZE, RELSIZE, MB, CF/TA, and HTECH; (ii) Country-level variables: ECOFREE, TAX, and MCGDP. The empirical model is as follows: (2) $\begin{array}{r}CAS{H}_{i,t}={\beta }_0+{\beta }_{1}DiffCU{I}_{j,t-1}+{\beta }_{2}CONTROL{S}_{t-1}+{\gamma }_s+{\delta }_t+{\Omega }_g+{\zeta }_{i,t}\end{array}$(2) We use Target or Acquirer prefixes to indicate whether the variable is for the target country or bidder country. See the Appendix A for variable definitions. In all model specifications, we also include year, industry, and geographic location dummies to control for year (Y), industry (I), and geographic (G) effects, respectively. These models are chosen after checking for multicollinearity and model specification error. z-ratios are shown in parentheses. Symbols *** and ** indicate significance at the 1% and 5%, respectively.

4.5. The role of power distance

In this section, we examine whether the relation between host country uncertainty and bidder acquisition payment choice varies when there is a difference in the power distance index between home and host countries. We posit that there are potential competing effects with greater power distance strengthening the likelihood of the bidder’s method of payment choice being implemented (reinforcing the relation between host country uncertainty and cash method of payment choice). However, the perceived friendliness and support coming from target firms under such a power distance dimension will increase the likelihood of acquisition success and the ability to extract perceived synergy benefits, both of which should reduce underlying acquisition risk independent of the level of wider host country uncertainty and potentially increase the likelihood that bidders would be willing to pay with cash (moderating the relation between host country uncertainty and cash method of payment choice). We use the following empirical estimation to determine which of these influences of the power distance relationship dominate: (3) $\begin{array}{rl}CAS{H}_{i,t}& ={\beta }_0+{\beta }_{1}CU{I}_{j,t-1}+{\beta }_{2}DiffPD{I}_{j,t-1}\\ & +{\beta }_{3}CU{I}_{j,t-1}\ast DiffPD{I}_{j,t-1}{\beta }_{4}CONTROL{S}_{t-1}+{\gamma }_s+{\delta }_t+{\Omega }_g+{\zeta }_{i,t}\end{array}$(3) where, the dependent variable, CASH, takes on the value of one if an acquisition is paid by cash and zero otherwise. The main test variable is the level of target country-specific uncertainty (CUI). Diff PDI represents the difference in Hofstede’s Power Distance Index between the target and acquirer countries and proxies for the power distance between home and host countries. CONTROLS_t-1 is a set of deal-level, firm-level, and country-level control variables reported with a one-year lag. In all model specifications, we also control for year (δ_t), industry (γ_s), and geographic (Ω_g) effects.

We report the regression results in Table 9. We first find that the Target CUI variable remains negatively related with the probability of transactions being paid by cash, after controlling for all target country-specific characteristics and the difference in power distance between the target and acquirer countries (Panel A and B). We also find that the coefficients for the power distance difference variable are statistically insignificant, indicating that the choice of payment method in acquisition deals does not depend on the difference in power distance between home and host countries. However, when we interact the Target CUI variable with the difference in power distance between home and host countries (Diff PDI), we find that the coefficients on the CUI * Diff PDI interaction term are statistically positive in both two models. These results support the moderating perspective associated with hypothesis (H3) and imply that cultural hierarchy associated with power distance authority of bidders over target companies reduces the perceived risk associated with cross-border acquisitions, and to the extent that it moderates concerns about country-specific uncertainty and its influence on the choice of acquisition payment method. Overall, the results indicate that power distance relationships permeate into acquisition level decision-making and that the acquirer-target role hierarchy is an important indirect explanator of the behavior of acquirers towards targets in acquisition deals through its impact on acquisition risk perception.

Table 9. The role of power distance.

Panel A:		Panel B:
Target CUI	−0.187***	Target CUI	−0.114***
	(−14.08)		(−9.65)
Diff PDI	0.000	Diff PDI	0.000
	(0.53)		(0.97)
Target CUI*Diff PDI	0.002*	Target CUI*Diff PDI	0.003**
	(1.81)		(2.40)
RELATED	0.048***	RELATED	0.053***
	(4.21)		(4.80)
TOEHOLD	0.040***	TOEHOLD	0.066***
	(2.62)		(4.38)
SHARE SOUGHT	−0.004***	SHARE SOUGHT	−0.004***
	(−30.01)		(−28.65)
lnDEALVALUE	0.038***	lnDEALVALUE	0.038***
	(13.54)		(13.55)
CROSS-BORDER	0.007	CROSS−BORDER	0.009
	(0.56)		(0.86)
Target SIZE	0.016	Target SIZE	0.008
	(1.22)		(1.10)
Target RELSIZE	0.007	Target RELSIZE	0.007
	(1.07)		(1.07)
Target MB	0.003	Target MB	0.003
	(0.55)		(0.55)
Target HTECH	0.016*	Target HTECH	0.016*
	(1.68)		(1.68)
Acquirer SIZE	0.044*	Acquirer SIZE	0.017
	(1.82)		(1.43)
Acquirer CF/TA	1.082**	Acquirer CF/TA	1.008*
	(2.35)		(1.71)
Acquirer HTECH	0.064**	Acquirer HTECH	0.034*
	(2.48)		(1.82)
Target ECOFREE	−0.181***	Diff ECOFREE	−0.091***
	(−5.41)		(−2.90)
Target MCGDP	0.186***	Diff MCGDP	−0.029
	(6.16)		(−1.06)
Target TAX	0.280***	Diff TAX	0.466***
	(2.91)		(6.00)
Fixed-effects	YIG	Fixed-effects	YIG
Obs	61,852	Obs	61,852
Pseudo R^2	0.069	Pseudo R^2	0.064

Note: This table presents the empirical analysis results on the effect of power distance on the relation between country-specific uncertainty and payment method. We employ the difference in Hofstede’s Power distance Index (Diff PDI) between the target and acquirer countries to proxy for power distance between host and home countries. PDI measures the extent to which the less powerful members of organizations and institutions accept and expect that power is distributed unequally. This represents inequality (more versus less), but defined from below, not from above. It suggests that a society’s level of inequality is endorsed by the followers as much as by the leaders. The dependent variable, CASH, is an indicator variable taking on the value of one if an acquisition is paid by cash and zero if it is paid by acquirer’s stock or a mixed cash and stock payment. The main test variable is the level of target country-specific uncertainty (CUI). Control variables include: (i) Deal-specific and firm-level characteristics: RELATED, TOEHOLD, SHARE SOUGHT, CROSS-BORDER, lnDEALVALUE, SIZE, RELSIZE, MB, CF/TA, and HTECH; (ii) Country-level variables: ECOFREE, TAX, and MCGDP. The empirical model is as follows: (3) $\begin{array}{r}CAS{H}_{i,t}={\beta }_0+{\beta }_{1}CU{I}_{j,t-1}+{\beta }_{2}DiffPD{I}_{t-1}+{\beta }_{3}CU{I}_{j,t-1}\ast DiffPD{I}_{t-1}+{\beta }_{4}CONTROL{S}_{t-1}+{\gamma }_s+{\delta }_t+{\Omega }_g+{\zeta }_{i,t}\end{array}$(3) We use Target or Acquirer prefixes to indicate whether the variable is for the target country or bidder country. In all model specifications, we also include year, industry, and geographic location dummies to control for year (Y), industry (I), and geographic (G) effects, respectively. z-ratios are shown in parentheses. Symbols ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

5. Conclusion

This study investigates whether target country-specific uncertainty is associated with the payment method choice by acquirers in an acquisition sample from 58 countries over the period from 2000 to 2015. After controlling for deal-specific and country-level characteristics, we find that target country-specific uncertainty is negatively related with cash payment likelihood. More specifically, acquiring firms tend to offer non-cash payment if the deal takes place in a host country with a higher level of uncertainty. Further, we examine the influence of the difference in uncertainty between the target and acquirer countries on the choice of payment method offered in acquisitions. We find that the difference in country-specific uncertainty between target and bidder countries is also negatively related to cash payment propensity, indicating that relative risk perception is important to bidder payment decision-making. Lastly, we examine whether the interaction between the host country’s uncertainty and the difference in Power Distance Index between home and host countries is related to acquirers’ payment method choice. The findings indicate that host country uncertainty continues to have a significant negative relation with the decision to pay in cash, but this relation is moderated by the strength of the power distance dimension between bidder and target companies, implying that hierarchical influence for acquirers offsets other acquisition-related risk factors such as country-specific uncertainty.

Our results are significant from both an academic and practical perspective Academically, this study contributes to the theoretical understanding of the effect of country-level economic and political environments and culture on the payment method choice in acquisitions by acquirers. Put differently, if the choice of payment method is an important aspect of the risk management strategy adopted by firms in corporate acquisitions, then it is reasonable to expect that country-level characteristics should be associated with the bidder’s risk management strategy. This finding contributes further to the literature that method of payment choice is reflective of acquisition risk perception. However, a key extension of this paper is the identification of the relevance of the acquirer-target hierarchical role in determining acquisition risk and influencing method of payment decision-making, and potentially other related decisions such as bid premium pricing. This is an important incremental addition to the literature relating to the relevance of culture and cultural differences in acquisition activity, and especially within the cross-border takeover environment, and the specific role of power distance in acquisition decision-making for both bidding and target firms.

From a practitioner perspective, the findings are important for stakeholders seeking to understand the role of economic and political uncertainty and cultural differences on the M&A process, and particularly on the payment method in acquisitions. As a result, policy makers, corporate administrators and investors can implement appropriate actions to minimize adverse effects and build resilience towards unpredictable changes in economic and political policies. In addition, understanding the effect of the acquirer-target hierarchical role relationship can shed light on the nature of interactions with the market for corporate control as they relate to payment method decisions and the nature of the integration process assuming acquisition bid success, but could also provide further insights on important acquisition characteristics such as target hostility levels and the likelihood of bid competition. This study is, to the best of our knowledge, the most wide-ranging comparative study of the payment method decision in acquisitions to date, which allows us to generalize the statistical results and findings to the global market context.

Acknowledgement

The authors wish to thank Chris Adcock (the Editor), the Associate Editor, and three anonymous referees for very helpful comments and suggestions. Also, we would like to thank Ian Hirst, Jo Danbolt, and the participants at the 2019 Financial Markets and Corporate Governance Conference (Australia), the 2019 AsianFA (Vietnam), and seminars at Westminster Business School, and La Trobe Business School, for very fruitful comments and suggestions. We are grateful to the members of the UE-UD Teaching and Research Team in Corporate Finance and Asset pricing (TRT-CFAP) for their helpful comments and suggestions. We also thank the Project titled “Building a shared electronic library for higher education institutions in Vietnam” in promoting collaboration and sharing in research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 502.02-2019.315.

Notes on contributors

Man Dang

Associate Professor Man Dang joined The University of Danang, University of Economics in April 2007. He received his MBA in Corporate Finance from The University of Stirling (UK) and PhD in Finance from La Trobe University (Australia). Dang has published research papers in quality finance journals, including International Review of Financial Analysis, European Journal of Finance, Finance Research Letters, Pacific-Basin Finance Journal, Quarterly Review of Economics and Finance, Journal of Multinational Financial Management, Emerging Markets Finance and Trade, and Australian Journal of Management.

Viet Anh Hoang

Dr. Viet Anh Hoang worked at The University of Danang, University of Economics from 2007. Hoang has published research papers in quality finance journals, including European Journal of Finance, Journal of Multinational Financial Management, and Emerging Markets Finance and Trade.

Edward Jones

Associate Professor Edward Jones joined the School as Senior Lecturer in Finance on 1st December 2009 having previously been Lecturer in Financial Economics at the University of Edinburgh. Dr Jones obtained his PhD at Heriot-Watt University, Edinburgh and has also worked as a Lecturer in Finance at the University of Wales, Swansea. He has authored a number of papers on profit measurement, corporate governance, corporate growth opportunities and company investment decisions, including British Accounting Review, European Accounting Review, International Review of Financial Analysis, European Financial Management, European Journal of Finance, Accounting and Business Research, and International Journal of Finance and Economics.

Darren Henry

Professor Darren Henry joined the Department of Accounting and Management at La Trobe University as a Lecturer in February 1997. He transferred to the Department of Economics and Finance in January 2000, and was promoted to Senior Lecturer in 2005, Associate Professor in 2007 and Professor in 2019. Darren has published research papers in quality finance journals, including Journal of Financial Markets, Journal of Banking and Finance, European Journal of Finance, Pacific-Basin Finance Journal, Accounting and Finance, Journal of Multinational Financial Management, Accounting and Business Research, Emerging Markets Finance and Trade, and Australian Journal of Management.

Premkanth Puwanenthiren

Dr. Prem Puwanenthiren joined The University of Westminster (UK) from 2018. He received his PhD in Finance from La Trobe University (Australia). Prem has published research papers in quality finance journals, including International Review of Financial Analysis, European Journal of Finance, Accounting and Finance, Quarterly Review of Economics and Finance, and Australian Journal of Management.

Notes

1 We further select power distance as a culture proxy as it is less endogenous with (influenced by) country-level uncertainty.

2 It should also be highlighted here that a cross-border acquisition will generally not lead to a modification in the target country institutional environment, as legal systems, governmental structures, corporate regulations, taxation policy and the like are determined either constitutionally or through government decision-making.

3 It has long been empirically recognized that real options theory explains the reasons that drive foreign firms to make incremental investments, deferring part of the investment whilst gaining access to the new market (Delios and Henisz 2003; Brouthers and Dikova 2010; Cuypers and Martin 2010). Such access provides a valuable growth option to the acquirer (Kogut 1991; Leiblein 2003; Slangen and Van Tulder 2009; Ahammad et al. 2017). Similarly, Tong, Reuer, and Peng (2008), Cuypers and Martin (2010), and Li and Li (2010) have confirmed that joint venture investment may also constitute a real option by unlocking access for foreign firms to more information about domestic targets which in turn allows for better assessment of the intrinsic value and capability of firms and, thereby, tackle country-specific uncertainties.

4 For instance, country reports for Nigeria, South Africa, the United Kingdom and the United States for the year 2017.

5 The EIU provides country reports regularly for 189 countries, typically covering politics, economic policy, the domestic economy, foreign and trade payments events, and on their overall impact on the country risk. Put differently, the reports from this leading company in the field of country intelligence mainly examine and discuss the main economic, financial, and political trends in a country.

6 To make the WUI equivalent across nations, Ahir, Bloom, and Furceri (2018) scale the raw count by the number of words in each report.

7 In a similar stream of research, Dutta, Saadi, and Zhu (2013) also confirm the information asymmetry problem in cross-border acquisition deals. Accordingly, since there is an increase in geographic and cultural distances, it is more complicated for acquirers to communicate with targets.

8 National culture comprises of a group of values and beliefs jointly shared by members of one country attributed to early socialization in families and school (Hofstede 1980).

9 Maung et al. (2021) document that acquirers from more religious countries pay less and a smaller proportion of their payment is in the form of cash as opposed to stock. Their study suggests that a country’s religiosity can closely proxy the risk aversion of firms’ managers.

10 According to Hirsch (1986), since acquiring firms have legitimate control over acquired firms through ownership control, acquisitions are regularly likened to ‘conquests’, with the acquirer as the ‘conqueror’ and the acquired firm as the ‘conquered’ (Hambrick and Cannella 1993; Pablo 1994).

11 Initially, this paper employed a comprehensive sample of 82 target company countries collected from the SDC Platinum Database. Following a suggestion of an anonymous referee, we have included additional firm-level data and removed deal observations without firm-level financial data. The final sample includes only 58 target company countries, of which 26 are developed and 32 are developing.

12 Source: IMD World Competiveness Center Dataset and Transparency International.

13 See Appendix A.

14 Stock payment method is more likely to be used in M&A deals in related industries (Faccio and Masulis 2005).

15 The acquisition of foreign target firms involves higher information asymmetry, search costs and valuation difficulties (Shimizu et al. 2004), lowering the probability of stock financing (Myers and Majluf 1984; Hansen 1987; Travlos 1987).

16 As the bidder holds a specific proportion of target equity, they can access the inside information of targets, thereby lowering the problem of the asymmetric information. Consequently, cash financing is preferred (Myers and Majluf 1984; Hansen 1987; Travlos 1987).

17 Stock financing has a positive relation with M&A deal size (Faccio and Masulis 2005; Swieringa and Schauten 2007).

18 Capron and Shen (2007) argue that firms whose asset value is highly uncertain, such as high-tech firms, have difficulties in sending a credible signal of their value to bidders, thus potentially affecting the payment method choice.

19 Numerous bodies utilize this indicator for different purposes, for instance, government agencies use it for decisions regarding key policies, companies use it for investment and risk management decisions, and academics use it in curriculum development (Holmes 2014; Dang et al. 2018).

20 See De Haan and Sturm (2000) for a review on this variable.

21 According to the taxation hypothesis, the reason for the tax liability can affect payment method choice (Eckbo and Langohr 1989).

22 In a previous version of the paper we also controlled for an overall country-level government quality (GOVQLT) index proxy, however, this was removed from the analysis in the current version due to correlation concerns as identified by a reviewer. Note that our primary hypothesis findings were unchanged with controlling for this GOVQLT variable.

23 US$ is the currency in the study.

24 We re-estimate Equation (1) using a Tobit approach. After controlling for deal-specific, firm-level, and country-level variables, our findings remain qualitatively unchanged.

25 We thank the reviewer for this suggestion.

26 We thank the reviewer for this suggestion.

27 Sha, Kang, and Wang (2020) find that Chinese acquiring firms are less likely to use only cash for their acquisitions during periods of high economic policy uncertainty.

Appendix

Appendix A: variable definitions

Table

Variables	Acronym	Description	Data sources
A. Dependent variable
Payment methods	CASH	An indicator variable taking on the value of one if an acquisition is paid by cash and zero if it is paid by acquirer’s stock or a mixed cash and stock payment.	SDC Platinum
B. Independent variables
Country-specific uncertainty	CUI	This index indicates country-specific uncertainty developed by Ahir, Bloom, and Furceri (2018) and defined using the frequency of the word ‘uncertainty' in the quarterly Economist Intelligence Unit country reports. These reports discuss major political and economic developments in each country, along with analysis and forecasts of policy implications, and political and economic conditions. The index captures uncertainty related to economic and political developments.	Ahir, Bloom, and Furceri (2018) policyuncertainty.com
Economic Policy Uncertainty	EPU	This index is constructed by three types of underlying components. One component quantifies newspaper coverage of policy-related economic uncertainty. A second component reflects the number of federal tax code provisions set to expire in future years. The third component uses disagreement among economic forecasters as a proxy for uncertainty.	Baker et al. (2016); policyuncertainty.com
Control of corruption	CORRCTRL	This index relates to the levels of control of corruption and captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as capture of the state by elites and private interests. Its value is from −2.5 to +2.5, where the higher the index the less the corruption indicated.	Worldwide Governance Indicators, World Bank
Corruption perceptions	CORRPER	This index relates to the levels of corruption perceptions. It ranks 180 countries and territories by their perceived levels of public sector corruption according to experts and business people, and uses a scale of 0–100, where 0 is highly corrupt and 100 is very clean.	Transparency International
C. Control variables
C.1. Firm-level characteristics
Firm size	SIZE	The natural logarithm of total market value of a firm’s equity in the preceding year of M&A event.	Worldscope
Relative size	RELSIZE	The ratio of transaction value to market value of a target firm’s equity.	SDC Platinum, Worldscope
Growth potential	CF/TA	The ratio of the acquiring firm’s operating cash flow to total assets in the prior year of acquisition.	Worldscope
Market-to-book ratio	MB	The ratio of a firm’s market value to book value in the prior year.	Worldscope
High-tech acquisitions	HTECH	An indicator variable taking on the value of one if target firm operates in a high-tech industry and zero otherwise.	SDC Platinum
C.2. Deal-specific characteristics
Relatedness acquisitions	RELATED	An indicator variable taking on the value of one if the target and the acquirer are in same areas of operations and zero for unrelated acquisitions.	SDC Platinum
Acquisition locations	CROSS-BORDER	An indicator variable taking on the value of one if the bidder and target firm come from different countries and zero otherwise.	SDC Platinum
Payment methods	SHARE SOUGHT	The percentage of the target firm’s shares sought by the bidding firm.	SDC Platinum
Toehold transactions	TOEHOLD	The percentage of target equity held by the bidder before the acquisition.	SDC Platinum
Transaction value	DEALVALUE	The value of the transaction (US$ million) in logarithm.	SDC Platinum
C.3. Institutional environments and other country-level characteristics
Economic freedom	ECOFREE	This index relates to A country’s quality of economic freedom. The index measures the country’s degree of economic freedom in five wide-ranging areas: (1) Size of government, (2) Legal structure and security of property rights, (3) Access to sound money, (4) Freedom to trade internationally, and (5) Regulation of credit, labor, and business.	Fraser Institute
Market capital to GDP	MCGDP	The stock market capitalization to GDP ratio.	Global Financial Development Database
Corporate income tax ratio	TAX	This variable relates to the tax level on income and capital gains.	World Development Indicators, World Bank
Power distance value	PDV	This index is a measurement of the acceptance of a hierarchy of power and wealth by the individuals who make up the general population of a nation, culture, or business.	geerthofstede.com

Table

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)	(16)	(17)	(18)	(19)	(20)	(21)	(22)	(23)	(24)
Target CUI	1
CASH	−0.03	1
RELATED	−0.03	−0.01	1.00
TOEHOLD	0.00	0.08	−0.03	1
SHARE SOUGHT	−0.04	−0.13	0.06	−0.40	1
CROSS-BORDER	−0.08	0.00	0.01	0.01	−0.05	1
lnDEALVALUE	−0.02	0.02	0.05	0.00	0.17	0.06	1
Target SIZE	0.01	0.03	0.03	0.06	0.11	−0.01	0.04	1
Target RELSIZE	0.00	0.01	0.01	0.02	0.04	−0.01	0.02	0.32	1
Target MB	0.01	0.00	0.04	0.08	0.09	0.03	0.06	0.26	0.05	1
Acquirer SIZE	0.01	0.14	0.15	0.09	0.12	0.09	0.08	0.04	0.12	0.14	1
Acquirer CF/TA	0.06	0.28	0.17	0.03	0.14	0.12	0.13	0.01	0.09	0.15	0.26	1
Target ECOFREE	−0.04	−0.01	0.04	−0.13	0.29	−0.11	0.06	0.03	0.02	0.03	0.03	0.05	1
Target MCGDP	−0.08	0.01	0.01	−0.13	0.27	−0.06	0.05	0.01	0.01	0.02	0.01	0.03	0.52	1
Target TAX	−0.20	−0.01	0.07	−0.20	0.33	−0.09	−0.03	0.01	0.00	0.02	0.03	0.14	0.55	0.39	1
Target HTECH	0.03	0.03	0.00	−0.04	0.04	0.00	0.00	0.12	0.04	0.01	0.03	0.03	0.09	0.03	0.02	1
Target CORRCTRL	−0.07	0.02	0.05	−0.09	0.25	−0.09	0.05	0.01	0.00	0.02	0.01	0.04	0.84	0.41	0.43	0.06	1
Target CORRPER	0.00	0.03	0.03	−0.07	0.19	−0.10	0.01	0.02	0.01	0.03	0.01	0.04	0.68	0.37	0.46	0.05	0.42	1
Target EPU	0.25	0.04	0.01	0.01	−0.02	−0.02	−0.01	0.00	0.00	0.01	0.02	0.06	−0.07	−0.22	−0.10	0.01	−0.01	0.03	1
Diff CUI	0.31	−0.01	0.00	0.01	0.00	−0.01	0.00	0.01	0.00	0.01	0.02	0.03	0.00	−0.02	−0.03	0.00	−0.03	−0.01	0.03	1
Diff ECOFREE	0.02	0.02	−0.01	−0.04	0.07	−0.26	0.01	0.02	0.02	0.03	0.00	0.02	0.45	0.22	0.17	0.03	0.34	0.30	0.05	−0.02	1
Diff MCGDP	0.00	0.01	−0.01	−0.03	0.05	−0.16	0.00	0.01	0.02	0.01	0.00	0.02	0.20	0.36	0.10	0.03	0.20	0.19	0.01	−0.05	0.57	1
Diff TAX	−0.04	0.04	−0.01	−0.03	0.02	0.13	0.01	0.03	0.02	0.02	0.01	0.02	0.14	0.12	0.31	0.02	0.14	0.12	0.02	−0.04	0.29	0.19	1
Diff PDI	−0.04	−0.02	0.03	0.03	−0.03	0.17	0.00	0.01	0.01	0.02	0.03	0.04	−0.23	−0.08	−0.15	−0.02	−0.27	−0.22	−0.06	−0.10	−0.50	−0.14	−0.25	1