UNDERSTANDING THE IMPACT OF ACQUISITION EXPERIENCE AND LOCAL SUBSIDIARIES WHEN ENGAGING IN CROSS-BORDER ACQUISITIONS

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UNDERSTANDING THE IMPACT OF ACQUISITION

EXPERIENCE AND LOCAL SUBSIDIARIES WHEN

ENGAGING IN CROSS-BORDER ACQUISITIONS

M.Sc. Strategic Innovation Management

July 16, 2018 Remco Pander rpander93@gmail.com s2150018 Supervisor: Dr. K.J. McCarthy k.j.mccarthy@rug.nl Co-assessor: Prof. dr. J.D.R. Oehmichen j.d.r.oehmichen@rug.nl

Keywords: acquisition performance, subsidiaries, cross-border acquisitions, national differences JEL classification: G14, G15, G34, M16

Word count: 11 986

ABSTRACT

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Introduction

The market for corporate mergers and acquisitions continues to grow. Global transaction volume rose to 3.9 trillion USD in 2016 (JPMorgan, 2017). This growth is increasingly driven by cross-border acquisitions, accounting for 36 percent of the transaction volume, an increase of 5 percent compared to 2015. Mergers and acquisitions are an attractive alternative to achieving organic growth. Specifically, cross-border acquisitions provide instant access to new geographical markets, technological resources not available domestically, more diversified knowledge and can also help in reducing political risks coming from import tariffs or other protectionist policies.

Yet, studies continue to report that most acquisitions do not achieve their expectations (Calipha et al., 2010). Even with all their potential benefits, many cross-border acquisitions fail to deliver acquisition performance. Moreover, cross-border acquisitions tend to perform worse than domestic acquisitions (Moeller & Schlingemann, 2005). This must mean that cross-border acquisitions carry some additional risks that acquirers fail to properly manage. Recognizing the increasing appetite for cross-border acquisitions, a better understanding of what these risks are and how they can be handled may be of insurmountable value.

One risk identified for cross-border acquisitions originates from the difficulties of acquiring a business in an unknown, foreign environment. Academic research labels the differences between the domestic country and foreign country as ‘national distance’. Four dimensions of national distance have been identified: cultural, institutional, economic and geographic distance. Acquirers incapable of managing the risks originating from national differences may see their acquisition performance diminish. Companies may simply be unaware of these risks. Alternatively, they may lack the knowledge required to manage them.

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experience and local subsidiaries on four dimensions of national differences: cultural, economic, institutional and geographic. Specifically, it will investigate whether acquisition experience and local subsidiary presence are helpful in mitigating the “liabilities of foreignness” (Zaheer, 1995). When going abroad, firms face difficulties due to their unfamiliarity with the local culture and practices. It is hypothesized that firms having more acquisition experience or an established subsidiary presence in a foreign country are better able to extract value from their cross-border acquisitions.

An improved understanding of these facets is highly relevant for serial acquirers and firms with no international acquisition experience alike. If it can be shown that acquisition experience or local subsidiaries have a positive relationship with performance, acquiring firms can employ these mechanisms. Serial acquirers with previous acquisition experience should capitalize on this experience as much as possible if they were not doing so already. Firms with no prior acquisition experience should recognize its value for potential future acquisitions. For both types of firms alike, a key component of their strategy may include the option to first establish a local presence in countries with high potential to build up relevant knowledge, before doing a major take-over. While acquisition effect may have a positive effect on acquisition experience, it must be built through numerous transactions, each carrying a hefty price tag. Uncovering mechanisms that may substitute for acquisition experience may thus be a more affordable alternative to improve acquisition performance.

The rest of this study is structured as follows. Section 2 discusses current research on M&A and the development of the hypotheses. Section 3 presents the methodological approach and data collection process. Section 4 presents the results. Section 5 interprets and discusses the results. Section 6 enumerates on limitations. Finally, section 7 concludes this study.

Theory

Acquisitions to Create Firm Value

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innovativeness and improving market power. Cost efficiencies result from economies of scale and scope, integrating redundant business functions and increased bargaining power versus suppliers (Seth, 1990). Together, they are categorized as the synergy potential underlying an acquisition. Usually, before a transaction takes place, the buying firm assesses the stand-alone value of the target plus the financial outcomes of the synergies. If this combined value exceeds the market value of the target, an offer will be made.

While acquisitions have the potential for value creation, academic literature finds that most acquisitions in practice fail to deliver the expected value. Looking at shareholder wealth creation, returns for the buyers’ shareholders float around zero (Bruner, 2002). Important variables such as strategic fit (Bauer & Matzler, 2014) and organizational fit (Datta D. , 1991) determine the potential for synergistic effects to be present by their influence on the potential for resource redistribution and integration effectiveness. Research also recognizes the acquisition process itself as an important determinant of performance (Haspeslagh & Jemison, 1991; Jemison & Sitkin, 1986). Increasingly, the post-acquisition integration process is being highlighted as critical to capturing acquisition performance (Birkinshaw & Bresman, 2000). The presence of numerous factors that impact acquisition performance makes it difficult to get everything right. The process is incredibly complex, and managers lack an understanding of how the acquisition should be properly executed. Acquisitions are infrequent events and only provide feedback several years after completion of the deal. Even then, this feedback is confounded with significant noise.

Cross-Border Acquisitions

One particular type of M&A is cross-border acquisitions. Cross-border acquisitions are becoming increasingly popular. In 2016, 36 percent of total M&A volume was due to cross-border transactions, which is an increase of 5 percent over 2015. Compared to domestic acquisitions, cross-border acquisitions provide several additional opportunities, while also introducing some specific additional risks.

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to domestic acquisitions. Cross-border acquisitions tend to underperform domestic transactions with respect to announcement returns by a statistically significant 1 percent.

Particular risks for cross-border acquisitions arise out of the difficulties acquirers face when operating in unknown, foreign markets. These difficulties are often aggregated under the term ‘liabilities of foreignness’ (Zaheer, 1995). Research has recognized four key components, namely cultural differences, institutional differences, political differences and geographical differences. While the term is most frequently used in the context of multinational enterprises, it is no less applicable to firms seeking to acquire or merge with a target in a different country (Shimizu, Hitt, Vaidyanath, & Pisano, 2004). Multiple authors have investigated the effect of national differences in the context of acquisitions. Dikova, Sahib and Witteloostuijn (2010) study the effect of countries’ formal and informal institutional context and how it impacts the likelihood of acquisition completion. Their results show that large institutional differences have a negative impact on the likelihood that an announced acquisition actually completes and also tend to lengthen the negotiation period. Others have considered the role of cultural differences. High cultural distance puts a brake on effective communication and stimulates misunderstanding. Datta and Puia (1995) find that cultural differences between host and target country negatively impact acquisition performance. Reus and Lamont (2009) consider the effectiveness of integration capabilities on bridging cultural distance. Only with strong capabilities may cultural distance be bridged and used as a source of value. Higher geographic differences increases the difficulty of coordination (Erel, Liao, & Weisbach, 2012), making it more time-consuming to for either party to visit the other and engage in communication. Higher geographic distance is also frequently associated with a change in timezone. An increase in geographical distance is frequently associated with a change in time zone. Relatedly, Ragozzino (2009) finds that geographical distance has a negative effect on the stake acquired in cross-border transactions. He argues that geographical distances increase information asymmetries for the acquirer, thus facing greater challenges when engaging in such transactions.

The Impact of Acquisition Experience

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that experience tends to improve performance. Performing acquisitions exposes the acquirer to diverse environments and causes an influx of new information. Organizational learning processes transfer this information into applicable knowledge that can result in a competitive advantage during the acquisition process ultimately improve acquisition performance (Hitt, Levitas, Arregle, & Borza, 2000). Having established acquisition routines alleviates the cognitive efforts of managers to successfully undertake acquisitions and allows them to effectively divide time between managing the acquisition and ensuring business continuity.

Hayward (2002) explores the concept of acquisition experience more in-depth, trying to uncover how specific experiences may improve performance. Taking an organizational learning perspective, Hayward (2002) argues that experience per se does not necessarily benefit acquirers. In line with the findings of Haleblian and Finkelstein (1999), the author states that experience is subject to contextual factors that may affect its benefit. Seeking these contextual factors, Hayward (2002) finds that previous acquisitions that are not too similar nor dissimilar, were not too successful and were temporally not too far away nor close to the focal acquisition stimulate learning and subsequently improve focal acquisition performance. Haleblian and Finkelstein (1999) and Nadolska and Barkema (2007) reason that acquisition experience is subject to negative transfer effects (Novick, 1988). Negative transfer refers to the misapplication of behavior learned in a familiar situation to a superficially similar situation, yielding poor outcomes.

In cross-border acquisitions, the acquirer faces all the risks of a domestic acquisition, plus the additional risks originating out of liabilities of foreignness. Acquisition experience may be an effective mechanism to overcome national differences. Organizational learning theory suggests that acquirers with more exposure to the implications of national differences may be more effective in understanding and managing them. Increased exposure to national differences causes acquirers to engage in an iterative learning process whereby they continuously engage in experiences and try to distill meaning from these experiences. The acquirer can seek patterns in its actions to manage national differences and what effect they have on the acquisition’s performance. Resultingly, acquirers with sufficient experience contain an internal repository of organizational knowledge on the effects of national differences. This knowledge can then be used by the acquirer to identify areas of risk in future acquisitions and manage them accordingly.

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increases the likelihood of future cross-border acquisition. Their work is based on a reasoning in line with Haleblian et al. (2006) and also builds on organizational learning theory. Acquisition experience establishes organizational routines and these routines are subject to organizational inertia, causing an acquirer to keep engaging in more acquisitions, building up momentum. This effect is strengthened by previous acquisition performance since acquirers find acquisitions less risky and more rewarding after previous successes. Barkema et al. (1996) find that cross-border acquisition experience can lessen the burden of high cultural differences and lengthen the longevity of acquisitions. More internationally experienced acquirers are likely to possess more knowledge on potential pitfalls while simultaneously being more adept at resolving conflicts stemming from cultural differences (Dikova & Sahib, 2013). Based on the works of Hayward (2002), Haleblian and Finkelstein (1999) and Nadolska and Barkema (2007) it seems likely that not all acquisition experience is equally applicable in mitigating national differences. Experience gained from domestic acquisitions will probably be of no use since the acquirer does not have to deal with national differences. Accordingly, these acquisitions cannot feed the iterative learning process that produces knowledge on managing these differences.

While the existing literature has looked at the role of acquisition experience in managing cultural differences and institutional differences in acquisitions, an overarching perspective is missing. Underlining all dimensions of national differences is, of course, the abstract notion of differences and that differences poses risks. Research has not looked at the effects of economic or geographic distance on acquisition performance. Similar to its effects on cultural and institutional differences, acquisition experience may also help in limiting the risk associated with economic and geographic differences. Acquirers having more experience will have had more exposure to these dimensions of national differences and developed knowledge and routines to manage them appropriately. Integration all dimensions of national differences in a single model provides a better understanding of the impact of each of these dimensions on acquisition performance. More interesting, it offers insight into the areas that acquisition experience is most helpful in. Based on the theories of liabilities of foreignness (Zaheer, 1995) and organizational learning, this study proposes the following hypothesis.

Hypothesis 1: Acquisition experience will have a positive effect on the relationship between national differences (institutional, cultural, economic, geographic) and acquisition performance (that is, it can moderate the negative effects of national differences)

Subsidiary Presence in Host Country

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differences. Their argument is based on the concept of ‘double-layered acculturation’ (Barkema et al., 1996), which is a concept similar to liabilities of foreignness. In addition to an acquirer having to adjust to country-level differences, it must also cope with differences at the company-level.

The advantages of having a local subsidiary in the context of international alliances may also very well apply to cross-border acquisitions. A local subsidiary in the same country as the acquisition target makes the acquirer more familiar with the country context. Local personnel has intense knowledge of the local environment in terms of cultural, political and institutional terms. The acquirer can employ organizational learning and transfer this knowledge from the local subsidiary level to the organizational level, directing it to the corporate function and business unit(s) responsible for the acquisition. Besides this role as passive knowledge sharer, the local subsidiary can plan an active role in the search for potential take-over candidates. Such a role would mitigate the risk of selecting an incompatible candidate that does not possess the required resources, capabilities or organizational culture. This can mitigate information asymmetries faced by the corporate parent because of national differences. Subsequently, it can also help in mitigating the risk stemming from geographical differences, by taking the lead in the post-acquisition integration phase (Lavie & Miller, 2008). Since the local subsidiary is in the same country, difficulties arising out of travel distance or time zone differences are circumvented. Following their reasoning, the following hypothesis is presented.

Hypothesis 2: Having a subsidiary in the target’s country of origin will have a positive effect on the relationship between national differences and acquisition performance.

Discussing the combined role of acquisition experience and having a subsidiary in the partner’s country of origin, these may function as substitutes or complements. Firstly, local subsidiaries may substitute for acquisition experience through the availability of local knowledge in the subsidiary that is more directly applicable than previous acquisition experience. The subsidiary can provide information on local business practices, market conditions and cultural values that may prove itself meaningful during the post-acquisition integration process. In addition, having employees domiciled in the host country makes that they are able to take the lead in this integration phase, thereby largely omitting the need for headquarters experience.

Hypothesis 3a: Having a subsidiary in the target’s country of origin will have a negative effect on the impact of experience in mitigating national differences (substituting effect)

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since many decisions must be made on all the different business functions to integrate and to what extent. Moreover, the literature has shown that the speed with which this process occurs has an important effect on overall acquisition performance (Bauer & Matzler, 2014). In this regard, previous acquisition experience can provide a vital benefit since the acquirer has a better understanding of the tasks to be performed and some insight into how it should be done. Combined with the influx of local knowledge, the acquirer can adapt its approach to local customs.

Hypotheses 3b: Having a subsidiary in the target’s country of origin will have a positive effect on the impact of experience in mitigating national differences (complementing effect)

Methodology

Data Collection

Online databases are available that encompass a large amount of information on mergers and acquisitions. For this study, acquisition event data from Zephyr - made available by intelligence provider Bureau van Dijk - is used. This database includes a wide array of data per transaction, such as acquisition histories, announcement dates, target and acquirers’ shareholder structure, financial metrics as well as accountancy measures. Moreover, this dataset can easily be combined with information from Orbis – also by Bureau van Dijk – which provides additional company data and a full information set on company subsidiaries and their geographical locations. Information from Thomson DataStream is used to calculate acquisition performance. National differences are based on information from Geert Hofstede and the World Bank.

The sample includes completed cross-border acquisitions executed between 2014 and 2018, where the acquiring company was a listed European company. All other types of transactions, such as share buybacks and MBO’s are excluded from the sample. To only include deals that represent true acquisitions and not just stake-building, it is further restricted to a maximum pre-deal acquirer stake of 10% and a minimum post-deal stake of 100% of shareholder equity. Finally, only deals with a minimum value of EUR10 million are included. A second sample from 2010 to 2014 with the same restrictions is drawn to implement one of the moderating variables, acquisition experience. This sample is restricted to this four-year period following the reasoning of Hayward (2002), stating that acquisition experience is only effective if it is recent.

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Measures

Dependent variable – Acquisition Performance

In the M&A literature, previous research has made extensive use of event studies to measure acquisition performance. Two examples come from Hayward (2002) and Haleblian and Finkelstein (1999), who also investigate acquisition experience. Such an approach is implemented by measuring the cumulative abnormal stock returns (CAR) around the day of the event announcement. Abnormal returns are calculated as the difference between the actual stock returns observed and the expected return based on a model of stock returns. The following formula shows how stock returns are calculated.

𝑅𝑖,𝑡 = ln⁡( 𝑅𝐼𝑖,𝑡

𝑅𝐼_{𝑖,𝑡−1}) (1)

Returns are based on the total return index (RI) from Thomson DataStream, which accounts for dividend reinvestments. Thus, the return for firm i on day t is calculated by taking the natural logarithm of the RI divided by the RI of the preceding day. After having calculated a measure for stock returns, the following formula demonstrates how abnormal returns are calculated:

𝐴𝑅𝑖,𝑡 = ⁡ 𝑅𝑖,𝑡− 𝐸(𝑅𝑖,𝑡) (2)

In this formula, 𝐴𝑅𝑖,𝑡 represents the abnormal return, 𝑅𝑖,𝑡, represents the return from the preceding formula and 𝐸(𝑅𝑖,𝑡) represents the expected return for firm i between day t and the prior day. Furthermore, t0 is the day of the event announcement. Implementing the expected return may be done using a variety of models. In this study, consistent with Hayward (2002) and Haleblian and Finkelstein (1999), the market model is used to calculate the expected return. The market model is a statistical model which relates the return of any given security to the return of the market portfolio (MacKinlay, 1997).

𝐸(𝑅𝑖,𝑡) = ⁡ 𝑎𝑖+ ⁡ 𝛽𝑖𝑅𝑚,𝑡+ ⁡ 𝜀𝑖,𝑡 (3)

The previous formula depicts the market model, with 𝑎𝑖, 𝛽𝑖 and 𝜀𝑖,𝑡 representing the intercept, slope and error term respectively for the relationship between stock returns for firm i and the market return 𝑅𝑚,𝑡 at time t. Market portfolio returns are often derived from a broad-based stock portfolio. Since the sample includes only European acquirers, the MSCI Europe Index is an appropriate proxy for the market portfolio. Finally, the following formula shows how cumulative abnormal returns are calculated. Abnormal returns are summed over the chosen event window which runs from 𝜏1 to 𝜏2. This study uses the timeframe starting 1 day before the announcement to 1 day after the announcement (-1, +1).

𝐶𝐴𝑅𝑖(𝜏1, 𝜏2) = ⁡ ∑𝜏𝑡=𝜏2 1𝐴𝑅𝑖,𝑡 (4)

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random-effects models. A Hausman test is conducted to determine the best alternative. Since the null-hypothesis of the Hausman test cannot be rejected, a random-effects model is both consistent and the most efficient estimator. Huber-White heteroscedasticity-consistent standard errors are applied to all models.

Independent variable – National Differences

Literature on cross-border acquisitions and alliances identifies four main dimensions of national differences: cultural difference, institutional differences, economic differences and geographical differences. A company’s country of origin was defined as the location of its corporate headquarters. A primary component of differences is cultural differences. Several frameworks have been constructed to provide a fine-grained measure of country-level cultural differences. The framework of Hofstede is most widely used and contains measures on the highest number of countries. Therefore, this study employs the Hofstede framework. An index of cultural distance can be constructed using the famous formula of Kogut and Singh (1988).

A second component is institutional differences, which is also the component under investigation in Dikova, Sahib and Witeloostuijn (2010). Data on institutional differences is made available by the World Bank, which makes available six indicators of country governance policy: voice and accountability (VA), political stability and absence of violence (PV), government effectiveness (GE), regulatory quality (RQ), rule of law (RL) and control of corruption (CC).

The third component of national differences is economic differences. This component is recognized in the literature on international alliances but has not been used in research on cross-border acquisitions, to the best of my knowledge. The level of economic development has much impact on the operations of businesses and associated business customs. Additionally, it provides a sense of the efficiency and effectiveness with which the companies are run. The World Bank provides information on the gross domestic product per capita (𝐺𝐷𝑃𝑝𝑐).

The fourth and final component is geographical distance. Thus, this component captures the difficulties associated with communicating which becomes increasingly difficult as distance increases (Erel, Liao, & Weisbach, 2012). It is operationalized as the distance in kilometers between the capital cities of the acquirer and target’s countries.

Moderating variable – Acquisition Experience

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Moderating variable – Subsidiary Presence in Host Country

The presence of a subsidiary in the host country is a binary variable indicating whether the acquiring firm has set up a subsidiary in the country of the target firm. Orbis makes available the full corporate affiliations structure of the focal firm. Large enterprises may have multiple levels of corporate affiliations, i.e. subsidiaries of subsidiaries. The effect of a subsidiary being able to make a useful contribution to the integration of an acquired company will likely decline with its increasingly deep position within the corporate structure. The ability of corporate headquarters to effectively communicate with a deeply nested subsidiary will be decreased due to the absence of direct lines of communication. Moreover, a deeply nested subsidiary will likely focus on a specific business function, thus not possessing more general knowledge valuable in the integration phase. Thus, the presence of a subsidiary in the host country is restricted to subsidiaries to a maximum of one level from the corporate headquarters. Additionally, the firm must have a minimum stake of 75 percent in the subsidiary, giving it dominant control over the subsidiary and its resource allocation. Lower stakes lessen the value of a subsidiary in the context of this study since other shareholders have influence over what the subsidiary is doing. If these conditions are met, this variable takes on the value of 1 and 0 otherwise.

Control variables

The literature on mergers and acquisition has established several variables that have an impact on acquisition performance. To properly delineate the effect of national differences, acquisition experience and having a subsidiary in the host country from other factors, it is necessary to control for firm-specific and deal-specific factors.

Firm-specific control variables include the acquirer’s firm size and Tobin’s Q. Both these variables are calculated using data from the fiscal year prior to the acquisition. The first variable, acquirer firm size, is calculated as the natural logarithm of book value of assets of the acquirer. Research finds that larger acquirers tend to make worse acquisitions in terms of acquisition performance compared to smaller acquirers. Moeller et al. (2004) argue that this is because the incentives of managers and shareholders are less likely to be aligned in larger corporations. The second variable, Tobin’s Q, is included to control for the quality of acquirer management (Lang, Stulz, & Walkling, 1991). It is calculated by dividing the acquirer’s market value of equity over book value of equity.

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effects are used to control for general economic conditions present in the year of the acquisition. Mode of payment is of importance considering the effect of paying cash or with stock has on the risk assumed in a deal. A dummy variable is included indicating if the acquisition was paid by stocks only.

Research Method

To investigate the effect of acquisition experience and subsidiaries in the context of cross-border acquisitions, an event study methodology is employed. Event studies (MacKinlay, 1997) are common in the economic and financial literature to uncover the effect of an event on the market value of a firm. The event study methodology derives its value from the efficient market hypothesis. The efficient market theory states that asset prices fully reflect all available (public) information so that asset prices are accurate measures of value. Moreover, any new information that becomes available to the world will be immediately reflected in the price. In the context of acquisitions, this would mean that the acquisition announcement would have an immediate effect on the acquirer’s stock price equal to all expected value created by the acquisition (Stahl & Voigt, 2004). Based on this reasoning, a measure capturing the abnormal change in stock price around the acquisition announcement is an appropriate measure for acquisition performance.

Regression is employed to test the proposed effects on acquisition performance. For the purposes of this study, all independent variables are centered around their mean. Centering variables is a common methodology when analyzing interaction effects. Its benefits include easier interpretation of the regression coefficients and the alleviation of potential multicollinearity issues due to the interaction terms. As a result, interpretation of regression coefficients should be with respect to their mean values. Before devising a model with full interaction effects included, it makes sense to create a baseline model including only the national differences and control variables.

𝐶𝐴𝑅𝑖 = ⁡ 𝛼0+ ⁡ 𝛽′𝑋𝑖+ ⁡ ∑5𝑗=1𝐶𝑗𝐼𝑖,𝑗+ ⁡ 𝜀𝑖 (5) This formula demonstrates the baseline model, with i representing firm i, 𝛼0 represents the intercept and 𝑋𝑖 represents a vector of control variables which are elaborated on later. The variable 𝐼𝑖,𝑗 (j = 1, …, 5) represents each of the five identified dimensions of national differences. Finally, 𝜀𝑖 is the regression error term. The vector 𝛽′ and each 𝐶𝑗 represent the regression coefficients to be estimated. To test hypothesis 1, this baseline model must be adapted to include the moderating effect of acquisition experience on the national differences dimension. The following equation depicts this model.

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instead of including acquisition experience as a moderator, subsidiary presence in the host country should be included.

𝐶𝐴𝑅𝑖 = ⁡ 𝛼2+ ⁡ 𝛽′𝑋𝑖+ ⁡ ∑𝑗=15 𝐷𝑗𝐼𝑖,𝑗+ ⁡ ∑5𝑗=1𝐸𝑗𝐼𝑖,𝑗𝑆𝑈𝐵𝑆𝐼𝐷𝐼𝐴𝑅𝑌𝑖+ ⁡ 𝜀𝑖 (7) The preceding equation shows the model used to investigate the effect of having a subsidiary in the target’s country on the national differences – acquisition performance relationship. In the equation, 𝑆𝑈𝐵𝑆𝐼𝐷𝐼𝐴𝑅𝑌𝑖 is a binary variable indicating whether firm i had a subsidiary in the country at the time of the acquisition announcement.

Finally, the following formula depicts how hypotheses 3a and 3b can be investigated. In addition to estimating the individual effects of acquisition experience and subsidiary presence, this equation includes an interaction effect between acquisition experience and subsidiary presence. The regression coefficient of this interaction term, 𝐽𝑗, gives an indication of the joint effect of acquisition experience and subsidiary presence.

𝐶𝐴𝑅𝑖 = ⁡ 𝛼3+ ⁡ 𝛽′𝑋𝑖+ ⁡ ∑5𝑗=1𝐷𝑗𝐼𝑖,𝑗+ ∑5𝑗=1𝐸𝑗𝐼𝑖,𝑗𝐸𝑋𝑃𝑖+ ∑5𝑗=1𝐹𝑗𝐼𝑖,𝑗𝑆𝑈𝐵𝑆𝐼𝐷𝐼𝐴𝑅𝑌𝑖+

⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡∑5𝑗=1𝐽𝑗𝐼𝑖,𝑗𝐸𝑋𝑃𝑖𝑆𝑈𝐵𝑆𝐼𝐷𝐼𝐴𝑅𝑌𝑖+ ⁡ 𝜀𝑖 (8)

Results

Sample statistics

The sample criteria lead to an initial 739 acquisitions during the four-year period between 2014 and 2018, which are executed by 459 different firms. Initial analysis shows that for 242 observations, data is missing for one or more of the variables under study. Accordingly, these observations were dropped from the sample. Closer inspection reveals some unusual values for Tobin’s Q warranting further investigation. Some of the observations are mergers rather than acquisitions and

are thus dropped from the sample. After sanitization, 411 observations are included in the final sample. In total, 46 distinct countries were the target of one or more acquisitions between 2014 and 2018. While most countries only had one acquisition, some countries saw considerably higher activity. Figure 1 shows the top ten target countries ranked by number of acquisitions. Striking from the figure is the dominance of the United States as host country in cross-border acquisitions. Of the 411 acquisitions in

0 50 100 150 200 250 300 Czech Republic Israel Netherlands South-Africa Brazil Great Britain Germany Austria Canada United States

Top 10 Target Countries for

Cross-Border Acquisitions

Number of acquisitions

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the entire sample, more than half is with a target firm based in the United States. Considering the importance of the United States in the global economy, this result is not surprising.

Table 1 presents the summary statistics for the dependent, independent and control variables. The average cumulative abnormal return is 1.28% which is comparable to results from other studies where these values typically lie between -5% and +5%. Noteworthy is the wide range in this variable, ranging from negative 21% to positive 19% over the three-day window. This is not surprising given the importance of an M&A event on the prospects of a firm. Looking at acquisition experience, most companies in the sample appear to have done at least one deal in the four-year period from 2010 to 2014. The maximum value of 7 indicates that some firms are so-called ‘serial acquirers’ who have made M&A a continuous process. Subsidiary presence is a dummy variable. It indicates that about 68% of the firms in the sample have a subsidiary in the country of the target firm. Looking at relative deal size, the average deal represents about 11% of the pre-deal acquirer’s asset size. There exists significant variance in deal size, with some deals so minor they represent only an extremely small percentage of the acquiring firm, whereas other deals represent 75% of the acquiring firm.

For some variables, the standard deviation, skewness and kurtosis are particularly large. Geographic differences, acquirer assets and deal value all suffer from this problem due to their absolute nature. As such, it makes sense to transform these variables logarithmically to improve normality. These transformed variables will be used in all subsequent analyses.

Table 1. Descriptive statistics

This table presents descriptive statistics for the dataset containing cross-border acquisitions done by European acquirers in the period between 2014 and 2018 following the data collection strategy described. The sample includes 411 acquisitions done in 46 different countries.

Mean Median Std. Dev. Min. Max. Skew. Kurt. Dependent variable CAR (-1, +1) 1.28% 0.7% 4.6% -21.1% 18.6% -.02 7.34 Independent variables Cultural differences 3.74 2.65 3.20 0.04 12.8 .51 1.60 Economic differences .91 .98 .36 .02 2.02 -.80 3.06 Institutional differences .49 .10 .96 .01 5.85 3.14 12.92 Geographic differences (km) 6156 5834 3408 107 18807 1.42 6.55 Acquisition experience 1.22 1 1.54 0 7 1.32 4.11

Subsidiary presence (dummy) 68% 1 46.5% 0 1 -.79 1.63

Control variables

Acquirer assets (€mm) 15278 2892 29190 24 125717 2.64 9.28

Deal value (€mm) 719 113 2656 10 28692 7.17 61.12

Relative deal size 11% 5% 14% 0.1% 75% 2.02 7.26

Tobin’s Q 1.42 1.19 .72 .52 3.78 1.08 3.51

All-stock payment .01 0 .09 0 1 10 101

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Table 4 in the appendix provides a correlation matrix for the variables. Cultural differences show a statistically significant negative correlation with three-day CAR, hinting at a direct negative effect of cultural differences on acquisition performance. Not unexpected, it shows multiple statistically significant correlations between the various dimensions of national differences. For example, there is a strong positive correlation between cultural differences and geographical differences. It is likely that countries that are further spaced from each other differ more in culture compared than more proximate countries. A further interesting finding is the positive correlation between cultural differences and acquisition experience. Possibly, acquirers with more experience are more likely to acquire targets that differ more. Focusing on the control variables, the correlation matrix shows high correlations between acquirer assets, deal value and relative deal size. It makes sense that these variables are correlated, since larger firms would tend to do bigger deals. Also, with higher deal value, it is likely that this represents a higher relative deal size.

To check for possible multicollinearity, Variance Inflation Factors (VIF) are checked for all estimated models. Their results show that acquirer assets and deal value should be excluded since their VIF values exceed the threshold of 10.

Model estimation

Table 2 presents model estimation output for a baseline model excluding interaction effects according to Equation (5) in column (1). It also displays results for models including interaction effects between acquisition experience and subsidiary presence following Equation (6) and (7) in columns (2) and (3).

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Table 2. Panel regression: explaining CAR (-1, +1) for a sample of cross-border acquisitions

This table presents the results of panel regression models for the sample of cross-border acquisitions done by European acquirers. The dependent variable is three-day cumulative abnormal returns. For brevity, the constant is not shown in these results. For each variable, the number shows the regression coefficient, while the number between parentheses shows the standard error using Huber-White heteroscedasticity-consistent standard errors. *, ** and *** indicate statistical significance at the 10%, 5% and 1% level respectively. Information on model fit is included at the bottom with the Wald Chi2_{and R}2_.

Variable (1) (2) (3) Independent variables Cultural differences -.001 (.001) -.000 (.001) -.002 (.001) * Economic differences -.014 (.010) -.015 (.010) -.015 (.013) Institutional differences -.002 (.003) -.002 (.003) -.002 (.005) Geographic differences -.004 (.003) -.005 (.003) * -.003 (.005) Acquisition experience .000 (.001) -.001 (.001) -.001 (.001) Subsidiary presence -.004 (.006) -.003 (.006) -.004 (.006)

Interaction with acquisition experience

Cultural differences x Acquisition experience .001 (.001) ***

Economic differences x Acquisition experience -.008 (.008)

Institutional differences x Acquisition experience .002 (.002)

Geographic differences x Acquisition experience .003 (.002)

Interaction with subsidiary presence

Cultural differences x Subsidiary presence .002 (.001)

Economic differences x Subsidiary presence -.000 (.013)

Institutional differences x Subsidiary presence -.002 (.007)

Geographic differences x Subsidiary presence -.002 (.005)

Relative deal size .076 (.025) *** .077 (.026) *** .076 (.025) ***

Tobin’s Q -.001 (.002) .000 (.002) .001 (.001)

All-stock payment (dummy) -.016 (.039) -.016 (.039) -.017 (.038)

Industry relatedness (dummy) .004 (.004) .004 (.004) .004 (.004)

Year dummies Yes Yes Yes

Model statistics

Wald Chi2 _{565 ***} _{1661 ***} _{1563 ***}

R2 _.090 _.109 _.094

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Plotting the observed interaction effect between cultural differences and acquisition experience in the second model supports interpretation. Figure 2 shows this effect for three groups. For the groups with below average and average acquisition experience, the small positive effect of cultural differences decreases as these cultural differences increase. In contrast, for acquirers with above average acquisition experience, cultural differences increase acquisition performance. This provides partial support for Hypothesis 1. Acquisition experience

does not interact with any other of the national differences.

None of the interaction effects are statistically significant in the third model. The statistically significant main effect for cultural differences indicates that for acquirers with no local subsidiaries, cultural differences negatively impact acquisition performance. In interaction models, main effects should be interpreted with respect to the other term in the interaction term equaling zero. An interaction plot similar to that of Figure 2, however, shows that local subsidiaries are not effective enough in alleviating the negative impact of cultural differences. This is reflected in the fact that the interaction term between cultural differences and subsidiary presence is in the expected direction, but not significant. Concluding, Hypothesis 2 must be rejected.

Hypothesis 3 proposes a joint effect between acquisition experience and subsidiary presence on the national differences – acquisition performance relationship. Two alternating joint effects are discussed: a complementing effect or a substituting effect. A proper regression model capturing these effects should include the main effects of the national differences, subsidiaries and acquisition experience, the interaction effects with acquisition experience, the interaction effects with subsidiary presence and the joint effects. Equation (8) represents such a model. Table 3 presents selected output for this model. Table 5 in the appendix displays all results. In this model, multicollinearity was a potential problem as indicated by the Variance Inflation Factors. This problem was alleviated by removing the interaction term between economic differences, acquisition experience and subsidiary presence.

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The results allow for some inferences. First, the interaction effect between acquisition experience and subsidiary presence is statistically significantly negative. This implies that higher acquisition experience is not beneficial when the company also has a local subsidiary. Instead, it may hurt acquisition performance. Second, in line with the results from Table 2, acquisition experience shows a statistically significant positive interaction effect with cultural differences. This provides additional evidence for Hypotheses 1. Third, the joint interaction effect between cultural differences, subsidiary presence and acquisition experience is statistically significantly negative. This would suggest that having both acquisition experience and a local subsidiary does not provide additional benefits in mitigating cultural differences. Rather, considering the positive effects for acquisition experience and subsidiary presence on cultural differences independently, it would imply a substituting effect. This supports Hypothesis 3a.

Table 3. Panel regression testing the joint effects of subsidiary presence and acquisition experience on CAR (-1, +1)

This table presents the results of a panel regression model explaining acquisition performance for a sample of cross-border acquisitions. The dependent variable is three-day cumulative abnormal returns. For brevity, control variables and the constant have been omitted in these results. For each variable, the number indicates the regression coefficient, while the number between parentheses shows the standard error using Huber-White heteroscedasticity-consistent standard errors. *, ** and *** indicate statistical significance at the 10%, 5% and 1% level respectively.

Variable (4) Independent variables Cultural differences -.001 (.001) Economic differences -.011 (.013) Institutional differences -.002 (.004) Geographic differences -.003 (.004) Acquisition experience .005 (.003) Subsidiary presence -.005 (.006)

Acquisition experience x Subsidiary presence -.007 (.002) ***

Joint interaction effect

Cultural differences x Subsidiary presence x Acquisition experience -.002 (.001) *

Economic differences x Subsidiary presence x Acquisition experience -

Institutional differences x Subsidiary presence x Acquisition experience .002 (.003)

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Robustness Checks

To test the robustness of the results, an alternative event window is used for the measure of cumulative abnormal returns to ensure that the acquisition announcement is fully absorbed in the stock price. While the primary analysis employs a (-1, +1) event window, results for the event window (-2, +2) are also presented. Using a larger event window has the benefit that it may better be able to capture acquisition performance if it takes longer to fully price the acquisition’s implications. However, it also carries the risk of including unrelated events.

Table 6 in the Appendix presents these results for the interaction models with acquisition experience and subsidiary presence. Comparing these to the main findings, they are almost identical. The interaction effect between acquisition experience and cultural difference remains and even becomes slightly more pronounced. This provides further evidence for Hypothesis 1. Interaction effects between subsidiary presence and national difference remain absent. In addition, Table 7 in the Appending shows results for the joint interaction effect analysis. Results are similar to the main results. For the joint interaction effect between cultural differences, subsidiary presence and acquisition experience, the substituting effect is again visible, in line with Hypothesis 3a.

Figure 1 shows a large skew of observations towards acquisitions with targets in the United States. Recognizing that the United States’ economy is the largest worldwide based on national GDP, this result is not surprising. However, due to this importance, it also may mean that European companies are more aware of local conditions and national differences with the United States, making them less relevant. The United States has a considerable impact on popular culture, news, politics and general knowledge in European countries compared to other countries. Similarly, Europe tends to have a stronger focus on the United States than on for example Asia or Latin America, increasing awareness of doing business in the United States. Consequently, the relevance of acquisition experience and the presence of local subsidiaries may change.

Results for the regression models estimated on a dataset excluding acquisitions of U.S. targets are provided in Table 8 and Table 9 in the Appendix. Only 166 acquisitions meet this additional criterium. Results for the models including the interaction effects separately look largely similar to the main results. The interaction effect between acquisition experience and cultural differences is again statistically significantly negative. Interestingly, economic differences are now also statistically significantly negative, which was not the case in the main results. Also, the control variable relative deal size has lost its significance. Instead, the all-stock payment dummy is now statistically significantly negative. In Table 9, joint interaction effects are shown. In contrast to the main findings, the joint interaction effect between cultural differences, subsidiary presence and acquisition experience is no longer present.

Discussion

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national differences. More specific, the results indicate that for acquirers with zero or one previous acquisitions under their belt, higher cultural differences negatively impact acquisition performance. Acquirers with more acquisition experience (in this analysis being four or more acquisitions) are able to counter this negative effect. These findings generally suggest that the acquisition experience is not an effective mechanism in this context. It may be that acquirers fail to recognize the importance of national differences at all, therefore never learning to recognize them. Alternatively, it may be the case that while acquirers recognize the importance of national difference, they do not develop the knowledge required to effectively manage them. Managing national differences may simply not be a topic of high importance on the agendas of managers handling the acquisition.

From the results, local subsidiary presence is not a useful mechanism in alleviating the potential issues arising out of national differences. These subsidiaries are not effective in circumventing the problem of double-layered acculturation. These findings stand in stark contrast to Lavie and Miller (2008), who note that an overlapping subsidiary profile improves international alliance performance. As a result, it must be concluded that the positive effects are not transferable to cross-border acquisitions. A likely explanation is that the knowledge of local conditions available at local subsidiaries differs from the knowledge required in doing cross-border acquisitions.

Even starker, the results indicate that local subsidiaries even have a negative effect on acquisition performance. This effect is twofold. First, the presence of a substituting effect results in a situation wherein acquisition experience becomes less valuable in handling cultural differences. Second, acquirers with above-average acquisition experience show decreased acquisition performance when they also have a local subsidiary (see interaction effect in Table 3). While this research cannot provide a conclusive reason for this finding, one possibility is that the acquirer tries to employ its acquisition experience while the local subsidiaries are also getting involved with the acquisition. They may provide contrasting or otherwise confusing knowledge and information leaving the acquirer in an ambiguous situation. Future research may look specifically at situations in which such a conflict arises and what opportunities the acquirer has to resolve this conflict.

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subsidiaries in countries they consider it likely they will make future acquisitions in. Moreover, when acquiring in countries they already have a local subsidiary presence, the acquirer should not include the local subsidiary in the acquisition process.

This study also has inferences for academics. Organizational learning theory is employed to theorize on the possible effects of acquisition experience. Contrary to the expectations derived from this theory, this study finds only a limitedly advantageous effect on the acquirer’s ability to mitigate national differences. This suggests that organizational learning theory is only limitedly applicable in this situation. Future research may investigate the root causes of this result. This may provide additional insights into the boundaries and contextual factors required for effective organizational learning. Second, opposing the work of Lavie and Miller (2008), this study does not find any positive effect of having a local subsidiary presence when dealing with managing national differences. In fact, the results are highly unfavorable for local subsidiaries. This study theorizes on some explanations for these findings, but these should be followed up with dedicated studies. This provides an interesting angle for future research.

Limitations

One major limitation of this study lies in the measurement of acquisition performance. In line with the majority of research on M&A, the event study methodology is adopted, using cumulative abnormal returns as a measure of acquisition performance. This approach is not without criticism. Zollo and Meier (2008) find that short-term stock performance is only a crude proxy for long-term acquisition performance. In the context of the current research topic, the effects of national differences may not be immediately obvious for stock market investors. Similarly, the moderating effects of acquisition experience and local subsidiary presence may not be considered. As a result, they will not be included in the share price, making cumulative abnormal returns inaccurate for the purposes of this study. Instead, the effects of national differences and the moderating effects of acquisition experience and local subsidiary presence may only become visible after some time, during the post-deal integration process. Long-term metrics such as 3-year change in return on assets may, therefore, be more accurate measures of acquisition performance for capturing these effects.

A second limitation comes from the measurement of local subsidiary presence. The data from Orbis does not include a date when the subsidiary started. As a result, it may be that the analysis included some subsidiaries that were only started after the acquisition. In these cases, local subsidiary presence obviously cannot mitigate national differences.

Conclusion

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finds that neither acquisition experience nor local subsidiaries are particularly effective in mitigating these risks. Only for cultural differences is acquisition experience an effective mitigation mechanism. Fascinatingly, the findings show that acquirers with considerable acquisition experience, while also having a local subsidiary presence may even see their acquisition performance deteriorate. These results provide interesting implications for companies seeking to engage in cross-border M&A. Researchers focusing on M&A are also provided with future research directions.

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Appendix

Table 4. Correlation matrix for variables under study

This table presents the correlation matrix for all variables under study. *, ** and *** illustrate statistical significance at the 10%, 5% and 1% level respectively.

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This table presents the results of a panel regression model explaining acquisition performance for a sample of cross-border acquisitions. The dependent variable is three-day cumulative abnormal returns. Interaction effects for acquisition experience, subsidiary presence, and a joint interaction effect are included. For brevity, the constant has been omitted in these results. For each variable, the number indicates the regression coefficient, while the number between parentheses shows the standard error using Huber-White heteroscedasticity-consistent standard errors. *, ** and *** indicate statistical significance at the 10%, 5% and 1% level respectively. Model statistics show model fit using R2_.

Cultural differences x Subsidiary presence x Acquisition experience -.002 (.001) *

Geographic differences x Subsidiary presence x Acquisition experience -.002 (.004)

Relative deal size .082 (.026) ***

Tobin’s Q .000 (.001)

All-stock payment (dummy) -.018 (.038)

Industry relatedness (dummy) .004 (.004)

Year dummies Yes

R2 _.136

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Table 6. Panel regression: explaining CAR (-2, +2) for a sample of cross-border acquisitions

This table presents the results of panel regression models for the sample of cross-border acquisitions done by European acquirers. The dependent variable is five-day cumulative abnormal returns. For brevity, the constant is not shown in these results. For each variable, the number shows the regression coefficient, while the number between parentheses shows the standard error using Huber-White heteroscedasticity-consistent standard errors. *, ** and *** indicate statistical significance at the 10%, 5% and 1% level respectively. Information on model fit is included at the bottom with the Wald Chi2_{and R}2_for

all models. Variable (5) (6) Independent variables Cultural differences -.001 (.001) -.002 (.002) Economic differences -.020 (.013) -.014 (.014) Institutional differences -.003 (.004) .001 (.006) Geographic differences -.006 (.003) * -.003 (.006) Acquisition experience -.002 (.002) -.001 (.002) Subsidiary presence -.004 (.008) -.003 (.008)

Institutional differences x Acquisition experience -.000 (.003)

Relative deal size .084 (.023) *** .080 (.023) ***

Tobin’s Q .002 (.002) .001 (.002)

All-stock payment (dummy) -.033 (.062) -.035 (.058)

Industry relatedness (dummy) .000 (.006) .001 (.006)

Year dummies Yes Yes

Wald Chi2 _{1151 ***} _{1030 ***}

R2 _.104 _.090

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This table presents the results of a panel regression model explaining acquisition performance for a sample of cross-border acquisitions. The dependent variable is five-day cumulative abnormal returns. For brevity, the constant has been omitted in these results. For each variable, the number indicates the regression coefficient, while the number between parentheses shows the standard error using Huber-White heteroscedasticity-consistent standard errors. *, ** and *** indicate statistical significance at the 10%, 5% and 1% level respectively. Model statistics show model fit using R2_.

Cultural differences x Subsidiary presence x Acquisition experience -.004 (.002) **

Geographic differences x Subsidiary presence x Acquisition experience -.002 (.004)

Relative deal size .102 (.030) ***

Tobin’s Q .003 (.002)

All-stock payment (dummy) -.040 (.057)

Industry relatedness (dummy) .002 (.008)

Year dummies Yes

R2 _0.128