Do cross-border acquisitions enhance firm value? Acquiror leverage, institutional distance and their effects on cross-border acquisition performance

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Do cross-border acquisitions enhance firm value?

Acquiror leverage, institutional distance and their effects on

cross-border acquisition performance

Author: Lennart Harmsen Student number: S2215772

Email: L.harmsen.2@student.rug.nl or Lennartharmsen@gmail.com Supervisor: Prof. Dr. H (Hans) van Ees

Co-assessor: Dr. C. Schlägel Word count: 14833

Faculty of Economics and Business University of Groningen

Duisenberg Building, Nettelbosje 2, 9747 AE Groningen, The Netherlands P.O. Box 800, 9700 AV Groningen, The Netherlands

http://www.rug.nl/feb

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2 Abstract

The following paper examines the relationship between acquiror leverage, institutional distance and firm performance in cross-border acquisitions (CBAs). More precisely, it focuses on short-term and long-short-term firm performance measured as two dependent variables through short-short-term market capitalization and long-term investor value appropriation (LIVA) scores of the acquiring firm. With regards to acquiror leverage, it hypothesizes that acquiror leverage has a negative effect on firm performance. With regards to institutional distance, it first divides institutional distance into two components, formal and informal distance. It argues that the direct and indirect effect of formal and informal institutional distance is negative with regards to short term market capitalization. With regard to long-term performance, it underlines that formal distance can have positive effects, whereas informal distance is still regarded as a negative factor in cross-border acquisitions and performance. The results highlight that acquiror leverage has a negative and significant effect on short- and long-term performance, but for the other hypothesis significance levels do not reach the required threshold. Extant literature exists regarding M&As but the role of acquiror leverage and the institutional perspective is still relatively underexplored. This paper, therefore, explores the combination of internal financial resources and context. Hence, it contributes to a very limited amount of research on cross-border acquisitions. Lastly, it uses LIVA as a measurement for long-term performance which has only very recently been constructed.

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1. Introduction

“ Sometimes your best investments are the ones you don’t make”- (Donald Trump) (Wilking, 2013)

Research into Mergers and Acquisitions (M&As) has become increasingly prevalent (Kengelbach et al., 2019). M&As are regarded as an investment mode to develop competitive advantages (Ferraz & Hamaguchi, 2002; Harrison et al., 1991) but arguable around 75% of them fail to create wealth for the share owners of the acquiring company (Selden & Colvin, 2003). Hence, the reported positive effect of M&As on performance is not unchallenged. Bradley, Desai and Kim (1988) argue that M&As in fields where business is related create synergies, synergies refer to the ability of two or more business actors to generate greater value working together than working apart (Benecke, Schuring & Rood, 2007; King et al., 2003). In contrast, research has shown that similar or complementary M&A parties do not necessarily generate synergies and often fail to significantly improve firm performance (Lubatkin, 1983). Hence, with pertinent conflicts in the existing literature, questions remain surrounding how and why firms can create value in M&A contexts, a topic addressed by this study.

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integration process of the target firm due to a loss of financial flexibility (Killi, Rapp & Schmid, 2011), thereby negatively influencing the post-acquisition performance. Hence, the question remains whether a restriction on capital will actually put it to better use and, therefore, deserves further examination. Basically, companies always have some form of leverage when making (cross-border) acquisitions due their financial capital structure. This further underlines the relevance of acquiror leverage.

Moreover, when considering the potential of M&A’s, it is important to take the institutional environment, which are guided by the existing institutions, into account. Institutions can be defined as “humanly devised constraints that shape human interaction” (North, 1990: 3). Extant literature has demonstrated that institutions widely vary between countries (Xu & Shenkar, 2002). Consequently, institutional distances exist between countries, whereby institutional distance refers to the differences between the institutional profiles of two countries (Kostova et al., 2019). Furthermore, Kostova and Zaheer (1999) argue in their work that home and host country institutional distance affects the organizational legitimacy of the MNE. Ghemawat (2001) and Hasan, Ibrahim and Uddin (2016) have further elaborated on the importance of institutional distance and its effects on firm performance when crossing borders. While M&A research has taken a predominantly inward, internally-focussed perspective, research has delineated a need to integrate externally-focussed perspectives to fully understand mechanisms like M&As (Peng, 2001) because institutional distance can have multiple effects on firm performance.

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complicates the understanding of host country context and can hinder the post-integration process of the target company. Stated differently, institutional distance increases the likelihood that non-routine tasks, that are already under scrutiny in case of high leverage, receive even less attention, thereby, enhancing the negative effect on firm performance (Harrison et al., 2014). In sum, the direct effects of institutional distance have been elaborated by some researchers, but its moderating effect in the field of acquiror leverage and performance remains an unexplored issue despite the fact that both acquiror leverage and institutional distance always exist in some mode when completing cross-border acquisitions (CBAs). Hence, this paper further contributes to the existing literature by examining the direct effects of institutional distance on firm performance and its interaction effects on the relationship between acquiror leverage and firm performance.

Zollo and Meier (2008) outline the difficulties with the conceptualization of acquisition performance, arguing that M&A performance is a multifaceted construct which cannot be measured by one overarching factor. They highlight that short-term stock-based measures can be misguiding and, therefore, recommend long term measurement in addition to provide valid results regarding M&A performance. This view is further supported by the work of Dobbs and Koller (2005) who argue that long-term measurements depict the true value and performance of the firm despite the commonly used short-term measurements. Consequently, performance will be measured in two ways. Firstly, short-term performance is captured as stock market response to the specific M&A (Franks, Harris & Titman, 1991; Healy, Palepu & Rubak, 1990) through market capitalization in order to examine whether CBAs enhance firm value in the short run. Secondly, long-term performance is measured as long-term investor value appropriation (LIVA), a newly devised construct to measure long-term firm performance, developed by Wibbens and Siggelkow (2019). Hence, both performance measurements view performance from the perspective of the shareholder or investor and signal whether the CBA contributes to their value. Therefore, it provides valuable information for investors and shareholders and future investments.

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effect of acquiror leverage and institutional distance, separately and interactively, on short- and long-term cross-border acquisition performance?”

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2. Theoretical overview and Hypotheses development

2.1 Mergers & Acquisitions

M&As are formal processes to unify two previously distinct firms and used for all kinds of corporate strategy. For example, M&As are used as a method to diversify the organization (Butler & Sauska, 2014), to contribute to (stockholder) value of the firm (Alexandridis, Antypas & Travlos, 2017;) and to help the firm grow (McKinsey, 2012). Multiple reasons are put forward in order to explain this positive transfer effect of M&As (Haleblian & Finkelstein, 2002). For example, researchers have focused on the creation of value due to financial advantages derived from M&As like tax benefits and diversification (Mandelker, 1974; Boisot & Meyer, 2008) or monopoly benefits (Pavlou, 2015), and non-financial factors like agency issues (Weston and Weaver, 2001), or managerialism (Seth et al., 2002). Despite these differences in focus between financial and non-financial factors, there exists general agreement that M&As take place with the aim to generate value by realizing synergies by the newly-combined firm (Zhu & Moeller, 2016).

These synergies can enhance firm performance, through the combination of internal and acquired resources from the target and acquirer (Ahuja & Katila, 2001; King et al., 2004; Laamanen & Keil, 2008; Lin & Wu, 2010) and occur when “the value of the newly-combined firm exceeds the sum of the values of the two merging firms, when acting independently” (Capron, 1999: 988). Synergies can develop in the field of finance, operations and management (Zhu & Moeller, 2016) and through economies of scale (Farrell & Shapiro, 2001; Adnan & Hossain, 2016). Moreover, gaining new knowledge and learning through M&As is often regarded as a way to create value independently or in the process of synergy realization (Cefis & Marsili, 2014; Haleblian & Finkelstein, 1999; Stahl et al., 2004; Vermeulen & Barkema, 2001).

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behaviour and CBAs. Hence, the organizational fit between acquiror and target can seemingly be flawless, but as long as the acquiror firm does not comprehend the institutional context wherein it operates, optimal synergies cannot be realized because it simply fails to fully exploit that specific environment. Therefore, this work focuses on the institutional context wherein M&As take place, referring to M&As as CBAs. CBAs are defined by Sarala (2010, p40) as “an acquisition, where one company takes a controlling interest (over 50%) of another company, regardless of the size of the companies, whereby the headquarters of the acquirer and the acquired firm are geographically located in different countries”. The next section explains the two performance measurements, then it continues to the effects of leverage power of acquirors and concludes with the role of the institutional context on the value creation of the firm through CBAs.

2.2 Measuring CBA performance 2.2.1 Short-term Market Capitalization

The literature regarding the short term performance of M&As is mostly dominated by event studies focusing on cumulative abnormal returns (Zhu & Moeller, 2016; Haleblian et al, 2009; Moeller & Schlingemann & Stulz, 2004; Harrison et al., 2014, Gubbi et al., 2010). However, measuring firm performance can be done by a variety of ways. As Zollo and Meier (2008) discovered in their meta-analysis, short-term financial performance is the most commonly used measure, with the dominant methods of short-term financial performance being ‘return on assets’ and ‘stock market response’. Stock market response is argued to be the best measurement as it reflects market interests, which include more different aspects than solely ‘return on assets’ (Healy et al., 1990; Franks et al., 1991; Zhang, Wang & Fung, 2014).

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Zollo and Meier (2008) argue that stock market response can also represent market sentiment because investors can have high future expectations based on emotions or synergies that are yet to be realized by the acquisition. Hence, cumulative abnormal returns are speculative in nature and do not reflect the actual acquisition performance (Ramakrishnan, 2008). Therefore, market capitalization is a better alternative because it reduces the likelihood of speculation since it is measured only once at the end of the year and not a limited amount of days before and after the acquisition. In addition, Zollo and Meier (2008) recommend to include a long-term performance measure while analysing CBA performance to gain more reliable results with regards to real performance. Therefore, the long-term performance measurement construct is elaborated next.

2.2.2 Long term performance : Long-term investor value appropriation (LIVA)

As the previous section indicated, measuring stock price performance is not enough in order to capture long-term performance of CBA. Therefore, an alternative measure next to market capitalization has to be explored to test for the long-term effects. Long-term performance measures are not only necessary to provide a balanced overview of the situation (Tangen, 2003) but also to correct for market sentiment (Capron, 1999). Moreover, long-term performance measurements are mandatory in order to properly capture synergies based on economies of scale, scope and revenues (Capron, 1999). In addition, innovations are often regarded as valuable strategies to improve a company’s performance (Gunday et al., 2011), especially through CBAs (Hsu et al., 2021), but their full effect can only be captured if one measures long-term performance since innovations take time to develop.

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their absolute profits and long-term net present value (NPV), however, long-term performance is often measured using short-term ratios like ROE or ROA. The disadvantage of using accounting returns stems from the fact that they can deviate significantly from the underlying economic returns since they are easy to manipulate through, for example, overstating assets or overvaluing inventory (Garcia, 2017). Moreover, firms aim to increase value in absolute size, not in terms of ratios (Wibbens & Siggelkow, 2019). Therefore, Wibbens and Siggelkow (2019) have developed an empirical measure of long-term firm performance called “Long-term investor value appropriation (LIVA)” that will be employed in this research.

LIVA measures whether firms created value for their investor in the long run (Wibbens & Siggelkow, 2019). LIVA can be regarded as a model of backward-looking NPV of returns over time. It is developed by using publicly available data for listed companies and estimates the ex post discounted value of all cash flows to and from investors between two time points. Its theoretical model will further be explained in the methodology section. Basically, LIVA measures the earnings investors make when they invest in a company at one point in time, borrowing the money at a cost equal to the average market return at that moment, sold all its shares at the final year at the market price, using that money as well as intermediate (cash) returns like dividends to repay its debt. The outcome, either positive or negative, is relative to the market. For example, the earnings are compared to what investors would have made if they had invested their money in a market-wide index over the same period. Hence, when analysing LIVA, the average across all firms is zero because some firms perform better than others, thus it shows if this particular company or investment actually created value or whether one would have been better off investing in a different company.

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event. Thirdly, LIVA is a good measurement construct when analysing performance over a long time period because then one just adds the company’s annual scores. Thus, LIVA combines stock market ratios, general market capital cost ratios and dividends or other annual payments to discover the value for investors. The next section outlines the role of acquiror leverage and its expected effects on short- and long-term value creation.

2.3 Acquiror leverage and performance

In business research, agency theory addresses the problems arising due to different interests between firm shareholders (the principals or owners) and firm managers (the agents) (Solomon et al., 2020) . It has been used to devise a theoretical framework of incentives and rules guiding behavior (Shapiro, 2005), based on the argument that if both parties want to maximize their own utility, there are valid reasons to assume that the agent’s actions will deviate from the interests of the principal (Jensen & Meckling, 1976). The principal has the opportunity to correct for opportunistic behavior of the agent by incentivizing managers to act in the principal’s interest by, for example, offering performance-related compensation or closely monitoring (Shapiro, 2005). However, despite the options to guide managerial behavior, monitoring can never be perfect. Thus, although monitoring and incentives can mitigate the problem of moral hazard and adverse selection, the principal-agent problem maintain to exist due to different perspectives, interest and information, even if the agent aims to maximize the owners interest (Shapiro, 2005).

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depreciation and profit volatility. This view is partially supported by Ibhagui and Olokoyo (2018), who found a significant and negative relation for small-size firms between leverage and firm performance, but saw this relationship vanishing if firm size exceeded an estimated threshold level. Therefore, they argued that the demerits of leverage, like more financial distress, weight greater on small firms than large firms, lowering small firm’s performance. Lastly, Miller and Bromiley (1990) findings show that risk, since the likelihood of bankruptcy increases with leverage (Ruland & Zhou, 2005), has a significant and negative effect on performance. Therefore, the potential effects of acquiror leverage and post-acquisition performance deserves further explanation.

Theoretical arguments for the effect of leverage on firm performance is twofold. On the one hand, it is argued that high leverage reduces agency costs because it limits managerial discretion, since resources are more scarce their allocation will be under more scrutiny and put to more efficient use (Maloney et al., 1993). Moreover, high debt ratios force managers to perform well because of the threat of bankruptcy (Grossman and Hart, 1982) and leaves no room to waste resources, therefore, enhancing their effort and energy to making the acquisition successful (Jensen, 1986), contributing to the value of the firm. Lastly, based on the debt-financing hypothesis, by debt-financing acquisitions with debt, acquirors intend to minimize their internal agency costs (Eldomiaty, Choi & Cheng, 2005). This sends a signal to the market that they take this investment very seriously and expect high future earnings from this acquisition. The high expectations for future earnings results in a reduction of the cost of capital since external investors want to be part of such an investment, adding to the firm’s growth opportunities and, therefore, it contributes to a company’s post-performance (Chen et al., 2020)

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more efficient (Killi, Rapp & Schmid, 2011). Another negative effect is that resources are scarce with too much leverage and its allocation is under so much scrutiny that important non-routine tasks like customer retention and firm integration receive not enough attention, thereby negatively influencing the post-acquisition performance (Harrison et al., 2014).).

Thus, there is a variety of arguments suggesting a positive, a negative and even a non-linear quadratic relationship between acquiror leverage and firm performance. However, based on the arguments of higher leveraged firms having to let opportunities pass at the expense of performance (Myers & Majluf, 1984), a loss of financial flexibility hindering the post-integration process (Killi et al., 2011) and an increased risk for bankruptcy (Harrison et al., 2014; Jensen, 1986; Grossman and Hart, 1982), a negative linear relationship between acquiror leverage and firm performance is expected. Since these factors are relevant for both short- as well as long-term, the following hypotheses are proposed:

Hypothesis 1: Acquiror leverage has a negative effect on short-term performance of the acquiror firm, measured as “market capitalization”.

Hypothesis 2: Acquiror leverage has a negative effect on long-term performance, measured as “Long-term investor value appropriation”.

Thus far, this research has only included the potential effects of acquiror leverage. The next section discusses how the context, wherein cross-acquisitions are completed, can affect acquiror firm performance. Therefore, the following chapter discusses institutional theory in relation with CBAs, acquiror leverage and firm performance, proposing institutional distance both as an independent variable and as a moderator.

2.4 Institutional theory

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normative pillar setting out the domain of social values, acceptable norms and culture are comparable to North’s use of informal institutions.

In the literature regarding institutional distance, many researchers build on the separation between formal and informal institutions (Li et al., 2020; Keig, Brouthers & Marshall, 2019; Fuentelsaz et al., 2020; Gaur & Lu, 2007; Dikova et al., 2010; Du & Boateng, 2015). Informal institutional distance is also referred to as cultural distance by Ahammed et al., (2016) and Slangen (2016) based on the previous works of Kogut and Singh (1988) and Hofstede (1984; 1986), on national and organizational culture. Hence, referring to soft, cultural differences between the home and the host country that shape the environment. Whereas researchers like Du & Boateng (2015) and Basuil & Datta (2015) argue that cultural dissimilarity serves as a deterrent to knowledge transfer and reduces legitimacy, others underline that cultural distance can also positively affect CBA performance in certain circumstances (Dikova & Sahib, 2013; Boateng et al., 2019). Moreover, Slangen (2016) argued that the negative effect of cultural or informal distance on CBA performance depends on the level of post-acquisition integration. This variety in output highlights the need for further research regarding the potential effects of cultural distance on CBA performance. With regards to formal institutions, the work of Du, Boateng and Newton (2015) argues that there is a significant and positive effect of formal institutional distance and long-term acquirer returns, whereas Rottig and Reus (2009) focus more on the negative effects, arguing that formal institutional distance enhances organizational legitimacy concerns and, therefore, increases transaction costs for acquiror firms resulting in lower post-acquisition performance. Hence, due to the different outcomes regarding the effects of informal and formal institutional distance on post-acquisition performance, this research will incorporate both.

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2.4.1. Institutional distance and its effects

Institutional distance enhances costs for the foreign multinational in CBAs through information asymmetry (Li & Xie, 2013) and the lack of legitimacy (Li et al., 2020). These social costs of doing business abroad are also regarded as the liability of foreignness (LOF) examined by Eden & Miller (2004) and Zaheer (1995). From an organizational point of view, the success of the firm depends on the recognition by its environment or stakeholders (Xu & Shenkar, 2002) and, therefore, institutions affect organizations in their quest for legitimacy (Zaheer & Kostova, 1999; David, Tolbert, & Boghossian, 2019) by setting out the structure within which companies have to (inter)act. According to DiMaggio and Powell (1983) institutional differences even force companies to behave in conformance with the host-country structure through coercive isomorphism pressures, normative pressures and mimetic pressures.

The lack of legitimacy enhances transaction costs of doing business abroad, because legitimacy is regarded as a key instrument to access external resources (Zimmernan & Zeitz, 2002). Gaining legitimacy is an extensive process and a lack thereof has significant negative effects on the survival and growth of new entrants according to the work of Kostova and Zaheer (1999). Moreover, institutional distance not only exacerbates the problem of understanding and correctly interpreting host country’s institutional environment but also hinders the communication with and understanding of other important local stakeholders (Xu & Shenkar, 2002; Ionascu et al., 2004). Thus, if institutional distance becomes larger, the way of doing business and methods of communication will become more and more different than the company is used to. Hence, the host environment becomes more complex, making the process of gaining legitimacy for the foreign multinational even harder (Xu et al., 2004).

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attempt to make the best possible judgements, both negatively affecting its performance. Secondly, in a world where information is transparent and equally easily accessible, it is easy to check for ‘bad’ behaviour of the partner or target firms. However, when information is indefinite or ambiguous, it is not only more difficult to explore the ‘true’ intentions of the target firm but also harder to enforce contract obligations, resulting in resources spend less efficient (Li & Xie, 2013). Moreover, since this further complicates target assessment, it can adversely affect purchase success, hence causing serious problems for the post-acquisition integration process (Dikova, Sahib & Witteloostuijn, 2010; Buono & Bowditch, 1989). In sum, information asymmetry and the lack of legitimacy can seriously hinder resource internalization, environmental acceptation and reduce efficiency (Jensen and Heckling, 1995).

On the other hand, institutional distance can positively affect firm performance in two ways. Firstly, it offers firms the opportunity to profit from institutional arbitrage, defined by Boisot and Meyer (2008, p356) as “the exploitation of the differences between different institutional arrangements operating in different jurisdictions”. Although Boisot and Meyer (2008) mainly focus on Chinese firms going abroad, institutional arbitrage can be used by any firm going international and refers to the possibility internationalization provides to multinational firms by moving business to countries where institutions serve their (international) operations best. This feature, which can also be referred to as comparative institutional advantage (Gaur and Lu, 2007), highlights that, although the firm knows the home country best, some (of its) activities are more efficient in specific institutional environments and, therefore, creates advantages. Examples of these advantages being ownership types, tax levels and production levels. This advantage is, however, solely realizable due to the existence of institutional distance between countries (Gaur and Lu, 2007).

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performance (Dikova et al., 2010; Galavotti et al., 2017; Collins et al., 2009; Ahammad et al., 2016; Barkema & Schijven, 2008). Therefore, experience with institutional differences help companies gain new insights and innovate, which strengthens their competitive advantage (Li et al., 2020)

2.4.2. Institutional distance and short-term CBA performance.

As previously explained, institutional distance can have positive and negative effects on CBA performance. In addition, both effects can occur simultaneously but its power can vary depending on informal and formal institutions (Li et al., 2020). According to Zaheer (1995), organizational legitimacy is crucial for good post-acquisition performance, however, this is a time-consuming process and cannot be fully constituted already shortly after the acquisition. Furthermore, during the acquisition process, acquiror firms are confronted with additional costs derived from information asymmetry, information searching costs, negotiation costs and acquiring firms paying premiums (Eccles, Lanes & Wilson, 1999; Datta & Pinches, 1992) directly hampering the acquirors performance. On the other hand, the upsides from institutional distance, the opportunity to profit from institutional arbitrage and the positive effects of accumulating new knowledge are not immediately available after acquisition and require a longer period for the acquiror firm to familiarize itself with the new environment (Li et al., 2020). Therefore, it is likely that the negative effects of institutional distance will outweigh the positive effects at least in the short run.

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Hypothesis 3: formal institutional distance has a negative effect on short-term performance of the acquiror firm, measured as “market capitalization”.

Hypothesis 4: informal institutional distance has a negative effect on short-term performance of the acquiror firm, measured as “market capitalization”.

As explained, both informal and formal institutional distance increase the opportunities for information asymmetry. Hence, it incentivizes self-centric behaviour from an acquiring firm’s managerial perspective by increasing manager’s options to take advantage of the principal-agent problem, which is likely to negatively affect the firms performance (Shapiro, 2005). This counterbalances the positive effect of acquiror leverage on performance, since acquiror leverage reduces the principal-agent problem. Moreover, with institutional distance also increases the need to gain legitimacy and one way of gaining legitimacy is by reaching out for local stakeholders in the host country (Zaheer, 1995; Freeman & McVea, 2001). This often requires investments in new, non-routine tasks to enhance the company’s local understanding. However, high leverage reduces the availability of resources and institutional distance complicates how and to what extent these resources should be spend on these non-routine tasks in order to strengthen legitimacy. Therefore, institutional distance is expected to strengthen the negative relationship between acquiror leverage and firm performance, resulting in hypothesis 5 and 6:

Hypothesis 5: formal institutional distance strengthens the negative relationship between acquiror leverage and short-term performance of the acquiror firm, measured as “market capitalization”.

Hypothesis 6: informal institutional distance strengthens the negative relationship between acquiror leverage and short-term performance of the acquiror firm, measured as “market capitalization”.

2.4.3. Institutional distance and long-term CBA performance

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On the one hand, formal institutions are regulations and laws. This kind of information is clearly coded and easily accessible through media and the public (Xu & Shenkar, 2002; Kostova, 1999). Therefore, acquiring firms can quickly assimilate this kind of knowledge and enhance its skills. On the other hand, informal institutions, with its roots in culture and experience, require a much more exhaustive approach to properly reap its benefits (Keig et al., 2019). Moreover, informal institutions are less subject to change compared to formal institutions. In order to change culture and customs one needs many years whereas regulations or laws change every few years (Li et al., 2020). Therefore, formal institutional difference is more helpful in expanding the knowledge pool of the firm and, consequently, its performance (Fuentelsaz, Garrido & Maicas, 2020).

General information asymmetry and legitimacy concerns are expected to decrease when the company is settled in the host environments since the company is getting more and more familiar with its environment. However, also here, this is more likely to occur in case of formal institutional differences because local rules and regulations are easier to understand than tradition and culture. Therefore, the opportunity to benefit from information asymmetry for the manager in the principal-agent relationship, when the firm is present for a longer time in the host environment, is less for formal institutional distance than for informal institutional distance (Li et al., 2020).

Moreover, informal institutions are expected to negatively affect the post-acquisition performance in the long-run for three reason. Firstly, it is difficult to understand informal institutions, hence it impedes gaining new knowledge for enhancing performance (Dikova et al., 2010). Secondly, the positive effects of institutional arbitrage cannot be appointed to informal institutions since institutional arbitrage comes from formal institutions through local laws and regulation (Boisot & Meyer, 2008). Therefore, the potential positive effects of institutional distance are not likely to occur due to informal institutions in the long run. Thirdly, as informal institutions are complicated to understand, also after years, it offers the opportunities for information asymmetry and, therefore, hampers the performance of the firm in the long run. Hence, the positive effects of institutional distance on performance are likely to occur in case of formal institutional distance but not with informal institutional distance, Therefore, hypotheses 7 and 8 state that:

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Hypothesis 8: informal institutional distance has a negative effect on long-term performance, measured as “Long term investor value appropriation”.

With regards to the relationship between acquiror leverage and firm performance, informal institutional distance is expected to strengthen the already negative relationship between acquiror leverage and long-term performance because also in the long run it complicates the information stream available for managers and principals. Formal institutional distance, however, is expected to weaken the negative relationship between acquiror leverage and long-term performance because it contributes to knowledge accumulation, thereby reducing the opportunities for information asymmetry. Moreover, formal institutional distance enables firms to make profit from institutional arbitrage. Through making use of profitable or suitable tax levels, formal institutional distance enables firms to correct for capital structure problems and can, therefore, help reduce their risk of bankruptcy (Bradley, Jarrell & Kim, 1984; Kane, Marcus & McDonald, 1984). Thus, hypotheses 9 and 10 state about the moderating effect of institutional distance that:

Hypothesis 9: formal institutional distance weakens the negative relationship between acquiror leverage and long-term performance, measured as “Long term investor value appropriation”. Hypothesis 10: informal institutional distance strengthens the negative relationship between acquiror leverage and long-term performance, measured as “Long term investor value appropriation”.

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22 2.5 Conceptual model

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3. Methodology

The following section describes how the research is executed. It starts with an explanation of the sample data gathered in this study. Then it proceeds to how the data is collected and the way the variables have been constructed. Lastly, it shows which statistical tests are used to analyse the data.

3.1 Empirical set up

This study focuses on the effect of acquiror leverage on CBA performance. Performance is measured for both short- and long-term using two different variables. Moreover, the moderating effect of institutional distance, both formal and informal, is examined in order to clarify whether institutional distance positively or negatively affects the relationship between acquiror leverage and CBA performance. The study does not focus on a specific industry, since this would seriously hamper the sample size. Moreover, despite that the literature review highlighted that acquiring firms can profit from institutional arbitrage through CBAs, the effects can differ a lot per industry (Brouthers, 1998). Therefore, reducing this sample size to one specific industry would result in a loss of very valuable information regarding the effects of institutional distance in general.

3.2 Data & Sample

Since this study is based on firm-level data, secondary data will be used in order to perform a quantitative analysis. Secondary data will be gathered through multiple sources. Bureau van Dijk’s Orbis Zephyr will be the only source used to collect M&A data because SDC-Platina is no longer available. Data regarding firm assets, stockholder equity and total liabilities will be derived from Compustat IQ. With regards to the dependent variables, Orbis Zephyr is used to gather information regarding market capitalization and WHARTON is employed to access the LIVA database. Moreover, the World Bank is utilized to construct formal institutional distance measurements and informal distance is measured using Hofstede’s work (1980) on cultural distance in accordance with the works of Li et al., (2020), Du & Boateng (2015), Xu & Shenkar (2002), Kogut & Singh (1988) and Keig et al., 2019.

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company, regardless of the sizes of the companies, and with the headquarter of the acquiring firm being located in a different country than that of the target firm”.

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25 Table 1. Target countries

Target Country Freq. Percent Cum.

Argentina 1 0.27 0.27 Australia 13 3.45 3.71 Austria 2 0.53 4.24 Belgium 6 1.59 5.84 Brasil 7 1.86 7.69 Bulgaria 2 0.53 8.22 Canada 50 13.26 21.49 Chile 2 0.53 22.02 China 8 2.12 24.14 Denmark 8 2.12 26.26 Egypt 1 0.27 26.53 Finland 2 0.53 27.06 France 24 6.37 33.42 Germany 37 9.81 43.24 Great Britain 83 22.02 65.25 Iceland 1 0.27 65.52 India 5 1.33 66.84 Ireland 4 1.06 67.90 Israel 19 5.04 72.94 Italy 16 4.24 77.19 Japan 3 0.80 77.98 Luxembourg 6 1.59 79.58 Mexico 7 1.86 81.43 Netherlands 15 3.98 85.41 New Zealand 1 0.27 85.68 Norway 7 1.86 87.53 Peru 1 0.27 87.80 Poland 2 0.53 88.33 Republic of Korea 3 0.80 89.12 Russian Federation 1 0.27 89.39 Saudi Arabia 1 0.27 89.66 Singapore 3 0.80 90.45 South Africa 2 0.53 90.98 Spain 9 2.39 93.37 Sweden 11 2.92 96.29 Switzerland 11 2.92 99.20 Turkey 1 0.27 99.47

United Arab Emirates 1 0.27 99.73

Vietnam 1 0.27 100.00

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3.3 Variables

The next section provides an overview of the dependent, independent an control variables used in this study.

3.3.1 Dependent variables

Market capitalization. In order to investigate the impact of M&As on short-term performance, I will use market capitalization. As explained by Zollo & Meier (2008), measuring CBA performance is a multifaceted construct, meaning that there are multiple ways of measuring it and not one specific. Moreover, they argue that stock measures are an appropriate measure for short term performance. Therefore, short-term performance will be measured using the Market Capitalization Value data from Orbis. This variable represents the sum of market value for all relevant issue level share types. It is calculated by multiplying the requested shares type by the last closing price for the shares. This variable is measured on the final day of each year. It totals the overall share value over time and, therefore, reflects firms value from the perspective of the market, which can be used for short term measures when looking at value of the firm (Shrimal, 2014). The Market Capitalization data from the year before till the year after the M&A announcement will be used to explore whether there is an effect on short-term firm value. Finally, the variable will be measured as the natural logarithm of market capitalization in order to normalize the data (Sloan, 1996)

LIVA scores. The second dependent variable in this study is the LIVA score. LIVA is based on Net Present Value (NPV) of the firm and estimates the ex post discounted value of all cash flows to and from investors between two time points with t=0 and t=T, T being the time investors sell the firm (shares) for its market value at VT (value T), while V0 represents the amount investors invest at the moment they buy the firm (shares) at the start. In the period between the buy-in and the sell-out, investors receive the free cash flow that is generated by the firm every year (FCFt). This free cash flow is equal to the cash flow from operations net of capital expenditures. Hence, in order to calculate LIVA, one must take the sum of the present value of these cash flows at time t+T, with r being the average cost of capital (Wibbens & Siggelkow, 2019), resulting in the following equation (Formula 1).

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Stating it differently, the authors prove that the above equation is equal to the following equation when taking the starting and ending value Vt equal to the enterprise value of the firm (formula 2):

Formula 2.

Here, excess return (ERt) is the total shareholder return above the cost of equity over period t, and market capitalization (MCt-1) is the market value of all shares at the beginning of period t. Therefore, LIVA is considered to be equal to the sum of the discounted absolute excess returns to shareholders over a given period (Wibbens & Siggelkow, 2019).

The LIVA scores used are accessed through Wharton Research Data Services and provide a good overview of investor value appropriation, in other words, whether in this case CBAs have generated value in the long run or whether the acquiring firm would have been better off having taken a different decision compared to the market and the cost of capital. In consistency with the timeframe of the short-term variable, LIVA scores from the year before the acquisition and the year after the acquisition will be used.

3.3.2 Independent variable

Acquiror leverage. The independent variable used in this research is acquiror leverage. Acquiror leverage is measured in accordance Harrison et al., (2014), as the book value of long and short term debt to the book value of total assets at year -1, the year before the acquisition was completed.

3.3.3 Independent and moderating variables

Formal Institutional distance.

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calculation is used to calculate formal institutional distance with the following formula 3:

Formula 3.

FIDj represents the formal institutional distance of the host country j to the United States at the time of the merger. Here, Iij refers to the score of host country j in dimension I of the World Governance Indicators. Iic depicts the score for the United States on that dimension and Vi refers to the variance in dimension I of the World Governance Indicators. The symbol 6 refers to the number of indicators used and the sigma signifies that FIDj is the square of the total sum for each indicator (Li et al,.2020; Keig et al., 2019; Kogut & Singh 1988).

Informal institutional distance.

In accordance with the work of Xu and Shenkar (2002), the variable of informal institutional distance is derived from cultural distance in accordance with Kogut and Singh (1988). Informal institutional distance is measured using the six dimensions from Hofstede’s work. This way of measuring informal institutional distance is an often used method. Whereas the work of Keig et al.,(2019), Du and Boateng (2015) and Fuentelsaz et al., (2020) only focus on the first four dimension of Hofstede, this research uses the updated version of six dimension similar to the work of Li et al., (2020) resulting in formula 4.

Formula 4.

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3.3.4 Control variables

Several control variables have to be taken into account because these can affect the independent variables, dependent and moderating variables in this research. Therefore, this list consists of firm-, time- and industry level variables.

Firm level:

Firm size: The literature argues that firm size effects firm performance. For example, Moeller, Schlingemann and Stulz (2004) argue that firms size influences after acquisition performance, highlighting that small firms receive larger abnormal announcement returns. Moreover, Ibhagui and Olokoyo (2018) argue that there is a negative relation between leverage and firm performance depending on firm size. Although they argue that is relationship disappears when firm size exceeds an estimated threshold level, it remains important to control for it. Based on the work of Kuncova, Hedija and Fiala, (2016), this research controls for firm size by taking the natural logarithms of total assets as the indicator of firm size. Hence, firm size is measured by the natural logarithm of the total assets of the acquirer at the year before the acquisition (Li et al., 2020).

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Reputation acquirer: reputation is regarded by the literature as a resource that can effect performance. Moreover, especially with regards to CBA, good acquirer reputation can help smoothen the integration of the target by lowering target management resistance (Daverat 2018). Therefore, Fortune’s Most Admired list is used to determine whether the acquiror firm has a good reputation (Fombrun & Shanley, 1990). The list is derived from Fortune’s website and the company is valued a “1” if it is on the list the year before the acquisition, if not, it gets a “0”.

Deal level:

Relative deal size: relative deal size can also affect our model since it signifies the importance of the deal to the acquiring company. Therefore, higher relative deal sizes can be regarded as bigger investments for the acquiring firm. Consequently, it can be expected that post-acquisition integration will be taken great care off, hence influencing the post-acquisition performance. Deal size is measured by the ratio of the M&A deal price to the total assets of the acquirer at the beginning of year T, thus at the end of the last year before the acquisition (Li et al., 2020)

Industry similarity: Industry similarity between the target and acquiring firm can also influence our model and, therefore, needs to be controlled for. Industries can have a specific culture such as reward systems, which can affect the success of an acquisition, either positive or negative. Those specific activities of companies operating in an industry can affect how an acquirer firm manages acquisitions from target selection to integration (Haleblian & Finkelstein, 2002). Moreover, industry similarity can enhance firm value through the creation of economies of scale (Du & Boateng, 2015). Therefore, it is important to control for industry (Datta, 1991). Industry similarity or relatedness is labelled with a “1” and non-similarity a “0” based on NAICS 4-digit coding, since this codes companies grouped in the same industry (NAICS Codes, 2020).

Year effects:

Year fixed effects. Dummy variables are included for each year from 2010 to 2017 to control for year fixed effects. Stata removed year dummies that were redundant.

3.4 Statistical Technique

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4. Results

In this section the results from the research will be presented. Firstly, an overview of the descriptive statistics will be provided, followed by a closer look at the correlations between the variables. Consequently, the results of the OLS regression will be shown in order to test for the hypotheses.

4.1 Descriptive statistics and correlations

In Table 2. there is summary of the descriptive statistics. Here, one can collect the mean value, standard deviation, the minimum and maximum of each variable. As mentioned in the methodology, the final sample was reduced to 377 observations. The two dependent variables differ significantly in their mean and standard deviation. The variable that depicts LIVA growth from the year before the acquisition till the year after, has a very high standard deviation (42.864) compared to the mean (7.896), signalling high variability in the data. This is supported by its large difference in minimum (-223.077) and maximum (353.051) scores. The large standard deviation is the result of the variable being calculated using absolute numbers, derived from the database provided by Siggelkow and Wibbens (2019), and the large difference between firm values in the sample. The sample contains some of the best performing firms, Apple and Amazon, according to Siggelkow and Wibbens (2019), but also many normal ones. The second dependent variable, the market capitalization of the firm the first year after the acquisition, has a normal distribution and standard deviation due to the natural logarithm taken. Moreover, if compared to LOGMCBefore, the mean of LOGMCAfter is slightly higher, suggesting a small increase in firm value based on market capitalisation after CBAs. In addition, the independent variable, Leverage and the control variable firm size logarithm show similar mean and standard deviation values, since these are also measured as a ratio.

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33 Table 2. Descriptive Statistics

Variable Obs Mean Std. Dev. Min Max

LIVAGROWTH 377 7.896 42.864 -223.077 353.051 LOGMCAfter 377 16.19 1.714 12.21 20.254 Leverage 377 .584 .217 .013 1.32 FID 377 .736 1.316 .023 8.626 IID 377 1.187 1.039 .026 4.959 RelDealSize 377 .148 .243 0 2.976 LogFirmSize 377 8.995 1.954 1.791 14.761 Reputation 377 .69 .463 0 1 Experience 377 .326 .469 0 1 IndustrySim 377 .684 .465 0 1 LOGMCBefore 377 16.054 1.745 10.61 19.887 LIVABef 377 3.364 17.88 -89.951 184.385

In table 3, one can find the correlation matrix between the variables. On average, correlation between the variables is relatively weak to moderate, with some exceptions. Firstly, the correlation between LIVA growth and LIVA before is quite high (0.692) at the 1 percent significance level, which makes sense because they are constructed in the same manner and LIVA before depicts the starting point for LIVA growth in this study. Secondly, both LogFirmSize and LOGMCBefore load high on LOGMCAfter and are significant at the 1 percentage level, suggesting that they are measuring the same, which is further strengthened by looking at the factor loadings of LOGMCBefore on LogFirmSize (0.836) at the 1% significance level. Therefore, the control variable LOGMCBefore will be dropped for the regression analysis. In general, the sample consists of 65 correlations of which 42 (64,6%) are statistically significant.

With regards to the two dependent variables in table 2, Market Capitalization after loads significantly (p<0.01) and positive (r=0.317) at LIVA growth because part of the LIVA score calculation includes a firm’s market capitalization. Nevertheless, the calculation of LIVA scores includes also other variables, therefore, multicollinearity does not have to be an issue, as will be shown by the Variance Inflation Test (VIF) later. Furthermore, the independent variable, Leverage has a negative and insignificant correlation (r=-.058) with LIVA growth but a positive significant correlation with Market Capitalization after (r=0.219, p<0.01). Based on these results, one can state that if Leverage increases, the Market Capitalization after also increases, however, that cannot be stated for its effects on LIVA growth. Moreover, the

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After. This implies that if Informal Institutional Distance increases, Market Capitalization After increases as well, contrary to prediction. Nevertheless, the association between both variables is weak.

With regards to the control variables, there is much similarity between their effects on the dependent variables. On the one hand, the model with Market Capitalization after as dependent variable has Log Firm Size (r=0.835, p<0.01), Market Capitalization Before (r=0.981, p<0.01), Reputation (r=0.563, p<0.01) and Experience (r=0.347, p<0.01) all load significantly and positively on Market Capitalization after, whereas Relative Deal Size (r=-0.432, p<0.01) has negative and significant loadings. This implies that, except for Relative Deal Size, if one of the control variables goes up, Market Capitalization After also increases, hence the market value of the firm, based on its market capitalization, increases. For Relative Deal Size, however, it means that if the relative deal size increases, the firm’s market value decreases, suggesting that relatively expensive deals negatively affect its market capitalization. On the other hand, the model with LIVA growth shows similar correlations with Log Firm Size (r=0.245, p<0.01), LIVA Before (r=0.692, p<0.01), Reputation (r=0.120, p<0.05) and Experience (r=0.194, p< 0.01) having positive and significant loadings. Moreover, in coherence with Market Capitalization after, Relative Deal Size (r=-0.108, p<0.05) also loads negative and significant on LIVA growth. Therefore, the implications of the control variables for LIVA growth are similar to that of Market Capitalization after, meaning that if the control variables, except for Relative Deal Size, go up, LIVA growth increases as well.

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35 Table 3. Correlation Matrix

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4.2 Regressions and testing hypotheses

In table 4, the results of the different regressions are shown. An Ordinary Least Squares (OLS) regression is used in order to test the hypotheses. As mentioned in the previous section and in chapter three, necessary steps have been undertaken in order to verify that the model equals the OLS assumptions for using OLS as a method to test the hypotheses. Consequently, multiple regressions have been performed as can be found in table 3. For each of the dependent variables, Market Capitalization and LIVA growth, 4 regressions are performed. Firstly, a regression between the dependent variable and all control variables is performed in order to measure the impact of the control variables in the model. Secondly, a regression with the independent variable, Leverage, together with all control variables is performed on both dependent variables, separately, in order to test for hypothesis 1 and hypothesis 2. Thirdly, in order to examine the independent and moderating power of both Formal Institutional Distance and Informal Institutional Distance, a third and fourth regression was necessary. Therefore, the table includes 8 models, with model 1 and 5 showing regressions with only control variables included and models 2,3,4,6,7 and 8 testing the different hypotheses. The models on market capitalization have an R-squared of around 0.745 and the models on LIVA have an R-squared of around 0.519, suggesting they are relatively capable of explaining the variation of the dependent variables respectively.

Testing hypotheses on Market Capitalization

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However, both effects are not significant and, therefore, hypotheses 3 and 5 have to be rejected despite the interaction effect having the correct direction. Moreover, the effect of Leverage on Market Capitalization becomes insignificant when Formal Institutional Distance is included in the regression. Lastly, model 4 includes the direct and interaction effect of Informal Institutional Distance in order to test hypotheses 4 and 6. Similar to model 3, the direct effect of Informal Institutional Distance is almost none existent (r= -0.09). Moreover, its interaction effect is also very weak (r= 0.083) and not significant, therefore, hypotheses 4 and 6 are rejected.

Testing hypotheses on Long Term Investor Value Appropriation

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38 Table 4. Regression Models Control on MC Leverage on MC Formal Distance on MC Informal Distance on MC Control on LIVA Leverage on LIVA Formal Distance on LIVA Informal Distance on LIVA

Relative Deal Size -.007 -.003 -.006 -.008 1.209 1.386 1.496 1.453

(.239) (.247) (.246) (.248) (3.593) (3.673) (3.683) (3.704) Reputation .734*** .763*** .77*** .779*** 3.231 4.481 4.334 4.309 (.138) (.137) (.139) (.14) (3.508) (3.336) (3.338) (3.388) Experience .271** .262** .266** .268** 3.948 3.594 3.548 3.565 (.114) (.114) (.114) (.114) (3.452) (3.45) (3.436) (3.511) Industry Similarity .11 .103 .112 .106 -.399 -.779 -.904 -.856 (.098) (.098) (.098) (.096) (3.305) (3.21) (3.166) (3.136) Firm Size .623*** .638*** .638*** .639*** 1.2 1.922 1.927 1.913 (.047) (.052) (.052) (.052) (1.294) (1.29) (1.294) (1.283) Leverage -.555** -.471 -.661** -24.595*** -25.232*** -24.335*** (.278) (.305) (.323) (7.731) (8.01) (8.391) FID .062 -.23 (.122) (2.11) Leverage x FID -.129 1.034 (.208) (3.78) IID -.09 .554 (.107) (2.61) Leverage x IID .083 -.075 (.19) (5.251) LIVABef 1.604*** 1.589*** 1.59*** 1.588*** (.245) (.226) (.228) (.226) _cons 10.175*** 10.34*** 10.251*** 10.395*** -23.948* -16.919 1.904 1.15 (.482) (.463) (.396) (.407) (12.951) (11.995) (8.26) (7.92) Observations 377 377 377 377 377 377 377 377 R-squared .737 .741 .741 .742 .505 .518 .519 .519

Year Dummy YES YES YES YES YES YES YES YES

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5. Discussion

This section discusses the previous findings in the context of the hypotheses presented in the literature. It provides an interpretation of the results regarding the predictions made and the theoretical and managerial takeaways from the research. Moreover, it concludes by providing the limitations of this study and offers suggestions for future research.

Theoretical implications

The main question, this study addresses, is “what is the effect of acquiror leverage and institutional distance, separately and interactively, on short- and long-term cross-border acquisition performance?” Mergers and acquisitions have long been the subject of empirical research. Despite the many reasons for undertaking mergers and acquisitions, like creating competitive advantages and synergies (Bardley, Desai & Kim, 1988; Ferraz & Hamaguchi, 2002; Harrison et al., 1991; Ahuja & Katila, 2001; King et al., 2004; Laamanen & Keil, 2008; Lin & Wu, 2010; Zhu & Moeller, 2016), and the ever increasing amount of mergers and acquisitions annually, research has shown that around 75% of mergers and acquisitions fail to create wealth (Selden & Colvin, 2003) or enhance firm performance (Lubatkin, 1983). Moreover, several researches focus either on resources (Harrison et al., 2014; Jensen, 1986; Sharpiro, 2005) or on context (Xu & Shenkar, 2002; Kostova et al.,2019; Kostavo and Zaheer, 1999; Ghemawat, 2001), while this study focuses on combining context with internal resources. Therefore, it aims to contribute to the M&A literature by exploring the effects of acquiror leverage and institutional distance on firm performance.

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the prospects of firm performance in the short-term. In addition, this study found that acquiror leverage also has a negative and significant effect on long-term firm performance. This result is in accordance with the prediction made. Again, the previous arguments of Killi et al. (2011) and Harrison et al. (2014) are likely to apply here, highlighting that higher debt-to-assets ratios hinder the post-integration process and are, therefore, regarded with suspicion by the market. Moreover, high leverage can also force managers to retain from taking risks, due to reduced resources available, even if that comes at the expense of value maximization in the long run (Lin & Chang, 2012; Myers & Majluf, 1984). As Harrison et al. (2014) outlined, the implications of leverage in acquisitions on long-term performance is an underexplored issue and, therefore, this study adds new knowledge to the existing literature. In sum, the literature on acquisitions is ambiguous with regards to the market predicting post-acquisition performance (Oler, Harrison & Allen, 2008; Agrawal & Jaffe, 2000; Dutta & Jog, 2009), but based on the results in this study, high leverage ratios seem to strengthen the pessimistic view of the market on the firm’s market value, both short-term and long-term.

Furthermore, this research has attempted to clarify the role of the institutional context wherein acquisitions are completed. Based on the early work of North (1990), this study tried to measure the effects of institutional distance on acquisition performance. Following the models of Li et al., (2020), Keig et al., (2019), Fuentelsaz et al., (2020), Gaur and Lu (2007) and Du and Boateng (2015) a distinction between formal and informal institutional difference was established. Consequently, both variables have been explored to discover direct and indirect effects in order to test hypotheses 3 to 10.

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Informal Institutional Distance affects Market Capitalization negatively (r=-0.09) and LIVA growth positively (r=0.554). Nevertheless, all relations are insignificant and very weak. With regards to the moderating effects on institutional distance, formal institutional distance negatively moderates (r= -0.129) the relationship between acquiror leverage and short-term performance and positively affects the relationship between acquiror leverage and long-term performance (r= 1.034) as predicted. However, both effects are insignificant. With regards to the correlation coefficients of informal institutional distance, its effect on the relationship between acquiror leverage and short-term performance is positive (r= 0.083), contrary to expectation, although very weak and insignificant. Its effect on the relationship between acquiror leverage and long-term performance is negative (r= -0.075), in accordance with the hypothesis, but also insignificant. Hence, one has to conclude that the direct and interaction effect of institutional distance was not conclusive in this study. Therefore, both variables are not as important as the literature suggests (Li et al., 2020; Du & Boateng, 2020; Dikova et al., 2010; Fuentelsaz et a., 2020) or their failure to deliver significant results is due to other factors in this research. Since formal institutional distance and informal institutional distance are well-known and well-developed concepts in the literature of International Business, grounded in strong arguments, the results should not downgrade the importance of both variables, but rather beg the question why they failed to be significant by looking at their construction or other variables in this study.

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Yang (2016) that large firms are expected to better assimilate knowledge and have more funding for new product development projects both enhancing a firm’s long-term performance.

Managerial implications

The findings of this study have multiple managerial implications. Firstly, as the regression results for acquiror leverage and performance indicate, the market tends to react negatively on CBAs when valuing the acquiror firm. Therefore, in order to contribute to shareholder wealth, managers should take into account their debt-to-assets ratio when considering CBAs, being aware of the fact that high leverage results in negative reactions from the market on acquiror stock price and market value. Secondly, although this study failed to provide significant results for the direct and interaction effects of institutional distance, it should not be neglected. Extensive literature has shown that in many cases institutional distance, either formal or informal, affects firm performance after CBAs. Therefore, managers should still carefully consider the implications institutional distance can have.

Thirdly, a firm’s reputation and experience positively affect its market capitalization. Therefore, managers and investors should take a company’s position with regards to these variables into account in the case of CBAs. It provides a stimulus to improve one’s reputation in order to lower target firm resistance and supports the argument that through knowledge accumulation companies can enhance their performance. However, the effect of these variables become insignificant if analysing long-term performance. Hence, they tend to diminish, begging the question why, which would be an interesting topic for future research. Lastly, firm size does not affect LIVA score. Hence, if using LIVA to evaluate performance, investors should not focus on firm size nor any of the other control variables in this model specifically.

In general, these implications further underline the complicated challenges firms face when considering CBAs. It highlights the ambiguity of CBAs with regards to short-term and long-term performance and stipulates that acquiror leverage should be taken into account if serving the interests of its shareholders.

Limitations and future research

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constant in the regression, this might explain the insignificance of both institutional distance variables in this study. Hence, future research can conduct similar analyses while focusing on a more diversified group of target firm host countries. Moreover, it can help to not necessarily focus on the United States as the home country, but take a broader variety of home countries. This will enhance the effect of institutional distance.

Secondly, this study focuses on CBAs valued at least 100 million USD. Hence, it excludes many CBAs that do not reach this threshold but can still be of relevance. Moreover, the sample size is not limited to one industry. Therefore, it includes companies from a large variety of industries. However, the effects of institutional distance can vary per industry since some industries require higher levels of target integration than others. Therefore, it is harder to establish an effect of institutional distance in this study, because the effects it has on one industry can be counterbalanced by its effects on another industry in this sample.

Thirdly, long-term performance is measured according to the works of Siggelkow and Wibbens (2019) as long term investor value appropriation. This is a relatively recent developed long term performance measurement and data is derived from their database. Therefore, the sample only includes companies that are included in their work, further limiting the sample size. Moreover, the lack of significant variables in the model on long-term performance suggests that it might be more fruitful to include other variables. Lastly, as the authors also outline, LIVA is especially interesting in the case of event studies and long-term performance. Therefore, it constitutes a potential topic for future research in this area.

Fourthly, the control variable Experience only controls for CBAs within the time period of this research (2010-2017). Hence, there exists the possibility that companies have gained acquisition experience in the period before 2010, but due to a lack of data and limited resources this is not included in the study. Therefore, the (first) dummy variable of experience can be inaccurate, highlighting the problem of experience. Moreover, an interesting topic of research would be to explore what previous experience really means and measures. What takeaways have companies gained from their experience and how does this affect their future deal making, since experience can be both heterogeneous as well as homogeneous.

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6. Conclusion

The purpose of this study was to investigate whether CBAs create value for the acquiring firm’s owners, through examining the relationship between acquiror leverage and Institutional Distance on market capitalization and on LIVA, respectively. Therefore, the main question guiding this research was “what is the effect of acquiror leverage and institutional distance, separately and interactively, on short- and long-term cross-border acquisition performance?” Based on a sample of 377 CBAs, ten hypotheses were tested. However, only the relationship between acquiror leverage and performance was found to be significant and negative, arguing that higher leverage in CBAs results in lower firm value measured as market capitalization and/or LIVA score. Therefore, besides the theoretical relevance of this subject and the data gathered, the results of this study lacked significance

Do cross-border acquisitions enhance firm value? Acquiror leverage, institutional distance and their effects on cross-border acquisition performance